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@MISC{Seemann_undated-ra,
  title       = "Barrnap: Bacterial ribosomal {RNA} predictor",
  author      = "Seemann, Torsten",
  abstract    = ":microscope: :leo: Bacterial ribosomal RNA predictor -
                 tseemann/barrnap",
  institution = "Github"
}

@MISC{Li2012-wr,
  title     = "seqtk Toolkit for processing sequences in {FASTA/Q} formats",
  author    = "Li, Heng",
  publisher = "GitHub",
  year      =  2012
}

@ARTICLE{Song2014-hn,
  title    = "Lighter: fast and memory-efficient sequencing error correction
              without counting",
  author   = "Song, Li and Florea, Liliana and Langmead, Ben",
  abstract = "Lighter is a fast, memory-efficient tool for correcting
              sequencing errors. Lighter avoids counting k-mers. Instead, it
              uses a pair of Bloom filters, one holding a sample of the input
              k-mers and the other holding k-mers likely to be correct. As long
              as the sampling fraction is adjusted in inverse proportion to the
              depth of sequencing, Bloom filter size can be held constant while
              maintaining near-constant accuracy. Lighter is parallelized, uses
              no secondary storage, and is both faster and more
              memory-efficient than competing approaches while achieving
              comparable accuracy.",
  journal  = "Genome Biol.",
  volume   =  15,
  number   =  11,
  pages    = "509",
  year     =  2014,
  language = "en"
}

@ARTICLE{Page2015-ob,
  title    = "Roary: rapid large-scale prokaryote pan genome analysis",
  author   = "Page, Andrew J and Cummins, Carla A and Hunt, Martin and Wong,
              Vanessa K and Reuter, Sandra and Holden, Matthew T G and Fookes,
              Maria and Falush, Daniel and Keane, Jacqueline A and Parkhill,
              Julian",
  abstract = "UNLABELLED: A typical prokaryote population sequencing study can
              now consist of hundreds or thousands of isolates. Interrogating
              these datasets can provide detailed insights into the genetic
              structure of prokaryotic genomes. We introduce Roary, a tool that
              rapidly builds large-scale pan genomes, identifying the core and
              accessory genes. Roary makes construction of the pan genome of
              thousands of prokaryote samples possible on a standard desktop
              without compromising on the accuracy of results. Using a single
              CPU Roary can produce a pan genome consisting of 1000 isolates in
              4.5 hours using 13 GB of RAM, with further speedups possible
              using multiple processors. AVAILABILITY AND IMPLEMENTATION: Roary
              is implemented in Perl and is freely available under an open
              source GPLv3 license from http://sanger-pathogens.github.io/Roary
              CONTACT: roary@sanger.ac.uk SUPPLEMENTARY INFORMATION:
              Supplementary data are available at Bioinformatics online.",
  journal  = "Bioinformatics",
  volume   =  31,
  number   =  22,
  pages    = "3691--3693",
  month    =  nov,
  year     =  2015,
  language = "en"
}

@MISC{Andrews2016-kz,
  title  = "{FastQC} A Quality Control tool for High Throughput Sequence Data.
            Babraham Bioinformatics. 2012",
  author = "Andrews, S and Krueger, F and Seconds-Pichon, A and Biggins, F and
            Wingett, S",
  year   =  2016
}

@ARTICLE{Hoang2018-mu,
  title    = "{UFBoot2}: Improving the Ultrafast Bootstrap Approximation",
  author   = "Hoang, Diep Thi and Chernomor, Olga and von Haeseler, Arndt and
              Minh, Bui Quang and Vinh, Le Sy",
  abstract = "The standard bootstrap (SBS), despite being computationally
              intensive, is widely used in maximum likelihood phylogenetic
              analyses. We recently proposed the ultrafast bootstrap
              approximation (UFBoot) to reduce computing time while achieving
              more unbiased branch supports than SBS under mild model
              violations. UFBoot has been steadily adopted as an efficient
              alternative to SBS and other bootstrap approaches. Here, we
              present UFBoot2, which substantially accelerates UFBoot and
              reduces the risk of overestimating branch supports due to
              polytomies or severe model violations. Additionally, UFBoot2
              provides suitable bootstrap resampling strategies for
              phylogenomic data. UFBoot2 is 778 times (median) faster than SBS
              and 8.4 times (median) faster than RAxML rapid bootstrap on
              tested data sets. UFBoot2 is implemented in the IQ-TREE software
              package version 1.6 and freely available at
              http://www.iqtree.org.",
  journal  = "Mol. Biol. Evol.",
  volume   =  35,
  number   =  2,
  pages    = "518--522",
  month    =  feb,
  year     =  2018,
  keywords = "maximum likelihood; model violation; phylogenetic inference;
              polytomies; ultrafast bootstrap",
  language = "en"
}

@ARTICLE{Buchfink2015-yu,
  title    = "Fast and sensitive protein alignment using {DIAMOND}",
  author   = "Buchfink, Benjamin and Xie, Chao and Huson, Daniel H",
  abstract = "The alignment of sequencing reads against a protein reference
              database is a major computational bottleneck in metagenomics and
              data-intensive evolutionary projects. Although recent tools offer
              improved performance over the gold standard BLASTX, they exhibit
              only a modest speedup or low sensitivity. We introduce DIAMOND,
              an open-source algorithm based on double indexing that is 20,000
              times faster than BLASTX on short reads and has a similar degree
              of sensitivity.",
  journal  = "Nat. Methods",
  volume   =  12,
  number   =  1,
  pages    = "59--60",
  month    =  jan,
  year     =  2015,
  language = "en"
}

@MISC{noauthor_undated-dt,
  title       = "Vcflib: A C++ library for parsing and manipulating {VCF} files",
  abstract    = "a simple C++ library for parsing and manipulating VCF files, +
                 many command-line utilities - vcflib/vcflib",
  institution = "Github"
}

@ARTICLE{Di_Tommaso2017-jf,
  title    = "Nextflow enables reproducible computational workflows",
  author   = "Di Tommaso, Paolo and Chatzou, Maria and Floden, Evan W and
              Barja, Pablo Prieto and Palumbo, Emilio and Notredame, Cedric",
  journal  = "Nat. Biotechnol.",
  volume   =  35,
  number   =  4,
  pages    = "316--319",
  month    =  apr,
  year     =  2017,
  language = "en"
}

@ARTICLE{Katz2019-cp,
  title    = "Mashtree: a rapid comparison of whole genome sequence files",
  author   = "Katz, Lee and Griswold, Taylor and Morrison, Shatavia and
              Caravas, Jason and Zhang, Shaokang and Bakker, Henk and Deng,
              Xiangyu and Carleton, Heather",
  abstract = "Software archive",
  journal  = "JOSS",
  volume   =  4,
  number   =  44,
  pages    = "1762",
  month    =  dec,
  year     =  2019
}

@ARTICLE{Laslett2004-li,
  title    = "{ARAGORN}, a program to detect {tRNA} genes and {tmRNA} genes in
              nucleotide sequences",
  author   = "Laslett, Dean and Canback, Bjorn",
  abstract = "A computer program, ARAGORN, identifies tRNA and tmRNA genes. The
              program employs heuristic algorithms to predict tRNA secondary
              structure, based on homology with recognized tRNA consensus
              sequences and ability to form a base-paired cloverleaf. tmRNA
              genes are identified using a modified version of the BRUCE
              program. ARAGORN achieves a detection sensitivity of 99\% from a
              set of 1290 eubacterial, eukaryotic and archaeal tRNA genes and
              detects all complete tmRNA sequences in the tmRNA database,
              improving on the performance of the BRUCE program. Recently
              discovered tmRNA genes in the chloroplasts of two species from
              the 'green' algae lineage are detected. The output of the program
              reports the proposed tRNA secondary structure and, for tmRNA
              genes, the secondary structure of the tRNA domain, the tmRNA gene
              sequence, the tag peptide and a list of organisms with matching
              tmRNA peptide tags.",
  journal  = "Nucleic Acids Res.",
  volume   =  32,
  number   =  1,
  pages    = "11--16",
  month    =  jan,
  year     =  2004,
  language = "en"
}

@ARTICLE{Rognes2016-qx,
  title    = "{VSEARCH}: a versatile open source tool for metagenomics",
  author   = "Rognes, Torbj{\o}rn and Flouri, Tom{\'a}{\v s} and Nichols, Ben
              and Quince, Christopher and Mah{\'e}, Fr{\'e}d{\'e}ric",
  abstract = "BACKGROUND: VSEARCH is an open source and free of charge
              multithreaded 64-bit tool for processing and preparing
              metagenomics, genomics and population genomics nucleotide
              sequence data. It is designed as an alternative to the widely
              used USEARCH tool (Edgar, 2010) for which the source code is not
              publicly available, algorithm details are only rudimentarily
              described, and only a memory-confined 32-bit version is freely
              available for academic use. METHODS: When searching nucleotide
              sequences, VSEARCH uses a fast heuristic based on words shared by
              the query and target sequences in order to quickly identify
              similar sequences, a similar strategy is probably used in
              USEARCH. VSEARCH then performs optimal global sequence alignment
              of the query against potential target sequences, using full
              dynamic programming instead of the seed-and-extend heuristic used
              by USEARCH. Pairwise alignments are computed in parallel using
              vectorisation and multiple threads. RESULTS: VSEARCH includes
              most commands for analysing nucleotide sequences available in
              USEARCH version 7 and several of those available in USEARCH
              version 8, including searching (exact or based on global
              alignment), clustering by similarity (using length pre-sorting,
              abundance pre-sorting or a user-defined order), chimera detection
              (reference-based or de novo), dereplication (full length or
              prefix), pairwise alignment, reverse complementation, sorting,
              and subsampling. VSEARCH also includes commands for FASTQ file
              processing, i.e., format detection, filtering, read quality
              statistics, and merging of paired reads. Furthermore, VSEARCH
              extends functionality with several new commands and improvements,
              including shuffling, rereplication, masking of low-complexity
              sequences with the well-known DUST algorithm, a choice among
              different similarity definitions, and FASTQ file format
              conversion. VSEARCH is here shown to be more accurate than
              USEARCH when performing searching, clustering, chimera detection
              and subsampling, while on a par with USEARCH for paired-ends read
              merging. VSEARCH is slower than USEARCH when performing
              clustering and chimera detection, but significantly faster when
              performing paired-end reads merging and dereplication. VSEARCH is
              available at https://github.com/torognes/vsearch under either the
              BSD 2-clause license or the GNU General Public License version
              3.0. DISCUSSION: VSEARCH has been shown to be a fast, accurate
              and full-fledged alternative to USEARCH. A free and open-source
              versatile tool for sequence analysis is now available to the
              metagenomics community.",
  journal  = "PeerJ",
  volume   =  4,
  pages    = "e2584",
  month    =  oct,
  year     =  2016,
  keywords = "Alignment; Chimera detection; Clustering; Dereplication; Masking;
              Metagenomics; Parallellization; Searching; Sequences; Shuffling",
  language = "en"
}

@ARTICLE{Garrison2012-sy,
  title         = "Haplotype-based variant detection from short-read sequencing",
  author        = "Garrison, Erik and Marth, Gabor",
  abstract      = "The direct detection of haplotypes from short-read DNA
                   sequencing data requires changes to existing small-variant
                   detection methods. Here, we develop a Bayesian statistical
                   framework which is capable of modeling multiallelic loci in
                   sets of individuals with non-uniform copy number. We then
                   describe our implementation of this framework in a
                   haplotype-based variant detector, FreeBayes.",
  month         =  jul,
  year          =  2012,
  archivePrefix = "arXiv",
  primaryClass  = "q-bio.GN",
  eprint        = "1207.3907"
}

@ARTICLE{Joensen2014-gi,
  title    = "Real-time whole-genome sequencing for routine typing,
              surveillance, and outbreak detection of verotoxigenic Escherichia
              coli",
  author   = "Joensen, Katrine Grimstrup and Scheutz, Flemming and Lund, Ole
              and Hasman, Henrik and Kaas, Rolf S and Nielsen, Eva M and
              Aarestrup, Frank M",
  abstract = "Fast and accurate identification and typing of pathogens are
              essential for effective surveillance and outbreak detection. The
              current routine procedure is based on a variety of techniques,
              making the procedure laborious, time-consuming, and expensive.
              With whole-genome sequencing (WGS) becoming cheaper, it has huge
              potential in both diagnostics and routine surveillance. The aim
              of this study was to perform a real-time evaluation of WGS for
              routine typing and surveillance of verocytotoxin-producing
              Escherichia coli (VTEC). In Denmark, the Statens Serum Institut
              (SSI) routinely receives all suspected VTEC isolates. During a
              7-week period in the fall of 2012, all incoming isolates were
              concurrently subjected to WGS using IonTorrent PGM. Real-time
              bioinformatics analysis was performed using web-tools
              (www.genomicepidemiology.org) for species determination,
              multilocus sequence type (MLST) typing, and determination of
              phylogenetic relationship, and a specific VirulenceFinder for
              detection of E. coli virulence genes was developed as part of
              this study. In total, 46 suspected VTEC isolates were
              characterized in parallel during the study. VirulenceFinder
              proved successful in detecting virulence genes included in
              routine typing, explicitly verocytotoxin 1 (vtx1), verocytotoxin
              2 (vtx2), and intimin (eae), and also detected additional
              virulence genes. VirulenceFinder is also a robust method for
              assigning verocytotoxin (vtx) subtypes. A real-time clustering of
              isolates in agreement with the epidemiology was established from
              WGS, enabling discrimination between sporadic and outbreak
              isolates. Overall, WGS typing produced results faster and at a
              lower cost than the current routine. Therefore, WGS typing is a
              superior alternative to conventional typing strategies. This
              approach may also be applied to typing and surveillance of other
              pathogens.",
  journal  = "J. Clin. Microbiol.",
  volume   =  52,
  number   =  5,
  pages    = "1501--1510",
  month    =  may,
  year     =  2014,
  language = "en"
}

@ARTICLE{Hyatt2010-wx,
  title    = "Prodigal: prokaryotic gene recognition and translation initiation
              site identification",
  author   = "Hyatt, Doug and Chen, Gwo-Liang and Locascio, Philip F and Land,
              Miriam L and Larimer, Frank W and Hauser, Loren J",
  abstract = "BACKGROUND: The quality of automated gene prediction in microbial
              organisms has improved steadily over the past decade, but there
              is still room for improvement. Increasing the number of correct
              identifications, both of genes and of the translation initiation
              sites for each gene, and reducing the overall number of false
              positives, are all desirable goals. RESULTS: With our years of
              experience in manually curating genomes for the Joint Genome
              Institute, we developed a new gene prediction algorithm called
              Prodigal (PROkaryotic DYnamic programming Gene-finding
              ALgorithm). With Prodigal, we focused specifically on the three
              goals of improved gene structure prediction, improved translation
              initiation site recognition, and reduced false positives. We
              compared the results of Prodigal to existing gene-finding methods
              to demonstrate that it met each of these objectives. CONCLUSION:
              We built a fast, lightweight, open source gene prediction program
              called Prodigal http://compbio.ornl.gov/prodigal/. Prodigal
              achieved good results compared to existing methods, and we
              believe it will be a valuable asset to automated microbial
              annotation pipelines.",
  journal  = "BMC Bioinformatics",
  volume   =  11,
  pages    = "119",
  month    =  mar,
  year     =  2010,
  language = "en"
}

@ARTICLE{Alcock2020-cx,
  title    = "{CARD} 2020: antibiotic resistome surveillance with the
              comprehensive antibiotic resistance database",
  author   = "Alcock, Brian P and Raphenya, Amogelang R and Lau, Tammy T Y and
              Tsang, Kara K and Bouchard, M{\'e}gane and Edalatmand, Arman and
              Huynh, William and Nguyen, Anna-Lisa V and Cheng, Annie A and
              Liu, Sihan and Min, Sally Y and Miroshnichenko, Anatoly and Tran,
              Hiu-Ki and Werfalli, Rafik E and Nasir, Jalees A and Oloni,
              Martins and Speicher, David J and Florescu, Alexandra and Singh,
              Bhavya and Faltyn, Mateusz and Hernandez-Koutoucheva, Anastasia
              and Sharma, Arjun N and Bordeleau, Emily and Pawlowski, Andrew C
              and Zubyk, Haley L and Dooley, Damion and Griffiths, Emma and
              Maguire, Finlay and Winsor, Geoff L and Beiko, Robert G and
              Brinkman, Fiona S L and Hsiao, William W L and Domselaar, Gary V
              and McArthur, Andrew G",
  abstract = "The Comprehensive Antibiotic Resistance Database (CARD;
              https://card.mcmaster.ca) is a curated resource providing
              reference DNA and protein sequences, detection models and
              bioinformatics tools on the molecular basis of bacterial
              antimicrobial resistance (AMR). CARD focuses on providing
              high-quality reference data and molecular sequences within a
              controlled vocabulary, the Antibiotic Resistance Ontology (ARO),
              designed by the CARD biocuration team to integrate with software
              development efforts for resistome analysis and prediction, such
              as CARD's Resistance Gene Identifier (RGI) software. Since 2017,
              CARD has expanded through extensive curation of reference
              sequences, revision of the ontological structure, curation of
              over 500 new AMR detection models, development of a new
              classification paradigm and expansion of analytical tools. Most
              notably, a new Resistomes \& Variants module provides analysis
              and statistical summary of in silico predicted resistance
              variants from 82 pathogens and over 100 000 genomes. By adding
              these resistance variants to CARD, we are able to summarize
              predicted resistance using the information included in CARD,
              identify trends in AMR mobility and determine previously
              undescribed and novel resistance variants. Here, we describe
              updates and recent expansions to CARD and its biocuration
              process, including new resources for community biocuration of AMR
              molecular reference data.",
  journal  = "Nucleic Acids Res.",
  volume   =  48,
  number   = "D1",
  pages    = "D517--D525",
  month    =  jan,
  year     =  2020,
  language = "en"
}

@ARTICLE{Ondov2016-cn,
  title    = "Mash: fast genome and metagenome distance estimation using
              {MinHash}",
  author   = "Ondov, Brian D and Treangen, Todd J and Melsted, P{\'a}ll and
              Mallonee, Adam B and Bergman, Nicholas H and Koren, Sergey and
              Phillippy, Adam M",
  abstract = "Mash extends the MinHash dimensionality-reduction technique to
              include a pairwise mutation distance and P value significance
              test, enabling the efficient clustering and search of massive
              sequence collections. Mash reduces large sequences and sequence
              sets to small, representative sketches, from which global
              mutation distances can be rapidly estimated. We demonstrate
              several use cases, including the clustering of all 54,118 NCBI
              RefSeq genomes in 33 CPU h; real-time database search using
              assembled or unassembled Illumina, Pacific Biosciences, and
              Oxford Nanopore data; and the scalable clustering of hundreds of
              metagenomic samples by composition. Mash is freely released under
              a BSD license ( https://github.com/marbl/mash ).",
  journal  = "Genome Biol.",
  volume   =  17,
  number   =  1,
  pages    = "132",
  month    =  jun,
  year     =  2016,
  keywords = "Alignment; Comparative genomics; Genomic distance; Metagenomics;
              Nanopore; Sequencing",
  language = "en"
}

% The entry below contains non-ASCII chars that could not be converted
% to a LaTeX equivalent.
@ARTICLE{Huerta-Cepas2017-sn,
  title    = "Fast {Genome-Wide} Functional Annotation through Orthology
              Assignment by {eggNOG-Mapper}",
  author   = "Huerta-Cepas, Jaime and Forslund, Kristoffer and Coelho, Luis
              Pedro and Szklarczyk, Damian and Jensen, Lars Juhl and von
              Mering, Christian and Bork, Peer",
  abstract = "Orthology assignment is ideally suited for functional inference.
              However, because predicting orthology is computationally
              intensive at large scale, and most pipelines are relatively
              inaccessible (e.g., new assignments only available through
              database updates), less precise homology-based functional
              transfer is still the default for (meta-)genome annotation. We,
              therefore, developed eggNOG-mapper, a tool for functional
              annotation of large sets of sequences based on fast orthology
              assignments using precomputed clusters and phylogenies from the
              eggNOG database. To validate our method, we benchmarked Gene
              Ontology (GO) predictions against two widely used homology-based
              approaches: BLAST and InterProScan. Orthology filters applied to
              BLAST results reduced the rate of false positive assignments by
              11\%, and increased the ratio of experimentally validated terms
              recovered over all terms assigned per protein by 15\%. Compared
              with InterProScan, eggNOG-mapper achieved similar proteome
              coverage and precision while predicting, on average, 41 more
              terms per protein and increasing the rate of experimentally
              validated terms recovered over total term assignments per protein
              by 35\%. EggNOG-mapper predictions scored within the top-5
              methods in the three GO categories using the CAFA2 NK-partial
              benchmark. Finally, we evaluated eggNOG-mapper for functional
              annotation of metagenomics data, yielding better performance than
              interProScan. eggNOG-mapper runs ∼15$\times$ faster than BLAST
              and at least 2.5$\times$ faster than InterProScan. The tool is
              available standalone and as an online service at
              http://eggnog-mapper.embl.de.",
  journal  = "Mol. Biol. Evol.",
  volume   =  34,
  number   =  8,
  pages    = "2115--2122",
  month    =  aug,
  year     =  2017,
  keywords = "comparative genomics; functional annotation; gene function;
              genomics; metagenomics; orthology",
  language = "en"
}

@ARTICLE{Walker2014-lr,
  title     = "Pilon: An Integrated Tool for Comprehensive Microbial Variant
               Detection and Genome Assembly Improvement",
  author    = "Walker, Bruce J and Abeel, Thomas and Shea, Terrance and Priest,
               Margaret and Abouelliel, Amr and Sakthikumar, Sharadha and
               Cuomo, Christina A and Zeng, Qiandong and Wortman, Jennifer and
               Young, Sarah K and Earl, Ashlee M",
  abstract  = "Advances in modern sequencing technologies allow us to generate
               sufficient data to analyze hundreds of bacterial genomes from a
               single machine in a single day. This potential for sequencing
               massive numbers of genomes calls for fully automated methods to
               produce high-quality assemblies and variant calls. We introduce
               Pilon, a fully automated, all-in-one tool for correcting draft
               assemblies and calling sequence variants of multiple sizes,
               including very large insertions and deletions. Pilon works with
               many types of sequence data, but is particularly strong when
               supplied with paired end data from two Illumina libraries with
               small e.g., 180 bp and large e.g., 3--5 Kb inserts. Pilon
               significantly improves draft genome assemblies by correcting
               bases, fixing mis-assemblies and filling gaps. For both haploid
               and diploid genomes, Pilon produces more contiguous genomes with
               fewer errors, enabling identification of more biologically
               relevant genes. Furthermore, Pilon identifies small variants
               with high accuracy as compared to state-of-the-art tools and is
               unique in its ability to accurately identify large sequence
               variants including duplications and resolve large insertions.
               Pilon is being used to improve the assemblies of thousands of
               new genomes and to identify variants from thousands of
               clinically relevant bacterial strains. Pilon is freely available
               as open source software.",
  journal   = "PLoS One",
  publisher = "Public Library of Science",
  volume    =  9,
  number    =  11,
  pages     = "e112963",
  month     =  nov,
  year      =  2014
}

@ARTICLE{Magoc2011-jn,
  title    = "{FLASH}: fast length adjustment of short reads to improve genome
              assemblies",
  author   = "Mago{\v c}, Tanja and Salzberg, Steven L",
  abstract = "MOTIVATION: Next-generation sequencing technologies generate very
              large numbers of short reads. Even with very deep genome
              coverage, short read lengths cause problems in de novo
              assemblies. The use of paired-end libraries with a fragment size
              shorter than twice the read length provides an opportunity to
              generate much longer reads by overlapping and merging read pairs
              before assembling a genome. RESULTS: We present FLASH, a fast
              computational tool to extend the length of short reads by
              overlapping paired-end reads from fragment libraries that are
              sufficiently short. We tested the correctness of the tool on one
              million simulated read pairs, and we then applied it as a
              pre-processor for genome assemblies of Illumina reads from the
              bacterium Staphylococcus aureus and human chromosome 14. FLASH
              correctly extended and merged reads >99\% of the time on
              simulated reads with an error rate of <1\%. With adequately set
              parameters, FLASH correctly merged reads over 90\% of the time
              even when the reads contained up to 5\% errors. When FLASH was
              used to extend reads prior to assembly, the resulting assemblies
              had substantially greater N50 lengths for both contigs and
              scaffolds. AVAILABILITY AND IMPLEMENTATION: The FLASH system is
              implemented in C and is freely available as open-source code at
              http://www.cbcb.umd.edu/software/flash. CONTACT:
              t.magoc@gmail.com.",
  journal  = "Bioinformatics",
  volume   =  27,
  number   =  21,
  pages    = "2957--2963",
  month    =  nov,
  year     =  2011,
  keywords = "PubMed S. aureus 2000-15/2011"
}

@ARTICLE{Petersen2011-nh,
  title    = "{SignalP} 4.0: discriminating signal peptides from transmembrane
              regions",
  author   = "Petersen, Thomas Nordahl and Brunak, S{\o}ren and von Heijne,
              Gunnar and Nielsen, Henrik",
  journal  = "Nat. Methods",
  volume   =  8,
  number   =  10,
  pages    = "785--786",
  month    =  sep,
  year     =  2011,
  language = "en"
}

@MISC{Skennerton_undated-gp,
  title       = "{MinCED}: Mining {CRISPRs} in Environmental Datasets",
  author      = "Skennerton, Connor T",
  abstract    = "Mining CRISPRs in Environmental Datasets. Contribute to
                 ctSkennerton/minced development by creating an account on
                 GitHub.",
  institution = "Github"
}

@ARTICLE{Kalyaanamoorthy2017-ea,
  title    = "{ModelFinder}: fast model selection for accurate phylogenetic
              estimates",
  author   = "Kalyaanamoorthy, Subha and Minh, Bui Quang and Wong, Thomas K F
              and von Haeseler, Arndt and Jermiin, Lars S",
  abstract = "Model-based molecular phylogenetics plays an important role in
              comparisons of genomic data, and model selection is a key step in
              all such analyses. We present ModelFinder, a fast model-selection
              method that greatly improves the accuracy of phylogenetic
              estimates by incorporating a model of rate heterogeneity across
              sites not previously considered in this context and by allowing
              concurrent searches of model space and tree space.",
  journal  = "Nat. Methods",
  volume   =  14,
  number   =  6,
  pages    = "587--589",
  month    =  jun,
  year     =  2017,
  language = "en"
}

@ARTICLE{Price2010-bv,
  title     = "{FastTree} 2 -- Approximately {Maximum-Likelihood} Trees for
               Large Alignments",
  author    = "Price, Morgan N. and Dehal, Paramvir S. and Arkin, Adam P.",
  abstract  = "Background We recently described FastTree, a tool for inferring
               phylogenies for alignments with up to hundreds of thousands of
               sequences. Here, we describe improvements to FastTree that
               improve its accuracy without sacrificing scalability.
               Methodology/Principal Findings Where FastTree 1 used
               nearest-neighbor interchanges (NNIs) and the minimum-evolution
               criterion to improve the tree, FastTree 2 adds minimum-evolution
               subtree-pruning-regrafting (SPRs) and maximum-likelihood NNIs.
               FastTree 2 uses heuristics to restrict the search for better
               trees and estimates a rate of evolution for each site (the
               ``CAT'' approximation). Nevertheless, for both simulated and
               genuine alignments, FastTree 2 is slightly more accurate than a
               standard implementation of maximum-likelihood NNIs (PhyML 3 with
               default settings). Although FastTree 2 is not quite as accurate
               as methods that use maximum-likelihood SPRs, most of the splits
               that disagree are poorly supported, and for large alignments,
               FastTree 2 is 100--1,000 times faster. FastTree 2 inferred a
               topology and likelihood-based local support values for 237,882
               distinct 16S ribosomal RNAs on a desktop computer in 22 hours
               and 5.8 gigabytes of memory. Conclusions/Significance FastTree 2
               allows the inference of maximum-likelihood phylogenies for huge
               alignments. FastTree 2 is freely available at
               http://www.microbesonline.org/fasttree .",
  journal   = "PLoS One",
  publisher = "Public Library of Science",
  volume    =  5,
  number    =  3,
  pages     = "e9490",
  month     =  mar,
  year      =  2010,
  keywords  = "00-Ungrouped"
}

@ARTICLE{Zankari2012-jn,
  title    = "Identification of acquired antimicrobial resistance genes",
  author   = "Zankari, Ea and Hasman, Henrik and Cosentino, Salvatore and
              Vestergaard, Martin and Rasmussen, Simon and Lund, Ole and
              Aarestrup, Frank M and Larsen, Mette Voldby",
  abstract = "OBJECTIVES: Identification of antimicrobial resistance genes is
              important for understanding the underlying mechanisms and the
              epidemiology of antimicrobial resistance. As the costs of
              whole-genome sequencing (WGS) continue to decline, it becomes
              increasingly available in routine diagnostic laboratories and is
              anticipated to substitute traditional methods for resistance gene
              identification. Thus, the current challenge is to extract the
              relevant information from the large amount of generated data.
              METHODS: We developed a web-based method, ResFinder that uses
              BLAST for identification of acquired antimicrobial resistance
              genes in whole-genome data. As input, the method can use both
              pre-assembled, complete or partial genomes, and short sequence
              reads from four different sequencing platforms. The method was
              evaluated on 1862 GenBank files containing 1411 different
              resistance genes, as well as on 23 de-novo-sequenced isolates.
              RESULTS: When testing the 1862 GenBank files, the method
              identified the resistance genes with an ID = 100\% (100\%
              identity) to the genes in ResFinder. Agreement between in silico
              predictions and phenotypic testing was found when the method was
              further tested on 23 isolates of five different bacterial
              species, with available phenotypes. Furthermore, ResFinder was
              evaluated on WGS chromosomes and plasmids of 30 isolates. Seven
              of these isolates were annotated to have antimicrobial
              resistance, and in all cases, annotations were compatible with
              the ResFinder results. CONCLUSIONS: A web server providing a
              convenient way of identifying acquired antimicrobial resistance
              genes in completely sequenced isolates was created. ResFinder can
              be accessed at www.genomicepidemiology.org. ResFinder will
              continuously be updated as new resistance genes are identified.",
  journal  = "J. Antimicrob. Chemother.",
  volume   =  67,
  number   =  11,
  pages    = "2640--2644",
  month    =  nov,
  year     =  2012,
  language = "en"
}

% The entry below contains non-ASCII chars that could not be converted
% to a LaTeX equivalent.
@ARTICLE{Li2018-be,
  title    = "Minimap2: pairwise alignment for nucleotide sequences",
  author   = "Li, Heng",
  abstract = "Motivation: Recent advances in sequencing technologies promise
              ultra-long reads of ∼100 kb in average, full-length mRNA or cDNA
              reads in high throughput and genomic contigs over 100 Mb in
              length. Existing alignment programs are unable or inefficient to
              process such data at scale, which presses for the development of
              new alignment algorithms. Results: Minimap2 is a general-purpose
              alignment program to map DNA or long mRNA sequences against a
              large reference database. It works with accurate short reads of
              $\geq$100 bp in length, $\geq$1 kb genomic reads at error rate
              ∼15\%, full-length noisy Direct RNA or cDNA reads and assembly
              contigs or closely related full chromosomes of hundreds of
              megabases in length. Minimap2 does split-read alignment, employs
              concave gap cost for long insertions and deletions and introduces
              new heuristics to reduce spurious alignments. It is 3-4 times as
              fast as mainstream short-read mappers at comparable accuracy, and
              is $\geq$30 times faster than long-read genomic or cDNA mappers
              at higher accuracy, surpassing most aligners specialized in one
              type of alignment. Availability and implementation:
              https://github.com/lh3/minimap2. Supplementary information:
              Supplementary data are available at Bioinformatics online.",
  journal  = "Bioinformatics",
  volume   =  34,
  number   =  18,
  pages    = "3094--3100",
  month    =  sep,
  year     =  2018,
  language = "en"
}

% The entry below contains non-ASCII chars that could not be converted
% to a LaTeX equivalent.
@ARTICLE{Nawrocki2013-ht,
  title    = "Infernal 1.1: 100-fold faster {RNA} homology searches",
  author   = "Nawrocki, Eric P and Eddy, Sean R",
  abstract = "SUMMARY: Infernal builds probabilistic profiles of the sequence
              and secondary structure of an RNA family called covariance models
              (CMs) from structurally annotated multiple sequence alignments
              given as input. Infernal uses CMs to search for new family
              members in sequence databases and to create potentially large
              multiple sequence alignments. Version 1.1 of Infernal introduces
              a new filter pipeline for RNA homology search based on
              accelerated profile hidden Markov model (HMM) methods and
              HMM-banded CM alignment methods. This enables ∼100-fold
              acceleration over the previous version and ∼10 000-fold
              acceleration over exhaustive non-filtered CM searches.
              AVAILABILITY: Source code, documentation and the benchmark are
              downloadable from http://infernal.janelia.org. Infernal is freely
              licensed under the GNU GPLv3 and should be portable to any
              POSIX-compliant operating system, including Linux and Mac OS/X.
              Documentation includes a user's guide with a tutorial, a
              discussion of file formats and user options and additional
              details on methods implemented in the software. CONTACT:
              nawrockie@janelia.hhmi.org",
  journal  = "Bioinformatics",
  volume   =  29,
  number   =  22,
  pages    = "2933--2935",
  month    =  nov,
  year     =  2013,
  language = "en"
}

@ARTICLE{Galata2019-wt,
  title    = "{PLSDB}: a resource of complete bacterial plasmids",
  author   = "Galata, Valentina and Fehlmann, Tobias and Backes, Christina and
              Keller, Andreas",
  abstract = "The study of bacterial isolates or communities requires the
              analysis of the therein included plasmids in order to provide an
              extensive characterization of the organisms. Plasmids harboring
              resistance and virulence factors are of especial interest as they
              contribute to the dissemination of antibiotic resistance. As the
              number of newly sequenced bacterial genomes is growing a
              comprehensive resource is required which will allow to browse and
              filter the available plasmids, and to perform sequence analyses.
              Here, we present PLSDB, a resource containing 13 789 plasmid
              records collected from the NCBI nucleotide database. The web
              server provides an interactive view of all obtained plasmids with
              additional meta information such as sequence characteristics,
              sample-related information and taxonomy. Moreover, nucleotide
              sequence data can be uploaded to search for short nucleotide
              sequences (e.g. specific genes) in the plasmids, to compare a
              given plasmid to the records in the collection or to determine
              whether a sample contains one or multiple of the known plasmids
              (containment analysis). The resource is freely accessible under
              https://ccb-microbe.cs.uni-saarland.de/plsdb/.",
  journal  = "Nucleic Acids Res.",
  volume   =  47,
  number   = "D1",
  pages    = "D195--D202",
  month    =  jan,
  year     =  2019,
  language = "en"
}

@ARTICLE{Feldgarden2019-il,
  title    = "Validating the {NCBI} {AMRFinder} Tool and Resistance Gene
              Database Using Antimicrobial Resistance {Genotype-Phenotype}
              Correlations in a Collection of {NARMS} Isolates",
  author   = "Feldgarden, Michael and Brover, Vyacheslav and Haft, Daniel H and
              Prasad, Arjun B and Slotta, Douglas J and Tolstoy, Igor and
              Tyson, Gregory H and Zhao, Shaohua and Hsu, Chih-Hao and
              McDermott, Patrick F and Tadesse, Daniel A and Morales, Cesar and
              Simmons, Mustafa and Tillman, Glenn and Wasilenko, Jamie and
              Folster, Jason P and Klimke, William",
  abstract = "Antimicrobial resistance (AMR) is a major public health problem
              that requires publicly available tools for rapid analysis. To
              identify AMR genes in whole genome sequences, the National Center
              for Biotechnology Information (NCBI) has produced AMRFinder, a
              tool that identifies AMR genes using a high-quality curated AMR
              gene reference database. The Bacterial Antimicrobial Resistance
              Reference Gene Database consists of up-to-date gene nomenclature,
              a set of hidden Markov models (HMMs), and a curated protein
              family hierarchy. Currently, it contains 4,579 antimicrobial
              resistance proteins and more than 560 HMMs.Here, we describe
              AMRFinder and its associated database. To assess the predictive
              ability of AMRFinder, we measured the consistency between
              predicted AMR genotypes from AMRFinder and resistance phenotypes
              of 6,242 isolates from the National Antimicrobial Resistance
              Monitoring System (NARMS). This included 5,425 Salmonella
              enterica, 770 Campylobacter spp., and 47 Escherichia coli
              phenotypically tested against various antimicrobial agents. Of
              87,679 susceptibility tests performed, 98.4\% were consistent
              with predictions.To assess the accuracy of AMRFinder, we compared
              its gene symbol output with that of a 2017 version of ResFinder,
              another publicly available resistance gene detection system. Most
              gene calls were identical, but there were 1,229 gene symbol
              differences (8.8\%) between them, with differences due to both
              algorithmic differences and database composition. AMRFinder
              missed 16 loci that Resfinder found, while Resfinder missed 216
              loci AMRFinder identified. Based on these results, AMRFinder
              appears to be a highly accurate AMR gene detection system.",
  journal  = "Antimicrob. Agents Chemother.",
  month    =  aug,
  year     =  2019,
  language = "en"
}

% The entry below contains non-ASCII chars that could not be converted
% to a LaTeX equivalent.
@ARTICLE{Zerbino2008-yg,
  title    = "Velvet: Algorithms for de novo short read assembly using de
              Bruijn graphs",
  author   = "Zerbino, Daniel R and Birney, Ewan",
  abstract = "We have developed a new set of algorithms, collectively called
              ``Velvet,'' to manipulate de Bruijn graphs for genomic sequence
              assembly. A de Bruijn graph is a compact representation based on
              short words (k-mers) that is ideal for high coverage, very short
              read (25--50 bp) data sets. Applying Velvet to very short reads
              and paired-ends information only, one can produce contigs of
              significant length, up to 50-kb N50 length in simulations of
              prokaryotic data and 3-kb N50 on simulated mammalian BACs. When
              applied to real Solexa data sets without read pairs, Velvet
              generated contigs of ∼8 kb in a prokaryote and 2 kb in a
              mammalian BAC, in close agreement with our simulated results
              without read-pair information. Velvet represents a new approach
              to assembly that can leverage very short reads in combination
              with read pairs to produce useful assemblies.",
  journal  = "Genome Res.",
  volume   =  18,
  number   =  5,
  pages    = "821--829",
  month    =  may,
  year     =  2008
}

@ARTICLE{Chaumeil2019-tf,
  title    = "{GTDB-Tk}: a toolkit to classify genomes with the Genome Taxonomy
              Database",
  author   = "Chaumeil, Pierre-Alain and Mussig, Aaron J and Hugenholtz, Philip
              and Parks, Donovan H",
  abstract = "SUMMARY: The GTDB Toolkit (GTDB-Tk) provides objective taxonomic
              assignments for bacterial and archaeal genomes based on the
              Genome Taxonomy Database (GTDB). GTDB-Tk is computationally
              efficient and able to classify thousands of draft genomes in
              parallel. Here we demonstrate the accuracy of the GTDB-Tk
              taxonomic assignments by evaluating its performance on a
              phylogenetically diverse set of 10,156 bacterial and archaeal
              metagenome-assembled genomes. AVAILABILITY: GTDB-Tk is
              implemented in Python and licensed under the GNU General Public
              License v3.0. Source code and documentation are available at:
              https://github.com/ecogenomics/gtdbtk. SUPPLEMENTARY INFORMATION:
              Supplementary data are available at Bioinformatics online.",
  journal  = "Bioinformatics",
  month    =  nov,
  year     =  2019,
  language = "en"
}

@MISC{Petit_undated-mv,
  title       = "fastq-dl - Download {FASTQ} files from {SRA} or {ENA}
                 repositories",
  author      = "Petit, III, Robert A",
  abstract    = "Download FASTQ files from SRA or ENA repositories. -
                 rpetit3/fastq-dl",
  institution = "Github"
}

@ARTICLE{Bayliss2019-cv,
  title    = "{PIRATE}: A fast and scalable pangenomics toolbox for clustering
              diverged orthologues in bacteria",
  author   = "Bayliss, Sion C and Thorpe, Harry A and Coyle, Nicola M and
              Sheppard, Samuel K and Feil, Edward J",
  abstract = "BACKGROUND: Cataloguing the distribution of genes within natural
              bacterial populations is essential for understanding evolutionary
              processes and the genetic basis of adaptation. Advances in whole
              genome sequencing technologies have led to a vast expansion in
              the amount of bacterial genomes deposited in public databases.
              There is a pressing need for software solutions which are able to
              cluster, catalogue and characterise genes, or other features, in
              increasingly large genomic datasets. RESULTS: Here we present a
              pangenomics toolbox, PIRATE (Pangenome Iterative Refinement and
              Threshold Evaluation), which identifies and classifies
              orthologous gene families in bacterial pangenomes over a wide
              range of sequence similarity thresholds. PIRATE builds upon
              recent scalable software developments to allow for the rapid
              interrogation of thousands of isolates. PIRATE clusters genes (or
              other annotated features) over a wide range of amino acid or
              nucleotide identity thresholds and uses the clustering
              information to rapidly identify paralogous gene families and
              putative fission/fusion events. Furthermore, PIRATE orders the
              pangenome using a directed graph, provides a measure of allelic
              variation, and estimates sequence divergence for each gene
              family. CONCLUSIONS: We demonstrate that PIRATE scales linearly
              with both number of samples and computation resources, allowing
              for analysis of large genomic datasets, and compares favorably to
              other popular tools. PIRATE provides a robust framework for
              analysing bacterial pangenomes, from largely clonal to panmictic
              species.",
  journal  = "Gigascience",
  volume   =  8,
  number   =  10,
  month    =  oct,
  year     =  2019,
  keywords = "bioinformatics; microbial genomics; next-generation sequencing;
              pangenomics",
  language = "en"
}

@ARTICLE{Ondov2019-dw,
  title    = "Mash Screen: high-throughput sequence containment estimation for
              genome discovery",
  author   = "Ondov, Brian D and Starrett, Gabriel J and Sappington, Anna and
              Kostic, Aleksandra and Koren, Sergey and Buck, Christopher B and
              Phillippy, Adam M",
  abstract = "The MinHash algorithm has proven effective for rapidly estimating
              the resemblance of two genomes or metagenomes. However, this
              method cannot reliably estimate the containment of a genome
              within a metagenome. Here, we describe an online algorithm
              capable of measuring the containment of genomes and proteomes
              within either assembled or unassembled sequencing read sets. We
              describe several use cases, including contamination screening and
              retrospective analysis of metagenomes for novel genome discovery.
              Using this tool, we provide containment estimates for every NCBI
              RefSeq genome within every SRA metagenome and demonstrate the
              identification of a novel polyomavirus species from a public
              metagenome.",
  journal  = "Genome Biol.",
  volume   =  20,
  number   =  1,
  pages    = "232",
  month    =  nov,
  year     =  2019,
  keywords = "Metagenomics; MinHash; Polyomavirus; SRA; Sequencing; Viral
              Discovery",
  language = "en"
}

@ARTICLE{Souvorov2018-tg,
  title    = "{SKESA}: strategic k-mer extension for scrupulous assemblies",
  author   = "Souvorov, Alexandre and Agarwala, Richa and Lipman, David J",
  abstract = "SKESA is a DeBruijn graph-based de-novo assembler designed for
              assembling reads of microbial genomes sequenced using Illumina.
              Comparison with SPAdes and MegaHit shows that SKESA produces
              assemblies that have high sequence quality and contiguity,
              handles low-level contamination in reads, is fast, and produces
              an identical assembly for the same input when assembled multiple
              times with the same or different compute resources. SKESA has
              been used for assembling over 272,000 read sets in the Sequence
              Read Archive at NCBI and for real-time pathogen detection. Source
              code for SKESA is freely available at
              https://github.com/ncbi/SKESA/releases .",
  journal  = "Genome Biol.",
  volume   =  19,
  number   =  1,
  pages    = "153",
  month    =  oct,
  year     =  2018,
  keywords = "Contamination; De-novo assembly; DeBruijn graphs; Illumina reads;
              Sequence quality",
  language = "en"
}

@ARTICLE{Chen2016-sb,
  title    = "{VFDB} 2016: hierarchical and refined dataset for big data
              analysis--10 years on",
  author   = "Chen, Lihong and Zheng, Dandan and Liu, Bo and Yang, Jian and
              Jin, Qi",
  abstract = "The virulence factor database (VFDB, http://www.mgc.ac.cn/VFs/)
              is dedicated to providing up-to-date knowledge of virulence
              factors (VFs) of various bacterial pathogens. Since its inception
              the VFDB has served as a comprehensive repository of bacterial
              VFs for over a decade. The exponential growth in the amount of
              biological data is challenging to the current database in regard
              to big data analysis. We recently improved two aspects of the
              infrastructural dataset of VFDB: (i) removed the redundancy
              introduced by previous releases and generated two hierarchical
              datasets--one core dataset of experimentally verified VFs only
              and another full dataset including all known and predicted VFs
              and (ii) refined the gene annotation of the core dataset with
              controlled vocabularies. Our efforts enhanced the data quality of
              the VFDB and promoted the usability of the database in the big
              data era for the bioinformatic mining of the explosively growing
              data regarding bacterial VFs.",
  journal  = "Nucleic Acids Res.",
  volume   =  44,
  number   = "D1",
  pages    = "D694--7",
  month    =  jan,
  year     =  2016,
  language = "en"
}

@ARTICLE{Turner2018-zv,
  title    = "Integrating long-range connectivity information into de Bruijn
              graphs",
  author   = "Turner, Isaac and Garimella, Kiran V and Iqbal, Zamin and McVean,
              Gil",
  abstract = "Motivation: The de Bruijn graph is a simple and efficient data
              structure that is used in many areas of sequence analysis
              including genome assembly, read error correction and variant
              calling. The data structure has a single parameter k, is
              straightforward to implement and is tractable for large genomes
              with high sequencing depth. It also enables representation of
              multiple samples simultaneously to facilitate comparison.
              However, unlike the string graph, a de Bruijn graph does not
              retain long range information that is inherent in the read data.
              For this reason, applications that rely on de Bruijn graphs can
              produce sub-optimal results given their input data. Results: We
              present a novel assembly graph data structure: the Linked de
              Bruijn Graph (LdBG). Constructed by adding annotations on top of
              a de Bruijn graph, it stores long range connectivity information
              through the graph. We show that with error-free data it is
              possible to losslessly store and recover sequence from a Linked
              de Bruijn graph. With assembly simulations we demonstrate that
              the LdBG data structure outperforms both our de Bruijn graph and
              the String Graph Assembler (SGA). Finally we apply the LdBG to
              Klebsiella pneumoniae short read data to make large (12 kbp)
              variant calls, which we validate using PacBio sequencing data,
              and to characterize the genomic context of drug-resistance genes.
              Availability and implementation: Linked de Bruijn Graphs and
              associated algorithms are implemented as part of McCortex, which
              is available under the MIT license at
              https://github.com/mcveanlab/mccortex. Supplementary information:
              Supplementary data are available at Bioinformatics online.",
  journal  = "Bioinformatics",
  volume   =  34,
  number   =  15,
  pages    = "2556--2565",
  month    =  aug,
  year     =  2018,
  language = "en"
}

@ARTICLE{Carattoli2014-gd,
  title    = "In silico detection and typing of plasmids using {PlasmidFinder}
              and plasmid multilocus sequence typing",
  author   = "Carattoli, Alessandra and Zankari, Ea and
              Garc{\'\i}a-Fern{\'a}ndez, Aurora and Voldby Larsen, Mette and
              Lund, Ole and Villa, Laura and M{\o}ller Aarestrup, Frank and
              Hasman, Henrik",
  abstract = "In the work presented here, we designed and developed two
              easy-to-use Web tools for in silico detection and
              characterization of whole-genome sequence (WGS) and whole-plasmid
              sequence data from members of the family Enterobacteriaceae.
              These tools will facilitate bacterial typing based on draft
              genomes of multidrug-resistant Enterobacteriaceae species by the
              rapid detection of known plasmid types. Replicon sequences from
              559 fully sequenced plasmids associated with the family
              Enterobacteriaceae in the NCBI nucleotide database were collected
              to build a consensus database for integration into a Web tool
              called PlasmidFinder that can be used for replicon sequence
              analysis of raw, contig group, or completely assembled and closed
              plasmid sequencing data. The PlasmidFinder database currently
              consists of 116 replicon sequences that match with at least at
              80\% nucleotide identity all replicon sequences identified in the
              559 fully sequenced plasmids. For plasmid multilocus sequence
              typing (pMLST) analysis, a database that is updated weekly was
              generated from www.pubmlst.org and integrated into a Web tool
              called pMLST. Both databases were evaluated using draft genomes
              from a collection of Salmonella enterica serovar Typhimurium
              isolates. PlasmidFinder identified a total of 103 replicons and
              between zero and five different plasmid replicons within each of
              49 S. Typhimurium draft genomes tested. The pMLST Web tool was
              able to subtype genomic sequencing data of plasmids, revealing
              both known plasmid sequence types (STs) and new alleles and ST
              variants. In conclusion, testing of the two Web tools using both
              fully assembled plasmid sequences and WGS-generated draft genomes
              showed them to be able to detect a broad variety of plasmids that
              are often associated with antimicrobial resistance in clinically
              relevant bacterial pathogens.",
  journal  = "Antimicrob. Agents Chemother.",
  volume   =  58,
  number   =  7,
  pages    = "3895--3903",
  month    =  jul,
  year     =  2014,
  language = "en"
}

@ARTICLE{Quinlan2010-mq,
  title     = "{BEDTools}: a flexible suite of utilities for comparing genomic
               features",
  author    = "Quinlan, Aaron R and Hall, Ira M",
  abstract  = "MOTIVATION: Testing for correlations between different sets of
               genomic features is a fundamental task in genomics research.
               However, searching for overlaps between features with existing
               web-based methods is complicated by the massive datasets that
               are routinely produced with current sequencing technologies.
               Fast and flexible tools are therefore required to ask complex
               questions of these data in an efficient manner. RESULTS: This
               article introduces a new software suite for the comparison,
               manipulation and annotation of genomic features in Browser
               Extensible Data (BED) and General Feature Format (GFF) format.
               BEDTools also supports the comparison of sequence alignments in
               BAM format to both BED and GFF features. The tools are extremely
               efficient and allow the user to compare large datasets (e.g.
               next-generation sequencing data) with both public and custom
               genome annotation tracks. BEDTools can be combined with one
               another as well as with standard UNIX commands, thus
               facilitating routine genomics tasks as well as pipelines that
               can quickly answer intricate questions of large genomic
               datasets. AVAILABILITY AND IMPLEMENTATION: BEDTools was written
               in C++. Source code and a comprehensive user manual are freely
               available at http://code.google.com/p/bedtools CONTACT:
               aaronquinlan@gmail.com; imh4y@virginia.edu SUPPLEMENTARY
               INFORMATION: Supplementary data are available at Bioinformatics
               online.",
  journal   = "Bioinformatics",
  publisher = "Oxford Univ Press",
  volume    =  26,
  number    =  6,
  pages     = "841--842",
  month     =  mar,
  year      =  2010
}

@ARTICLE{Parks2018-rx,
  title    = "A standardized bacterial taxonomy based on genome phylogeny
              substantially revises the tree of life",
  author   = "Parks, Donovan H and Chuvochina, Maria and Waite, David W and
              Rinke, Christian and Skarshewski, Adam and Chaumeil, Pierre-Alain
              and Hugenholtz, Philip",
  abstract = "Taxonomy is an organizing principle of biology and is ideally
              based on evolutionary relationships among organisms. Development
              of a robust bacterial taxonomy has been hindered by an inability
              to obtain most bacteria in pure culture and, to a lesser extent,
              by the historical use of phenotypes to guide classification.
              Culture-independent sequencing technologies have matured
              sufficiently that a comprehensive genome-based taxonomy is now
              possible. We used a concatenated protein phylogeny as the basis
              for a bacterial taxonomy that conservatively removes polyphyletic
              groups and normalizes taxonomic ranks on the basis of relative
              evolutionary divergence. Under this approach, 58\% of the 94,759
              genomes comprising the Genome Taxonomy Database had changes to
              their existing taxonomy. This result includes the description of
              99 phyla, including six major monophyletic units from the
              subdivision of the Proteobacteria, and amalgamation of the
              Candidate Phyla Radiation into a single phylum. Our taxonomy
              should enable improved classification of uncultured bacteria and
              provide a sound basis for ecological and evolutionary studies.",
  journal  = "Nat. Biotechnol.",
  volume   =  36,
  number   =  10,
  pages    = "996--1004",
  month    =  nov,
  year     =  2018,
  language = "en"
}

@MISC{Blin_undated-mi,
  title       = "ncbi-genome-download - Scripts to download genomes from the
                 {NCBI} {FTP} servers",
  author      = "Blin, Kai",
  abstract    = "Scripts to download genomes from the NCBI FTP servers -
                 kblin/ncbi-genome-download",
  institution = "Github"
}

@ARTICLE{Parks2015-xm,
  title    = "{CheckM}: assessing the quality of microbial genomes recovered
              from isolates, single cells, and metagenomes",
  author   = "Parks, Donovan H and Imelfort, Michael and Skennerton, Connor T
              and Hugenholtz, Philip and Tyson, Gene W",
  abstract = "Large-scale recovery of genomes from isolates, single cells, and
              metagenomic data has been made possible by advances in
              computational methods and substantial reductions in sequencing
              costs. Although this increasing breadth of draft genomes is
              providing key information regarding the evolutionary and
              functional diversity of microbial life, it has become impractical
              to finish all available reference genomes. Making robust
              biological inferences from draft genomes requires accurate
              estimates of their completeness and contamination. Current
              methods for assessing genome quality are ad hoc and generally
              make use of a limited number of ``marker'' genes conserved across
              all bacterial or archaeal genomes. Here we introduce CheckM, an
              automated method for assessing the quality of a genome using a
              broader set of marker genes specific to the position of a genome
              within a reference genome tree and information about the
              collocation of these genes. We demonstrate the effectiveness of
              CheckM using synthetic data and a wide range of isolate-,
              single-cell-, and metagenome-derived genomes. CheckM is shown to
              provide accurate estimates of genome completeness and
              contamination and to outperform existing approaches. Using
              CheckM, we identify a diverse range of errors currently impacting
              publicly available isolate genomes and demonstrate that genomes
              obtained from single cells and metagenomic data vary
              substantially in quality. In order to facilitate the use of draft
              genomes, we propose an objective measure of genome quality that
              can be used to select genomes suitable for specific gene- and
              genome-centric analyses of microbial communities.",
  journal  = "Genome Res.",
  volume   =  25,
  number   =  7,
  pages    = "1043--1055",
  month    =  jul,
  year     =  2015,
  language = "en"
}

@ARTICLE{Bushnell2016-oa,
  title   = "{BBMap} short read aligner",
  author  = "Bushnell, B",
  journal = "University of California, Berkeley, California. URL
             http://sourceforge. net/projects/bbmap",
  year    =  2016
}

@ARTICLE{Inouye2014-do,
  title    = "{SRST2}: Rapid genomic surveillance for public health and
              hospital microbiology labs",
  author   = "Inouye, Michael and Dashnow, Harriet and Raven, Lesley-Ann and
              Schultz, Mark B and Pope, Bernard J and Tomita, Takehiro and
              Zobel, Justin and Holt, Kathryn E",
  abstract = "Rapid molecular typing of bacterial pathogens is critical for
              public health epidemiology, surveillance and infection control,
              yet routine use of whole genome sequencing (WGS) for these
              purposes poses significant challenges. Here we present SRST2, a
              read mapping-based tool for fast and accurate detection of genes,
              alleles and multi-locus sequence types (MLST) from WGS data.
              Using >900 genomes from common pathogens, we show SRST2 is highly
              accurate and outperforms assembly-based methods in terms of both
              gene detection and allele assignment. We include validation of
              SRST2 within a public health laboratory, and demonstrate its use
              for microbial genome surveillance in the hospital setting. In the
              face of rising threats of antimicrobial resistance and emerging
              virulence among bacterial pathogens, SRST2 represents a powerful
              tool for rapidly extracting clinically useful information from
              raw WGS data. Source code is available from
              http://katholt.github.io/srst2/.",
  journal  = "Genome Med.",
  volume   =  6,
  number   =  11,
  pages    = "90",
  month    =  nov,
  year     =  2014
}

@MISC{Seemann_undated-jc,
  title       = "snp-dists - Pairwise {SNP} distance matrix from a {FASTA}
                 sequence alignment",
  author      = "Seemann, Torsten",
  abstract    = "Pairwise SNP distance matrix from a FASTA sequence alignment -
                 tseemann/snp-dists",
  institution = "Github"
}

@ARTICLE{Katoh2013-ov,
  title    = "{MAFFT} multiple sequence alignment software version 7:
              improvements in performance and usability",
  author   = "Katoh, Kazutaka and Standley, Daron M",
  abstract = "We report a major update of the MAFFT multiple sequence alignment
              program. This version has several new features, including options
              for adding unaligned sequences into an existing alignment,
              adjustment of direction in nucleotide alignment, constrained
              alignment and parallel processing, which were implemented after
              the previous major update. This report shows actual examples to
              explain how these features work, alone and in combination. Some
              examples incorrectly aligned by MAFFT are also shown to clarify
              its limitations. We discuss how to avoid misalignments, and our
              ongoing efforts to overcome such limitations.",
  journal  = "Mol. Biol. Evol.",
  volume   =  30,
  number   =  4,
  pages    = "772--780",
  month    =  apr,
  year     =  2013,
  language = "en"
}

@ARTICLE{Hunt2017-og,
  title    = "{ARIBA}: rapid antimicrobial resistance genotyping directly from
              sequencing reads",
  author   = "Hunt, Martin and Mather, Alison E and S{\'a}nchez-Bus{\'o},
              Leonor and Page, Andrew J and Parkhill, Julian and Keane,
              Jacqueline A and Harris, Simon R",
  abstract = "Antimicrobial resistance (AMR) is one of the major threats to
              human and animal health worldwide, yet few high-throughput tools
              exist to analyse and predict the resistance of a bacterial
              isolate from sequencing data. Here we present a new tool, ARIBA,
              that identifies AMR-associated genes and single nucleotide
              polymorphisms directly from short reads, and generates detailed
              and customizable output. The accuracy and advantages of ARIBA
              over other tools are demonstrated on three datasets from
              Gram-positive and Gram-negative bacteria, with ARIBA
              outperforming existing methods.",
  journal  = "Microb Genom",
  volume   =  3,
  number   =  10,
  pages    = "e000131",
  month    =  oct,
  year     =  2017,
  keywords = "antimicrobial resistance; bacteria; genotyping; sequence typing;
              whole genome sequencing",
  language = "en"
}

@ARTICLE{Seemann2014-yf,
  title    = "Prokka: rapid prokaryotic genome annotation",
  author   = "Seemann, Torsten",
  abstract = "UNLABELLED: The multiplex capability and high yield of current
              day DNA-sequencing instruments has made bacterial whole genome
              sequencing a routine affair. The subsequent de novo assembly of
              reads into contigs has been well addressed. The final step of
              annotating all relevant genomic features on those contigs can be
              achieved slowly using existing web- and email-based systems, but
              these are not applicable for sensitive data or integrating into
              computational pipelines. Here we introduce Prokka, a command line
              software tool to fully annotate a draft bacterial genome in about
              10 min on a typical desktop computer. It produces
              standards-compliant output files for further analysis or viewing
              in genome browsers. AVAILABILITY AND IMPLEMENTATION: Prokka is
              implemented in Perl and is freely available under an open source
              GPLv2 license from http://vicbioinformatics.com/.",
  journal  = "Bioinformatics",
  volume   =  30,
  number   =  14,
  pages    = "2068--2069",
  month    =  jul,
  year     =  2014,
  language = "en"
}

@ARTICLE{Titus_Brown2016-jy,
  title    = "sourmash: a library for {MinHash} sketching of {DNA}",
  author   = "Titus Brown, C and Irber, Luiz",
  abstract = "Software archive",
  journal  = "JOSS",
  volume   =  1,
  number   =  5,
  pages    = "27",
  month    =  sep,
  year     =  2016
}

@ARTICLE{Jolley2018-om,
  title    = "Open-access bacterial population genomics: {BIGSdb} software, the
              {PubMLST.org} website and their applications",
  author   = "Jolley, Keith A and Bray, James E and Maiden, Martin C J",
  abstract = "The PubMLST.org website hosts a collection of open-access,
              curated databases that integrate population sequence data with
              provenance and phenotype information for over 100 different
              microbial species and genera. Although the PubMLST website was
              conceived as part of the development of the first multi-locus
              sequence typing (MLST) scheme in 1998 the software it uses, the
              Bacterial Isolate Genome Sequence database (BIGSdb, published in
              2010), enables PubMLST to include all levels of sequence data,
              from single gene sequences up to and including complete, finished
              genomes. Here we describe developments in the BIGSdb software
              made from publication to June 2018 and show how the platform
              realises microbial population genomics for a wide range of
              applications. The system is based on the gene-by-gene analysis of
              microbial genomes, with each deposited sequence annotated and
              curated to identify the genes present and systematically
              catalogue their variation. Originally intended as a means of
              characterising isolates with typing schemes, the synthesis of
              sequences and records of genetic variation with provenance and
              phenotype data permits highly scalable (whole genome sequence
              data for tens of thousands of isolates) means of addressing a
              wide range of functional questions, including: the prediction of
              antimicrobial resistance; likely cross-reactivity with vaccine
              antigens; and the functional activities of different variants
              that lead to key phenotypes. There are no limitations to the
              number of sequences, genetic loci, allelic variants or schemes
              (combinations of loci) that can be included, enabling each
              database to represent an expanding catalogue of the genetic
              variation of the population in question. In addition to providing
              web-accessible analyses and links to third-party analysis and
              visualisation tools, the BIGSdb software includes a RESTful
              application programming interface (API) that enables access to
              all the underlying data for third-party applications and data
              analysis pipelines.",
  journal  = "Wellcome Open Res",
  volume   =  3,
  pages    = "124",
  month    =  sep,
  year     =  2018,
  keywords = "Database; epidemiology; evolution; population annotation; public
              health",
  language = "en"
}

@MISC{Tange2018-de,
  title  = "{GNU} Parallel 2018",
  author = "Tange, Ole",
  month  =  apr,
  year   =  2018
}

@MISC{Danecek_undated-kw,
  title       = "{BCFtools} - Utilities for variant calling and manipulating
                 {VCFs} and {BCFs}",
  author      = "Danecek, P",
  abstract    = "This is the official development repository for BCFtools. To
                 compile, the develop branch of htslib is needed: git clone
                 --branch=develop git://github.com/samtools/htslib.git htslib -
                 samtools/bcftools",
  institution = "Github"
}

@UNPUBLISHED{Parks2019-re,
  title    = "Selection of representative genomes for 24,706 bacterial and
              archaeal species clusters provide a complete genome-based
              taxonomy",
  author   = "Parks, Donovan H and Chuvochina, Maria and Chaumeil, Pierre-Alain
              and Rinke, Christian and Mussig, Aaron J and Hugenholtz, Philip",
  abstract = "Abstract We recently introduced the Genome Taxonomy Database
              (GTDB), a phylogenetically consistent, genome-based taxonomy
              providing rank normalized classifications for nearly 150,000
              genomes from domain to genus. However, nearly 40\% of the genomes
              used to infer the GTDB reference tree lack a species name,
              reflecting the large number of genomes in public repositories
              without complete taxonomic assignments. Here we address this
              limitation by proposing 24,706 species clusters which encompass
              all publicly available bacterial and archaeal genomes when using
              commonly accepted average nucleotide identity (ANI) criteria for
              circumscribing species. In contrast to previous ANI studies, we
              selected a single representative genome to serve as the
              nomenclatural type for circumscribing each species with type
              strains used where available. We complemented the 8,792 species
              clusters with validly or effectively published names with 15,914
              de novo species clusters in order to assign placeholder names to
              the growing number of genomes from uncultivated species. This
              provides the first complete domain to species taxonomic framework
              which will improve communication of scientific results.",
  journal  = "bioRxiv",
  pages    = "771964",
  month    =  nov,
  year     =  2019,
  language = "en"
}

@ARTICLE{Li2015-jz,
  title    = "{MEGAHIT}: an ultra-fast single-node solution for large and
              complex metagenomics assembly via succinct de Bruijn graph",
  author   = "Li, Dinghua and Liu, Chi-Man and Luo, Ruibang and Sadakane,
              Kunihiko and Lam, Tak-Wah",
  abstract = "MEGAHIT is a NGS de novo assembler for assembling large and
              complex metagenomics data in a time- and cost-efficient manner.
              It finished assembling a soil metagenomics dataset with 252 Gbps
              in 44.1 and 99.6 h on a single computing node with and without a
              graphics processing unit, respectively. MEGAHIT assembles the
              data as a whole, i.e. no pre-processing like partitioning and
              normalization was needed. When compared with previous methods on
              assembling the soil data, MEGAHIT generated a three-time larger
              assembly, with longer contig N50 and average contig length;
              furthermore, 55.8\% of the reads were aligned to the assembly,
              giving a fourfold improvement.",
  journal  = "Bioinformatics",
  volume   =  31,
  number   =  10,
  pages    = "1674--1676",
  month    =  may,
  year     =  2015,
  language = "en"
}

@MISC{Seemann_undated-fn,
  title       = "Shovill: De novo assembly pipeline for Illumina paired reads",
  author      = "Seemann, Torsten",
  abstract    = "Faster SPAdes assembly of Illumina reads. Contribute to
                 tseemann/shovill development by creating an account on GitHub.",
  institution = "Github"
}

@UNPUBLISHED{Gruber-Vodicka2019-ch,
  title    = "phyloFlash -- Rapid {SSU} {rRNA} profiling and targeted assembly
              from metagenomes",
  author   = "Gruber-Vodicka, Harald R and Seah, Brandon K B and Pruesse, Elmar",
  abstract = "ABSTRACT The SSU rRNA gene is the key marker in molecular ecology
              for all domains of life, but is largely absent from
              metagenome-assembled genomes that often are the only resource
              available for environmental microbes. Here we present phyloFlash,
              a pipeline to overcome this gap with rapid, SSU rRNA-centered
              taxonomic classification, targeted assembly, and graph-based
              binning of full metagenomic assemblies. We show that a cleanup of
              artifacts is pivotal even with a curated reference database. With
              such a filtered database, the general-purpose mapper BBmap
              extracts SSU rRNA reads five times faster than the
              rRNA-specialized tool SortMeRNA with similar sensitivity and
              higher selectivity on simulated metagenomes. Reference-based
              targeted assemblers yielded either highly fragmented assemblies
              or high levels of chimerism, so we employ the general-purpose
              genomic assembler SPAdes. Our optimized implementation is
              independent of reference database composition and has
              satisfactory levels of chimera formation. Using the phyloFlash
              workflow we could recover the first complete genomes of several
              enigmatic taxa, including Marinamargulisbacteria from surface
              ocean seawater. phyloFlash quickly processes Illumina
              (meta)genomic data, is straightforward to use, even as part of
              high-throughput quality control, and has user-friendly output
              reports. The software is available at
              https://github.com/HRGV/phyloFlash (GPL3 license) and is
              documented with an online manual.",
  journal  = "bioRxiv",
  pages    = "521922",
  month    =  jan,
  year     =  2019,
  language = "en"
}

@ARTICLE{Quast2013-dv,
  title    = "The {SILVA} ribosomal {RNA} gene database project: improved data
              processing and web-based tools",
  author   = "Quast, Christian and Pruesse, Elmar and Yilmaz, Pelin and Gerken,
              Jan and Schweer, Timmy and Yarza, Pablo and Peplies, J{\"o}rg and
              Gl{\"o}ckner, Frank Oliver",
  abstract = "SILVA (from Latin silva, forest, http://www.arb-silva.de) is a
              comprehensive web resource for up to date, quality-controlled
              databases of aligned ribosomal RNA (rRNA) gene sequences from the
              Bacteria, Archaea and Eukaryota domains and supplementary online
              services. The referred database release 111 (July 2012) contains
              3 194 778 small subunit and 288 717 large subunit rRNA gene
              sequences. Since the initial description of the project,
              substantial new features have been introduced, including advanced
              quality control procedures, an improved rRNA gene aligner, online
              tools for probe and primer evaluation and optimized browsing,
              searching and downloading on the website. Furthermore, the
              extensively curated SILVA taxonomy and the new non-redundant
              SILVA datasets provide an ideal reference for high-throughput
              classification of data from next-generation sequencing
              approaches.",
  journal  = "Nucleic Acids Res.",
  volume   =  41,
  number   = "Database issue",
  pages    = "D590--6",
  month    =  jan,
  year     =  2013
}

@MISC{Petit_undated-kp,
  title       = "{VCF-Annotator}: Add biological annotations to variants in a
                 {VCF} file",
  author      = "Petit, III, Robert A",
  abstract    = "Add biological annotations to variants in a given VCF file. -
                 rpetit3/vcf-annotator",
  institution = "Github"
}

@ARTICLE{Li2006-xi,
  title    = "Cd-hit: a fast program for clustering and comparing large sets of
              protein or nucleotide sequences",
  author   = "Li, Weizhong and Godzik, Adam",
  abstract = "MOTIVATION: In 2001 and 2002, we published two papers
              (Bioinformatics, 17, 282-283, Bioinformatics, 18, 77-82)
              describing an ultrafast protein sequence clustering program
              called cd-hit. This program can efficiently cluster a huge
              protein database with millions of sequences. However, the
              applications of the underlying algorithm are not limited to only
              protein sequences clustering, here we present several new
              programs using the same algorithm including cd-hit-2d, cd-hit-est
              and cd-hit-est-2d. Cd-hit-2d compares two protein datasets and
              reports similar matches between them; cd-hit-est clusters a
              DNA/RNA sequence database and cd-hit-est-2d compares two
              nucleotide datasets. All these programs can handle huge datasets
              with millions of sequences and can be hundreds of times faster
              than methods based on the popular sequence comparison and
              database search tools, such as BLAST.",
  journal  = "Bioinformatics",
  volume   =  22,
  number   =  13,
  pages    = "1658--1659",
  month    =  jul,
  year     =  2006,
  language = "en"
}

@ARTICLE{OLeary2016-cd,
  title    = "Reference sequence ({RefSeq}) database at {NCBI}: current status,
              taxonomic expansion, and functional annotation",
  author   = "O'Leary, Nuala A and Wright, Mathew W and Brister, J Rodney and
              Ciufo, Stacy and Haddad, Diana and McVeigh, Rich and Rajput,
              Bhanu and Robbertse, Barbara and Smith-White, Brian and
              Ako-Adjei, Danso and Astashyn, Alexander and Badretdin, Azat and
              Bao, Yiming and Blinkova, Olga and Brover, Vyacheslav and
              Chetvernin, Vyacheslav and Choi, Jinna and Cox, Eric and
              Ermolaeva, Olga and Farrell, Catherine M and Goldfarb, Tamara and
              Gupta, Tripti and Haft, Daniel and Hatcher, Eneida and Hlavina,
              Wratko and Joardar, Vinita S and Kodali, Vamsi K and Li, Wenjun
              and Maglott, Donna and Masterson, Patrick and McGarvey, Kelly M
              and Murphy, Michael R and O'Neill, Kathleen and Pujar, Shashikant
              and Rangwala, Sanjida H and Rausch, Daniel and Riddick, Lillian D
              and Schoch, Conrad and Shkeda, Andrei and Storz, Susan S and Sun,
              Hanzhen and Thibaud-Nissen, Francoise and Tolstoy, Igor and
              Tully, Raymond E and Vatsan, Anjana R and Wallin, Craig and Webb,
              David and Wu, Wendy and Landrum, Melissa J and Kimchi, Avi and
              Tatusova, Tatiana and DiCuccio, Michael and Kitts, Paul and
              Murphy, Terence D and Pruitt, Kim D",
  abstract = "The RefSeq project at the National Center for Biotechnology
              Information (NCBI) maintains and curates a publicly available
              database of annotated genomic, transcript, and protein sequence
              records (http://www.ncbi.nlm.nih.gov/refseq/). The RefSeq project
              leverages the data submitted to the International Nucleotide
              Sequence Database Collaboration (INSDC) against a combination of
              computation, manual curation, and collaboration to produce a
              standard set of stable, non-redundant reference sequences. The
              RefSeq project augments these reference sequences with current
              knowledge including publications, functional features and
              informative nomenclature. The database currently represents
              sequences from more than 55,000 organisms (>4800 viruses, >40,000
              prokaryotes and >10,000 eukaryotes; RefSeq release 71), ranging
              from a single record to complete genomes. This paper summarizes
              the current status of the viral, prokaryotic, and eukaryotic
              branches of the RefSeq project, reports on improvements to data
              access and details efforts to further expand the taxonomic
              representation of the collection. We also highlight diverse
              functional curation initiatives that support multiple uses of
              RefSeq data including taxonomic validation, genome annotation,
              comparative genomics, and clinical testing. We summarize our
              approach to utilizing available RNA-Seq and other data types in
              our manual curation process for vertebrate, plant, and other
              species, and describe a new direction for prokaryotic genomes and
              protein name management.",
  journal  = "Nucleic Acids Res.",
  volume   =  44,
  number   = "D1",
  pages    = "D733--45",
  month    =  jan,
  year     =  2016,
  language = "en"
}

@ARTICLE{Li2013-sq,
  title         = "Aligning sequence reads, clone sequences and assembly
                   contigs with {BWA-MEM}",
  author        = "Li, Heng",
  abstract      = "Summary: BWA-MEM is a new alignment algorithm for aligning
                   sequence reads or long query sequences against a large
                   reference genome such as human. It automatically chooses
                   between local and end-to-end alignments, supports paired-end
                   reads and performs chimeric alignment. The algorithm is
                   robust to sequencing errors and applicable to a wide range
                   of sequence lengths from 70bp to a few megabases. For
                   mapping 100bp sequences, BWA-MEM shows better performance
                   than several state-of-art read aligners to date.
                   Availability and implementation: BWA-MEM is implemented as a
                   component of BWA, which is available at
                   http://github.com/lh3/bwa. Contact:
                   hengli@broadinstitute.org",
  month         =  mar,
  year          =  2013,
  archivePrefix = "arXiv",
  primaryClass  = "q-bio.GN",
  eprint        = "1303.3997"
}

@ARTICLE{Gupta2014-oe,
  title    = "{ARG-ANNOT}, a new bioinformatic tool to discover antibiotic
              resistance genes in bacterial genomes",
  author   = "Gupta, Sushim Kumar and Padmanabhan, Babu Roshan and Diene,
              Seydina M and Lopez-Rojas, Rafael and Kempf, Marie and Landraud,
              Luce and Rolain, Jean-Marc",
  abstract = "ARG-ANNOT (Antibiotic Resistance Gene-ANNOTation) is a new
              bioinformatic tool that was created to detect existing and
              putative new antibiotic resistance (AR) genes in bacterial
              genomes. ARG-ANNOT uses a local BLAST program in Bio-Edit
              software that allows the user to analyze sequences without a Web
              interface. All AR genetic determinants were collected from
              published works and online resources; nucleotide and protein
              sequences were retrieved from the NCBI GenBank database. After
              building a database that includes 1,689 antibiotic resistance
              genes, the software was tested in a blind manner using 100 random
              sequences selected from the database to verify that the
              sensitivity and specificity were at 100\% even when partial
              sequences were queried. Notably, BLAST analysis results obtained
              using the rmtF gene sequence (a new aminoglycoside-modifying
              enzyme gene sequence that is not included in the database) as a
              query revealed that the tool was able to link this sequence to
              short sequences (17 to 40 bp) found in other genes of the rmt
              family with significant E values. Finally, the analysis of 178
              Acinetobacter baumannii and 20 Staphylococcus aureus genomes
              allowed the detection of a significantly higher number of AR
              genes than the Resfinder gene analyzer and 11 point mutations in
              target genes known to be associated with AR. The average time for
              the analysis of a genome was 3.35 $\pm$ 0.13 min. We have created
              a concise database for BLAST using a Bio-Edit interface that can
              detect AR genetic determinants in bacterial genomes and can
              rapidly and easily discover putative new AR genetic determinants.",
  journal  = "Antimicrob. Agents Chemother.",
  volume   =  58,
  number   =  1,
  pages    = "212--220",
  year     =  2014,
  keywords = "PubMed S. aureus 2000-15/2014;S. aureus Genomics/No Data/PDF"
}

@ARTICLE{Jain2018-zl,
  title    = "High throughput {ANI} analysis of {90K} prokaryotic genomes
              reveals clear species boundaries",
  author   = "Jain, Chirag and Rodriguez-R, Luis M and Phillippy, Adam M and
              Konstantinidis, Konstantinos T and Aluru, Srinivas",
  abstract = "A fundamental question in microbiology is whether there is
              continuum of genetic diversity among genomes, or clear species
              boundaries prevail instead. Whole-genome similarity metrics such
              as Average Nucleotide Identity (ANI) help address this question
              by facilitating high resolution taxonomic analysis of thousands
              of genomes from diverse phylogenetic lineages. To scale to
              available genomes and beyond, we present FastANI, a new method to
              estimate ANI using alignment-free approximate sequence mapping.
              FastANI is accurate for both finished and draft genomes, and is
              up to three orders of magnitude faster compared to
              alignment-based approaches. We leverage FastANI to compute
              pairwise ANI values among all prokaryotic genomes available in
              the NCBI database. Our results reveal clear genetic
              discontinuity, with 99.8\% of the total 8 billion genome pairs
              analyzed conforming to >95\% intra-species and <83\%
              inter-species ANI values. This discontinuity is manifested with
              or without the most frequently sequenced species, and is robust
              to historic additions in the genome databases.",
  journal  = "Nat. Commun.",
  volume   =  9,
  number   =  1,
  pages    = "5114",
  month    =  nov,
  year     =  2018,
  language = "en"
}

@MISC{Bankevich_undated-ft,
  title        = "{SPAdes}: A New Genome Assembly Algorithm and Its
                  Applications to {Single-Cell} Sequencing | Journal of
                  Computational Biology",
  booktitle    = "Mary Ann Liebert, Inc., publishers",
  author       = "Bankevich, Anton and Nurk, Sergey and Antipov, Dmitry and
                  Gurevich, Alexey A and Dvorkin, Mikhail and Kulikov,
                  Alexander S and Lesin, Valery M and Nikolenko, Sergey I and
                  Pham, Son and Prjibelski, Andrey D and Pyshkin, Alexey V",
  howpublished = "\url{https://www.liebertpub.com/doi/10.1089/cmb.2012.0021}",
  note         = "Accessed: 2018-11-8"
}

@ARTICLE{Li2009-re,
  title     = "The Sequence {Alignment/Map} format and {SAMtools}",
  author    = "Li, Heng and Handsaker, Bob and Wysoker, Alec and Fennell, Tim
               and Ruan, Jue and Homer, Nils and Marth, Gabor and Abecasis,
               Goncalo and Durbin, Richard and {1000 Genome Project Data
               Processing Subgroup}",
  abstract  = "SUMMARY: The Sequence Alignment/Map (SAM) format is a generic
               alignment format for storing read alignments against reference
               sequences, supporting short and long reads (up to 128 Mbp)
               produced by different sequencing platforms. It is flexible in
               style, compact in size, efficient in random access and is the
               format in which alignments from the 1000 Genomes Project are
               released. SAMtools implements various utilities for
               post-processing alignments in the SAM format, such as indexing,
               variant caller and alignment viewer, and thus provides universal
               tools for processing read alignments. AVAILABILITY:
               http://samtools.sourceforge.net.",
  journal   = "Bioinformatics",
  publisher = "Oxford Univ Press",
  volume    =  25,
  number    =  16,
  pages     = "2078--2079",
  month     =  aug,
  year      =  2009
}

@ARTICLE{Cock2009-ep,
  title    = "Biopython: freely available Python tools for computational
              molecular biology and bioinformatics",
  author   = "Cock, Peter J A and Antao, Tiago and Chang, Jeffrey T and
              Chapman, Brad A and Cox, Cymon J and Dalke, Andrew and Friedberg,
              Iddo and Hamelryck, Thomas and Kauff, Frank and Wilczynski,
              Bartek and de Hoon, Michiel J L",
  abstract = "SUMMARY: The Biopython project is a mature open source
              international collaboration of volunteer developers, providing
              Python libraries for a wide range of bioinformatics problems.
              Biopython includes modules for reading and writing different
              sequence file formats and multiple sequence alignments, dealing
              with 3D macro molecular structures, interacting with common tools
              such as BLAST, ClustalW and EMBOSS, accessing key online
              databases, as well as providing numerical methods for statistical
              learning. AVAILABILITY: Biopython is freely available, with
              documentation and source code at (www.biopython.org) under the
              Biopython license.",
  journal  = "Bioinformatics",
  volume   =  25,
  number   =  11,
  pages    = "1422--1423",
  month    =  jun,
  year     =  2009,
  language = "en"
}

@ARTICLE{Lakin2017-gd,
  title    = "{MEGARes}: an antimicrobial resistance database for high
              throughput sequencing",
  author   = "Lakin, Steven M and Dean, Chris and Noyes, Noelle R and
              Dettenwanger, Adam and Ross, Anne Spencer and Doster, Enrique and
              Rovira, Pablo and Abdo, Zaid and Jones, Kenneth L and Ruiz, Jaime
              and Belk, Keith E and Morley, Paul S and Boucher, Christina",
  abstract = "Antimicrobial resistance has become an imminent concern for
              public health. As methods for detection and characterization of
              antimicrobial resistance move from targeted culture and
              polymerase chain reaction to high throughput metagenomics,
              appropriate resources for the analysis of large-scale data are
              required. Currently, antimicrobial resistance databases are
              tailored to smaller-scale, functional profiling of genes using
              highly descriptive annotations. Such characteristics do not
              facilitate the analysis of large-scale, ecological sequence
              datasets such as those produced with the use of metagenomics for
              surveillance. In order to overcome these limitations, we present
              MEGARes (https://megares.meglab.org), a hand-curated
              antimicrobial resistance database and annotation structure that
              provides a foundation for the development of high throughput
              acyclical classifiers and hierarchical statistical analysis of
              big data. MEGARes can be browsed as a stand-alone resource
              through the website or can be easily integrated into sequence
              analysis pipelines through download. Also via the website, we
              provide documentation for AmrPlusPlus, a user-friendly Galaxy
              pipeline for the analysis of high throughput sequencing data that
              is pre-packaged for use with the MEGARes database.",
  journal  = "Nucleic Acids Res.",
  volume   =  45,
  number   = "D1",
  pages    = "D574--D580",
  month    =  jan,
  year     =  2017,
  language = "en"
}

@ARTICLE{Page2016-bi,
  title    = "{SNP-sites}: rapid efficient extraction of {SNPs} from
              {multi-FASTA} alignments",
  author   = "Page, Andrew J and Taylor, Ben and Delaney, Aidan J and Soares,
              Jorge and Seemann, Torsten and Keane, Jacqueline A and Harris,
              Simon R",
  abstract = "Rapidly decreasing genome sequencing costs have led to a
              proportionate increase in the number of samples used in
              prokaryotic population studies. Extracting single nucleotide
              polymorphisms (SNPs) from a large whole genome alignment is now a
              routine task, but existing tools have failed to scale efficiently
              with the increased size of studies. These tools are slow, memory
              inefficient and are installed through non-standard procedures. We
              present SNP-sites which can rapidly extract SNPs from a
              multi-FASTA alignment using modest resources and can output
              results in multiple formats for downstream analysis. SNPs can be
              extracted from a 8.3 GB alignment file (1842 taxa, 22 618 sites)
              in 267 seconds using 59 MB of RAM and 1 CPU core, making it
              feasible to run on modest computers. It is easy to install
              through the Debian and Homebrew package managers, and has been
              successfully tested on more than 20 operating systems. SNP-sites
              is implemented in C and is available under the open source
              license GNU GPL version 3.",
  journal  = "Microb Genom",
  volume   =  2,
  number   =  4,
  pages    = "e000056",
  month    =  apr,
  year     =  2016,
  keywords = "SNP calling; high throughput; software",
  language = "en"
}

@ARTICLE{Bolger2014-vp,
  title    = "Trimmomatic: a flexible trimmer for Illumina sequence data",
  author   = "Bolger, Anthony M and Lohse, Marc and Usadel, Bjoern",
  abstract = "MOTIVATION: Although many next-generation sequencing (NGS) read
              preprocessing tools already existed, we could not find any tool
              or combination of tools that met our requirements in terms of
              flexibility, correct handling of paired-end data and high
              performance. We have developed Trimmomatic as a more flexible and
              efficient preprocessing tool, which could correctly handle
              paired-end data. RESULTS: The value of NGS read preprocessing is
              demonstrated for both reference-based and reference-free tasks.
              Trimmomatic is shown to produce output that is at least
              competitive with, and in many cases superior to, that produced by
              other tools, in all scenarios tested. AVAILABILITY AND
              IMPLEMENTATION: Trimmomatic is licensed under GPL V3. It is
              cross-platform (Java 1.5+ required) and available at
              http://www.usadellab.org/cms/index.php?page=trimmomatic CONTACT:
              usadel@bio1.rwth-aachen.de SUPPLEMENTARY INFORMATION:
              Supplementary data are available at Bioinformatics online.",
  journal  = "Bioinformatics",
  volume   =  30,
  number   =  15,
  pages    = "2114--2120",
  month    =  aug,
  year     =  2014,
  language = "en"
}

@ARTICLE{Didelot2015-tg,
  title    = "{ClonalFrameML}: efficient inference of recombination in whole
              bacterial genomes",
  author   = "Didelot, Xavier and Wilson, Daniel J",
  abstract = "Recombination is an important evolutionary force in bacteria, but
              it remains challenging to reconstruct the imports that occurred
              in the ancestry of a genomic sample. Here we present
              ClonalFrameML, which uses maximum likelihood inference to
              simultaneously detect recombination in bacterial genomes and
              account for it in phylogenetic reconstruction. ClonalFrameML can
              analyse hundreds of genomes in a matter of hours, and we
              demonstrate its usefulness on simulated and real datasets. We
              find evidence for recombination hotspots associated with mobile
              elements in Clostridium difficile ST6 and a previously
              undescribed 310kb chromosomal replacement in Staphylococcus
              aureus ST582. ClonalFrameML is freely available at
              http://clonalframeml.googlecode.com/.",
  journal  = "PLoS Comput. Biol.",
  volume   =  11,
  number   =  2,
  pages    = "e1004041",
  month    =  feb,
  year     =  2015,
  language = "en"
}

@ARTICLE{Vaser2017-wd,
  title    = "Fast and accurate de novo genome assembly from long uncorrected
              reads",
  author   = "Vaser, Robert and Sovi{\'c}, Ivan and Nagarajan, Niranjan and {\v
              S}iki{\'c}, Mile",
  abstract = "The assembly of long reads from Pacific Biosciences and Oxford
              Nanopore Technologies typically requires resource-intensive
              error-correction and consensus-generation steps to obtain
              high-quality assemblies. We show that the error-correction step
              can be omitted and that high-quality consensus sequences can be
              generated efficiently with a SIMD-accelerated, partial-order
              alignment-based, stand-alone consensus module called Racon. Based
              on tests with PacBio and Oxford Nanopore data sets, we show that
              Racon coupled with miniasm enables consensus genomes with similar
              or better quality than state-of-the-art methods while being an
              order of magnitude faster.",
  journal  = "Genome Res.",
  volume   =  27,
  number   =  5,
  pages    = "737--746",
  month    =  may,
  year     =  2017,
  language = "en"
}

@MISC{Petit_undated-ke,
  title       = "fastq-scan: generate summary statistics of input {FASTQ}
                 sequences",
  author      = "Petit, III, Robert A",
  abstract    = "Output FASTQ summary statistics in JSON format. Contribute to
                 rpetit3/fastq-scan development by creating an account on
                 GitHub.",
  institution = "Github"
}

@ARTICLE{Camacho2009-vc,
  title    = "{BLAST+}: architecture and applications",
  author   = "Camacho, Christiam and Coulouris, George and Avagyan, Vahram and
              Ma, Ning and Papadopoulos, Jason and Bealer, Kevin and Madden,
              Thomas L",
  abstract = "BACKGROUND: Sequence similarity searching is a very important
              bioinformatics task. While Basic Local Alignment Search Tool
              (BLAST) outperforms exact methods through its use of heuristics,
              the speed of the current BLAST software is suboptimal for very
              long queries or database sequences. There are also some
              shortcomings in the user-interface of the current command-line
              applications. RESULTS: We describe features and improvements of
              rewritten BLAST software and introduce new command-line
              applications. Long query sequences are broken into chunks for
              processing, in some cases leading to dramatically shorter run
              times. For long database sequences, it is possible to retrieve
              only the relevant parts of the sequence, reducing CPU time and
              memory usage for searches of short queries against databases of
              contigs or chromosomes. The program can now retrieve masking
              information for database sequences from the BLAST databases. A
              new modular software library can now access subject sequence data
              from arbitrary data sources. We introduce several new features,
              including strategy files that allow a user to save and reuse
              their favorite set of options. The strategy files can be uploaded
              to and downloaded from the NCBI BLAST web site. CONCLUSION: The
              new BLAST command-line applications, compared to the current
              BLAST tools, demonstrate substantial speed improvements for long
              queries as well as chromosome length database sequences. We have
              also improved the user interface of the command-line
              applications.",
  journal  = "BMC Bioinformatics",
  volume   =  10,
  pages    = "421",
  month    =  dec,
  year     =  2009,
  language = "en"
}

@ARTICLE{Lagesen2007-ui,
  title    = "{RNAmmer}: consistent and rapid annotation of ribosomal {RNA}
              genes",
  author   = "Lagesen, Karin and Hallin, Peter and R{\o}dland, Einar Andreas
              and Staerfeldt, Hans-Henrik and Rognes, Torbj{\o}rn and Ussery,
              David W",
  abstract = "The publication of a complete genome sequence is usually
              accompanied by annotations of its genes. In contrast to protein
              coding genes, genes for ribosomal RNA (rRNA) are often poorly or
              inconsistently annotated. This makes comparative studies based on
              rRNA genes difficult. We have therefore created computational
              predictors for the major rRNA species from all kingdoms of life
              and compiled them into a program called RNAmmer. The program uses
              hidden Markov models trained on data from the 5S ribosomal RNA
              database and the European ribosomal RNA database project. A
              pre-screening step makes the method fast with little loss of
              sensitivity, enabling the analysis of a complete bacterial genome
              in less than a minute. Results from running RNAmmer on a large
              set of genomes indicate that the location of rRNAs can be
              predicted with a very high level of accuracy. Novel, unannotated
              rRNAs are also predicted in many genomes. The software as well as
              the genome analysis results are available at the CBS web server.",
  journal  = "Nucleic Acids Res.",
  volume   =  35,
  number   =  9,
  pages    = "3100--3108",
  month    =  apr,
  year     =  2007,
  language = "en"
}

@ARTICLE{Matsen2010-oc,
  title    = "pplacer: linear time maximum-likelihood and Bayesian phylogenetic
              placement of sequences onto a fixed reference tree",
  author   = "Matsen, Frederick A and Kodner, Robin B and Armbrust, E Virginia",
  abstract = "BACKGROUND: Likelihood-based phylogenetic inference is generally
              considered to be the most reliable classification method for
              unknown sequences. However, traditional likelihood-based
              phylogenetic methods cannot be applied to large volumes of short
              reads from next-generation sequencing due to computational
              complexity issues and lack of phylogenetic signal. ``Phylogenetic
              placement,'' where a reference tree is fixed and the unknown
              query sequences are placed onto the tree via a reference
              alignment, is a way to bring the inferential power offered by
              likelihood-based approaches to large data sets. RESULTS: This
              paper introduces pplacer, a software package for phylogenetic
              placement and subsequent visualization. The algorithm can place
              twenty thousand short reads on a reference tree of one thousand
              taxa per hour per processor, has essentially linear time and
              memory complexity in the number of reference taxa, and is easy to
              run in parallel. Pplacer features calculation of the posterior
              probability of a placement on an edge, which is a statistically
              rigorous way of quantifying uncertainty on an edge-by-edge basis.
              It also can inform the user of the positional uncertainty for
              query sequences by calculating expected distance between
              placement locations, which is crucial in the estimation of
              uncertainty with a well-sampled reference tree. The software
              provides visualizations using branch thickness and color to
              represent number of placements and their uncertainty. A
              simulation study using reads generated from 631 COG alignments
              shows a high level of accuracy for phylogenetic placement over a
              wide range of alignment diversity, and the power of edge
              uncertainty estimates to measure placement confidence.
              CONCLUSIONS: Pplacer enables efficient phylogenetic placement and
              subsequent visualization, making likelihood-based phylogenetics
              methodology practical for large collections of reads; it is
              freely available as source code, binaries, and a web service.",
  journal  = "BMC Bioinformatics",
  volume   =  11,
  pages    = "538",
  month    =  oct,
  year     =  2010,
  language = "en"
}

@ARTICLE{Nguyen2015-ow,
  title    = "{IQ-TREE}: a fast and effective stochastic algorithm for
              estimating maximum-likelihood phylogenies",
  author   = "Nguyen, Lam-Tung and Schmidt, Heiko A and von Haeseler, Arndt and
              Minh, Bui Quang",
  abstract = "Large phylogenomics data sets require fast tree inference
              methods, especially for maximum-likelihood (ML) phylogenies. Fast
              programs exist, but due to inherent heuristics to find optimal
              trees, it is not clear whether the best tree is found. Thus,
              there is need for additional approaches that employ different
              search strategies to find ML trees and that are at the same time
              as fast as currently available ML programs. We show that a
              combination of hill-climbing approaches and a stochastic
              perturbation method can be time-efficiently implemented. If we
              allow the same CPU time as RAxML and PhyML, then our software
              IQ-TREE found higher likelihoods between 62.2\% and 87.1\% of the
              studied alignments, thus efficiently exploring the tree-space. If
              we use the IQ-TREE stopping rule, RAxML and PhyML are faster in
              75.7\% and 47.1\% of the DNA alignments and 42.2\% and 100\% of
              the protein alignments, respectively. However, the range of
              obtaining higher likelihoods with IQ-TREE improves to
              73.3-97.1\%. IQ-TREE is freely available at
              http://www.cibiv.at/software/iqtree.",
  journal  = "Mol. Biol. Evol.",
  volume   =  32,
  number   =  1,
  pages    = "268--274",
  month    =  jan,
  year     =  2015,
  keywords = "maximum likelihood; phylogenetic inference; phylogeny; stochastic
              algorithm",
  language = "en"
}

% The entry below contains non-ASCII chars that could not be converted
% to a LaTeX equivalent.
@ARTICLE{Fu2012-aw,
  title    = "{CD-HIT}: accelerated for clustering the next-generation
              sequencing data",
  author   = "Fu, Limin and Niu, Beifang and Zhu, Zhengwei and Wu, Sitao and
              Li, Weizhong",
  abstract = "SUMMARY: CD-HIT is a widely used program for clustering
              biological sequences to reduce sequence redundancy and improve
              the performance of other sequence analyses. In response to the
              rapid increase in the amount of sequencing data produced by the
              next-generation sequencing technologies, we have developed a new
              CD-HIT program accelerated with a novel parallelization strategy
              and some other techniques to allow efficient clustering of such
              datasets. Our tests demonstrated very good speedup derived from
              the parallelization for up to ∼24 cores and a quasi-linear
              speedup for up to ∼8 cores. The enhanced CD-HIT is capable of
              handling very large datasets in much shorter time than previous
              versions. AVAILABILITY: http://cd-hit.org. CONTACT: liwz@sdsc.edu
              SUPPLEMENTARY INFORMATION: Supplementary data are available at
              Bioinformatics online.",
  journal  = "Bioinformatics",
  volume   =  28,
  number   =  23,
  pages    = "3150--3152",
  month    =  dec,
  year     =  2012,
  language = "en"
}

@ARTICLE{Petit2018-yq,
  title    = "Staphylococcus aureus viewed from the perspective of 40,000+
              genomes",
  author   = "Petit, 3rd, Robert A and Read, Timothy D",
  abstract = "Low-cost Illumina sequencing of clinically-important bacterial
              pathogens has generated thousands of publicly available genomic
              datasets. Analyzing these genomes and extracting relevant
              information for each pathogen and the associated clinical
              phenotypes requires not only resources and bioinformatic skills
              but organism-specific knowledge. In light of these issues, we
              created Staphopia, an analysis pipeline, database and application
              programming interface, focused on Staphylococcus aureus, a common
              colonizer of humans and a major antibiotic-resistant pathogen
              responsible for a wide spectrum of hospital and
              community-associated infections. Written in Python, Staphopia's
              analysis pipeline consists of submodules running open-source
              tools. It accepts raw FASTQ reads as an input, which undergo
              quality control filtration, error correction and reduction to a
              maximum of approximately 100$\times$ chromosome coverage. This
              reduction significantly reduces total runtime without
              detrimentally affecting the results. The pipeline performs de
              novo assembly-based and mapping-based analysis. Automated gene
              calling and annotation is performed on the assembled contigs.
              Read-mapping is used to call variants (single nucleotide
              polymorphisms and insertion/deletions) against a reference S.
              aureus chromosome (N315, ST5). We ran the analysis pipeline on
              more than 43,000 S. aureus shotgun Illumina genome projects in
              the public European Nucleotide Archive database in November 2017.
              We found that only a quarter of known multi-locus sequence types
              (STs) were represented but the top 10 STs made up 70\% of all
              genomes. methicillin-resistant S. aureus (MRSA) were 64\% of all
              genomes. Using the Staphopia database we selected 380 high
              quality genomes deposited with good metadata, each from a
              different multi-locus ST, as a non-redundant diversity set for
              studying S. aureus evolution. In addition to answering basic
              science questions, Staphopia could serve as a potential platform
              for rapid clinical diagnostics of S. aureus isolates in the
              future. The system could also be adapted as a template for other
              organism-specific databases.",
  journal  = "PeerJ",
  volume   =  6,
  pages    = "e5261",
  month    =  jul,
  year     =  2018,
  keywords = "Antibiotic resistance; Database; MLST; MRSA; MSSA; S. aureus",
  language = "en"
}

@ARTICLE{Gurevich2013-rp,
  title    = "{QUAST}: quality assessment tool for genome assemblies",
  author   = "Gurevich, Alexey and Saveliev, Vladislav and Vyahhi, Nikolay and
              Tesler, Glenn",
  abstract = "SUMMARY: Limitations of genome sequencing techniques have led to
              dozens of assembly algorithms, none of which is perfect. A number
              of methods for comparing assemblers have been developed, but none
              is yet a recognized benchmark. Further, most existing methods for
              comparing assemblies are only applicable to new assemblies of
              finished genomes; the problem of evaluating assemblies of
              previously unsequenced species has not been adequately
              considered. Here, we present QUAST-a quality assessment tool for
              evaluating and comparing genome assemblies. This tool improves on
              leading assembly comparison software with new ideas and quality
              metrics. QUAST can evaluate assemblies both with a reference
              genome, as well as without a reference. QUAST produces many
              reports, summary tables and plots to help scientists in their
              research and in their publications. In this study, we used QUAST
              to compare several genome assemblers on three datasets. QUAST
              tables and plots for all of them are available in the
              Supplementary Material, and interactive versions of these reports
              are on the QUAST website. AVAILABILITY:
              http://bioinf.spbau.ru/quast . SUPPLEMENTARY INFORMATION:
              Supplementary data are available at Bioinformatics online.",
  journal  = "Bioinformatics",
  volume   =  29,
  number   =  8,
  pages    = "1072--1075",
  month    =  feb,
  year     =  2013,
  keywords = "00-Ungrouped"
}

@ARTICLE{Tan2015-oi,
  title    = "Unified representation of genetic variants",
  author   = "Tan, Adrian and Abecasis, Gon{\c c}alo R and Kang, Hyun Min",
  abstract = "UNLABELLED: A genetic variant can be represented in the Variant
              Call Format (VCF) in multiple different ways. Inconsistent
              representation of variants between variant callers and analyses
              will magnify discrepancies between them and complicate variant
              filtering and duplicate removal. We present a software tool vt
              normalize that normalizes representation of genetic variants in
              the VCF. We formally define variant normalization as the
              consistent representation of genetic variants in an unambiguous
              and concise way and derive a simple general algorithm to enforce
              it. We demonstrate the inconsistent representation of variants
              across existing sequence analysis tools and show that our tool
              facilitates integration of diverse variant types and call sets.
              AVAILABILITY AND IMPLEMENTATION: The source code is available for
              download at http://github.com/atks/vt. More detailed
              documentation is available at
              http://genome.sph.umich.edu/wiki/Variant\_Normalization. CONTACT:
              hmkang@umich.edu SUPPLEMENTARY INFORMATION: Supplementary data
              are available at Bioinformatics online.",
  journal  = "Bioinformatics",
  volume   =  31,
  number   =  13,
  pages    = "2202--2204",
  month    =  jul,
  year     =  2015,
  language = "en"
}

@ARTICLE{Wick2017-nz,
  title     = "Unicycler: Resolving bacterial genome assemblies from short and
               long sequencing reads",
  author    = "Wick, Ryan R and Judd, Louise M and Gorrie, Claire L and Holt,
               Kathryn E",
  abstract  = "The Illumina DNA sequencing platform generates accurate but
               short reads, which can be used to produce accurate but
               fragmented genome assemblies. Pacific Biosciences and Oxford
               Nanopore Technologies DNA sequencing platforms generate long
               reads that can produce complete genome assemblies, but the
               sequencing is more expensive and error-prone. There is
               significant interest in combining data from these complementary
               sequencing technologies to generate more accurate ``hybrid''
               assemblies. However, few tools exist that truly leverage the
               benefits of both types of data, namely the accuracy of short
               reads and the structural resolving power of long reads. Here we
               present Unicycler, a new tool for assembling bacterial genomes
               from a combination of short and long reads, which produces
               assemblies that are accurate, complete and cost-effective.
               Unicycler builds an initial assembly graph from short reads
               using the de novo assembler SPAdes and then simplifies the graph
               using information from short and long reads. Unicycler uses a
               novel semi-global aligner to align long reads to the assembly
               graph. Tests on both synthetic and real reads show Unicycler can
               assemble larger contigs with fewer misassemblies than other
               hybrid assemblers, even when long-read depth and accuracy are
               low. Unicycler is open source (GPLv3) and available at
               github.com/rrwick/Unicycler.",
  journal   = "PLoS Comput. Biol.",
  publisher = "journals.plos.org",
  volume    =  13,
  number    =  6,
  pages     = "e1005595",
  month     =  jun,
  year      =  2017,
  language  = "en"
}

@MISC{Kwong_undated-ha,
  title       = "maskrc-svg - Masks recombination as detected by
                 {ClonalFrameML} or Gubbins and draws an {SVG}",
  author      = "Kwong, Jason",
  abstract    = "Masks recombination as detected by ClonalFrameML or Gubbins
                 and draws an SVG - kwongj/maskrc-svg",
  institution = "Github"
}

@ARTICLE{Hawkey2015-ul,
  title    = "{ISMapper}: identifying transposase insertion sites in bacterial
              genomes from short read sequence data",
  author   = "Hawkey, Jane and Hamidian, Mohammad and Wick, Ryan R and Edwards,
              David J and Billman-Jacobe, Helen and Hall, Ruth M and Holt,
              Kathryn E",
  abstract = "BACKGROUND: Insertion sequences (IS) are small transposable
              elements, commonly found in bacterial genomes. Identifying the
              location of IS in bacterial genomes can be useful for a variety
              of purposes including epidemiological tracking and predicting
              antibiotic resistance. However IS are commonly present in
              multiple copies in a single genome, which complicates genome
              assembly and the identification of IS insertion sites. Here we
              present ISMapper, a mapping-based tool for identification of the
              site and orientation of IS insertions in bacterial genomes,
              directly from paired-end short read data. RESULTS: ISMapper was
              validated using three types of short read data: (i) simulated
              reads from a variety of species, (ii) Illumina reads from 5
              isolates for which finished genome sequences were available for
              comparison, and (iii) Illumina reads from 7 Acinetobacter
              baumannii isolates for which predicted IS locations were tested
              using PCR. A total of 20 genomes, including 13 species and 32
              distinct IS, were used for validation. ISMapper correctly
              identified 97 \% of known IS insertions in the analysis of
              simulated reads, and 98 \% in real Illumina reads. Subsampling of
              real Illumina reads to lower depths indicated ISMapper was able
              to correctly detect insertions for average genome-wide read
              depths >20x, although read depths >50x were required to obtain
              confident calls that were highly-supported by evidence from
              reads. All ISAba1 insertions identified by ISMapper in the A.
              baumannii genomes were confirmed by PCR. In each A. baumannii
              genome, ISMapper successfully identified an IS insertion upstream
              of the ampC beta-lactamase that could explain phenotypic
              resistance to third-generation cephalosporins. The utility of
              ISMapper was further demonstrated by profiling genome-wide IS6110
              insertions in 138 publicly available Mycobacterium tuberculosis
              genomes, revealing lineage-specific insertions and multiple
              insertion hotspots. CONCLUSIONS: ISMapper provides a rapid and
              robust method for identifying IS insertion sites directly from
              short read data, with a high degree of accuracy demonstrated
              across a wide range of bacteria.",
  journal  = "BMC Genomics",
  volume   =  16,
  pages    = "667",
  month    =  sep,
  year     =  2015,
  language = "en"
}

@MISC{Seemann_undated-ie,
  title       = "Snippy: fast bacterial variant calling from {NGS} reads",
  author      = "Seemann, Torsten",
  abstract    = ":scissors: :zap: Rapid haploid variant calling and core genome
                 alignment - tseemann/snippy",
  institution = "Github"
}

@ARTICLE{Wheeler2013-it,
  title    = "nhmmer: {DNA} homology search with profile {HMMs}",
  author   = "Wheeler, Travis J and Eddy, Sean R",
  abstract = "SUMMARY: Sequence database searches are an essential part of
              molecular biology, providing information about the function and
              evolutionary history of proteins, RNA molecules and DNA sequence
              elements. We present a tool for DNA/DNA sequence comparison that
              is built on the HMMER framework, which applies probabilistic
              inference methods based on hidden Markov models to the problem of
              homology search. This tool, called nhmmer, enables improved
              detection of remote DNA homologs, and has been used in
              combination with Dfam and RepeatMasker to improve annotation of
              transposable elements in the human genome. AVAILABILITY: nhmmer
              is a part of the new HMMER3.1 release. Source code and
              documentation can be downloaded from http://hmmer.org. HMMER3.1
              is freely licensed under the GNU GPLv3 and should be portable to
              any POSIX-compliant operating system, including Linux and Mac
              OS/X.",
  journal  = "Bioinformatics",
  volume   =  29,
  number   =  19,
  pages    = "2487--2489",
  month    =  oct,
  year     =  2013,
  language = "en"
}

@ARTICLE{Huang2012-sj,
  title    = "{ART}: a next-generation sequencing read simulator",
  author   = "Huang, Weichun and Li, Leping and Myers, Jason R and Marth, Gabor
              T",
  abstract = "UNLABELLED: ART is a set of simulation tools that generate
              synthetic next-generation sequencing reads. This functionality is
              essential for testing and benchmarking tools for next-generation
              sequencing data analysis including read alignment, de novo
              assembly and genetic variation discovery. ART generates simulated
              sequencing reads by emulating the sequencing process with
              built-in, technology-specific read error models and base quality
              value profiles parameterized empirically in large sequencing
              datasets. We currently support all three major commercial
              next-generation sequencing platforms: Roche's 454, Illumina's
              Solexa and Applied Biosystems' SOLiD. ART also allows the
              flexibility to use customized read error model parameters and
              quality profiles. AVAILABILITY: Both source and binary software
              packages are available at
              http://www.niehs.nih.gov/research/resources/software/art.",
  journal  = "Bioinformatics",
  volume   =  28,
  number   =  4,
  pages    = "593--594",
  month    =  feb,
  year     =  2012,
  language = "en"
}

@MISC{Blin_undated-tr,
  title       = "{NCBI} Genome Download: Scripts to download genomes from the
                 {NCBI} {FTP} servers",
  author      = "Blin, Kai",
  abstract    = "Scripts to download genomes from the NCBI FTP servers -
                 kblin/ncbi-genome-download",
  institution = "Github"
}

@MISC{Seemann_undated-ma,
  title       = "Samclip: Filter {SAM} file for soft and hard clipped
                 alignments",
  author      = "Seemann, Torsten",
  abstract    = "Filter SAM file for soft and hard clipped alignments -
                 tseemann/samclip",
  institution = "Github"
}

@ARTICLE{Eddy2011-bh,
  title    = "Accelerated Profile {HMM} Searches",
  author   = "Eddy, Sean R",
  abstract = "Profile hidden Markov models (profile HMMs) and probabilistic
              inference methods have made important contributions to the theory
              of sequence database homology search. However, practical use of
              profile HMM methods has been hindered by the computational
              expense of existing software implementations. Here I describe an
              acceleration heuristic for profile HMMs, the ``multiple segment
              Viterbi'' (MSV) algorithm. The MSV algorithm computes an optimal
              sum of multiple ungapped local alignment segments using a striped
              vector-parallel approach previously described for fast
              Smith/Waterman alignment. MSV scores follow the same statistical
              distribution as gapped optimal local alignment scores, allowing
              rapid evaluation of significance of an MSV score and thus
              facilitating its use as a heuristic filter. I also describe a
              20-fold acceleration of the standard profile HMM Forward/Backward
              algorithms using a method I call ``sparse rescaling''. These
              methods are assembled in a pipeline in which high-scoring MSV
              hits are passed on for reanalysis with the full HMM
              Forward/Backward algorithm. This accelerated pipeline is
              implemented in the freely available HMMER3 software package.
              Performance benchmarks show that the use of the heuristic MSV
              filter sacrifices negligible sensitivity compared to
              unaccelerated profile HMM searches. HMMER3 is substantially more
              sensitive and 100- to 1000-fold faster than HMMER2. HMMER3 is now
              about as fast as BLAST for protein searches.",
  journal  = "PLoS Comput. Biol.",
  volume   =  7,
  number   =  10,
  pages    = "e1002195",
  month    =  oct,
  year     =  2011,
  language = "en"
}

@ARTICLE{McArthur2013-nq,
  title    = "The comprehensive antibiotic resistance database",
  author   = "McArthur, Andrew G and Waglechner, Nicholas and Nizam, Fazmin and
              Yan, Austin and Azad, Marisa A and Baylay, Alison J and Bhullar,
              Kirandeep and Canova, Marc J and De Pascale, Gianfranco and Ejim,
              Linda and Kalan, Lindsay and King, Andrew M and Koteva, Kalinka
              and Morar, Mariya and Mulvey, Michael R and O'Brien, Jonathan S
              and Pawlowski, Andrew C and Piddock, Laura J V and
              Spanogiannopoulos, Peter and Sutherland, Arlene D and Tang, Irene
              and Taylor, Patricia L and Thaker, Maulik and Wang, Wenliang and
              Yan, Marie and Yu, Tennison and Wright, Gerard D",
  abstract = "The field of antibiotic drug discovery and the monitoring of new
              antibiotic resistance elements have yet to fully exploit the
              power of the genome revolution. Despite the fact that the first
              genomes sequenced of free living organisms were those of
              bacteria, there have been few specialized bioinformatic tools
              developed to mine the growing amount of genomic data associated
              with pathogens. In particular, there are few tools to study the
              genetics and genomics of antibiotic resistance and how it impacts
              bacterial populations, ecology, and the clinic. We have initiated
              development of such tools in the form of the Comprehensive
              Antibiotic Research Database (CARD; http://arpcard.mcmaster.ca).
              The CARD integrates disparate molecular and sequence data,
              provides a unique organizing principle in the form of the
              Antibiotic Resistance Ontology (ARO), and can quickly identify
              putative antibiotic resistance genes in new unannotated genome
              sequences. This unique platform provides an informatic tool that
              bridges antibiotic resistance concerns in health care,
              agriculture, and the environment.",
  journal  = "Antimicrob. Agents Chemother.",
  volume   =  57,
  number   =  7,
  pages    = "3348--3357",
  month    =  jul,
  year     =  2013,
  language = "en"
}

@ARTICLE{Langmead2012-gp,
  title    = "Fast gapped-read alignment with Bowtie 2",
  author   = "Langmead, Ben and Salzberg, Steven L",
  abstract = "As the rate of sequencing increases, greater throughput is
              demanded from read aligners. The full-text minute index is often
              used to make alignment very fast and memory-efficient, but the
              approach is ill-suited to finding longer, gapped alignments.
              Bowtie 2 combines the strengths of the full-text minute index
              with the flexibility and speed of hardware-accelerated dynamic
              programming algorithms to achieve a combination of high speed,
              sensitivity and accuracy.",
  journal  = "Nat. Methods",
  volume   =  9,
  number   =  4,
  pages    = "357--359",
  month    =  mar,
  year     =  2012,
  language = "en"
}

@ARTICLE{Finn2011-zk,
  title    = "{HMMER} web server: interactive sequence similarity searching",
  author   = "Finn, Robert D and Clements, Jody and Eddy, Sean R",
  abstract = "HMMER is a software suite for protein sequence similarity
              searches using probabilistic methods. Previously, HMMER has
              mainly been available only as a computationally intensive UNIX
              command-line tool, restricting its use. Recent advances in the
              software, HMMER3, have resulted in a 100-fold speed gain relative
              to previous versions. It is now feasible to make efficient
              profile hidden Markov model (profile HMM) searches via the web. A
              HMMER web server (http://hmmer.janelia.org) has been designed and
              implemented such that most protein database searches return
              within a few seconds. Methods are available for searching either
              a single protein sequence, multiple protein sequence alignment or
              profile HMM against a target sequence database, and for searching
              a protein sequence against Pfam. The web server is designed to
              cater to a range of different user expertise and accepts batch
              uploading of multiple queries at once. All search methods are
              also available as RESTful web services, thereby allowing them to
              be readily integrated as remotely executed tasks in locally
              scripted workflows. We have focused on minimizing search times
              and the ability to rapidly display tabular results, regardless of
              the number of matches found, developing graphical summaries of
              the search results to provide quick, intuitive appraisement of
              them.",
  journal  = "Nucleic Acids Res.",
  volume   =  39,
  number   = "Web Server issue",
  pages    = "W29--37",
  month    =  jul,
  year     =  2011,
  language = "en"
}

@ARTICLE{Adler2015-sr,
  title   = "pigz: A parallel implementation of gzip for modern
             multi-processor, multi-core machines",
  author  = "Adler, Mark",
  journal = "Jet Propulsion Laboratory",
  year    =  2015
}

@ARTICLE{Huerta-Cepas2019-xa,
  title    = "{eggNOG} 5.0: a hierarchical, functionally and phylogenetically
              annotated orthology resource based on 5090 organisms and 2502
              viruses",
  author   = "Huerta-Cepas, Jaime and Szklarczyk, Damian and Heller, Davide and
              Hern{\'a}ndez-Plaza, Ana and Forslund, Sofia K and Cook, Helen
              and Mende, Daniel R and Letunic, Ivica and Rattei, Thomas and
              Jensen, Lars J and von Mering, Christian and Bork, Peer",
  abstract = "eggNOG is a public database of orthology relationships, gene
              evolutionary histories and functional annotations. Here, we
              present version 5.0, featuring a major update of the underlying
              genome sets, which have been expanded to 4445 representative
              bacteria and 168 archaea derived from 25 038 genomes, as well as
              477 eukaryotic organisms and 2502 viral proteomes that were
              selected for diversity and filtered by genome quality. In total,
              4.4M orthologous groups (OGs) distributed across 379 taxonomic
              levels were computed together with their associated sequence
              alignments, phylogenies, HMM models and functional descriptors.
              Precomputed evolutionary analysis provides fine-grained
              resolution of duplication/speciation events within each OG. Our
              benchmarks show that, despite doubling the amount of genomes, the
              quality of orthology assignments and functional annotations (80\%
              coverage) has persisted without significant changes across this
              update. Finally, we improved eggNOG online services for fast
              functional annotation and orthology prediction of custom genomics
              or metagenomics datasets. All precomputed data are publicly
              available for downloading or via API queries at
              http://eggnog.embl.de.",
  journal  = "Nucleic Acids Res.",
  volume   =  47,
  number   = "D1",
  pages    = "D309--D314",
  month    =  jan,
  year     =  2019,
  language = "en"
}

@MISC{Petit_undated-hw,
  title       = "assembly-scan: generate basic stats for an assembly",
  author      = "Petit, III, Robert A",
  abstract    = "Generate basic stats for an assembly. Contribute to
                 rpetit3/assembly-scan development by creating an account on
                 GitHub.",
  institution = "Github"
}

@ARTICLE{Cingolani2012-lb,
  title    = "A program for annotating and predicting the effects of single
              nucleotide polymorphisms, {SnpEff}: {SNPs} in the genome of
              Drosophila melanogaster strain w1118; iso-2; iso-3",
  author   = "Cingolani, Pablo and Platts, Adrian and Wang, Le Lily and Coon,
              Melissa and Nguyen, Tung and Wang, Luan and Land, Susan J and Lu,
              Xiangyi and Ruden, Douglas M",
  abstract = "We describe a new computer program, SnpEff, for rapidly
              categorizing the effects of variants in genome sequences. Once a
              genome is sequenced, SnpEff annotates variants based on their
              genomic locations and predicts coding effects. Annotated genomic
              locations include intronic, untranslated region, upstream,
              downstream, splice site, or intergenic regions. Coding effects
              such as synonymous or non-synonymous amino acid replacement,
              start codon gains or losses, stop codon gains or losses, or frame
              shifts can be predicted. Here the use of SnpEff is illustrated by
              annotating ~356,660 candidate SNPs in ~117 Mb unique sequences,
              representing a substitution rate of ~1/305 nucleotides, between
              the Drosophila melanogaster w(1118); iso-2; iso-3 strain and the
              reference y(1); cn(1) bw(1) sp(1) strain. We show that ~15,842
              SNPs are synonymous and ~4,467 SNPs are non-synonymous (N/S
              ~0.28). The remaining SNPs are in other categories, such as stop
              codon gains (38 SNPs), stop codon losses (8 SNPs), and start
              codon gains (297 SNPs) in the 5'UTR. We found, as expected, that
              the SNP frequency is proportional to the recombination frequency
              (i.e., highest in the middle of chromosome arms). We also found
              that start-gain or stop-lost SNPs in Drosophila melanogaster
              often result in additions of N-terminal or C-terminal amino acids
              that are conserved in other Drosophila species. It appears that
              the 5' and 3' UTRs are reservoirs for genetic variations that
              changes the termini of proteins during evolution of the
              Drosophila genus. As genome sequencing is becoming inexpensive
              and routine, SnpEff enables rapid analyses of whole-genome
              sequencing data to be performed by an individual laboratory.",
  journal  = "Fly",
  volume   =  6,
  number   =  2,
  pages    = "80--92",
  month    =  apr,
  year     =  2012,
  language = "en"
}
