@Manual{tidyverse,
    title = {tidyverse: Easily Install and Load the 'Tidyverse'},
    author = {Hadley Wickham},
    year = {2017},
    note = {R package version 1.2.1},
    url = {https://CRAN.R-project.org/package=tidyverse},
}

 @Article{decipher,
    title = {Using DECIPHER v2.0 to Analyze Big Biological Sequence Data in R},
    author = {Erik S. Wright},
    journal = {The R Journal},
    year = {2016},
    volume = {8},
    number = {1},
    pages = {352-359},
}


@ARTICLE{Wright2015-me,
  title    = "{DECIPHER}: harnessing local sequence context to improve protein
              multiple sequence alignment",
  author   = "Wright, Erik S",
  abstract = "BACKGROUND: Alignment of large and diverse sequence sets is a
              common task in biological investigations, yet there remains
              considerable room for improvement in alignment quality. Multiple
              sequence alignment programs tend to reach maximal accuracy when
              aligning only a few sequences, and then diminish steadily as more
              sequences are added. This drop in accuracy can be partly
              attributed to a build-up of error and ambiguity as more sequences
              are aligned. Most high-throughput sequence alignment algorithms
              do not use contextual information under the assumption that sites
              are independent. This study examines the extent to which local
              sequence context can be exploited to improve the quality of large
              multiple sequence alignments. RESULTS: Two predictors based on
              local sequence context were assessed: (i) single sequence
              secondary structure predictions, and (ii) modulation of gap costs
              according to the surrounding residues. The results indicate that
              context-based predictors have appreciable information content
              that can be utilized to create more accurate alignments.
              Furthermore, local context becomes more informative as the number
              of sequences increases, enabling more accurate protein alignments
              of large empirical benchmarks. These discoveries became the basis
              for DECIPHER, a new context-aware program for sequence alignment,
              which outperformed other programs on large sequence sets.
              CONCLUSIONS: Predicting secondary structure based on local
              sequence context is an efficient means of breaking the
              independence assumption in alignment. Since secondary structure
              is more conserved than primary sequence, it can be leveraged to
              improve the alignment of distantly related proteins. Moreover,
              secondary structure predictions increase in accuracy as more
              sequences are used in the prediction. This enables the scalable
              generation of large sequence alignments that maintain high
              accuracy even on diverse sequence sets. The DECIPHER R package
              and source code are freely available for download at
              DECIPHER.cee.wisc.edu and from the Bioconductor repository.",
  journal  = "BMC Bioinformatics",
  volume   =  16,
  pages    = "322",
  month    =  oct,
  year     =  2015,
  language = "en"
}


@Manual{ggpubr,
    title = {ggpubr: 'ggplot2' Based Publication Ready Plots},
    author = {Alboukadel Kassambara},
    year = {2017},
    note = {R package version 0.1.6},
    url = {https://CRAN.R-project.org/package=ggpubr},
}

@article{grady2016,
  title={Current knowledge and perspectives of Paenibacillus: a review},
  author={Grady, Elliot Nicholas and MacDonald, Jacqueline and Liu, Linda and Richman, Alex and Yuan, Ze-Chun},
  journal={Microbial cell factories},
  volume={15},
  number={1},
  pages={203},
  year={2016},
  publisher={BioMed Central}
}
@article{ouyang2008,
  title={Paenibacillus thiaminolyticus: a new cause of human infection, inducing bacteremia in a patient on hemodialysis},
  author={Ouyang, Jie and Pei, Zhiheng and Lutwick, Larry and Dalal, Sharvari and Yang, Liying and Cassai, Nicholas and Sandhu, Kuldip and Hanna, Bruce and Wieczorek, Rosemary L and Bluth, Martin and others},
  journal={Annals of Clinical \& Laboratory Science},
  volume={38},
  number={4},
  pages={393--400},
  year={2008},
  publisher={Assoc Clin Scientists}
}
