% Generated by Paperpile. Check out http://paperpile.com for more information.
% BibTeX export options can be customized via Settings -> BibTeX.

@ARTICLE{Narsai2017-ql,
  title    = "Dynamic and rapid changes in the transcriptome and epigenome
              during germination and in developing rice ( Oryza sativa )
              coleoptiles under anoxia and re-oxygenation",
  author   = "Narsai, Reena and Secco, David and Schultz, Matthew D and Ecker,
              Joseph R and Lister, Ryan and Whelan, James",
  abstract = "Summary Detailed molecular profiling of Oryza sativa (rice) was
              carried out to uncover the features that are essential for
              germination and early seedling growth under anoxic conditions.
              Temporal analysis of the transcriptome and methylome from
              germination to young seedlings under aerobic and anaerobic
              conditions revealed 82\% similarity in the transcriptome and no
              differences in the epigenome up to 24 h. Following germination,
              significant changes in the transcriptome and DNA methylation were
              observed between 4-day aerobically and anaerobically grown
              coleoptiles. A link between the epigenomic state and cell
              division versus cell elongation is suggested, as no differences
              in DNA methylation were observed between 24-h aerobically and
              anaerobically germinating embryos, when there is little cell
              division. After that, epigenetic changes appear to correlate with
              differences between cell elongation (anaerobic conditions) versus
              cell division (aerobic conditions) in the coleoptiles.
              Re-oxygenation of 3-day anaerobically grown seedlings resulted in
              rapid transcriptomic changes in DNA methylation in these
              coleoptiles. Unlike the transcriptome, changes in DNA methylation
              upon re-oxygenation did not reflect those seen in aerobic
              coleoptiles, but instead, reverted to a pattern similar to dry
              seeds. Reversion to the ?dry seed? state of DNA methylation upon
              re-oxygenation may act to ?reset the clock? for the rapid
              molecular changes and cell division that result upon
              re-oxygenation.",
  journal  = "Plant J.",
  volume   =  89,
  number   =  4,
  pages    = "805--824",
  month    =  feb,
  year     =  2017
}

@Manual{hdf5array,                                                                                                                        
    title = {HDF5Array: HDF5 backend for DelayedArray objects},                                                                    
    author = {Hervé Pagès},                                                                                                        
    year = {2020},                                                                                                                 
    note = {R package version 1.16.1},                                                                                             
  }   
@Manual{rhdf5,                                                                                                                        
    title = {rhdf5: R Interface to HDF5},                                                                                          
    author = {Bernd Fischer and Mike Smith and Gregoire Pau},                                                                      
    year = {2020},                                                                                                                 
    note = {R package version 2.32.2},                                                                                             
    url = {https://github.com/grimbough/rhdf5},                                                                                    
  }    

@Manual{xml2,                                                                                                                        
  title = {xml2: Parse XML},                                                                                                     
  author = {Hadley Wickham and Jim Hester and Jeroen Ooms},                                                                      
  year = {2020},                                                                                                                 
  note = {R package version 1.3.2},                                                                                              
  url = {https://CRAN.R-project.org/package=xml2},                                                                               
}   

@Manual{ggplotify,                                                                                                                        
  title = {ggplotify: Convert Plot to 'grob' or 'ggplot' Object},                                                                
  author = {Guangchuang Yu},                                                                                                     
  year = {2020},                                                                                                                 
  note = {R package version 0.0.5},                                                                                              
  url = {https://CRAN.R-project.org/package=ggplotify},                                                                          
}

@Article{biobase, 
  author = {W. Huber and V. J. Carey and R. Gentleman and S. An          
ders and M. Carlson and B. S. Carvalho and H. C. Bravo and S. Dav  
is and L. Gatto and T. Girke and R. Gottardo and F. Hahne and K. 
D. Hansen and R. A. Irizarry and M. Lawrence and M. I. Love and J 
. MacDonald and V. Obenchain and A. K. {Ole's} and H. {Pag`es} and A. Reyes and P. Shannon and G. K. Smyth and D. Tenenbaum and L. 
 Waldron and M. Morgan}, 
    title = {{O}rchestrating high-throughput genomic analysis wit
h {B}ioconductor},
    journal = {Nature Methods},
    year = {2015},
    volume = {12},
    number = {2},
    pages = {115--121},
    url = {http://www.nature.com/nmeth/journal/v12/n2/full/nmeth.3252.html},
  }  

@ARTICLE{Langfelder2012-nv,
  title    = {Fast R Functions for Robust Correlations and Hierarchical Clustering},
  author   = {P. Langfelder and S. Horvath},
  journal  = {J. Stat. Softw.},
  volume   =  {46},
  number   =  {11},
  year     =  {2012},
  url      = {https://www.ncbi.nlm.nih.gov/pubmed/23050260},
}

@Manual{biocstyle,                                                                                                                        
  title = {BiocStyle: Standard styles for vignettes and other B                                                                  
ioconductor documents},                                                                                                            
  author = {Andrzej Oleś and Martin Morgan and Wolfgang Huber},                                                                  
  year = {2020},                                                                                                                 
  note = {R package version 2.16.0},                                                                                             
  url = {https://github.com/Bioconductor/BiocStyle},                                                                             
}   

@Manual{rols,                                                                                                                        
  title = {rols: An R interface to the Ontology Lookup Service},                                                                                                                                  
  author = {Laurent Gatto},                                                                    
  year = {2019},                                                        
  note = {R package version 2.14.0},             
  url = {http://lgatto.github.com/rols/},
}  

@Manual{ebi,
  title = {ExpressionAtlas: Download datasets from EMBL-EBI Expression Atlas},
  author = {Maria Keays},
  year = {2019},
  note = {R package version 1.14.0}
}

@Article{geo,
  author = {Sean Davis and Paul Meltzer},
  title = {GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor},
  journal = {Bioinformatics},
  year = {2007},
  volume = {14},
  pages = {1846--1847}
}

@Manual{graphics,
  title = {R: A Language and Environment for Statistical Computing},
  author = {{R Core Team}},
  organization = {R Foundation for Statistical Computing},
  address = {Vienna, Austria},
  year = {2018},
  url = {https://www.R-project.org/},
}
@Manual{stats,
  title = {R: A Language and Environment for Statistical Computing},
  author = {{R Core Team}},
  organization = {R Foundation for Statistical Computing},
  address = {Vienna, Austria},
  year = {2018},
  url = {https://www.R-project.org/},
}
@Manual{methods,
  title = {R: A Language and Environment for Statistical Computing},
  author = {{R Core Team}},
  organization = {R Foundation for Statistical Computing},
  address = {Vienna, Austria},
  year = {2018},
  url = {https://www.R-project.org/},
}
@Manual{grDevices,
  title = {R: A Language and Environment for Statistical Computing},
  author = {{R Core Team}},
  organization = {R Foundation for Statistical Computing},
  address = {Vienna, Austria},
  year = {2018},
  url = {https://www.R-project.org/},
}
@Manual{grid,
  title = {R: A Language and Environment for Statistical Computing},
  author = {{R Core Team}},
  organization = {R Foundation for Statistical Computing},
  address = {Vienna, Austria},
  year = {2018},
  url = {https://www.R-project.org/},
}
@Manual{utils,
  title = {R: A Language and Environment for Statistical Computing},
  author = {{R Core Team}},
  organization = {R Foundation for Statistical Computing},
  address = {Vienna, Austria},
  year = {2018},
  url = {https://www.R-project.org/},
}

@Manual{gcrma,
  title = {gcrma: Background Adjustment Using Sequence Information}, 
  author = {Jean Wu and Rafael Irizarry with contributions from James MacDonald Jeff Gentry},
  year = {2018},
  note = {R package version 2.54.0},
}

@Article{affy,
 author = {Laurent Gautier and Leslie Cope and Benjamin M. Bolstad and Rafael A. Irizarry},
 title = {affy---analysis of Affymetrix GeneChip data at the probe level},
 journal = {Bioinformatics},
 volume = {20},
 number = {3},
 year = {2004},
 issn = {1367-4803},
 pages = {307--315},
 doi = {10.1093/bioinformatics/btg405},
 publisher = {Oxford University Press},
 address = {Oxford, UK},
}

@Article{DESeq2,
  title = {Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2},
  author = {Michael I. Love and Wolfgang Huber and Simon Anders},
  year = {2014},
  journal = {Genome Biology},
  doi = {10.1186/s13059-014-0550-8},
  volume = {15},
  issue = {12},
  pages = {550},
}

@Article{edgeR,
  author = {{McCarthy} and Davis J. and {Chen} and {Yunshun} and {Smyth} and Gordon K.},
  title = {Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation},
  year = {2012},
  journal = {Nucleic Acids Research},
  volume = {40},
  number = {10},
  pages = {4288-4297},
}

@Article{edgeR1,
    title = {edgeR: a Bioconductor package for differential expression analysis of digital gene expression data},
    author = {Mark D Robinson and Davis J McCarthy and Gordon K Smyth},                                                            
    journal = {Bioinformatics},                                                                                                    
    volume = {26},                                                                                                                 
    number = {1},                                                                                                                  
    pages = {139-140},                                                                                                             
    year = {2010},                                                                                                                 
    doi = {10.1093/bioinformatics/btp616},                                                                                         
}   
@ARTICLE{limma,
  title    = "limma powers differential expression analyses for
              {RNA-sequencing} and microarray studies",
  author   = "Ritchie, Matthew E and Phipson, Belinda and Wu, Di and Hu, Yifang
              and Law, Charity W and Shi, Wei and Smyth, Gordon K",
  abstract = "limma is an R/Bioconductor software package that provides an
              integrated solution for analysing data from gene expression
              experiments. It contains rich features for handling complex
              experimental designs and for information borrowing to overcome
              the problem of small sample sizes. Over the past decade, limma
              has been a popular choice for gene discovery through differential
              expression analyses of microarray and high-throughput PCR data.
              The package contains particularly strong facilities for reading,
              normalizing and exploring such data. Recently, the capabilities
              of limma have been significantly expanded in two important
              directions. First, the package can now perform both differential
              expression and differential splicing analyses of RNA sequencing
              (RNA-seq) data. All the downstream analysis tools previously
              restricted to microarray data are now available for RNA-seq as
              well. These capabilities allow users to analyse both RNA-seq and
              microarray data with very similar pipelines. Second, the package
              is now able to go past the traditional gene-wise expression
              analyses in a variety of ways, analysing expression profiles in
              terms of co-regulated sets of genes or in terms of higher-order
              expression signatures. This provides enhanced possibilities for
              biological interpretation of gene expression differences. This
              article reviews the philosophy and design of the limma package,
              summarizing both new and historical features, with an emphasis on
              recent enhancements and features that have not been previously
              described.",
  journal  = "Nucleic Acids Res.",
  volume   =  43,
  number   =  7,
  pages    = "e47",
  month    =  apr,
  year     =  2015,
  language = "en"
}

@ARTICLE{distinct,
  title   = "distinct : a novel approach to differential distribution analyses",
  author  = "Tiberi, Simone and Crowell, Helena L and Weber, Lukas M and
             Samartsidis, Pantelis and Robinson, Mark D",
  journal = "bioRxiv"
}


@Article{igraph,
  title = {The igraph software package for complex network research},
  author = {Gabor Csardi and Tamas Nepusz},
  journal = {InterJournal},
  volume = {Complex Systems},
  pages = {1695},
  year = {2006},
  url = {http://igraph.org},
}
@Article{reshape2,
  title = {Reshaping Data with the {reshape} Package},
  author = {Hadley Wickham},
  journal = {Journal of Statistical Software},
  year = {2007},
  volume = {21},
  number = {12},
  pages = {1--20},
  url = {http://www.jstatsoft.org/v21/i12/},
}

@Manual{gplots,
  title = {gplots: Various R Programming Tools for Plotting Data},
  author = {Gregory R. Warnes and Ben Bolker and Lodewijk Bonebakker and Robert Gentleman and Wolfgang Huber Andy Liaw and Thomas Lumley and Martin Maechler and Arni Magnusson and Steffen Moeller and Marc Schwartz and Bill Venables}, 
  year = {2019},
  note = {R package version 3.0.1.1},
  url = {https://CRAN.R-project.org/package=gplots},
}
@Manual{SummarizedExperiment, 
    title = {SummarizedExperiment: SummarizedExperiment container}, 
    author = {Martin Morgan and Valerie Obenchain and Jim Hester and Hervé Pagès},
    year = {2018},                                                                                                                 
    note = {R package version 1.10.1},
  }

@Manual{visNetwork,
    title = {visNetwork: Network Visualization using 'vis.js' Library},
    author = {{Almende B.V.} and Benoit Thieurmel and Titouan Robert},
    year = {2018},
    note = {R package version 2.0.4},
    url = {https://CRAN.R-project.org/package=visNetwork}
  }
@Manual{data.table,
    title = {data.table: Extension of `data.frame`},
    author = {Matt Dowle and Arun Srinivasan},
    year = {2018},
    note = {R package version 1.11.8},
    url = {https://CRAN.R-project.org/package=data.table}
  }
@Manual{plotly,
    title = {plotly for R},
    author = {Carson Sievert},
    year = {2018},
    url = {https://plotly-book.cpsievert.me}
  }
@Manual{XML,
    title = {XML: Tools for Parsing and Generating XML Within R and S-Plus},
    author = {Duncan Temple Lang and the CRAN Team},
    year = {2018},
    note = {R package version 3.98-1.16},
    url = {https://CRAN.R-project.org/package=XML}
  }
@Manual{Cairo,
    title = {Cairo: R graphics device using cairo graphics library for creating high-quality bitmap (PNG, JPEG, TIFF), vector (PDF, SVG, PostScript) and display (X11 and Win32) output},
    author = {Simon Urbanek and Jeffrey Horner},
    year = {2015},
    note = {R package version 1.5-9},
    url = {https://CRAN.R-project.org/package=Cairo}
  }
@Manual{dynamicTreeCut,                                                                                                                        
    title = {dynamicTreeCut: Methods for Detection of Clusters in Hierarchical Clustering Dendrograms},                                                                                       
    author = {Peter Langfelder and Bin Zhang and with contributions from Steve Horvath},
    year = {2016},                                                                                                                 
    note = {R package version 1.63-1},                                                                                             
    url = {https://CRAN.R-project.org/package=dynamicTreeCut},                                                                     
  }

@Manual{ggdendro,
    title = {ggdendro: Create Dendrograms and Tree Diagrams Using 'ggplot2'},
    author = {Andrie {de Vries} and Brian D. Ripley},
    year = {2016},
    note = {R package version 0.1-20},
    url = {https://CRAN.R-project.org/package=ggdendro}
  }
@Manual{gridExtra,
    title = {gridExtra: Miscellaneous Functions for "Grid" Graphics},
    author = {Baptiste Auguie},
    year = {2017},
    note = {R package version 2.3},
    url = {https://CRAN.R-project.org/package=gridExtra}
  }
@Manual{DT,
    title = {DT: A Wrapper of the JavaScript Library 'DataTables'},
    author = {Yihui Xie},
    year = {2018},
    note = {R package version 0.4},
    url = {https://CRAN.R-project.org/package=DT}
  }
@Book{ggplot2,
    author = {Hadley Wickham},
    title = {ggplot2: Elegant Graphics for Data Analysis},
    publisher = {Springer-Verlag New York},
    year = {2016},
    isbn = {978-3-319-24277-4},
    url = {http://ggplot2.org}
  }
@Manual{rsvg,
    title = {rsvg: Render SVG Images into PDF, PNG, PostScript, or Bitmap Arrays},
    author = {Jeroen Ooms},
    year = {2018},
    note = {R package version 1.3},
    url = {https://CRAN.R-project.org/package=rsvg}
  }
@Manual{shinydashboard, 
    title = {shinydashboard: Create Dashboards with 'Shiny'}, 
    author={Winston Chang and Barbara {Borges Ribeiro}},
    year={2018},
    note={R package version 0.7.1},
    url={https://CRAN.R-project.org/package=shinydashboard}
} 
@Manual{shiny, 
    title = {shiny: Web Application Framework for R},
    author={Winston Chang and Joe Cheng and JJ Allaire and Yihui Xie and Jonathan McPherson},
    note={R package version 1.1.0},
    url={https://CRAN.R-project.org/package=shiny}
}
@misc{brain,
  author = {anatomybodysystem.com},
  title = {{HEAD ANATOMY}},
  howpublished = "\url{http://anatomybodysystem.com/lateral-view-of-the-brain-labeled/lateral-view-of-the-brain-labeled-brain-diagram-and-label-anatomy-body-list/}",
  year = {2017}, 
  note = "[Online; accessed 20-December-2018]"
}
@misc{Trip8.Co,
  author = {Trip8.Co},
  title = {{Us Map States}},
  howpublished = "\url{http://trip8.co/interactive-map-of-us-states-us-map-states-interactive-us-map/interactive-map-of-us-states-us-map-states-interactive-us-map-new-united-states-map-interactive-detail-color-usa-with-name-inside/}",
  year = {2018}, 
  note = "[Online; accessed 20-December-2018]"
}
@misc{US_Census_Bureau,
  author = {U.S. Census Bureau},
  title = {{Annual Population Estimates, Estimated Components of Resident Population Change, and Rates of the Components of Resident Population Change for the United States, States, and Puerto Rico: April 1, 2010 to July 1, 2018}},
  howpublished = "\url{https://www.census.gov/data/datasets/time-series/demo/popest/2010s-state-total.html}",
  year = {2018}, 
  note = "[Online; accessed 07-January-2019]"
}

@misc{hipp,
  author = {epilepsyresearch},
  title = {{The Hippocampus: What Is It?}},
  howpublished = "\url{https://www.epilepsyresearch.org.uk/the-hippocampus-what-is-it/}",
  year = {2017}, 
  note = "[Online; accessed 20-December-2018]"
}
@Manual{usmap,
    title = {usmap: US Maps Including Alaska and Hawaii},
    author = {Paolo {Di Lorenzo}},
    year = {2018},
    note = {R package version 0.4.0},
    url = {https://CRAN.R-project.org/package=usmap}
  }
@Manual{kableExtra,      
    title = {kableExtra: Construct Complex Table with 'kable' and Pipe Syntax},               
    author = {Hao Zhu},  
    year = {2019},    
    note = {R package version 1.1.0},
    url = {https://CRAN.R-project.org/package=kableExtra}
  }        
@InCollection{knitr,    
    booktitle = {Implementing Reproducible Computational Research},
    editor = {Victoria Stodden and Friedrich Leisch and Roger D. Peng}, 
    title = {knitr: A Comprehensive Tool for Reproducible Research in {R}},               
    author = {Yihui Xie}, 
    publisher = {Chapman and Hall/CRC},
    year = {2014},                                                       
    note = {ISBN 978-1466561595},  
    url = {http://www.crcpress.com/product/isbn/9781466561595}      
  } 

@ARTICLE{Lekschas2015-gd,
  title    = "Semantic Body Browser: graphical exploration of an organism and
              spatially resolved expression data visualization",
  author   = "Lekschas, Fritz and Stachelscheid, Harald and Seltmann, Stefanie
              and Kurtz, Andreas",
  abstract = "UNLABELLED: Advancing technologies generate large amounts of
              molecular and phenotypic data on cells, tissues and organisms,
              leading to an ever-growing detail and complexity while
              information retrieval and analysis becomes increasingly
              time-consuming. The Semantic Body Browser is a web application
              for intuitively exploring the body of an organism from the organ
              to the subcellular level and visualising expression profiles by
              means of semantically annotated anatomical illustrations. It is
              used to comprehend biological and medical data related to the
              different body structures while relying on the strong pattern
              recognition capabilities of human users. AVAILABILITY AND
              IMPLEMENTATION: The Semantic Body Browser is a JavaScript web
              application that is freely available at
              http://sbb.cellfinder.org. The source code is provided on
              https://github.com/flekschas/sbb.",
  journal  = "Bioinformatics",
  volume   =  31,
  number   =  5,
  pages    = "794--796",
  month    =  mar,
  year     =  2015,
  language = "en"
}

@ARTICLE{Muschelli2014-av,
  title    = "brainR: Interactive 3 and {4D} Images of High Resolution
              Neuroimage Data",
  author   = "Muschelli, John and Sweeney, Elizabeth and Crainiceanu, Ciprian",
  abstract = "We provide software tools for displaying and publishing
              interactive 3-dimensional (3D) and 4-dimensional (4D) figures to
              html webpages, with examples of high-resolution brain imaging.
              Our framework is based in the R statistical software using the
              rgl package, a 3D graphics library. We build on this package to
              allow manipulation of figures including rotation and translation,
              zooming, coloring of brain substructures, adjusting transparency
              levels, and addition/or removal of brain structures. The need for
              better visualization tools of ultra high dimensional data is ever
              present; we are providing a clean, simple, web-based option. We
              also provide a package (brainR) for users to readily implement
              these tools.",
  journal  = "R J.",
  volume   =  6,
  number   =  1,
  pages    = "41--48",
  month    =  jun,
  year     =  2014,
  language = "en"
}


@ARTICLE{Ravasz2002-db,
  title    = "Hierarchical organization of modularity in metabolic networks",
  author   = "Ravasz, E and Somera, A L and Mongru, D A and Oltvai, Z N and
              Barab{\'a}si, A L",
  abstract = "Spatially or chemically isolated functional modules composed of
              several cellular components and carrying discrete functions are
              considered fundamental building blocks of cellular organization,
              but their presence in highly integrated biochemical networks
              lacks quantitative support. Here, we show that the metabolic
              networks of 43 distinct organisms are organized into many small,
              highly connected topologic modules that combine in a hierarchical
              manner into larger, less cohesive units, with their number and
              degree of clustering following a power law. Within Escherichia
              coli, the uncovered hierarchical modularity closely overlaps with
              known metabolic functions. The identified network architecture
              may be generic to system-level cellular organization.",
  journal  = "Science",
  volume   =  297,
  number   =  5586,
  pages    = "1551--1555",
  month    =  aug,
  year     =  2002,
  language = "en"
}

@ARTICLE{Maag2018-gi,
  title    = "gganatogram: An {R} package for modular visualisation of
              anatograms and tissues based on ggplot2",
  author   = "Maag, Jesper L V",
  abstract = "Displaying data onto anatomical structures is a convenient
              technique to quickly observe tissue related information. However,
              drawing tissues is a complex task that requires both expertise in
              anatomy and the arts. While web based applications exist for
              displaying gene expression on anatograms, other non-genetic
              disciplines lack similar tools. Moreover, web based tools often
              lack the modularity associated with packages in programming
              languages, such as R. Here I present gganatogram, an R package
              used to plot modular species anatograms based on a combination of
              the graphical grammar of ggplot2 and the publicly available
              anatograms from the Expression Atlas. This combination allows for
              quick and easy, modular, and reproducible generation of
              anatograms. Using only one command and a data frame with tissue
              name, group, colour, and value, this tool enables the user to
              visualise specific human and mouse tissues with desired colours,
              grouped by a variable, or displaying a desired value, such as
              gene-expression, pharmacokinetics, or bacterial load across
              selected tissues. gganatogram consists of 5 highly annotated
              organisms, male/female human/mouse, and a cell anatogram. It
              further consists of 24 other less annotated organisms from the
              animal and plant kingdom. I hope that this tool will be useful by
              the wider community in biological sciences. Community members are
              welcome to submit additional anatograms, which can be
              incorporated into the package. A stable version gganatogram has
              been deposited to neuroconductor, and a development version can
              be found on github/jespermaag/gganatogram. An interactive shiny
              app of gganatogram can be found on
              https://jespermaag.shinyapps.io/gganatogram/, which allows for
              non-R users to create anatograms.",
  journal  = "F1000Res.",
  volume   =  7,
  pages    = "1576",
  month    =  sep,
  year     =  2018,
  keywords = "Anatograms; Anatomy; Expression Atlas; Organs; R; Shiny; Tissues;
              ggplot2",
  language = "en"
}


@ARTICLE{Waese2017-fx,
  title    = "ePlant: Visualizing and Exploring Multiple Levels of Data for
              Hypothesis Generation in Plant Biology",
  author   = "Waese, Jamie and Fan, Jim and Pasha, Asher and Yu, Hans and
              Fucile, Geoffrey and Shi, Ruian and Cumming, Matthew and Kelley,
              Lawrence A and Sternberg, Michael J and Krishnakumar, Vivek and
              Ferlanti, Erik and Miller, Jason and Town, Chris and
              Stuerzlinger, Wolfgang and Provart, Nicholas J",
  abstract = "A big challenge in current systems biology research arises when
              different types of data must be accessed from separate sources
              and visualized using separate tools. The high cognitive load
              required to navigate such a workflow is detrimental to hypothesis
              generation. Accordingly, there is a need for a robust research
              platform that incorporates all data and provides integrated
              search, analysis, and visualization features through a single
              portal. Here, we present ePlant (http://bar.utoronto.ca/eplant),
              a visual analytic tool for exploring multiple levels of
              Arabidopsis thaliana data through a zoomable user interface.
              ePlant connects to several publicly available web services to
              download genome, proteome, interactome, transcriptome, and 3D
              molecular structure data for one or more genes or gene products
              of interest. Data are displayed with a set of visualization tools
              that are presented using a conceptual hierarchy from big to
              small, and many of the tools combine information from more than
              one data type. We describe the development of ePlant in this
              article and present several examples illustrating its integrative
              features for hypothesis generation. We also describe the process
              of deploying ePlant as an ``app'' on Araport. Building on readily
              available web services, the code for ePlant is freely available
              for any other biological species research.",
  journal  = "Plant Cell",
  volume   =  29,
  number   =  8,
  pages    = "1806--1821",
  month    =  aug,
  year     =  2017,
  language = "en"
}


@ARTICLE{Winter2007-bq,
  title    = "An ``Electronic Fluorescent Pictograph'' browser for exploring
              and analyzing large-scale biological data sets",
  author   = "Winter, Debbie and Vinegar, Ben and Nahal, Hardeep and Ammar, Ron
              and Wilson, Greg V and Provart, Nicholas J",
  abstract = "BACKGROUND: The exploration of microarray data and data from
              other high-throughput projects for hypothesis generation has
              become a vital aspect of post-genomic research. For the
              non-bioinformatics specialist, however, many of the currently
              available tools provide overwhelming amounts of data that are
              presented in a non-intuitive way. METHODOLOGY/PRINCIPAL FINDINGS:
              In order to facilitate the interpretation and analysis of
              microarray data and data from other large-scale data sets, we
              have developed a tool, which we have dubbed the electronic
              Fluorescent Pictograph - or eFP - Browser, available at
              http://www.bar.utoronto.ca/, for exploring microarray and other
              data for hypothesis generation. This eFP Browser engine paints
              data from large-scale data sets onto pictographic representations
              of the experimental samples used to generate the data sets. We
              give examples of using the tool to present Arabidopsis gene
              expression data from the AtGenExpress Consortium (Arabidopsis eFP
              Browser), data for subcellular localization of Arabidopsis
              proteins (Cell eFP Browser), and mouse tissue atlas microarray
              data (Mouse eFP Browser). CONCLUSIONS/SIGNIFICANCE: The eFP
              Browser software is easily adaptable to microarray or other
              large-scale data sets from any organism and thus should prove
              useful to a wide community for visualizing and interpreting these
              data sets for hypothesis generation.",
  journal  = "PLoS One",
  volume   =  2,
  number   =  8,
  pages    = "e718",
  month    =  aug,
  year     =  2007,
  language = "en"
}


@ARTICLE{Prudencio2015-wd,
  title    = "Distinct brain transcriptome profiles in C9orf72-associated and
              sporadic {ALS}",
  author   = "Prudencio, Mercedes and Belzil, Veronique V and Batra, Ranjan and
              Ross, Christian A and Gendron, Tania F and Pregent, Luc J and
              Murray, Melissa E and Overstreet, Karen K and Piazza-Johnston,
              Amelia E and Desaro, Pamela and Bieniek, Kevin F and DeTure,
              Michael and Lee, Wing C and Biendarra, Sherri M and Davis, Mary D
              and Baker, Matthew C and Perkerson, Ralph B and van Blitterswijk,
              Marka and Stetler, Caroline T and Rademakers, Rosa and Link,
              Christopher D and Dickson, Dennis W and Boylan, Kevin B and Li,
              Hu and Petrucelli, Leonard",
  abstract = "Increasing evidence suggests that defective RNA processing
              contributes to the development of amyotrophic lateral sclerosis
              (ALS). This may be especially true for ALS caused by a repeat
              expansion in C9orf72 (c9ALS), in which the accumulation of RNA
              foci and dipeptide-repeat proteins are expected to modify RNA
              metabolism. We report extensive alternative splicing (AS) and
              alternative polyadenylation (APA) defects in the cerebellum of
              c9ALS subjects (8,224 AS and 1,437 APA), including changes in
              ALS-associated genes (for example, ATXN2 and FUS), and in
              subjects with sporadic ALS (sALS; 2,229 AS and 716 APA).
              Furthermore, heterogeneous nuclear ribonucleoprotein H (hnRNPH)
              and other RNA-binding proteins are predicted to be potential
              regulators of cassette exon AS events in both c9ALS and sALS.
              Co-expression and gene-association network analyses of gene
              expression and AS data revealed divergent pathways associated
              with c9ALS and sALS.",
  journal  = "Nat. Neurosci.",
  volume   =  18,
  number   =  8,
  pages    = "1175--1182",
  month    =  aug,
  year     =  2015,
  language = "en"
}


@ARTICLE{Cardoso-Moreira2019-yq,
  title    = "Gene expression across mammalian organ development",
  author   = "Cardoso-Moreira, Margarida and Halbert, Jean and Valloton,
              Delphine and Velten, Britta and Chen, Chunyan and Shao, Yi and
              Liechti, Ang{\'e}lica and Ascen{\c c}{\~a}o, Kelly and Rummel,
              Coralie and Ovchinnikova, Svetlana and Mazin, Pavel V and
              Xenarios, Ioannis and Harshman, Keith and Mort, Matthew and
              Cooper, David N and Sandi, Carmen and Soares, Michael J and
              Ferreira, Paula G and Afonso, Sandra and Carneiro, Miguel and
              Turner, James M A and VandeBerg, John L and Fallahshahroudi, Amir
              and Jensen, Per and Behr, R{\"u}diger and Lisgo, Steven and
              Lindsay, Susan and Khaitovich, Philipp and Huber, Wolfgang and
              Baker, Julie and Anders, Simon and Zhang, Yong E and Kaessmann,
              Henrik",
  abstract = "The evolution of gene expression in mammalian organ development
              remains largely uncharacterized. Here we report the
              transcriptomes of seven organs (cerebrum, cerebellum, heart,
              kidney, liver, ovary and testis) across developmental time points
              from early organogenesis to adulthood for human, rhesus macaque,
              mouse, rat, rabbit, opossum and chicken. Comparisons of gene
              expression patterns identified correspondences of developmental
              stages across species, and differences in the timing of key
              events during the development of the gonads. We found that the
              breadth of gene expression and the extent of purifying selection
              gradually decrease during development, whereas the amount of
              positive selection and expression of new genes increase. We
              identified differences in the temporal trajectories of expression
              of individual genes across species, with brain tissues showing
              the smallest percentage of trajectory changes, and the liver and
              testis showing the largest. Our work provides a resource of
              developmental transcriptomes of seven organs across seven
              species, and comparative analyses that characterize the
              development and evolution of mammalian organs.",
  journal  = "Nature",
  volume   =  571,
  number   =  7766,
  pages    = "505--509",
  month    =  jul,
  year     =  2019,
  language = "en"
}


@ARTICLE{Merkin2012-ak,
  title    = "Evolutionary dynamics of gene and isoform regulation in Mammalian
              tissues",
  author   = "Merkin, Jason and Russell, Caitlin and Chen, Ping and Burge,
              Christopher B",
  abstract = "Most mammalian genes produce multiple distinct messenger RNAs
              through alternative splicing, but the extent of splicing
              conservation is not clear. To assess tissue-specific
              transcriptome variation across mammals, we sequenced
              complementary DNA from nine tissues from four mammals and one
              bird in biological triplicate, at unprecedented depth. We find
              that while tissue-specific gene expression programs are largely
              conserved, alternative splicing is well conserved in only a
              subset of tissues and is frequently lineage-specific. Thousands
              of previously unknown, lineage-specific, and conserved
              alternative exons were identified; widely conserved alternative
              exons had signatures of binding by MBNL, PTB, RBFOX, STAR, and
              TIA family splicing factors, implicating them as ancestral
              mammalian splicing regulators. Our data also indicate that
              alternative splicing often alters protein phosphorylatability,
              delimiting the scope of kinase signaling.",
  journal  = "Science",
  volume   =  338,
  number   =  6114,
  pages    = "1593--1599",
  month    =  dec,
  year     =  2012,
  language = "en"
}


@ARTICLE{Chen-Plotkin2008-qn,
  title    = "Variations in the progranulin gene affect global gene expression
              in frontotemporal lobar degeneration",
  author   = "Chen-Plotkin, Alice S and Geser, Felix and Plotkin, Joshua B and
              Clark, Chris M and Kwong, Linda K and Yuan, Wuxing and Grossman,
              Murray and Van Deerlin, Vivianna M and Trojanowski, John Q and
              Lee, Virginia M-Y",
  abstract = "Frontotemporal lobar degeneration is a fatal neurodegenerative
              disease that results in progressive decline in behavior,
              executive function and sometimes language. Disease mechanisms
              remain poorly understood. Recently, however, the DNA- and
              RNA-binding protein TDP-43 has been identified as the major
              protein present in the hallmark inclusion bodies of
              frontotemporal lobar degeneration with ubiquitinated inclusions
              (FTLD-U), suggesting a role for transcriptional dysregulation in
              FTLD-U pathophysiology. Using the Affymetrix U133A microarray
              platform, we profiled global gene expression in both
              histopathologically affected and unaffected areas of human FTLD-U
              brains. We then characterized differential gene expression with
              biological pathway analyses, cluster and principal component
              analyses, and subgroup analyses based on brain region and
              progranulin (GRN) gene status. Comparing 17 FTLD-U brains to 11
              controls, we identified 414 upregulated and 210 downregulated
              genes in frontal cortex (P-value 1300 dysregulated genes in
              frontal cortex (P-value < 0.001), many participating in pathways
              uniquely dysregulated in the GRN+ cases. Our findings demonstrate
              a distinct molecular phenotype for GRN+ FTLD-U, not readily
              apparent on clinical or histopathological examination, suggesting
              distinct pathophysiological mechanisms for GRN+ and GRN- subtypes
              of FTLD-U. In addition, these data from a large number of human
              brains provide a valuable resource for future testing of disease
              hypotheses.",
  journal  = "Hum. Mol. Genet.",
  volume   =  17,
  number   =  10,
  pages    = "1349--1362",
  month    =  may,
  year     =  2008,
  language = "en"
}
@ARTICLE{Mustroph2009-nu,
  title    = "Profiling translatomes of discrete cell populations resolves
              altered cellular priorities during hypoxia in Arabidopsis",
  author   = "Mustroph, Angelika and Eugenia Zanetti, M and Jang, Charles J H
              and Holtan, Hans E and Repetti, Peter P and Galbraith, David W
              and Girke, Thomas and Bailey-Serres, Julia",
  abstract = "Multicellular organs are composed of distinct cell types with
              unique assemblages of translated mRNAs. Here, ribosome-associated
              mRNAs were immunopurified from specific cell populations of
              intact seedlings using Arabidopsis thaliana lines expressing a
              FLAG-epitope tagged ribosomal protein L18 (FLAG-RPL18) via
              developmentally regulated promoters. The profiling of mRNAs in
              ribosome complexes, referred to as the translatome, identified
              differentially expressed mRNAs in 21 cell populations defined by
              cell-specific expression of FLAG-RPL18. Phloem companion cells of
              the root and shoot had the most distinctive translatomes. When
              seedlings were exposed to a brief period of hypoxia, a pronounced
              reprioritization of mRNA enrichment in the cell-specific
              translatomes occurred, including a ubiquitous rise in 49 mRNAs
              encoding transcription factors, signaling proteins, anaerobic
              metabolism enzymes, and uncharacterized proteins. Translatome
              profiling also exposed an intricate molecular signature of
              transcription factor (TF) family member mRNAs that was markedly
              reconfigured by hypoxia at global and cell-specific levels. In
              addition to the demonstration of the complexity and plasticity of
              cell-specific populations of ribosome-associated mRNAs, this
              study provides an in silico dataset for recognition of
              differentially expressed genes at the cell-, region-, and
              organ-specific levels.",
  journal  = "Proc Natl Acad Sci U S A",
  volume   =  106,
  number   =  44,
  pages    = "18843--18848",
  month    =  nov,
  year     =  2009,
  keywords = "Tissue specific modules/Microarray;Tissue specific
              modules/Microarray/Data"
}
@ARTICLE{Geng2013-ek,
  title    = "A spatio-temporal understanding of growth regulation during the
              salt stress response in Arabidopsis",
  author   = "Geng, Yu and Wu, Rui and Wee, Choon Wei and Xie, Fei and Wei,
              Xueliang and Chan, Penny Mei Yeen and Tham, Cliff and Duan, Lina
              and Dinneny, Jos{\'e} R",
  abstract = "Plant environmental responses involve dynamic changes in growth
              and signaling, yet little is understood as to how progress
              through these events is regulated. Here, we explored the
              phenotypic and transcriptional events involved in the acclimation
              of the Arabidopsis thaliana seedling root to a rapid change in
              salinity. Using live-imaging analysis, we show that growth is
              dynamically regulated with a period of quiescence followed by
              recovery then homeostasis. Through the use of a new
              high-resolution spatio-temporal transcriptional map, we identify
              the key hormone signaling pathways that regulate specific
              transcriptional programs, predict their spatial domain of action,
              and link the activity of these pathways to the regulation of
              specific phases of growth. We use tissue-specific approaches to
              suppress the abscisic acid (ABA) signaling pathway and
              demonstrate that ABA likely acts in select tissue layers to
              regulate spatially localized transcriptional programs and promote
              growth recovery. Finally, we show that salt also regulates many
              tissue-specific and time point-specific transcriptional responses
              that are expected to modify water transport, Casparian strip
              formation, and protein translation. Together, our data reveal a
              sophisticated assortment of regulatory programs acting together
              to coordinate spatially patterned biological changes involved in
              the immediate and long-term response to a stressful shift in
              environment.",
  journal  = "Plant Cell",
  volume   =  25,
  number   =  6,
  pages    = "2132--2154",
  month    =  jun,
  year     =  2013,
  keywords = "Tissue specific modules/Microarray/Data",
  language = "en"
}
@MISC{Gentleman2018-xj,
  title        = "genefilter: methods for filtering genes from high-throughput
                  experiments",
  booktitle    = "Bioconductor",
  author       = "Gentleman, R and Carey, V and Huber, W and Hahne, F",
  year         =  2018,
  howpublished = "\url{http://bioconductor.uib.no/2.7/bioc/html/genefilter.html}",
  note         = "Accessed: 2018-10-24",
  keywords     = "Tissue specific modules/Algorithms, packages"
}
@ARTICLE{grImport,
  title     = "Importing vector graphics: The grImport package for {R}",
  author    = "Murrell, P",
  abstract  = "Abstract This article describes an approach to importing
               vector-based graphical images into statistical software as
               implemented in a package called grImport for the R statistical
               computing environment. This approach assumes that an original
               image can be transformed into a PostScript format (ie, the
               opriginal image is in a standard vector graphics format such as
               PostScript, PDF, or SVG). The grImport package consists of three
               components: a ...",
  journal   = "J. Stat. Softw.",
  publisher = "jstatsoft.org",
  year      =  2009,
  keywords  = "Tissue specific modules/Interactive graphs"
}
@ARTICLE{Langfelder2008-sg,
  title    = "{WGCNA}: an {R} package for weighted correlation network analysis",
  author   = "Langfelder, Peter and Horvath, Steve",
  abstract = "BACKGROUND: Correlation networks are increasingly being used in
              bioinformatics applications. For example, weighted gene
              co-expression network analysis is a systems biology method for
              describing the correlation patterns among genes across microarray
              samples. Weighted correlation network analysis (WGCNA) can be
              used for finding clusters (modules) of highly correlated genes,
              for summarizing such clusters using the module eigengene or an
              intramodular hub gene, for relating modules to one another and to
              external sample traits (using eigengene network methodology), and
              for calculating module membership measures. Correlation networks
              facilitate network based gene screening methods that can be used
              to identify candidate biomarkers or therapeutic targets. These
              methods have been successfully applied in various biological
              contexts, e.g. cancer, mouse genetics, yeast genetics, and
              analysis of brain imaging data. While parts of the correlation
              network methodology have been described in separate publications,
              there is a need to provide a user-friendly, comprehensive, and
              consistent software implementation and an accompanying tutorial.
              RESULTS: The WGCNA R software package is a comprehensive
              collection of R functions for performing various aspects of
              weighted correlation network analysis. The package includes
              functions for network construction, module detection, gene
              selection, calculations of topological properties, data
              simulation, visualization, and interfacing with external
              software. Along with the R package we also present R software
              tutorials. While the methods development was motivated by gene
              expression data, the underlying data mining approach can be
              applied to a variety of different settings. CONCLUSION: The WGCNA
              package provides R functions for weighted correlation network
              analysis, e.g. co-expression network analysis of gene expression
              data. The R package along with its source code and additional
              material are freely available at
              http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/Rpackages/WGCNA.",
  journal  = "BMC Bioinformatics",
  volume   =  9,
  pages    = "559",
  month    =  dec,
  year     =  2008,
  keywords = "Tissue specific modules/Tutorial",
  language = "en"
}

@ARTICLE{Papatheodorou2018-jy,
  title    = "{Expression Atlas: gene and protein expression across multiple
              studies and organisms}",
  author   = "Papatheodorou, Irene and Fonseca, Nuno A and Keays, Maria and
              Tang, Y Amy and Barrera, Elisabet and Bazant, Wojciech and Burke,
              Melissa and F{\"u}llgrabe, Anja and Fuentes, Alfonso
              Mu{\~n}oz-Pomer and George, Nancy and Huerta, Laura and Koskinen,
              Satu and Mohammed, Suhaib and Geniza, Matthew and Preece, Justin
              and Jaiswal, Pankaj and Jarnuczak, Andrew F and Huber, Wolfgang
              and Stegle, Oliver and Vizcaino, Juan Antonio and Brazma, Alvis
              and Petryszak, Robert",
  journal  = "Nucleic Acids Res.",
  volume   =  46,
  number   = "D1",
  pages    = "D246--D251",
  month    =  jan,
  year     =  2018,
  url      = "http://dx.doi.org/10.1093/nar/gkx1158",
  language = "en",
  issn     = "0305-1048, 1362-4962",
  pmid     = "29165655",
  doi      = "10.1093/nar/gkx1158",
  pmc      = "PMC5753389"
}

@ARTICLE{Mungall2012-ma,
  title    = "{Uberon, an integrative multi-species anatomy ontology}",
  author   = "Mungall, Christopher J and Torniai, Carlo and Gkoutos, Georgios V
              and Lewis, Suzanna E and Haendel, Melissa A",
  journal  = "Genome Biol.",
  volume   =  13,
  number   =  1,
  pages    = "R5",
  month    =  jan,
  year     =  2012,
  url      = "http://dx.doi.org/10.1186/gb-2012-13-1-r5",
  language = "en",
  issn     = "1465-6906",
  pmid     = "22293552",
  doi      = "10.1186/gb-2012-13-1-r5",
  pmc      = "PMC3334586"
}


