title: EPIG-Seq: extracting patterns and identifying co-expressed genes from RNA-Seq data.
abstract: BACKGROUND
  RNA sequencing (RNA-Seq) measures genome-wide gene expression. RNA-Seq data is count-based rendering normal distribution models for analysis inappropriate. Normalization of RNA-Seq data to transform the data has limitations which can adversely impact the analysis. Furthermore, there are a few count-based methods for analysis of RNA-Seq data but they are essentially for pairwise analysis of treatment groups or multiclasses but not pattern-based to identify co-expressed genes.
  
  
  RESULTS
  We adapted our extracting patterns and identifying genes methodology for RNA-Seq (EPIG-Seq) count data. The software uses count-based correlation to measure similarity between genes, quasi-Poisson modelling to estimate dispersion in the data and a location parameter to indicate magnitude of differential expression. EPIG-Seq is different than any other software currently available for pattern analysis of RNA-Seq data in that EPIG-Seq 1) uses count level data and supports cases of inflated zeros, 2) identifies statistically significant clusters of genes that are co-expressed across experimental conditions, 3) takes into account dispersion in the replicate data and 4) provides reliable results even with small sample sizes. EPIG-Seq operates in two steps: 1) extract the pattern profiles from data as seeds for clustering co-expressed genes and 2) cluster the genes to the pattern seeds and compute statistical significance of the pattern of co-expressed genes. EPIG-Seq provides a table of the genes with bootstrapped p-values and profile plots of the patterns of co-expressed genes. In addition, EPIG-Seq provides a heat map and principal component dimension reduction plot of the clustered genes as visual aids. We demonstrate the utility of EPIG-Seq through the analysis of toxicogenomics and cancer data sets to identify biologically relevant co-expressed genes. EPIG-Seq is available at: sourceforge.net/projects/epig-seq.
  
  
  CONCLUSIONS
  EPIG-Seq is unlike any other software currently available for pattern analysis of RNA-Seq count level data across experimental groups. Using the EPIG-Seq software to analyze RNA-Seq count data across biological conditions permits the ability to extract biologically meaningful co-expressed genes associated with coordinated regulation.
authors.affiliation: Integrative Bioinformatics Group, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, USA.|Microarray and Genome Informatics Group, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, USA.|Kelly Government Solutions, Research Triangle Park, NC, 27709, USA.
authors.forename: Jianying
authors.initials: J
authors.lastname: Li
authors.affiliation: Microarray and Genome Informatics Group, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, USA. bushel@niehs.nih.gov.|Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, 111 T.W. Alexander Drive, P.O. Box 12233, Research Triangle Park, NC, 27709, USA. bushel@niehs.nih.gov.
authors.forename: Pierre R
authors.initials: PR
authors.lastname: Bushel
mesh_terms: D001943:Breast Neoplasms / Q000235:genetics; D016000:Cluster Analysis; D019295:Computational Biology; D005260:Female; D006801:Humans; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software; D043922:Toxicogenetics; D059467:Transcriptome
keywords: Cancer; Clustering; EPIG-Seq; Gene expression; Pattern analysis; RNA-Seq; Toxicogenomics