title: Reusable, extensible, and modifiable R scripts and Kepler workflows for comprehensive single set ChIP-seq analysis.
abstract: BACKGROUND
  There has been an enormous expansion of use of chromatin immunoprecipitation followed by sequencing (ChIP-seq) technologies. Analysis of large-scale ChIP-seq datasets involves a complex series of steps and production of several specialized graphical outputs. A number of systems have emphasized custom development of ChIP-seq pipelines. These systems are primarily based on custom programming of a single, complex pipeline or supply libraries of modules and do not produce the full range of outputs commonly produced for ChIP-seq datasets. It is desirable to have more comprehensive pipelines, in particular ones addressing common metadata tasks, such as pathway analysis, and pipelines producing standard complex graphical outputs. It is advantageous if these are highly modular systems, available as both turnkey pipelines and individual modules, that are easily comprehensible, modifiable and extensible to allow rapid alteration in response to new analysis developments in this growing area. Furthermore, it is advantageous if these pipelines allow data provenance tracking.
  
  
  RESULTS
  We present a set of 20 ChIP-seq analysis software modules implemented in the Kepler workflow system; most (18/20) were also implemented as standalone, fully functional R scripts. The set consists of four full turnkey pipelines and 16 component modules. The turnkey pipelines in Kepler allow data provenance tracking. Implementation emphasized use of common R packages and widely-used external tools (e.g., MACS for peak finding), along with custom programming. This software presents comprehensive solutions and easily repurposed code blocks for ChIP-seq analysis and pipeline creation. Tasks include mapping raw reads, peakfinding via MACS, summary statistics, peak location statistics, summary plots centered on the transcription start site (TSS), gene ontology, pathway analysis, and de novo motif finding, among others.
  
  
  CONCLUSIONS
  These pipelines range from those performing a single task to those performing full analyses of ChIP-seq data. The pipelines are supplied as both Kepler workflows, which allow data provenance tracking, and, in the majority of cases, as standalone R scripts. These pipelines are designed for ease of modification and repurposing.
authors.affiliation: Department of Biochemistry and Molecular Biology, University of Calgary Cumming School of Medicine, Rm HSC1151, 3330 Hospital Dr. NW, Calgary, AB, T2N4N1, Canada.
authors.forename: Nathan
authors.initials: N
authors.lastname: Cormier
authors.affiliation: Department of Biochemistry and Molecular Biology, University of Calgary Cumming School of Medicine, Rm HSC1151, 3330 Hospital Dr. NW, Calgary, AB, T2N4N1, Canada.
authors.forename: Tyler
authors.initials: T
authors.lastname: Kolisnik
authors.affiliation: Department of Biochemistry and Molecular Biology, University of Calgary Cumming School of Medicine, Rm HSC1151, 3330 Hospital Dr. NW, Calgary, AB, T2N4N1, Canada. mbieda@ucalgary.ca.
authors.forename: Mark
authors.identifier: http://orcid.org/0000-0002-0429-1373
authors.initials: M
authors.lastname: Bieda
mesh_terms: D047369:Chromatin Immunoprecipitation / Q000379:methods*; D019295:Computational Biology / Q000379:methods; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans
keywords: Bioconductor; ChIP-seq analysis; Scientific workflows; Software packages