Omics Pipe: A Community-based Framework for Reproducible Multi-Omics Data Analysis

Omics Pipe is a computational framework that automates multi-omics data analysis pipelines on high performance compute clusters and in the cloud. It supports best practice published pipelines for RNA-seq, miRNA-seq, Exome-seq, Whole Genome sequencing, ChIP-seq analyses and automatic processing of data from The Cancer Genome Atlas (TCGA). Omics Pipe provides researchers with a tool for reproducible, open source and extensible next generation sequencing analysis. The goal of Omics Pipe is to democratize NGS analysis by dramatically increasing the accessibility and reproducibility of best practice computational pipelines, which will enable researchers to generate biologically meaningful and interpretable results.

rna-seq

Using Omics Pipe, researchers from the Scripps Research Institute analyzed 100 TCGA breast invasive carcinoma paired tumor-normal data sets based on the latest UCSC hg19 RefSeq annotation. Omics Pipe automatically downloaded and processed the desired TCGA samples on a high throughput compute cluster to produce a results report for each sample. They aggregated the individual sample results and compared them to the analysis in the original publications. This comparison revealed high overlap between the analyses, as well as novel findings due to the use of updated annotations and methods.

Availability and Implementation: Source code for Omics Pipe is freely available on the web (https://bitbucket.org/sulab/omics_pipe). Omics Pipe is distributed as a standalone Python package for installation (https://pypi.python.org/pypi/omics_pipe) and as an Amazon Machine Image (AMI) in Amazon Web Services (AWS) Elastic Compute Cloud (EC2) that contains all necessary third-party software dependencies and databases (https://pythonhosted.org/omics_pipe/AWS_installation.html).

Contact: asu@scripps.edu or kfisch@ucsd.edu

Fisch KM, Meißner T, Gioia L, Ducom JC, Carland TM, Loguercio S, Su AI. (2015) Omics Pipe: A Community-based Framework for Reproducible Multi-Omics Data Analysis. Bioinformatics [Epub ahead of print]. [abstract]

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