g.nome, A Transparent Bioinformatics Pipeline that Enables Differential Expression and Alternative Splicing Analysis by Non-Computational Biologists

Abstract

Reproducibility and accessibility are cardinal principles in the rapidly evolving field of bioinformatics. As the collection of biological data grows, proper use of pipelines to analyze datasets can become a bottleneck restricting efficient analysis. Biologists who collect data and test hypotheses may not have strong computational backgrounds and may not be able to fully understand the underlying strengths and weaknesses of computational approaches or fully exploit their data. Some data may be misunderstood and, perhaps more importantly, critical findings may remain unobserved. High throughput RNA sequencing (RNAseq) has advanced our understanding of transcriptomics across diverse applications. Here we introduce g.nome, a bioinformatics platform that integrates contemporary tools necessary for independent analysis. A user-friendly graphical interface simplifies running jobs and allows simplified analysis of different datasets by non-bioinformaticians. g.nome was used to analyze the consequences of localizing the critical RNAi factor argonaute (AGO) to nuclei of colorectal cancer cell line HCT116. Analysis using the pipeline facilitated the straightforward identification of splicing changes and the prioritization of these splicing changes for validation and further experimental analysis. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/652286v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@f0fa7org.highwire.dtl.DTLVardef@ccac5borg.highwire.dtl.DTLVardef@147c331org.highwire.dtl.DTLVardef@5fe66c_HPS_FORMAT_FIGEXP M_FIG C_FIG