BackgroundGene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets.ResultsTo address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments.ConclusionsGSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.

We report the generation and analysis of functional data from multiple, diverse experiments performed on a targeted 1% of the human genome as part of the pilot phase of the ENCODE Project. These data have been further integrated and augmented by a number of evolutionary and computational analyses. Together, our results advance the collective knowledge about human genome function in several major areas. First, our studies provide convincing evidence that the genome is pervasively transcribed, such that the majority of its bases can be found in primary transcripts, including non-protein-coding transcripts, and those that extensively overlap one another. Second, systematic examination of transcriptional regulation has yielded new understanding about transcription start sites, including their relationship to specific regulatory sequences and features of chromatin accessibility and histone modification. Third, a more sophisticated view of chromatin structure has emerged, including its inter-relationship with DNA replication and transcriptional regulation. Finally, integration of these new sources of information, in particular with respect to mammalian evolution based on inter- and intra-species sequence comparisons, has yielded new mechanistic and evolutionary insights concerning the functional landscape of the human genome. Together, these studies are defining a path for pursuit of a more comprehensive characterization of human genome function.

Fas exon 6 can be included or skipped to generate mRNAs encoding, respectively, a membrane bound form of the receptor that promotes apoptosis or a soluble isoform that prevents programmed cell death. We report that the apoptosis-inducing protein TIA-1 promotes U1 snRNP binding to the 5' splice site of intron 6, which in turn facilitates exon definition by enhancing U2AF binding to the 3' splice site of intron 5. The polypyrimidine tract binding protein (PTB) promotes exon skipping by binding to an exonic splicing silencer and inhibiting the association of U2AF and U2 snRNP with the upstream 3' splice site, without affecting recognition of the downstream 5' splice site by U1. Remarkably, U1 snRNP-mediated recognition of the 5' splice site is required both for efficient U2AF binding and for U2AF inhibition by PTB. We propose that TIA-1 and PTB regulate Fas splicing and possibly Fas-mediated apoptosis by targeting molecular events that lead to exon definition.