Gene 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. To 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. GSVA 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
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/pdf/gsva-gene-set-variation-analysis-for-microarray-and-rna-seq-1bwfkmry7e.pdf

GSVA: gene set variation analysis for microarray and RNA-seq data.

New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments. The voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline. This opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. Two case studies illustrate the use of linear modeling and gene set testing methods.

/pdf/voom-precision-weights-unlock-linear-model-analysis-tools-5bgw8lj2b8.pdf

voom: precision weights unlock linear model analysis tools for RNA-seq read counts

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers approximately 99% of the euchromatic genome and is accurate to an error rate of approximately 1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human genome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead.

/pdf/finishing-the-euchromatic-sequence-of-the-human-genome-24n2xnoegg.pdf

Finishing the euchromatic sequence of the human genome

Neandertals, the closest evolutionary relatives of present-day humans, lived in large parts of Europe and western Asia before disappearing 30,000 years ago. We present a draft sequence of the Neandertal genome composed of more than 4 billion nucleotides from three individuals. Comparisons of the Neandertal genome to the genomes of five present-day humans from different parts of the world identify a number of genomic regions that may have been affected by positive selection in ancestral modern humans, including genes involved in metabolism and in cognitive and skeletal development. We show that Neandertals shared more genetic variants with present-day humans in Eurasia than with present-day humans in sub-Saharan Africa, suggesting that gene flow from Neandertals into the ancestors of non-Africans occurred before the divergence of Eurasian groups from each other.

/pdf/a-draft-sequence-of-the-neandertal-genome-4nrrmzdsiy.pdf

A Draft Sequence of the Neandertal Genome

In female mammals, most genes on one X chromosome are silenced as a result of X-chromosome inactivation. However, some genes escape X-inactivation and are expressed from both the active and inactive X chromosome. Such genes are potential contributors to sexually dimorphic traits, to phenotypic variability among females heterozygous for X-linked conditions, and to clinical abnormalities in patients with abnormal X chromosomes. Here, we present a comprehensive X-inactivation profile of the human X chromosome, representing an estimated 95% of assayable genes in fibroblast-based test systems. In total, about 15% of X-linked genes escape inactivation to some degree, and the proportion of genes escaping inactivation differs dramatically between different regions of the X chromosome, reflecting the evolutionary history of the sex chromosomes. An additional 10% of X-linked genes show variable patterns of inactivation and are expressed to different extents from some inactive X chromosomes. This suggests a remarkable and previously unsuspected degree of expression heterogeneity among females.

X-inactivation profile reveals extensive variability in X-linked gene expression in females

The male-specific region of the Y chromosome, the MSY, differentiates the sexes and comprises 95% of the chromosome's length. Here, we report that the MSY is a mosaic of heterochromatic sequences and three classes of euchromatic sequences: X-transposed, X-degenerate and ampliconic. These classes contain all 156 known transcription units, which include 78 protein-coding genes that collectively encode 27 distinct proteins. The X-transposed sequences exhibit 99% identity to the X chromosome. The X-degenerate sequences are remnants of ancient autosomes from which the modern X and Y chromosomes evolved. The ampliconic class includes large regions (about 30% of the MSY euchromatin) where sequence pairs show greater than 99.9% identity, which is maintained by frequent gene conversion (non-reciprocal transfer). The most prominent features here are eight massive palindromes, at least six of which contain testis genes.

/pdf/the-male-specific-region-of-the-human-y-chromosome-is-a-qmabegned6.pdf

Philip Latrielle

Papers

The male-specific region of the human Y chromosome is a mosaic of discrete sequence classes.