Enhancer Evolution across 20 Mammalian Species

Our first meeting of 2016 is scheduled for 12:00 on the 25th of January in room 3160 in the Discovery building. The room may be subject to change. The paper selection is Enhancer Evolution across 20 Mammalian Species, available online at the link. We will allot some time at the beginning of our meeting to discuss paper suggestions and themes we would like to cover this semester.

The abstract of the paper is as follows. Please feel free to begin our discussion in the comments section below.

The mammalian radiation has corresponded with rapid changes in noncoding regions of the genome, but we lack a comprehensive understanding of regulatory evolution in mammals. Here, we track the evolution of promoters and enhancers active in liver across 20 mammalian species from six diverse orders by profiling genomic enrichment of H3K27 acetylation and H3K4 trimethylation. We report that rapid evolution of enhancers is a universal feature of mammalian genomes. Most of the recently evolved enhancers arise from ancestral DNA exaptation, rather than lineage-specific expansions of repeat elements. In contrast, almost all liver promoters are partially or fully conserved across these species. Our data further reveal that recently evolved enhancers can be associated with genes under positive selection, demonstrating the power of this approach for annotating regulatory adaptations in genomic sequences. These results provide important insight into the functional genetics underpinning mammalian regulatory evolution.

We look forward to seeing those who can attend soon.

Sharing and Specificity of Co-expression Networks across 35 Human Tissues

Our paper selection for Monday, October 26th is about an analysis of RNAseq data from the GTEx collaboration, titled Sharing and Specificity of Co-expression Networks across 35 Human Tissues. It is available at the PLOS Computational Biology website. The abstract reads as follows.

To understand the regulation of tissue-specific gene expression, the GTEx Consortium generated RNA-seq expression data for more than thirty distinct human tissues. This data provides an opportunity for deriving shared and tissue specific gene regulatory networks on the basis of co-expression between genes. However, a small number of samples are available for a majority of the tissues, and therefore statistical inference of networks in this setting is highly underpowered. To address this problem, we infer tissue-specific gene co-expression networks for 35 tissues in the GTEx dataset using a novel algorithm, GNAT, that uses a hierarchy of tissues to share data between related tissues. We show that this transfer learning approach increases the accuracy with which networks are learned. Analysis of these networks reveals that tissue-specific transcription factors are hubs that preferentially connect to genes with tissue specific functions. Additionally, we observe that genes with tissue-specific functions lie at the peripheries of our networks. We identify numerous modules enriched for Gene Ontology functions, and show that modules conserved across tissues are especially likely to have functions common to all tissues, while modules that are upregulated in a particular tissue are often instrumental to tissue-specific function. Finally, we provide a web tool, available at mostafavilab.stat.ubc.ca/GNAT, which allows exploration of gene function and regulation in a tissue-specific manner.

We look forward to seeing those who can attend on the 26th, and please feel free to start the discussion section below.