Expanding the Landscape of Chromatin Modification (CM)-Related Functional Domains and Genes in Human

Chromatin modification (CM) plays a key role in regulating transcription, DNA replication, repair and recombination. However, our knowledge of these processes in humans remains very limited. Here we use computational approaches to study proteins and functional domains involved in CM in humans. We analyze the abundance and the pair-wise domain-domain co-occurrences of 25 well-documented CM domains in 5 model organisms: yeast, worm, fly, mouse and human. Results show that domains involved in histone methylation, DNA methylation, and histone variants are remarkably expanded in metazoan, reflecting the increased demand for cell type-specific gene regulation. We find that CM domains tend to co-occur with a limited number of partner domains and are hence not promiscuous. This property is exploited to identify 47 potentially novel CM domains, including 24 DNA-binding domains, whose role in CM has received little attention so far. Lastly, we use a consensus Machine Learning approach to predict 379 novel CM genes (coding for 329 proteins) in humans based on domain compositions. Several of these predictions are supported by very recent experimental studies and others are slated for experimental verification. Identification of novel CM genes and domains in humans will aid our understanding of fundamental epigenetic processes that are important for stem cell differentiation and cancer biology. Information on all the candidate CM domains and genes reported here is publicly available

doi:10.1093/nar/gkn1005 Up-to-date catalogues of yeast protein complexes

Gold standard datasets on protein complexes are key to inferring and validating protein–protein interactions. Despite much progress in characterizing protein complexes in the yeast Saccharomyces cerevisiae, numerous researchers still use as reference the manually curated complexes catalogued by the Munich Information Center of Protein Sequences database. Although this catalogue has served the community extremely well, it no longer reflects the current state of knowledge. Here, we report two catalogues of yeast protein complexes as results of systematic curation efforts. The first one, denoted as CYC2008, is a comprehensive catalogue of 408 manually curated heteromeric protein complexes reliably backed by small-scale experiments reported in the current literature. This catalogue represents an up-to-date reference set for biologists interested in discovering protein interactions and protein complexes. The second catalogue, denoted as YHTP2008, comprises 400 high-throughput complexes annotated with current literature evidence. Among them, 262 correspond, at least partially, to CYC2008 complexes. Evidence for interacting subunits is collected for 68 complexes that have only partial or no overlap with CYC2008 complexes, whereas no literature evidence was found for 100 complexes. Some of these partially supported and as yet unsupported complexes may be interesting candidates for experimental follow up. Both catalogues are freely available at

Finding maximum edge bicliques in convex bipartite graphs

A bipartite graph G=(A, B, E) is convex on B if there exists an ordering of the vertices of B such that for any vertex v εA, vertices adjacent to v are consecutive in B. A complete bipartite subgraph of a graph G is called a biclique of G. In this paper, we study the problem of finding the maximum edge-cardinality biclique in convex bipartite graphs. Given a bipartite graph G=(A, B, E) which is convex on B, we present a new algorithm that computes the maximum edge-cardinality biclique of G in O(n log3 n loglogn) time and O(n) space, where n=|A|. This improves the current O(n 2) time bound available for the problem

Finding maximum edge bicliques in convex bipartite graphs

A bipartite graph G = (A,B,E) is convex on B if there exists an ordering of the vertices of B such that for any vertex v ? A, vertices adjacent to v are consecutive in B. A complete bipartite subgraph of a graph G is called a biclique of G. Motivated by an application to analyzing DNA microarray data, we study the problem of finding maximum edge bicliques in convex bipartite graphs. Given a bipartite graph G = (A,B,E) which is convex on B, we present a new algorithm that computes a maximum edge biclique of G in O(nlog3 n log log n) time and O(n) space, where n = |A|. This improves the current O(n 2) time bound available for the problem. We also show that for two special subclasses of convex bipartite graphs, namely for biconvex graphs and bipartite permutation graphs, a maximum ed