Benjamin Schuster-Böckler

Pfam: clans, web tools and services

Pfam is a database of protein families that currently contains 7973 entries (release 18.0). A recent development in Pfam has enabled the grouping of related families into clans. Pfam clans are described in detail, together with the new associated web pages. Improvements to the range of Pfam web tools and the first set of Pfam web services that allow programmatic access to the database and associated tools are also presented. Pfam is available on the web in the UK (), the USA (), France () and Sweden ().

Chromatin organization is a major influence on regional mutation rates in human cancer cells

Cancer genome sequencing provides the first direct information on how mutation rates vary across the human genome in somatic cells. Testing diverse genetic and epigenetic features, here we show that mutation rates in cancer genomes are strikingly related to chromatin organization. Indeed, at the megabase scale, a single feature—levels of the heterochromatin-associated histone modification H3K9me3—can account for more than 40% of mutation-rate variation, and a combination of features can account for more than 55%. The strong association between mutation rates and chromatin organization is upheld in samples from different tissues and for different mutation types. This suggests that the arrangement of the genome into heterochromatin- and euchromatin-like domains is a dominant influence on regional mutation-rate variation in human somatic cells.

HMM Logos for visualization of protein families

BackgroundProfile Hidden Markov Models (pHMMs) are a widely used tool for protein family research. Up to now, however, there exists no method to visualize all of their central aspects graphically in an intuitively understandable way.ResultsWe present a visualization method that incorporates both emission and transition probabilities of the pHMM, thus extending sequence logos introduced by Schneider and Stephens. For each emitting state of the pHMM, we display a stack of letters. The stack height is determined by the deviation of the positions letter emission frequencies from the background frequencies. The stack width visualizes both the probability of reaching the state (the hitting probability) and the expected number of letters the state emits during a pass through the model (the states expected contribution).A web interface offering online creation of HMM Logos and the corresponding source code can be found at the Logos web server of the Max Planck Institute for Molecular Genetics http://logos.molgen.mpg.de.ConclusionsWe demonstrate that HMM Logos can be a useful tool for the biologist: We use them to highlight differences between two homologous subfamilies of GTPases, Rab and Ras, and we show that they are able to indicate structural elements of Ras.

Protein interactions in human genetic diseases

We present a novel method that combines protein structure information with protein interaction data to identify residues that form part of an interaction interface. Our prediction method can retrieve interaction hotspots with an accuracy of 60% (at a 20% false positive rate). The method was applied to all mutations in the Online Mendelian Inheritance in Man (OMIM) database, predicting 1,428 mutations to be related to an interaction defect. Combining predicted and hand-curated sets, we discuss how mutations affect protein interactions in general.

Dosage sensitivity shapes the evolution of copy-number varied regions.

Dosage sensitivity is an important evolutionary force which impacts on gene dispensability and duplicability. The newly available data on human copy-number variation (CNV) allow an analysis of the most recent and ongoing evolution. Provided that heterozygous gene deletions and duplications actually change gene dosage, we expect to observe negative selection against CNVs encompassing dosage sensitive genes. In this study, we make use of several sources of population genetic data to identify selection on structural variations of dosage sensitive genes. We show that CNVs can directly affect expression levels of contained genes. We find that genes encoding members of protein complexes exhibit limited expression variation and overlap significantly with a manually derived set of dosage sensitive genes. We show that complexes and other dosage sensitive genes are underrepresented in CNV regions, with a particular bias against frequent variations and duplications. These results suggest that dosage sensitivity is a significant force of negative selection on regions of copy-number variation.

Reuse of structural domain–domain interactions in protein networks

BackgroundProtein interactions are thought to be largely mediated by interactions between structural domains. Databases such as i Pfam relate interactions in protein structures to known domain families. Here, we investigate how the domain interactions from the i Pfam database are distributed in protein interactions taken from the HPRD, MPact, BioGRID, DIP and IntAct databases.ResultsWe find that known structural domain interactions can only explain a subset of 4–19% of the available protein interactions, nevertheless this fraction is still significantly bigger than expected by chance. There is a correlation between the frequency of a domain interaction and the connectivity of the proteins it occurs in. Furthermore, a large proportion of protein interactions can be attributed to a small number of domain interactions. We conclude that many, but not all, domain interactions constitute reusable modules of molecular recognition. A substantial proportion of domain interactions are conserved between E. coli, S. cerevisiae and H. sapiens. These domains are related to essential cellular functions, suggesting that many domain interactions were already present in the last universal common ancestor.ConclusionOur results support the concept of domain interactions as reusable, conserved building blocks of protein interactions, but also highlight the limitations currently imposed by the small number of available protein structures.

Visualizing profile–profile alignment: pairwise HMM logos

UNLABELLED The availability of advanced profile-profile comparison tools, such as PRC or HHsearch demands sophisticated visualization tools not presently available. We introduce an approach built upon the concept of HMM logos. The method illustrates the similarities of pairs of protein family profiles in an intuitive way. Two HMM logos, one for each profile, are drawn one upon the other. The aligned states are then highlighted and connected. AVAILABILITY A web interface offering online creation of pairwise HMM logos is available at http://www.sanger.ac.uk/Software/analysis/logomat-p. Furthermore, software developers may download a Perl package that includes methods for creation of pairwise HMM logos locally. CONTACT bsb@sanger.ac.uk.

The importance of p53 pathway genetics in inherited and somatic cancer genomes

Decades of research have shown that mutations in the p53 stress response pathway affect the incidence of diverse cancers more than mutations in other pathways. However, most evidence is limited to somatic mutations and rare inherited mutations. Using newly abundant genomic data, we demonstrate that commonly inherited genetic variants in the p53 pathway also affect the incidence of a broad range of cancers more than variants in other pathways. The cancer-associated single nucleotide polymorphisms (SNPs) of the p53 pathway have strikingly similar genetic characteristics to well-studied p53 pathway cancer-causing somatic mutations. Our results enable insights into p53-mediated tumour suppression in humans and into p53 pathway-based cancer surveillance and treatment strategies.

An Introduction to Hidden Markov Models

This unit introduces the concept of hidden Markov models in computational biology. It describes them using simple biological examples, requiring as little mathematical knowledge as possible. The unit also presents a brief history of hidden Markov models and an overview of their current applications before concluding with a discussion of their limitations.

BEARscc determines robustness of single-cell clusters using simulated technical replicates

Single-cell messenger RNA sequencing (scRNA-seq) has emerged as a powerful tool to study cellular heterogeneity within complex tissues. Subpopulations of cells with common gene expression profiles can be identified by applying unsupervised clustering algorithms. However, technical variance is a major confounding factor in scRNA-seq, not least because it is not possible to replicate measurements on the same cell. Here, we present BEARscc, a tool that uses RNA spike-in controls to simulate experiment-specific technical replicates. BEARscc works with a wide range of existing clustering algorithms to assess the robustness of clusters to technical variation. We demonstrate that the tool improves the unsupervised classification of cells and facilitates the biological interpretation of single-cell RNA-seq experiments.Single cell messenger RNAseq allows the study of heterogeneity in tissue samples. Here the authors present BEARscc, a tool that uses RNA spike-in controls to simulate experiment-specific technical replicates to estimate the robustness of computational predictions of subpopulations of cells.