46 research outputs found
The Database of Macromolecular Motions: new features added at the decade mark
Get PDFabstract: The database of molecular motions, MolMovDB (http://molmovdb.org), has been in existence for the past decade. It classifies macromolecular motions and provides tools to interpolate between two conformations (the Morph Server) and predict possible motions in a single structure. In 2005, we expanded the services offered on MolMovDB. In particular, we further developed the Morph Server to produce improved interpolations between two submitted structures. We added support for multiple chains to the original adiabatic mapping interpolation, allowing the analysis of subunit motions. We also added the option of using FRODA interpolation, which allows for more complex pathways, potentially overcoming steric barriers. We added an interface to a hinge prediction service, which acts on single structures and predicts likely residue points for flexibility. We developed tools to relate such points of flexibility in a structure to particular key residue positions, i.e. active sites or highly conserved positions. Lastly, we began relating our motion classification scheme to function using descriptions from the Gene Ontology Consortium.The final version of this article, as published in Nucleic Acids Research, can be viewed online at: https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkj04
Author Correction: Expanded encyclopaedias of DNA elements in the human and mouse genomes
Get PDFIn the version of this article initially published, two members of the ENCODE Project Consortium were missing from the author list. Rizi Ai (Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, USA) and Shantao Li (Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA) are now included in the author list. These errors have been corrected in the online version of the article
Integrated Assessment of Genomic Correlates of Protein Evolutionary Rate
Get PDFRates of evolution differ widely among proteins, but the causes and consequences of such differences remain under debate. With the advent of high-throughput functional genomics, it is now possible to rigorously assess the genomic correlates of protein evolutionary rate. However, dissecting the correlations among evolutionary rate and these genomic features remains a major challenge. Here, we use an integrated probabilistic modeling approach to study genomic correlates of protein evolutionary rate in Saccharomyces cerevisiae. We measure and rank degrees of association between (i) an approximate measure of protein evolutionary rate with high genome coverage, and (ii) a diverse list of protein properties (sequence, structural, functional, network, and phenotypic). We observe, among many statistically significant correlations, that slowly evolving proteins tend to be regulated by more transcription factors, deficient in predicted structural disorder, involved in characteristic biological functions (such as translation), biased in amino acid composition, and are generally more abundant, more essential, and enriched for interaction partners. Many of these results are in agreement with recent studies. In addition, we assess information contribution of different subsets of these protein properties in the task of predicting slowly evolving proteins. We employ a logistic regression model on binned data that is able to account for intercorrelation, non-linearity, and heterogeneity within features. Our model considers features both individually and in natural ensembles ("meta-features") in order to assess joint information contribution and degree of contribution independence. Meta-features based on protein abundance and amino acid composition make strong, partially independent contributions to the task of predicting slowly evolving proteins; other meta-features make additional minor contributions. The combination of all meta-features yields predictions comparable to those based on paired species comparisons, and approaching the predictive limit of optimal lineage-insensitive features. Our integrated assessment framework can be readily extended to other correlational analyses at the genome scale.
Author Summary
Proteins encoded within a given genome are known to evolve at drastically different rates. Through recent large-scale studies, researchers have measured a wide variety of properties for all proteins in yeast. We are interested to know how these properties relate to one another and to what extent they explain evolutionary rate variation. Protein properties are a heterogeneous mix, a factor which complicates research in this area. For example, some properties (e.g., protein abundance) are numerical, while others (e.g., protein function) are descriptive; protein properties may also suffer from noise and hidden redundancies. We have addressed these issues within a flexible and robust statistical framework. We first ranked a large list of protein properties by the strength of their relationships with evolutionary rate; this confirms many known evolutionary relationships and also highlights several new ones. Similar protein properties were then grouped and applied to predict slowly evolving proteins. Some of these groups were as effective as paired species comparison in making correct predictions, although in both cases a great deal of evolutionary rate variation remained to be explained. Our work has helped to refine the set of protein properties that researchers should consider as they investigate the mechanisms underlying protein evolution.PhRMA Foundation; National Science Foundation (DGE-0654108); National Institues of Health; Williams professorship fun
Author Correction: Perspectives on ENCODE
Get PDFIn this Article, the authors Rizi Ai (Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, USA) and Shantao Li (Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA) were mistakenly omitted from the ENCODE Project Consortium author list. The original Article has been corrected online
Information assessment on predicting protein-protein interactions
Get PDFAbstract Background Identifying protein-protein interactions is fundamental for understanding the molecular machinery of the cell. Proteome-wide studies of protein-protein interactions are of significant value, but the high-throughput experimental technologies suffer from high rates of both false positive and false negative predictions. In addition to high-throughput experimental data, many diverse types of genomic data can help predict protein-protein interactions, such as mRNA expression, localization, essentiality, and functional annotation. Evaluations of the information contributions from different evidences help to establish more parsimonious models with comparable or better prediction accuracy, and to obtain biological insights of the relationships between protein-protein interactions and other genomic information. Results Our assessment is based on the genomic features used in a Bayesian network approach to predict protein-protein interactions genome-wide in yeast. In the special case, when one does not have any missing information about any of the features, our analysis shows that there is a larger information contribution from the functional-classification than from expression correlations or essentiality. We also show that in this case alternative models, such as logistic regression and random forest, may be more effective than Bayesian networks for predicting interactions. Conclusions In the restricted problem posed by the complete-information subset, we identified that the MIPS and Gene Ontology (GO) functional similarity datasets as the dominating information contributors for predicting the protein-protein interactions under the framework proposed by Jansen et al. Random forests based on the MIPS and GO information alone can give highly accurate classifications. In this particular subset of complete information, adding other genomic data does little for improving predictions. We also found that the data discretizations used in the Bayesian methods decreased classification performance.</p
Author Correction: Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples.
Get PDFCorrection to this paper has been published: https://doi.org/10.1038/s41467-020-20128-w
Author Correction: Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples
No full textThe original version of this Article omitted from the author list the 9th author Yize Li, who is from the ‘The McDonnell Genome Institute at Washington University, St. Louis, MO 63108, USA and Department of Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, MO 63108, USA’. This has been corrected in both the PDF and HTML versions of the Article
Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples.
Get PDFThe Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts
Author Correction: Identification of genetic effects underlying type 2 diabetes in South Asian and European populations
Get PDFAuthor Correction to: Identification of genetic effects underlying type 2 diabetes in South Asian and European population
Selection and Characterization of Small Random
No full textthis article can be found at doi: 10.1016/j.jmb.2004.03.044 E-mail address of the corresponding author: [email protected] Abbreviations used: PDGF, platelet-derived growth factor; CAT, chloramphenicol acetyltransferas
