OLS Client and OLS Dialog: Open Source Tools to Annotate Public Omics Datasets

The availability of user‐friendly software to annotate biological datasets and experimental details is becoming essential in data management practices, both in local storage systems and in public databases. The Ontology Lookup Service (OLS, http://www.ebi.ac.uk/ols ) is a popular centralized service to query, browse and navigate biomedical ontologies and controlled vocabularies. Recently, the OLS framework has been completely redeveloped (version 3.0), including enhancements in the data model, like the added support for Web Ontology Language based ontologies, among many other improvements. However, the new OLS is not backwards compatible and new software tools are needed to enable access to this widely used framework now that the previous version is no longer available. We here present the OLS Client as a free, open‐source Java library to retrieve information from the new version of the OLS. It enables rapid tool creation by providing a robust, pluggable programming interface and common data model to programmatically access the OLS. The library has already been integrated and is routinely used by several bioinformatics resources and related data annotation tools. Secondly, we also introduce an updated version of the OLS Dialog (version 2.0), a Java graphical user interface that can be easily plugged into Java desktop applications to access the OLS. The software and related documentation are freely available at https://github.com/PRIDE-Utilities/ols-client and https://github.com/PRIDE-Toolsuite/ols-dialog .     

大熊猫古名研究

有关在中国古籍中是否曾出现过对大熊猫的某种谓称问题, 以往曾有"騶虞"说、"貘"说、"貔貅"说, 等等, 认为"騶虞"和"貘"的说法不足采信, 对于"貔貅"说, 作者通过考证, 提出清代以前的"貔貅"并非指大熊猫, 而清初《译峨籁》一书中谓称之"貔貅"实为该书作者转述峨眉山人所称"皮裘"的借音而转义, 即使是指大熊猫, 也只是在一相对极其狭小的历史时空范围之内可供援引; 另一方面, 并不排斥在中国历史上某个时期, 曾一度出现过对大熊猫相对准确的记载和描述, 而这一点有待于更多的考证与发掘。     

Robust recognition of zinc binding sites in proteins

Metals play a variety of roles in biological processes, and hence their presence in a protein structure can yield vital functional information. Because the residues that coordinate a metal often undergo conformational changes upon binding, detection of binding sites based on simple geometric criteria in proteins without bound metal is difficult. However, aspects of the physicochemical environment around a metal binding site are often conserved even when this structural rearrangement occurs. We have developed a Bayesian classifier using known zinc binding sites as positive training examples and nonmetal binding regions that nonetheless contain residues frequently observed in zinc sites as negative training examples. In order to allow variation in the exact positions of atoms, we average a variety of biochemical and biophysical properties in six concentric spherical shells around the site of interest. At a specificity of 99.8%, this method achieves 75.5% sensitivity in unbound proteins at a positive predictive value of 73.6%. We also test its accuracy on predicted protein structures obtained by homology modeling using templates with 30%-50% sequence identity to the target sequences. At a specificity of 99.8%, we correctly identify at least one zinc binding site in 65.5% of modeled proteins. Thus, in many cases, our model is accurate enough to identify metal binding sites in proteins of unknown structure for which no high sequence identity homologs of known structure exist. Both the source code and a Web interface are available to the public at http://feature.stanford.edu/metals.     

Integrating T-cell epitope annotations with sequence and structural information using DAS

Immunoinformatics is an emerging new field that benefits from computational analyses and tools that facilitate the understanding of the immune system. A large number of immunoinformatics resources such as immune-related databases and analysis software are available through the World Wide Web for the benefit of the research community. However, immunoinformatics developments have sometimes remained isolated from mainstream bioinformatics. Therefore, there is clearly a need for integration, which will empower the exchange of data and annotations within the scientific community in a quick and efficient fashion. Here, we have chosen the Distributed Annotation System (DAS), for integrating in house annotations on experimental and predicted HLA I-restriction elements of CD8 T-cell epitopes with sequence and structural information.     

Novel strategies to mine alcoholism-related haplotypes and genes by combining existing knowledge framework

High-throughout single nucleotide polymorphism detection technology and the existing knowledge provide strong support for mining the disease-related haplotypes and genes. In this study, first, we apply four kinds of haplotype identification methods (Confidence Intervals, Four Gamete Tests, Solid Spine of LD and fusing method of haplotype block) into high-throughout SNP genotype data to identify blocks, then use cluster analysis to verify the effectiveness of the four methods, and select the alcoholism-related SNP haplotypes through risk analysis. Second, we establish a mapping from haplotypes to alcoholism-related genes. Third, we inquire NCBI SNP and gene databases to locate the blocks and identify the candidate genes. In the end, we make gene function annotation by KEGG, Biocarta, and GO database. We find 159 haplotype blocks, which relate to the alcoholism most possibly on chromosome 1∼22, including 227 haplotypes, of which 102 SNP haplotypes may increase the risk of alcoholism. We get 121 alcoholism-related genes and verify their reliability by the functional annotation of biology. In a word, we not only can handle the SNP data easily, but also can locate the disease-related genes precisely by combining our novel strategies of mining alcoholism-related haplotypes and genes with existing knowledge framework. Supported by the National Natural Science Foundation of China (Grant Nos. 30570424, 60601010 and 30600367), the National High-Tech Research and Development Program of China, (Grant No.2007AA02Z329), the Key Science and Technology Program of Heilongjiang Province(Grant No.GB03C602-4), Natural Science Foundation of Heilongjiang Province (Grant No. F2008-02), Youth Science Foundation of Harbin Medical University (Grant No. 060045) and Science Foundation of Heilongjiang Province Education Department (Grant Nos. 11531113 and 1152hq28).     

Recurrent use of evolutionary importance for functional annotation of proteins based on local structural similarity

The annotation of protein function has not kept pace with the exponential growth of raw sequence and structure data. An emerging solution to this problem is to identify 3D motifs or templates in protein structures that are necessary and sufficient determinants of function. Here, we demonstrate the recurrent use of evolutionary trace information to construct such 3D templates for enzymes, search for them in other structures, and distinguish true from spurious matches. Serine protease templates built from evolutionarily important residues distinguish between proteases and other proteins nearly as well as the classic Ser-His-Asp catalytic triad. In 53 enzymes spanning 33 distinct functions, an automated pipeline identifies functionally related proteins with an average positive predictive power of 62%, including correct matches to proteins with the same function but with low sequence identity (the average identity for some templates is only 17%). Although these template building, searching, and match classification strategies are not yet optimized, their sequential implementation demonstrates a functional annotation pipeline which does not require experimental information, but only local molecular mimicry among a small number of evolutionarily important residues.     

Estrogenic regulation of skeletal muscle proteome: a study of premenopausal women and postmenopausal MZ cotwins discordant for hormonal therapy

Female middle age is characterized by a decline in skeletal muscle mass and performance, predisposing women to sarcopenia, functional limitations, and metabolic dysfunction as they age. Menopausal loss of ovarian function leading to low circulating level of 17β‐estradiol has been suggested as a contributing factor to aging‐related muscle deterioration. However, the underlying molecular mechanisms remain largely unknown and thus far androgens have been considered as a major anabolic hormone for skeletal muscle. We utilized muscle samples from 24 pre‐ and postmenopausal women to establish proteome‐wide profiles, associated with the difference in age (30–34 years old vs. 54–62 years old), menopausal status (premenopausal vs. postmenopausal), and use of hormone replacement therapy (HRT; user vs. nonuser). None of the premenopausal women used hormonal medication while the postmenopausal women were monozygotic (MZ) cotwin pairs of whom the other sister was current HRT user or the other had never used HRT. Label‐free proteomic analyses resulted in the quantification of 797 muscle proteins of which 145 proteins were for the first time associated with female aging using proteomics. Furthermore, we identified 17β‐estradiol as a potential upstream regulator of the observed differences in muscle energy pathways. These findings pinpoint the underlying molecular mechanisms of the metabolic dysfunction accruing upon menopause, thus having implications for understanding the complex functional interactions between female reproductive hormones and health.