329 results found
Leal LG, David A, Jarvelin M-R, et al., 2019, Identification of disease-associated loci using machine learning for genotype and network data integration., Bioinformatics
MOTIVATION: Integration of different omics data could markedly help to identify biological signatures, understand the missing heritability of complex diseases and ultimately achieve personalised medicine. Standard regression models used in Genome Wide Association Studies (GWAS) identify loci with a strong effect size, whereas GWAS meta-analyses are often needed to capture weak loci contributing to the missing heritability. Development of novel machine learning algorithms for merging genotype data with other omics data is highly needed as it could enhance the prioritisation of weak loci. RESULTS: We developed cNMTF (Corrected Non-negative Matrix Tri-Factorisation), an integrative algorithm based on clustering techniques of biological data. This method assesses the interrelatedness between genotypes, phenotypes, the damaging effect of the variants and gene networks in order to identify loci-trait associations. cNMTF was used to prioritise genes associated with lipid traits in two population cohorts. We replicated 129 genes reported in GWAS world-wide and provided evidence that supports 85% of our findings (226 out of 265 genes), including recent associations in literature (NLGN1), regulators of lipid metabolism (DAB1) and pleiotropic genes for lipid traits (CARM1). Moreover, cNMTF performed efficiently against strong population structures by accounting for the individuals' ancestry. As the method is flexible in the incorporation of diverse omics data sources, it can be easily adapted to the user's research needs. AVAILABILITY: An R package (cnmtf) is available at https://lgl15.github.io/cnmtf_web/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Ofoegbu TC, David A, Kelley LA, et al., 2019, PhyreRisk: A Dynamic Web Application to Bridge Genomics, Proteomics and 3D Structural Data to Guide Interpretation of Human Genetic Variants., J Mol Biol
PhyreRisk is an open-access, publicly-accessible web application for interactively bridging genomic, proteomic and structural data facilitating the mapping of human variants onto protein structures. A major advance over other tools for sequence-structure variant mapping is that PhyreRisk provides information on 20,214 human canonical proteins and an additional 22,271 alternative protein sequences (isoforms). Specifically, PhyreRisk provides structural coverage (partial or complete) for 70% (14,035 of 20,214 canonical proteins) of the human proteome, by storing 18,874 experimental structures and 84,818 pre-built models of canonical proteins and their isoforms generated using our in house Phyre2. PhyreRisk reports 55,732 experimentally, multi-validated protein interactions from IntAct and 24,260 experimental structures of protein complexes. Another major feature of PhyreRisk is that, rather than presenting a limited set of precomputed variant-structure mapping of known genetic variants, it allows the user to explore novel variants using, as input, genomic coordinates formats (Ensembl, VCF, reference SNP ID and HGVS notations) and Human Build GRCh37 and GRCh38. PhyreRisk also supports mapping variants using amino acid coordinates and searching for genes or proteins of interest. PhyreRisk is designed to empower researchers to translate genetic data into protein structural information thereby providing a more comprehensive appreciation of the functional impact of variants. PhyreRisk is freely available at http://phyrerisk.bc.ic.ac.uk.
Ittisoponpisan S, Islam SA, Khanna T, et al., 2019, Can Predicted Protein 3D Structures Provide Reliable Insights into whether Missense Variants Are Disease Associated?, J Mol Biol
Knowledge of protein structure can be used to predict the phenotypic consequence of a missense variant. Since structural coverage of the human proteome can be roughly tripled to over 50% of the residues if homology-predicted structures are included in addition to experimentally determined coordinates, it is important to assess the reliability of using predicted models when analyzing missense variants. Accordingly, we assess whether a missense variant is structurally damaging by using experimental and predicted structures. We considered 606 experimental structures and show that 40% of the 1965 disease-associated missense variants analyzed have a structurally damaging change in the mutant structure. Only 11% of the 2134 neutral variants are structurally damaging. Importantly, similar results are obtained when 1052 structures predicted using Phyre2 algorithm were used, even when the model shares low (<40%) sequence identity to the template. Thus, structure-based analysis of the effects of missense variants can be effectively applied to homology models. Our in-house pipeline, Missense3D, for structurally assessing missense variants was made available at http://www.sbg.bio.ic.ac.uk/~missense3d.
Ittisoponpisan S, Islam S, Khanna T, et al., Can Predicted Protein 3D-structures provide Reliable Insights into whether Missense Variants are Disease-associated?, Journal of Molecular Biology, ISSN: 0022-2836
Ciezarek AG, Osborne OG, Shipley ON, et al., 2019, Phylotranscriptomic Insights into the Diversification of Endothermic Thunnus Tunas, MOLECULAR BIOLOGY AND EVOLUTION, Vol: 36, Pages: 84-96, ISSN: 0737-4038
Ciezarek A, Osbourne O, Shipley ON, et al., Diversification of characteristics related to regional endothermy in Thunnus tunas, Molecular Biology and Evolution, ISSN: 1537-1719
Birds, mammals, and certain fishes, including tunas, opahs and lamnid sharks, are endothermic, conserving internally generated, metabolic heat to maintain body or tissue temperatures above that of the environment. Bluefin tunas, among the most threatened, but commercially important, fishes worldwide are renowned regional endotherms, maintaining elevated temperatures of the oxidative locomotor muscle, viscera, brain and eyes, and occupying cold, productive high-latitude waters. Less cold-tolerant tuna, such as yellowfin, by contrast, remain in warm-temperate to tropical waters year-round, reproducing more rapidly than temperate bluefin tuna. Thereby, they are more resilient to fisheries, whereas bluefins have declined steeply. Despite the importance of these traits to not only fisheries, but response to climate change, little is known of the genetic processes underlying the diversification of tuna. In collecting and analysing sequence data across 29,556 genes, we found that parallel selection on standing genetic variation has driven the evolution of endothermy in bluefin tunas. This includes two shared substitutions in genes encoding glycerol-3 phosphate dehydrogenase, an enzyme which underlies thermogenesis in bumblebees and mammals, as well as four genes involved in the Krebs cycle, oxidative phosphorylation, β-oxidation and superoxide removal. Using phylogenetic techniques, we further illustrate that the eight Thunnus species are genetically distinct, but found evidence of mitochondrial genome introgression across two species. Phylogeny-based metrics highlight conservation needs for some of these species.
David A, Ittisoponpisan S, Sternberg MJE, 2018, PROTEIN STRUCTURE ANALYSIS AIDS IN THE INTERPRETATION OF GENETIC VARIANTS OF UNCERTAIN CLINICAL SIGNIFICANCE IDENTIFIED IN THE LDL RECEPTOR, HEART UK 32nd Annual Medical and Scientific Conference on Hot Topics in Atheroscloerosis and Cardiovascular Disease, Publisher: ELSEVIER IRELAND LTD, Pages: E2-E3, ISSN: 1567-5688
Reynolds CR, Islam SA, Sternberg MJE, 2018, EzMol: A Web Server Wizard for the Rapid Visualization and Image Production of Protein and Nucleic Acid Structures, JOURNAL OF MOLECULAR BIOLOGY, Vol: 430, Pages: 2244-2248, ISSN: 0022-2836
Sternberg MJE, Yosef N, 2018, Computation Resources for Molecular Biology: Special Issue 2018, JOURNAL OF MOLECULAR BIOLOGY, Vol: 430, Pages: 2181-2183, ISSN: 0022-2836
Cornish AJ, David A, Sternberg MJE, 2018, PhenoRank: reducing study bias in gene prioritization through simulation, BIOINFORMATICS, Vol: 34, Pages: 2087-2095, ISSN: 1367-4803
Greener JG, Sternberg MJE, 2018, Structure-based prediction of protein allostery, CURRENT OPINION IN STRUCTURAL BIOLOGY, Vol: 50, Pages: 1-8, ISSN: 0959-440X
Alhuzimi E, Leal LG, Sternberg MJE, et al., 2018, Properties of human genes guided by their enrichment in rare and common variants, HUMAN MUTATION, Vol: 39, Pages: 365-370, ISSN: 1059-7794
Bryant WA, Stentz R, Le Gall G, et al., 2017, In Silico Analysis of the Small Molecule Content of Outer Membrane Vesicles Produced by Bacteroides thetaiotaomicron Indicates an Extensive Metabolic Link between Microbe and Host, FRONTIERS IN MICROBIOLOGY, Vol: 8, ISSN: 1664-302X
Waese J, Fan J, Pasha A, et al., 2017, ePlant: Visualizing and Exploring Multiple Levels of Data for Hypothesis Generation in Plant Biology, PLANT CELL, Vol: 29, Pages: 1806-1821, ISSN: 1040-4651
Scales M, Chubb D, Dobbins SE, et al., 2017, Search for rare protein altering variants influencing susceptibility to multiple myeloma, ONCOTARGET, Vol: 8, Pages: 36203-36210, ISSN: 1949-2553
Sundriyal S, Moniot S, Mahmud Z, et al., 2017, Thienopyrimidinone Based Sirtuin-2 (SIRT2)-Selective Inhibitors Bind in the Ligand Induced Selectivity Pocket, JOURNAL OF MEDICINAL CHEMISTRY, Vol: 60, Pages: 1928-1945, ISSN: 0022-2623
Greener JG, Filippis I, Sternberg MJE, 2017, Predicting Protein Dynamics and Allostery Using Multi-Protein Atomic Distance Constraints, STRUCTURE, Vol: 25, Pages: 546-558, ISSN: 0969-2126
Ittisoponpisan S, Alhuzimi E, Sternberg MJE, et al., 2017, Landscape of Pleiotropic Proteins Causing Human Disease: Structural and System Biology Insights, HUMAN MUTATION, Vol: 38, Pages: 289-296, ISSN: 1059-7794
Ainsworth D, Sternberg MJE, Raczy C, et al., 2017, k-SLAM: accurate and ultra-fast taxonomic classification and gene identification for large metagenomic data sets, NUCLEIC ACIDS RESEARCH, Vol: 45, Pages: 1649-1656, ISSN: 0305-1048
Ostankovitch MI, Sternberg MJE, 2017, Computation Resources for Molecular Biology: Special Issue 2017, JOURNAL OF MOLECULAR BIOLOGY, Vol: 429, Pages: 345-347, ISSN: 0022-2836
Metherell LA, Guerra-Assuncao JA, Sternberg MJ, et al., 2016, Three-Dimensional Model of Human Nicotinamide Nucleotide Transhydrogenase (NNT) and Sequence-Structure Analysis of its Disease-Causing Variations, HUMAN MUTATION, Vol: 37, Pages: 1074-1084, ISSN: 1059-7794
Jiang Y, Oron TR, Clark WT, et al., 2016, An expanded evaluation of protein function prediction methods shows an improvement in accuracy, GENOME BIOLOGY, Vol: 17, ISSN: 1474-760X
Howard SR, Guasti L, Ruiz-Babot G, et al., 2016, IGSF10 mutations dysregulate gonadotropin-releasing hormone neuronal migration resulting in delayed puberty, EMBO MOLECULAR MEDICINE, Vol: 8, Pages: 626-642, ISSN: 1757-4676
Mezulis S, Sternberg MJE, Kelley LA, 2016, PhyreStorm: A Web Server for Fast Structural Searches Against the PDB, JOURNAL OF MOLECULAR BIOLOGY, Vol: 428, Pages: 702-708, ISSN: 0022-2836
Sternberg MJE, Ostankovitch MI, 2016, Computation Resources for Molecular Biology: A Special Issue, JOURNAL OF MOLECULAR BIOLOGY, Vol: 428, Pages: 669-670, ISSN: 0022-2836
Greener JG, Sternberg MJE, 2015, AlloPred: prediction of allosteric pockets on proteins using normal mode perturbation analysis, BMC BIOINFORMATICS, Vol: 16, ISSN: 1471-2105
Reynolds CR, Muggleton SH, Sternberg MJE, 2015, Incorporating Virtual Reactions into a Logic-based Ligand-based Virtual Screening Method to Discover New Leads, MOLECULAR INFORMATICS, Vol: 34, Pages: 615-625, ISSN: 1868-1743
Cornish AJ, Filippis I, David A, et al., 2015, Exploring the cellular basis of human disease through a large-scale mapping of deleterious genes to cell types, GENOME MEDICINE, Vol: 7, ISSN: 1756-994X
David A, Sternberg MJ, 2015, The contribution of missense mutations in core and rim residues of protein-protein interfaces to human disease., Journal of Molecular Biology, Vol: 427, Pages: 2886-2898, ISSN: 1089-8638
Missense mutations at protein-protein interaction (PPIs) sites, called interfaces, are important contributors to human disease. Interfaces are non-uniform surface areas characterized by two main regions, 'core' and 'rim', which differ in terms of evolutionary conservation and physico-chemical properties. Moreover, within interfaces, only a small subset of residues ('hot spots') is crucial for the binding free energy of the protein-protein complex. We performed a large-scale structural analysis of human single amino acid variations (SAVs) and demonstrated that disease-causing mutations are preferentially located within the interface core, as opposed to the rim (p< 0.01). In contrast, the interface rim is significantly enriched in polymorphisms, similar to the remaining non-interacting surface. Energetic hot spots tend to be enriched in disease-causing mutations compared to non-hot spots (p=0.05), regardless of their occurrence in core or rim residues. For individual amino acids, the frequency of substitution into a polymorphism or disease-causing mutation differed to other amino acids and was related to its structural location, as was the type of physico-chemical change introduced by the SAV. In conclusion, this study demonstrated the different distribution and properties of disease-causing SAVs and polymorphisms within different structural regions and in relation to the energetic contribution of amino acid in protein-protein interfaces, thus highlighting the importance of a structural system biology approach for predicting the effect of SAVs.
Kelley LA, Sternberg MJ, 2015, Partial protein domains: evolutionary insights and bioinformatics challenges., Genome Biology, Vol: 16, Pages: 100-100, ISSN: 1474-760X
Protein domains are generally thought to correspond to units of evolution. New research raises questions about how such domains are defined with bioinformatics tools and sheds light on how evolution has enabled partial domains to be viable.
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