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2011 ACM Conference on Bioinformatics, computational biology and Biomedicine, ACM-BCB 2011

作者： Xue, Li C. Walia, Rasna EL-Manzalawy, Yasser Dobbs, Drena Honavar, Vasant Bioinformatics and Computational Biology Program Iowa State University Ames IA 50011 United States Department of Genetics Development and Cell Biology Iowa State University Ames IA 50011 United States Department of Computer Science Iowa State University Ames IA 50011 United States Department of Systems and Computer Engineering Al-Azhar University Cairo Egypt

ISBN: (纸本)9781450307963

Protein-RNA interactions play important roles in cellular processes like protein synthesis, RNA processing, and gene expression regulation. Reliable identification of the interfaces involved in RNA-protein interactions is essential for comprehending the mechanisms and the functional implications of these interactions and provides a valuable guide for rational drug discovery and design. Because the determination of 3D structures of protein-RNA complexes has various technical limitations and is typically costly, reliable in silico interface prediction methods that require only the sequence information are urgently needed. We present HomPRIP, a homologous sequence based method for predicting protein-RNA interfaces, based on our conservation analysis of protein-RNA interfaces. We test Hom-PRIP on a benchmark dataset of 199 proteins and compare it with the state-of-the-art protein-RNA interface prediction methods. Our results show that HomPRIP can reliably identify protein-RNA interface residues in 71% of test proteins with at least one putative sequence homolog passing the similarity thresholds of HomPRIP. Moreover, to facilitate predictions for proteins with no identified homologs, we develop HomPRIP-NB, a method combining the HomPRIP predictor and a Naive Bayes (NB) classifier trained using evolutionary information derived from alignments against the NCBI nr database. Our results suggest that HomPRIP-NB significantly outperforms the state-of-the-art machine learning methods for predicting protein-RNA interface residues. Copyright © 2011 ACM.

关键词： Proteins

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Ranking docked models of protein-protein complexes using predicted partner-specific protein-protein interfaces: A preliminary study 11

Ranking docked models of protein-protein complexes using pre...

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2011 ACM Conference on Bioinformatics, computational biology and Biomedicine, ACM-BCB 2011

作者： Xue, Li C. Jordan, Rafael A. El-Manzalawy, Yasser Dobbs, Drena Honavar, Vasant Bioinformatics and Computational Biology Program Iowa State University Ames IA 50011 United States Department of Genetics Development and Cell Biology Iowa State University Ames 50011 United States Department of Computer Science Iowa State University Ames IA 50011 United States Department of Computer Science Pontificia Universidad Javeriana Cali Colombia Department of Systems and Computer Engineering AI-Azhar University Cairo Egypt

ISBN: (纸本)9781450307963

computational protein-protein docking is a valuable tool for determining the conformation of complexes formed by interacting proteins. Selecting near-native conformations from the large number of possible models generated by docking software presents a significant challenge in practice. We introduce a novel method for ranking docked conformations based on the degree of overlap between the interface residues of a docked conformation formed by a pair of proteins with the set of predicted interface residues between them. Our approach relies on a method, called PS-HomPPI, for reliably predicting proteinprotein interface residues by taking into account information derived from both interacting proteins. PS-HomPPI infers the residues of a query protein that are likely to interact with a partner protein based on known interface residues of the homo-interologs of the query-partner protein pair, i.e., pairs of interacting proteins that are homologous to the query protein and partner protein. Our results on Docking Benchmark 3.0 show that the quality of the ranking of docked conformations using our method is consistently superior to that produced using ClusPro cluster-size-based and energy-based criteria for 61 out of the 64 docking complexes for which PS-HomPPI produces interface predictions. An implementation of our method for ranking docked models is freely available at: http://***/DockRank/. Copyright © 2011 ACM.

关键词： Proteins

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Potential role of HE4 in multimodal screening for epithelial ovarian cancer

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Journal of the National cancer Institute 2011年第21期103卷 1630-1634页

作者： Urban, Nicole Thorpe, Jason D. Bergan, Lindsay A. Forrest, Robin M. Kampani, Archana V. Scholler, Nathalie Anderson, Garnet L. Cramer, Daniel W. Berg, Christine D. McIntosh, Martin W. Hartge, Patricia Drescher, Charles W. Molecular Diagnostics Program Fred Hutchinson Cancer Research Center M2-B230 Seattle WA 98109-1024 1100 Fairview Ave N. United States Computational Biology Program Fred Hutchinson Cancer Research Center Seattle WA United States Cancer Prevention Program Fred Hutchinson Cancer Research Center Seattle WA United States University of Pennsylvania Perelman School of Medicine Penn Ovarian Cancer Research Center Philadelphia PA United States Department of Obstetrics and Gynecology Epidemiology Center Brigham and Women's Hospital Boston MA United States Early Detection Research Group Division of Cancer Prevention National Cancer Institute Bethesda MD United States Division of Cancer Epidemiology and Genetics National Cancer Institute National Institutes of Health Bethesda MD United States

In screening for epithelial ovarian cancer, unnecessary surgery can be reduced by limiting use of transvaginal ultrasound (TVU) to women with increasing CA125 serum levels. Replacing or augmenting TVU with measurement of a serum marker specific for malignancy might further improve screening performance. Serum samples from 112 invasive ovarian cancer patients and 706 matched control subjects from the Prostate, Lung, Colorectal, and Ovarian trial were used to evaluate human epididymis protein 4 (HE4), mesothelin, matrix metalloproteinase 7 (MMP7), SLPI, Spondin2, and insulin-like growth factor binding protein 2 (IGFBP2) for their potential use in screening. TVU results were available for a subset of 84 patients and 516 control subjects used to compare the best marker with TVU. HE4 was found to perform better than TVU as a second-line screen, confirming 27 of 39 cancers with increasing CA125 serum levels compared with 17 cancers confirmed by TVU (P =. 03). Serum HE4 levels were found to increase with age and smoking status, suggesting that a longitudinal algorithm might improve its performance. © 2011 The Author.

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Treatment with 5-azacytidine accelerates acute promyelocytic Leukemia leukemogenesis in a transgenic mouse model

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Genes and cancer 2011年第2期2卷 160-165页

作者： Scaglioni, Pier Paolo Cai, Lu Fan Majid, Samia M. Yung, Thomas M. Socci, Nicholas D. Kogan, Scott C. Kopelovich, Levy Pandolfi, Pier Paolo Cancer Biology and Genetics Program Memorial Sloan-Kettering Cancer Center New York NY United States Department of Medicine Memorial Sloan-Kettering Cancer Center New York NY United States Department of Pathology Memorial Sloan-Kettering Cancer Center New York NY United States Computational Biology Center Memorial Sloan-Kettering Cancer Center New York NY United States Department of Laboratory Medicine and Comprehensive Cancer Center University of California San Francisco CA United States Division of Cancer Prevention National Cancer Institute Bethesda MD United States Division of Hematology Oncology University of Texas Southwestern Medical Center Dallas TX United States Beth Israel Deaconess Cancer Center Department of Medicine and Pathology Beth Israel Deaconess Medical Center Harvard Medical School Boston MA United States

A key oncogenic force in acute promyelocytic leukemia (APL) is the ability of the promyelocytic leukemia-retinoic acid receptor α (PML-RARA) oncoprotein to recruit transcriptional repressors and DNA methyltransferases at retinoic acid-responsive elements. Pharmacological doses of retinoic acid relieve transcriptional repression inducing terminal differentiation/apoptosis of the leukemic blasts. APL blasts often harbor additional recurrent chromosomal abnormalities, and significantly, APL prevalence is increased in Latino populations. These observations suggest that multiple genetic and environmental/dietary factors are likely implicated in APL. We tested whether dietary or targeted chemopreventive strategies relieving PML-RARA transcriptional repression would be effective in a transgenic mouse model. Surprisingly, we found that 1) treatment with a demethylating agent, 5-azacytidine, results in a striking acceleration of APL;2) a high fat, low folate/choline-containing diet resulted in a substantial but nonsignificant APL acceleration;and 3) all-trans retinoic acid (ATRA) is ineffective in preventing leukemia and results in ATRA-resistant APL. Our findings have important clinical implications because ATRA is a drug of choice for APL treatment and indicate that global demethylation, whether through dietary manipulations or through the use of a pharmacologic agent such as 5-azacytidine, may have unintended and detrimental consequences in chemopreventive regimens. © The Author(s) 2011.

关键词： 5-azacytidine Apl Atra Chemoprevention Western diet

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Functional proteomics can define prognosis and predict pathologic complete response in patients with breast cancer

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Clinical Proteomics 2011年第1期8卷 1-15页

作者： Gonzalez-Angulo, Ana M. Hennessy, Bryan T. Meric-Bernstam, Funda Sahin, Aysegul Liu, Wenbin Ju, Zhenlin Carey, Mark S. Myhre, Simen Speers, Corey Deng, Lei Broaddus, Russell Lluch, Ana Aparicio, Sam Brown, Powel Pusztai, Lajos Symmans, W Fraser Alsner, Jan Overgaard, Jens Borresen-Dale, Anne-Lise Hortobagyi, Gabriel N. Coombes, Kevin R. Mills, Gordon B. Department of Breast Medical Oncology and Systems Biology University of Texas MD Anderson Cancer Center Houston TX 77030 1515 Holcombe Blvd United States Department of Gynecology Medical Oncology and Systems Biology University of Texas MD Anderson Cancer Center Houston TX 77030 1515 Holcombe Blvd United States Department of Surgical Oncology University of Texas MD Anderson Cancer Center Houston TX 77030 1515 Holcombe Blvd United States Department of Pathology University of Texas MD Anderson Cancer Center Houston TX 77030 1515 Holcombe Blvd United States Department of Bioinformatics University of Texas MD Anderson Cancer Center Houston TX 77030 1515 Holcombe Blvd United States Department of Systems Biology University of Texas MD Anderson Cancer Center Houston TX 77030 1515 Holcombe Blvd United States Department of Genetics Institute for Cancer Research Norwegian Radium Hospital Oslo 0027 Sognsvannsveien 20 Norway Faculty Division University of Oslo Norwegian Radium Hospital Oslo 0027 Sognsvannsveien 20 Norway Lester and Sue Smith Breast Center Baylor College of Medicine Houston TX 77030 1 Baylor Plaza # Bcm600 United States Department of Hematology and Oncology Hospital Clinico Universitario de Valencia Valencia 46010 Avenida Blasco Ibáñez 17 Spain Molecular Oncology and Breast Cancer Program University of British Columbia 2211 Wesbrook Mall Vancouver BC V6T 2B5 Canada Department of Cancer Prevention University of Texas MD Anderson Cancer Center Houston TX 77030 1515 Holcombe Blvd United States Department of Breast Medical Oncology University of Texas MD Anderson Cancer Center Houston TX 77030 1515 Holcombe Blvd United States Department of Experimental Clinical Oncology Aarhus University Hospital DK-8000 Aarhus Nordre Ringgade 1 Denmark

Purpose. To determine whether functional proteomics improves breast cancer classification and prognostication and can predict pathological complete response (pCR) in patients receiving neoadjuvant taxane and anthracycline- taxane-based systemic therapy (NST). Methods. Reverse phase protein array (RPPA) using 146 antibodies to proteins relevant to breast cancer was applied to three independent tumor sets. Supervised clustering to identify subgroups and prognosis in surgical excision specimens from a training set (n = 712) was validated on a test set (n = 168) in two cohorts of patients with primary breast cancer. A score was constructed using ordinal logistic regression to quantify the probability of recurrence in the training set and tested in the test set. The score was then evaluated on 132 FNA biopsies of patients treated with NST to determine ability to predict pCR. Results: Six breast cancer subgroups were identified by a 10-protein biomarker panel in the 712 tumor training set. They were associated with different recurrence-free survival (RFS) (log-rank p = 8.8 E-10). The structure and ability of the six subgroups to predict RFS was confirmed in the test set (log-rank p = 0.0013). A prognosis score constructed using the 10 proteins in the training set was associated with RFS in both training and test sets (p = 3.2E-13, for test set). There was a significant association between the prognostic score and likelihood of pCR to NST in the FNA set (p = 0.0021). Conclusion: We developed a 10-protein biomarker panel that classifies breast cancer into prognostic groups that may have potential utility in the management of patients who receive anthracycline-taxane-based NST. © 2011 Gonzalez-Angulo et al;licensee BioMed Central Ltd.

关键词： Breast cancer Functional Proteomics Prediction Prognosis

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Reply to Parsons: Many tumor types follow the monoclonal model of tumor initiation

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Proceedings of the National Academy of Sciences 2011年第5期108卷 E16-E16页

作者： Yu-Kang Cheng Rameen Beroukhim Ross L. Levine Ingo K. Mellinghoff Franziska Michor Department of Biostatistics and Computational Biology Dana–Farber Cancer Institute and Department of Biostatistics Harvard School of Public Health Boston MA 02115 Tri-Institutional Training Program in Computational Biology and Medicine Weill Cornell Medical College New York NY 10065 Cancer Biology and Genetics Program Memorial Sloan-Kettering Cancer Center New York NY 10065 Department of Cancer Biology Dana–Farber Cancer Institute and The Broad Institute of Massachusetts Institute of Technology and Harvard Cambridge MA 02139 and Human Oncology and Pathogenesis Program Memorial Sloan-Kettering Cancer Center New York NY 10065

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Annotation of gene promoters by integrative data-mining of ChIP-seq Pol-II enrichment data

Annotation of gene promoters by integrative data-mining of C...

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8th Asia-Pacific Bioinformatics Conference, APBC 2010

作者： Gupta, Ravi Wikramasinghe, Priyankara Bhattacharyya, Anirban Perez, Francisco A. Pal, Sharmistha Davuluri, Ramana V. Center for Systems and Computational Biology Molecular and Cellular Oncogenesis Program Wistar Institute Philadelphia PA United States Graduate Group in Genomics and Computational Biology Department of Genetics University of Pennsylvania Philadelphia PA United States

Background: Use of alternative gene promoters that drive widespread cell-type, tissue-type or developmental gene regulation in mammalian genomes is a common phenomenon. Chromatin immunoprecipitation methods coupled with DNA microarray (ChIP-chip) or massive parallel sequencing (ChIP-seq) are enabling genome-wide identification of active promoters in different cellular conditions using antibodies against Pol-II. However, these methods produce enrichment not only near the gene promoters but also inside the genes and other genomic regions due to the non-specificity of the antibodies used in ChIP. Further, the use of these methods is limited by their high cost and strong dependence on cellular type and context. Methods: We trained and tested different state-of-art ensemble and meta classification methods for identification of Pol-II enriched promoter and Pol-II enriched non-promoter sequences, each of length 500 bp. The classification models were trained and tested on a bench-mark dataset, using a set of 39 different feature variables that are based on chromatin modification signatures and various DNA sequence features. The best performing model was applied on seven published ChIP-seq Pol-II datasets to provide genome wide annotation of mouse gene promoters. Results: We present a novel algorithm based on supervised learning methods to discriminate promoter associated Pol-II enrichment from enrichment elsewhere in the genome in ChIP-chip/seq profiles. We accumulated a dataset of 11,773 promoter and 46,167 non-promoter sequences, each of length 500 bp, generated from RNA Pol-II ChIP-seq data of five tissues (Brain, Kidney, Liver, Lung and Spleen). We evaluated the classification models in building the best predictor and found that Bagging and Random Forest based approaches give the best accuracy. We implemented the algorithm on seven different published ChIP-seq datasets to provide a comprehensive set of promoter annotations for both protein-coding and non-coding genes in

关键词： Mammals

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An integrative ChIP-chip and gene expression profiling to model SMAD regulatory modules

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BMC systems biology 2009年第1期3卷 1-20页

作者： Qin, Huaxia Chan, Michael W.Y. Liyanarachchi, Sandya Balch, Curtis Potter, Dustin Souriraj, Irene J. Cheng, Alfred S.L. Agosto-Perez, Francisco J. Nikonova, Elena V. Yan, Pearlly S. Lin, Huey-Jen Nephew, Kenneth P. Saltz, Joel H. Showe, Louise C. Huang, Tim H.M. Davuluri, Ramana V. Human Cancer Genetics Program Department of Molecular Virology Immunology and Medical Genetics The Ohio State University Columbus OH 43210 United States Division of Medical Technology School of Allied Medical Professions The Ohio State University Columbus OH 43210 United States Department of Biomedical Informatics The Ohio State University Columbus OH 43210 United States Department of Medical Sciences Indiana University School of Medicine Bloomington IN 47405 United States Department of Life Science Institute of Molecular Biology National Chung Cheng University Min-Hsiung Chia-Yi 621 Taiwan Institute of Digestive Disease The Chinese University of Hong Kong Hong Kong Hong Kong Center for Systems and Computational Biology Molecular and Cellular Oncogenesis Program The Wistar Institute Philadelphia PA United States

Background: The TGF-β/SMAD pathway is part of a broader signaling network in which crosstalk between pathways occurs. While the molecular mechanisms of TGF-β/SMAD signaling pathway have been studied in detail, the global networks downstream of SMAD remain largely unknown. The regulatory effect of SMAD complex likely depends on transcriptional modules, in which the SMAD binding elements and partner transcription factor binding sites (SMAD modules) are present in specific context. Results: To address this question and develop a computational model for SMAD modules, we simultaneously performed chromatin immunoprecipitation followed by microarray analysis (ChIP-chip) and mRNA expression profiling to identify TGF-β/SMAD regulated and synchronously coexpressed gene sets in ovarian surface epithelium. Intersecting the ChIP-chip and gene expression data yielded 150 direct targets, of which 141 were grouped into 3 co-expressed gene sets (sustained up-regulated, transient up-regulated and down-regulated), based on their temporal changes in expression after TGF-β activation. We developed a data-mining method driven by the Random Forest algorithm to model SMAD transcriptional modules in the target sequences. The predicted SMAD modules contain SMAD binding element and up to 2 of 7 other transcription factor binding sites (E2F, P53, LEF1, ELK1, COUPTF, PAX4 and DR1). Conclusion: Together, the computational results further the understanding of the interactions between SMAD and other transcription factors at specific target promoters, and provide the basis for more targeted experimental verification of the co-regulatory modules. © 2009 Qin et al;licensee BioMed Central Ltd.

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The Broad Institute-Six Years Later

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CHEMISTRY & biology 2010年第4期17卷 311-312页

作者： McCarthy, Alice Main Text “In June 2003 the scientific and medical communities at MIT Harvard University and its affiliated hospitals and the Whitehead Institute banded together as collaborating partners to form the Eli and Edythe L. Broad Institute based in Cambridge MA. The Broad Institute established with initial funding from a $100 million philanthropic donation from the Los Angeles-based Broad family was primarily viewed as a marriage between the Whitehead Institute's Center for Genome Research (WICGR) and the Harvard Institute of Chemistry and Cell Biology (ICCB). Eli Broad founder and chairman of AIG SunAmerica Inc. explained “the purpose of the Broad Institute is to create a new type of research institute to build on the accomplishments of the human genome project and to move to clinical applications to both prevent and cure diseases.” Every Thursday morning we meet with perhaps 20 faculty members and 100 other researchers to discuss what we're all doing and should be doing next. -David Altschuler This paragraph was written five years ago when the Broad Institute was in its very earliest days as a life science research community (McCarthy 2005). Since that time “the Broad” as it's known has kept true to Eli Broad's vision having attracted a talented group of researchers faculty trainees and professional staff. This 1600 person research community known internally as “Broadies” includes faculty staff and students from throughout the MIT and Harvard biomedical research communities and beyond with collaborations spanning over a hundred private and public institutions in more than 40 countries worldwide. “What is special about the Broad is that we have people from Harvard MIT and the Harvard hospitals come together and work on problems of shared interest that could not be solved in their own individuals labs” explains David Altshuler M.D. Ph.D. Deputy Director and one of the Broad's six core faculty members. “These problems require expertise beyond any one principal investigator and in

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Meeting Report from the Second "Minimum Information for Biological and Biomedical Investigations" (MIBBI) workshop

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Standards in genomic sciences 2010年第3期3卷 259-66页

作者： Carsten Kettner Dawn Field Susanna-Assunta Sansone Chris Taylor Jan Aerts Nigel Binns Andrew Blake Cedrik M Britten Ario de Marco Jennifer Fostel Pascale Gaudet Alejandra González-Beltrán Nigel Hardy Jan Hellemans Henning Hermjakob Nick Juty Jim Leebens-Mack Eamonn Maguire Steffen Neumann Sandra Orchard Helen Parkinson William Piel Shoba Ranganathan Philippe Rocca-Serra Annapaola Santarsiero David Shotton Peter Sterk Andreas Untergasser Patricia L Whetzel Beilstein-Institut Frankfurt am Main Germany Centre for Ecology & Hydrology UK Oxford e-Research Centre University of Oxford UK The European Bioinformatics Institute (EBI) UK Faculty of Engineering - ESAT/SCD Leuven University Leuven-Heverlee Belgium Division of Pathway Medicine University of Edinburgh Medical School Edinburgh UK MRC Harwell UK Medical Department University Medical Center Johannes Gutenberg University-Mainz Mainz Germany Consortium for Genomic Technology Milano Italy University of Nova Gorica Nova Gorica Slovenia NIEHS NIH US National Toxicology Program USA Swiss Institute of Bioinformatics Geneva Switzerland Computational and Systems Medicine and Department of Computer Science University College London London UK Department of Computer Science Aberystwyth University Aberystwyth UK Center for Medical Genetics Ghent University Ghent Belgium Department of Plant Biology University of Georgia Athens USA Department of Stress- and Developmental Biology Institute for Plant Biochemistry Halle Germany Peabody Museum of Natural History Yale University New Haven USA Macquarie University Sydney Australia National University of Singapore Singapore Cancer Pharmacology The Mario Negri Institute for Pharmacological Research Milan Italy Image Bioinformatics Research Group Department of Zoology University of Oxford Oxford UK Wellcome Trust Sanger Institute UK Zentrum für Molekulare Biologie der Universität Heidelberg Heidelberg Germany The National Center for Biomedical Ontology / Stanford Center for Biomedical Informatics Research Stanford University Stanford USA

This report summarizes the proceedings of the second workshop of the 'Minimum Information for Biological and Biomedical Investigations' (MIBBI) consortium held on Dec 1-2, 2010 in Rüdesheim, Germany through the sponsorship of the Beilstein-Institute. MIBBI is an umbrella organization uniting communities developing Minimum Information (MI) checklists to standardize the description of data sets, the workflows by which they were generated and the scientific context for the work. This workshop brought together representatives of more than twenty communities to present the status of their MI checklists and plans for future development. Shared challenges and solutions were identified and the role of MIBBI in MI checklist development was discussed. The meeting featured some thirty presentations, wide-ranging discussions and breakout groups. The top outcomes of the two-day workshop as defined by the participants were: 1) the chance to share best practices and to identify areas of synergy; 2) defining a series of tasks for updating the MIBBI Portal; 3) reemphasizing the need to maintain independent MI checklists for various communities while leveraging common terms and workflow elements contained in multiple checklists; and 4) revision of the concept of the MIBBI Foundry to focus on the creation of a core set of MIBBI modules intended for reuse by individual MI checklist projects while maintaining the integrity of each MI project. Further information about MIBBI and its range of activities can be found at http://***/.

关键词： Minimum Information European Bioinformatics Institute Biomedical Ontology Stable Link Breakout Group

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