In Silico Genomes
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A Method to Infer Changed Activity of Metabolic Function from Transcript Profiles
ModeScore: A Method to Infer Changed Activity of Metabolic Function from Transcript Profiles Andreas Hoppe and Hermann-Georg Holzhütter Charité University Medicine Berlin, Institute for Biochemistry, Computational Systems Biochemistry Group [email protected] Abstract Genome-wide transcript profiles are often the only available quantitative data for a particular perturbation of a cellular system and their interpretation with respect to the metabolism is a major challenge in systems biology, especially beyond on/off distinction of genes. We present a method that predicts activity changes of metabolic functions by scoring reference flux distributions based on relative transcript profiles, providing a ranked list of most regulated functions. Then, for each metabolic function, the involved genes are ranked upon how much they represent a specific regulation pattern. Compared with the naïve pathway-based approach, the reference modes can be chosen freely, and they represent full metabolic functions, thus, directly provide testable hypotheses for the metabolic study. In conclusion, the novel method provides promising functions for subsequent experimental elucidation together with outstanding associated genes, solely based on transcript profiles. 1998 ACM Subject Classification J.3 Life and Medical Sciences Keywords and phrases Metabolic network, expression profile, metabolic function Digital Object Identifier 10.4230/OASIcs.GCB.2012.1 1 Background The comprehensive study of the cell’s metabolism would include measuring metabolite concentrations, reaction fluxes, and enzyme activities on a large scale. Measuring fluxes is the most difficult part in this, for a recent assessment of techniques, see [31]. Although mass spectrometry allows to assess metabolite concentrations in a more comprehensive way, the larger the set of potential metabolites, the more difficult [8]. -
The Kyoto Encyclopedia of Genes and Genomes (KEGG)
Kyoto Encyclopedia of Genes and Genome Minoru Kanehisa Institute for Chemical Research, Kyoto University HFSPO Workshop, Strasbourg, November 18, 2016 The KEGG Databases Category Database Content PATHWAY KEGG pathway maps Systems information BRITE BRITE functional hierarchies MODULE KEGG modules KO (KEGG ORTHOLOGY) KO groups for functional orthologs Genomic information GENOME KEGG organisms, viruses and addendum GENES / SSDB Genes and proteins / sequence similarity COMPOUND Chemical compounds GLYCAN Glycans Chemical information REACTION / RCLASS Reactions / reaction classes ENZYME Enzyme nomenclature DISEASE Human diseases DRUG / DGROUP Drugs / drug groups Health information ENVIRON Health-related substances (KEGG MEDICUS) JAPIC Japanese drug labels DailyMed FDA drug labels 12 manually curated original DBs 3 DBs taken from outside sources and given original annotations (GENOME, GENES, ENZYME) 1 computationally generated DB (SSDB) 2 outside DBs (JAPIC, DailyMed) KEGG is widely used for functional interpretation and practical application of genome sequences and other high-throughput data KO PATHWAY GENOME BRITE DISEASE GENES MODULE DRUG Genome Molecular High-level Practical Metagenome functions functions applications Transcriptome etc. Metabolome Glycome etc. COMPOUND GLYCAN REACTION Funding Annual budget Period Funding source (USD) 1995-2010 Supported by 10+ grants from Ministry of Education, >2 M Japan Society for Promotion of Science (JSPS) and Japan Science and Technology Agency (JST) 2011-2013 Supported by National Bioscience Database Center 0.8 M (NBDC) of JST 2014-2016 Supported by NBDC 0.5 M 2017- ? 1995 KEGG website made freely available 1997 KEGG FTP site made freely available 2011 Plea to support KEGG KEGG FTP academic subscription introduced 1998 First commercial licensing Contingency Plan 1999 Pathway Solutions Inc. -
3 13437143.Pdf
Title Integrative Annotation of 21,037 Human Genes Validated by Full-Length cDNA Clones Imanishi, Tadashi; Itoh, Takeshi; Suzuki, Yutaka; O'Donovan, Claire; Fukuchi, Satoshi; Koyanagi, Kanako O.; Barrero, Roberto A.; Tamura, Takuro; Yamaguchi-Kabata, Yumi; Tanino, Motohiko; Yura, Kei; Miyazaki, Satoru; Ikeo, Kazuho; Homma, Keiichi; Kasprzyk, Arek; Nishikawa, Tetsuo; Hirakawa, Mika; Thierry-Mieg, Jean; Thierry-Mieg, Danielle; Ashurst, Jennifer; Jia, Libin; Nakao, Mitsuteru; Thomas, Michael A.; Mulder, Nicola; Karavidopoulou, Youla; Jin, Lihua; Kim, Sangsoo; Yasuda, Tomohiro; Lenhard, Boris; Eveno, Eric; Suzuki, Yoshiyuki; Yamasaki, Chisato; Takeda, Jun-ichi; Gough, Craig; Hilton, Phillip; Fujii, Yasuyuki; Sakai, Hiroaki; Tanaka, Susumu; Amid, Clara; Bellgard, Matthew; Bonaldo, Maria de Fatima; Bono, Hidemasa; Bromberg, Susan K.; Brookes, Anthony J.; Bruford, Elspeth; Carninci, Piero; Chelala, Claude; Couillault, Christine; Souza, Sandro J. de; Debily, Marie-Anne; Devignes, Marie-Dominique; Dubchak, Inna; Endo, Toshinori; Estreicher, Anne; Eyras, Eduardo; Fukami-Kobayashi, Kaoru; R. Gopinath, Gopal; Graudens, Esther; Hahn, Yoonsoo; Han, Michael; Han, Ze-Guang; Hanada, Kousuke; Hanaoka, Hideki; Harada, Erimi; Hashimoto, Katsuyuki; Hinz, Ursula; Hirai, Momoki; Hishiki, Teruyoshi; Hopkinson, Ian; Imbeaud, Sandrine; Inoko, Hidetoshi; Kanapin, Alexander; Kaneko, Yayoi; Kasukawa, Takeya; Kelso, Janet; Kersey, Author(s) Paul; Kikuno, Reiko; Kimura, Kouichi; Korn, Bernhard; Kuryshev, Vladimir; Makalowska, Izabela; Makino, Takashi; Mano, Shuhei; -
In Silico Tools for Splicing Defect Prediction: a Survey from the Viewpoint of End Users
© American College of Medical Genetics and Genomics REVIEW In silico tools for splicing defect prediction: a survey from the viewpoint of end users Xueqiu Jian, MPH1, Eric Boerwinkle, PhD1,2 and Xiaoming Liu, PhD1 RNA splicing is the process during which introns are excised and informaticians in relevant areas who are working on huge data sets exons are spliced. The precise recognition of splicing signals is critical may also benefit from this review. Specifically, we focus on those tools to this process, and mutations affecting splicing comprise a consider- whose primary goal is to predict the impact of mutations within the able proportion of genetic disease etiology. Analysis of RNA samples 5′ and 3′ splicing consensus regions: the algorithms used by different from the patient is the most straightforward and reliable method to tools as well as their major advantages and disadvantages are briefly detect splicing defects. However, currently, the technical limitation introduced; the formats of their input and output are summarized; prohibits its use in routine clinical practice. In silico tools that predict and the interpretation, evaluation, and prospection are also discussed. potential consequences of splicing mutations may be useful in daily Genet Med advance online publication 21 November 2013 diagnostic activities. In this review, we provide medical geneticists with some basic insights into some of the most popular in silico tools Key Words: bioinformatics; end user; in silico prediction tool; for splicing defect prediction, from the viewpoint of end users. Bio- medical genetics; splicing consensus region; splicing mutation INTRODUCTION TO PRE-mRNA SPLICING AND small nuclear ribonucleoproteins and more than 150 proteins, MUTATIONS AFFECTING SPLICING serine/arginine-rich (SR) proteins, heterogeneous nuclear ribo- Sixty years ago, the milestone discovery of the double-helix nucleoproteins, and the regulatory complex (Figure 1). -
Genbank by Walter B
GenBank by Walter B. Goad o understand the significance of the information stored in memory—DNA—to the stuff of activity—proteins. The idea that GenBank, you need to know a little about molecular somehow the bases in DNA determine the amino acids in proteins genetics. What that field deals with is self-replication—the had been around for some time. In fact, George Gamow suggested in T process unique to life—and mutation and 1954, after learning about the structure proposed for DNA, that a recombination—the processes responsible for evolution-at the triplet of bases corresponded to an amino acid. That suggestion was fundamental level of the genes in DNA. This approach of working shown to be true, and by 1965 most of the genetic code had been from the blueprint, so to speak, of a living system is very powerful, deciphered. Also worked out in the ’60s were many details of what and studies of many other aspects of life—the process of learning, for Crick called the central dogma of molecular genetics—the now example—are now utilizing molecular genetics. firmly established fact that DNA is not translated directly to proteins Molecular genetics began in the early ’40s and was at first but is first transcribed to messenger RNA. This molecule, a nucleic controversial because many of the people involved had been trained acid like DNA, then serves as the template for protein synthesis. in the physical sciences rather than the biological sciences, and yet These great advances prompted a very distinguished molecular they were answering questions that biologists had been asking for geneticist to predict, in 1969, that biology was just about to end since years. -
Integrative Annotation of 21,037 Human Genes Validated by Full-Length Cdna Clones
Title Integrative Annotation of 21,037 Human Genes Validated by Full-Length cDNA Clones Imanishi, Tadashi; Itoh, Takeshi; Suzuki, Yutaka; O'Donovan, Claire; Fukuchi, Satoshi; Koyanagi, Kanako O.; Barrero, Roberto A.; Tamura, Takuro; Yamaguchi-Kabata, Yumi; Tanino, Motohiko; Yura, Kei; Miyazaki, Satoru; Ikeo, Kazuho; Homma, Keiichi; Kasprzyk, Arek; Nishikawa, Tetsuo; Hirakawa, Mika; Thierry-Mieg, Jean; Thierry-Mieg, Danielle; Ashurst, Jennifer; Jia, Libin; Nakao, Mitsuteru; Thomas, Michael A.; Mulder, Nicola; Karavidopoulou, Youla; Jin, Lihua; Kim, Sangsoo; Yasuda, Tomohiro; Lenhard, Boris; Eveno, Eric; Suzuki, Yoshiyuki; Yamasaki, Chisato; Takeda, Jun-ichi; Gough, Craig; Hilton, Phillip; Fujii, Yasuyuki; Sakai, Hiroaki; Tanaka, Susumu; Amid, Clara; Bellgard, Matthew; Bonaldo, Maria de Fatima; Bono, Hidemasa; Bromberg, Susan K.; Brookes, Anthony J.; Bruford, Elspeth; Carninci, Piero; Chelala, Claude; Couillault, Christine; Souza, Sandro J. de; Debily, Marie-Anne; Devignes, Marie-Dominique; Dubchak, Inna; Endo, Toshinori; Estreicher, Anne; Eyras, Eduardo; Fukami-Kobayashi, Kaoru; R. Gopinath, Gopal; Graudens, Esther; Hahn, Yoonsoo; Han, Michael; Han, Ze-Guang; Hanada, Kousuke; Hanaoka, Hideki; Harada, Erimi; Hashimoto, Katsuyuki; Hinz, Ursula; Hirai, Momoki; Hishiki, Teruyoshi; Hopkinson, Ian; Imbeaud, Sandrine; Inoko, Hidetoshi; Kanapin, Alexander; Kaneko, Yayoi; Kasukawa, Takeya; Kelso, Janet; Kersey, Author(s) Paul; Kikuno, Reiko; Kimura, Kouichi; Korn, Bernhard; Kuryshev, Vladimir; Makalowska, Izabela; Makino, Takashi; Mano, Shuhei; -
In Silico Protein Design: a Combinatorial and Global Optimization Approach by John L
From SIAM News, Volume 37, Number 1, January/February 2004 In Silico Protein Design: A Combinatorial and Global Optimization Approach By John L. Klepeis and Christodoulos A. Floudas The use of computational techniques to create peptide- and protein-based therapeutics is an important challenge in medicine. The ultimate goal, defined about two decades ago, is to use computer algorithms to identify amino acid sequences that not only adopt particular three-dimensional structures but also perform specific functions. To those familiar with the field of structural biology, it is certainly not surprising that this problem has been described as “inverse protein folding” [16]. That is, while the grand challenge of protein folding is to understand how a particular protein, defined by its amino acid sequence, finds its unique three-dimensional structure, protein design involves the discovery of sets of amino acid sequences that form functional proteins and fold into specific target structures. Experimental, computational, and hybrid approaches have all contributed to advances in protein design. Applying mutagenesis and rational design techniques, for example, experimentalists have created enzymes with altered functionalities and increased stability. The coverage of sequence space is highly restricted for these techniques, however [4]. An approach that samples more diverse sequences, called directed protein evolution, iteratively applies the techniques of genetic recombination and in vitro functional assays [1]. These methods, although they do a better job of sampling sequence space and generating functionally diverse proteins, are still restricted to the screening of 103 – 106 sequences [22]. Challenges of Generic Computational Protein Design The limitations of experimental techniques serve to highlight the importance of computational protein design. -
7 Systems Biotechnology: Combined in Silico and Omics Analyses for The
0195300815_0193-0231_ Ch-07.qxd 23/6/06 4:56 PM Page 193 7 Systems Biotechnology: Combined in Silico and Omics Analyses for the Improvement of Microorganisms for Industrial Applications Sang Yup Lee, Dong-Yup Lee, Tae Yong Kim, Byung Hun Kim, & Sang Jun Lee Biotechnology plays an increasingly important role in the healthcare, pharmaceutical, chemical, food, and agricultural industries. Microorganisms have been successfully employed for the production of recombinant proteins [1–4] and various primary and secondary metabolites [5–8]. As in other engineering disciplines, one of the ulti- mate goals of industrial biotechnology is to develop lower-cost and higher-yield processes. Toward this goal, fermentation and down- stream processes have been significantly improved thanks to the effort of biochemical engineers [9]. In addition to the effort of making these mid- to downstream processes more efficient, microbial strains have been improved by recombinant and other molecular biological methods, leading to the increase in microbial metabolic activities toward desired goals [10]. However, these conventional attempts have not always been successful owing to unexpected changes in the physiology and metabolism of host cells. Alternatively, rational meta- bolic and cellular engineering approaches have been tried to solve such problems in a number of cases, but they were also still limited to the manipulation of only a handful of genes encoding enzymes and regulatory proteins. In this regard, systematic approaches are indeed required not only for understanding the global context of the meta- bolic system but also for designing better metabolic engineering strategies. Recent advances in high-throughput experimental techniques have resulted in rapid accumulation of a wide range of biological data and information at different levels: genome, transcriptome, pro- teome, metabolome, and fluxome [11–17]. -
In Silico Prediction of Protein Flexibility with Local Structure Approach
Bioinformatics protein flexibility prediction Preprint – accepted for publication in Biochimie in silico prediction of protein flexibility with local structure approach. Tarun J. Narwani1,2,3,+, Catherine Etchebest1,2,3,+, Pierrick Craveur1,2,3,4, Sylvain Léonard1,2,3, Joseph Rebehmed1,2,3,5, Narayanaswamy Srinivasan6, Aurélie Bornot1,2,3, #, Jean-Christophe Gelly1,2,3 & Alexandre G. de Brevern1,2,3,4,* 1 INSERM, U 1134, DSIMB, Univ Paris, Univ de la Réunion, Univ des Antilles, F-75739 Paris, France. 2 Institut National de la Transfusion Sanguine (INTS), F-75739 Paris, France. 3 Laboratoire d'Excellence GR-Ex, F-75739 Paris, France. 4 Molecular Graphics Laboratory, Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA. 5 Department of Computer Science and Mathematics, Lebanese American University, Byblos 1h401 2010, Lebanon. 9 MBU, IISc, Bangalore, India # Present address: AstraZeneca, Discovery Sciences, Computational Biology, Alderley Park UK. Short title: bioinformatics protein flexibility prediction * Corresponding author: Mailing address: Dr. de Alexandre G. de Brevern, INSERM UMR_S 1134, DSIMB, Université Paris, Institut National de Transfusion Sanguine (INTS), 6, rue Alexandre Cabanel, 75739 Paris cedex 15, France e-mail : [email protected] 1 Bioinformatics protein flexibility prediction Abstract Flexibility is an intrinsic essential feature of protein structures, directly linked to their functions. To this day, most of the prediction methods use the crystallographic data (namely B-factors) as the only indicator of protein’s inner flexibility and predicts them as rigid or flexible. PredyFlexy stands differently from other approaches as it relies on the definition of protein flexibility (i) not only taken from crystallographic data, but also (ii) from Root Mean Square Fluctuation (RMSFs) observed in Molecular Dynamics simulations. -
Bioinformatics Glossary Based Database of Biological Databases: DBD
J Biochem Tech (2009) 1(3):88-90 ISSN: 0974-2328 Bioinformatics glossary based Database of Biological Databases: DBD Siva Kiran RR, Setty MVN, Hanumatha Rao G* Received: 16 May 2009 / Received in revised form: 17 May 2009, Accepted: 18 May 2009, Published online: 6 June 2009 © Sevas Educational Society 2008 Abstract Database of Biological/Bioinformatics Databases (DBD) is a Databases of Biological Databases such as DOD – Database of collection of 1669 databases and online resources collected from Databases (http://www.progenebio.in/DoD/DoD.htm), MetaBase - NAR Database Summary Papers (http://www.oxfordjournals.org The Database of Biological Database (http://biodatabase.org) and /nar/database/a/) & Internet search engines. The database has been The Molecular Biology Database Collection – Updates developed based on 437 keywords (Glossary) available in (Baxevanis 2000), published annually by journal entitled “Nucleic http://falcon.roswellpark.org/labweb/glossary.html. Keywords with Acids Research” help researchers to identify and correlate important their relevant databases are arranged in alphabetic order which queries beside providing a common platform for various molecular enables quick accession of databases by researchers. Database biology databases. Databases in DOD, Metabase & others have been description provides brief information about the database with a link grouped into categories as conceived by the respective authors. For to main web page. DBD is available online and can be accessed at instance, DOD has grouped 719 databases into 14 major categories http://www.biodbs.info. (Galperin 2005) while Metabase has grouped 1119 databases into 21 categories. Keywords: Databases, biological databases, bioinformatics databases, bioinformatics glossary The uniqueness of the present DBD is the alphabetic listing of all databases based on technical terms (keywords) viz. -
I S C B N E W S L E T T
ISCB NEWSLETTER FOCUS ISSUE {contents} President’s Letter 2 Member Involvement Encouraged Register for ISMB 2002 3 Registration and Tutorial Update Host ISMB 2004 or 2005 3 David Baker 4 2002 Overton Prize Recipient Overton Endowment 4 ISMB 2002 Committees 4 ISMB 2002 Opportunities 5 Sponsor and Exhibitor Benefits Best Paper Award by SGI 5 ISMB 2002 SIGs 6 New Program for 2002 ISMB Goes Down Under 7 Planning Underway for 2003 Hot Jobs! Top Companies! 8 ISMB 2002 Job Fair ISCB Board Nominations 8 Bioinformatics Pioneers 9 ISMB 2002 Keynote Speakers Invited Editorial 10 Anna Tramontano: Bioinformatics in Europe Software Recommendations11 ISCB Software Statement volume 5. issue 2. summer 2002 Community Development 12 ISCB’s Regional Affiliates Program ISCB Staff Introduction 12 Fellowship Recipients 13 Awardees at RECOMB 2002 Events and Opportunities 14 Bioinformatics events world wide INTERNATIONAL SOCIETY FOR COMPUTATIONAL BIOLOGY A NOTE FROM ISCB PRESIDENT This newsletter is packed with information on development and dissemination of bioinfor- the ISMB2002 conference. With over 200 matics. Issues arise from recommendations paper submissions and over 500 poster submis- made by the Society’s committees, Board of sions, the conference promises to be a scientific Directors, and membership at large. Important feast. On behalf of the ISCB’s Directors, staff, issues are defined as motions and are discussed EXECUTIVE COMMITTEE and membership, I would like to thank the by the Board of Directors on a bi-monthly Philip E. Bourne, Ph.D., President organizing committee, local organizing com- teleconference. Motions that pass are enacted Michael Gribskov, Ph.D., mittee, and program committee for their hard by the Executive Committee which also serves Vice President work preparing for the conference. -
UCLA UCLA Electronic Theses and Dissertations
UCLA UCLA Electronic Theses and Dissertations Title Bipartite Network Community Detection: Development and Survey of Algorithmic and Stochastic Block Model Based Methods Permalink https://escholarship.org/uc/item/0tr9j01r Author Sun, Yidan Publication Date 2021 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA Los Angeles Bipartite Network Community Detection: Development and Survey of Algorithmic and Stochastic Block Model Based Methods A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Statistics by Yidan Sun 2021 © Copyright by Yidan Sun 2021 ABSTRACT OF THE DISSERTATION Bipartite Network Community Detection: Development and Survey of Algorithmic and Stochastic Block Model Based Methods by Yidan Sun Doctor of Philosophy in Statistics University of California, Los Angeles, 2021 Professor Jingyi Li, Chair In a bipartite network, nodes are divided into two types, and edges are only allowed to connect nodes of different types. Bipartite network clustering problems aim to identify node groups with more edges between themselves and fewer edges to the rest of the network. The approaches for community detection in the bipartite network can roughly be classified into algorithmic and model-based methods. The algorithmic methods solve the problem either by greedy searches in a heuristic way or optimizing based on some criteria over all possible partitions. The model-based methods fit a generative model to the observed data and study the model in a statistically principled way. In this dissertation, we mainly focus on bipartite clustering under two scenarios: incorporation of node covariates and detection of mixed membership communities.