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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; -
Cancer Informatics: New Tools for a Data-Driven Age in Cancer Research Warren Kibbe1, Juli Klemm1, and John Quackenbush2
Cancer Focus on Computer Resources Research Cancer Informatics: New Tools for a Data-Driven Age in Cancer Research Warren Kibbe1, Juli Klemm1, and John Quackenbush2 Cancer is a remarkably adaptable and formidable foe. Cancer Precision Medicine Initiative highlighted the importance of data- exploits many biological mechanisms to confuse and subvert driven cancer research, translational research, and its application normal physiologic and cellular processes, to adapt to thera- to decision making in cancer treatment (https://www.cancer.gov/ pies, and to evade the immune system. Decades of research and research/key-initiatives/precision-medicine). And the National significant national and international investments in cancer Strategic Computing Initiative highlighted the importance of research have dramatically increased our knowledge of the computing as a national competitive asset and included a focus disease, leading to improvements in cancer diagnosis, treat- on applying computing in biomedical research. Articles in the ment, and management, resulting in improved outcomes for mainstream media, such as that by Siddhartha Mukherjee in many patients. the New Yorker in April of 2017 (http://www.newyorker.com/ In melanoma, the V600E mutation in the BRAF gene is now magazine/2017/04/03/ai-versus-md), have emphasized the targetable by a specific therapy. BRAF is a serine/threonine protein growing importance of computing, machine learning, and data kinase activating the MAP kinase (MAPK)/ERK signaling pathway, in biomedicine. and both BRAF and MEK inhibitors, such as vemurafenib and The NCI (Rockville, MD) recognized the need to invest in dabrafenib, have shown dramatic responses in patients carrying informatics. In 2011, it established a funding opportunity, the mutation. -
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. -
Finding New Order in Biological Functions from the Network Structure of Gene Annotations
Finding New Order in Biological Functions from the Network Structure of Gene Annotations Kimberly Glass 1;2;3∗, and Michelle Girvan 3;4;5 1Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA 2Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA 3Physics Department, University of Maryland, College Park, MD, USA 4Institute for Physical Science and Technology, University of Maryland, College Park, MD, USA 5Santa Fe Institute, Santa Fe, NM, USA The Gene Ontology (GO) provides biologists with a controlled terminology that describes how genes are associated with functions and how functional terms are related to each other. These term-term relationships encode how scientists conceive the organization of biological functions, and they take the form of a directed acyclic graph (DAG). Here, we propose that the network structure of gene-term annotations made using GO can be employed to establish an alternate natural way to group the functional terms which is different from the hierarchical structure established in the GO DAG. Instead of relying on an externally defined organization for biological functions, our method connects biological functions together if they are performed by the same genes, as indicated in a compendium of gene annotation data from numerous different experiments. We show that grouping terms by this alternate scheme is distinct from term relationships defined in the ontological structure and provides a new framework with which to describe and predict the functions of experimentally identified sets of genes. Tools and supplemental material related to this work can be found online at http://www.networks.umd.edu. -
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; -
Extracting Biological Meaning from High-Dimensional Datasets John
Extracting Biological Meaning from High-Dimensional Datasets John Quackenbush Dana-Farber Cancer Institute and the Harvard School of Public Health, U.S.A. The revolution of genomics has come not from the ”completed” genome sequences of hu- man, mouse, rat, and other species. Nor has it come from the preliminary catalogues of genes that have been produced in these species. Rather, the genomic revolution has been in the creation of technologies - transcriptomics, proteomics, metabolomics - that allow us to rapidly assemble data on large numbers of samples that provide information on the state of tens of thousands of biological entities. Although the gene-by-gene hypothesis testing approach remains the standard for dissecting biological function, ’omic technolo- gies have become a standard laboratory tool for generating new, testable hypotheses. The challenge is now no longer generating the data, but rather in analyzing and inter- preting it. Although new statistical and data mining techniques are being developed, they continue to wrestle with the problem of having far fewer samples than necessary to constrain the analysis. One way to deal with this problem is to use the existing body of biological data, including genotype, phenotype, the genome, its annotation and the vast body of biological literature. Through examples, we will demonstrate show how diverse datasets can be used in conjunction with computational tools to constrain ’omics datasets and extract meaningful results that reveal new features of the underlying biology. [ John Quackenbush, 44 Binney Street, SM822 Boston, MA 02115-6084, U.S.A.; [email protected]]. -
Molecular Processes During Fat Cell Development Revealed by Gene
Open Access Research2005HackletVolume al. 6, Issue 13, Article R108 Molecular processes during fat cell development revealed by gene comment expression profiling and functional annotation Hubert Hackl¤*, Thomas Rainer Burkard¤*†, Alexander Sturn*, Renee Rubio‡, Alexander Schleiffer†, Sun Tian†, John Quackenbush‡, Frank Eisenhaber† and Zlatko Trajanoski* * Addresses: Institute for Genomics and Bioinformatics and Christian Doppler Laboratory for Genomics and Bioinformatics, Graz University of reviews Technology, Petersgasse 14, 8010 Graz, Austria. †Research Institute of Molecular Pathology, Dr Bohr-Gasse 7, 1030 Vienna, Austria. ‡Dana- Farber Cancer Institute, Department of Biostatistics and Computational Biology, 44 Binney Street, Boston, MA 02115. ¤ These authors contributed equally to this work. Correspondence: Zlatko Trajanoski. E-mail: [email protected] Published: 19 December 2005 Received: 21 July 2005 reports Revised: 23 August 2005 Genome Biology 2005, 6:R108 (doi:10.1186/gb-2005-6-13-r108) Accepted: 8 November 2005 The electronic version of this article is the complete one and can be found online at http://genomebiology.com/2005/6/13/R108 © 2005 Hackl et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which deposited research permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Gene-expression<p>In-depthadipocytecell development.</p> cells bioinformatics were during combined fat-cell analyses with development de of novo expressed functional sequence annotation tags fo andund mapping to be differentially onto known expres pathwayssed during to generate differentiation a molecular of 3 atlasT3-L1 of pre- fat- Abstract Background: Large-scale transcription profiling of cell models and model organisms can identify novel molecular components involved in fat cell development. -
Day 1 Session 4
USING NETWORKS TO RE-EXAMINE THE GENOME-PHENOME CONNECTION John Quackenbush Dana-Farber Cancer Institute Harvard School of Public Health The WØRD When you feel it in your gut, you know it must be right. 697 SNPs explain 20% of height ~2,000 SNPs explain 21% of height ~3,700 SNPs explain 24% of height ~9,500 SNPs explain 29% of height 97 SNPs explain 2.7% of BMI All common SNPs may explain 20% of BMI Do we give up on GWAS, fine map everything, or think differently? eQTL Analysis Use genome-wide data on genetic variants (SNPs = Single Nucleotide Polymorphisms) and gene expression data together Treat gene expression as a quantitative trait Ask, “Which SNPs are correlated with the degree of gene expression?” Most people concentrate on cis-acting SNPs What about trans-acting SNPs? John Platig eQTL Networks: A simple idea • eQTLs should group into communities with core SNPs regulating particular cellular functions • Perform a “standard eQTL” analysis using Matrix_EQTL: Y = β0 + β1 ADD + ε where Y is the quantitative trait and ADD is the allele dosage of a genotype. John Platig Which SNPs affect function? Many strong eQTLs are found near the target gene. But what about multiple SNPs that are correlated with multiple genes? SNPs Can a network of SNP- gene associations inform the functional roles of these SNPs? Genes John Platig Results: COPD John Platig What about GWAS SNPs? John Platig What about GWAS SNPs? The “hubs” are a GWAS desert! John Platig What are the critical areas? Abraham Wald: Put the armor where the bullets aren’t! http://cameronmoll.com/Good_vs_Great.pdf Network Structure Matters? • Are “disease” SNPs skewed towards the top of my SNP list as ranked by the overall out degree? • No! • The collection of highest-degree SNPs is devoid of disease-related SNPs • Highly deleterious SNPs that affect many processes are probably removed by strong negative selection. -
The Human Proteome
Vidal et al. Clinical Proteomics 2012, 9:6 http://www.clinicalproteomicsjournal.com/content/9/1/6 CLINICAL PROTEOMICS MEETING REPORT Open Access The human proteome – a scientific opportunity for transforming diagnostics, therapeutics, and healthcare Marc Vidal1, Daniel W Chan2, Mark Gerstein3, Matthias Mann4, Gilbert S Omenn5*, Danilo Tagle6, Salvatore Sechi7* and Workshop Participants Abstract A National Institutes of Health (NIH) workshop was convened in Bethesda, MD on September 26–27, 2011, with representative scientific leaders in the field of proteomics and its applications to clinical settings. The main purpose of this workshop was to articulate ways in which the biomedical research community can capitalize on recent technology advances and synergize with ongoing efforts to advance the field of human proteomics. This executive summary and the following full report describe the main discussions and outcomes of the workshop. Executive summary human proteome, including the antibody-based Human A National Institutes of Health (NIH) workshop was Protein Atlas, the NIH Common Fund Protein Capture convened in Bethesda, MD on September 26–27, 2011, Reagents, the mass spectrometry-based Peptide Atlas with representative scientific leaders in the field of pro- and Selected Reaction Monitoring (SRM) Atlas, and the teomics and its applications to clinical settings. The main Human Proteome Project organized by the Human purpose of this workshop was to articulate ways in which Proteome Organization. Several leading laboratories the biomedical research community can capitalize on re- have demonstrated that about 10,000 protein products, cent technology advances and synergize with ongoing of the about 20,000 protein-coding human genes, can efforts to advance the field of human proteomics. -
Roles of the Mathematical Sciences in Bioinformatics Education
Roles of the Mathematical Sciences in Bioinformatics Education Curtis Huttenhower 09-09-14 Harvard School of Public Health Department of Biostatistics U. Oregon Who am I, and why am I here? • Assoc. Prof. of Computational Biology in Biostatistics dept. at Harvard Sch. of Public Health – Ph.D. in Computer Science – B.S. in CS, Math, and Chemistry • My teaching focuses on graduate level: – Survey courses in bioinformatics for biologists – Applied training in systems for research computing – Reproducibility and best practices for “computational experiments” 2 HSPH BIO508: Genomic Data Manipulation http://huttenhower.sph.harvard.edu/bio508 3 Teaching quantitative methods to biologists: challenges • Biologists aren’t interested in stats as taught to biostatisticians – And they’re right! – Biostatisticians don’t usually like biology, either – Two ramifications: excitement gap and pedagogy • Differences in research culture matter – Hands-on vs. coursework – Lab groups – Rotations – Length of Ph.D. –… • There are few/no textbooks that are comprehensive, recent, and audience-appropriate – Choose any two of three 4 Teaching quantitative methods to biologists: (some of the) solutions • Teach intuitions – Biologists will never need to prove anything about a t-distribution – They will benefit minimally from looking up when to use a t-test – They will benefit a lot from seeing when a t-test lies (or doesn’t) • Teach applications – Problems/projects must be interactive – IPython Notebook / RStudio / etc. are invaluable • Teach writing • Demonstrate