Presentation of NETTAB 2009
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The EMBL-European Bioinformatics Institute the Hub for Bioinformatics in Europe
The EMBL-European Bioinformatics Institute The hub for bioinformatics in Europe Blaise T.F. Alako, PhD [email protected] www.ebi.ac.uk What is EMBL-EBI? • Part of the European Molecular Biology Laboratory • International, non-profit research institute • Europe’s hub for biological data, services and research The European Molecular Biology Laboratory Heidelberg Hamburg Hinxton, Cambridge Basic research Structural biology Bioinformatics Administration Grenoble Monterotondo, Rome EMBO EMBL staff: 1500 people Structural biology Mouse biology >60 nationalities EMBL member states Austria, Belgium, Croatia, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Israel, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland and the United Kingdom Associate member state: Australia Who we are ~500 members of staff ~400 work in services & support >53 nationalities ~120 focus on basic research EMBL-EBI’s mission • Provide freely available data and bioinformatics services to all facets of the scientific community in ways that promote scientific progress • Contribute to the advancement of biology through basic investigator-driven research in bioinformatics • Provide advanced bioinformatics training to scientists at all levels, from PhD students to independent investigators • Help disseminate cutting-edge technologies to industry • Coordinate biological data provision throughout Europe Services Data and tools for molecular life science www.ebi.ac.uk/services Browse our services 9 What services do we provide? Labs around the -
Functional Effects Detailed Research Plan
GeCIP Detailed Research Plan Form Background The Genomics England Clinical Interpretation Partnership (GeCIP) brings together researchers, clinicians and trainees from both academia and the NHS to analyse, refine and make new discoveries from the data from the 100,000 Genomes Project. The aims of the partnerships are: 1. To optimise: • clinical data and sample collection • clinical reporting • data validation and interpretation. 2. To improve understanding of the implications of genomic findings and improve the accuracy and reliability of information fed back to patients. To add to knowledge of the genetic basis of disease. 3. To provide a sustainable thriving training environment. The initial wave of GeCIP domains was announced in June 2015 following a first round of applications in January 2015. On the 18th June 2015 we invited the inaugurated GeCIP domains to develop more detailed research plans working closely with Genomics England. These will be used to ensure that the plans are complimentary and add real value across the GeCIP portfolio and address the aims and objectives of the 100,000 Genomes Project. They will be shared with the MRC, Wellcome Trust, NIHR and Cancer Research UK as existing members of the GeCIP Board to give advance warning and manage funding requests to maximise the funds available to each domain. However, formal applications will then be required to be submitted to individual funders. They will allow Genomics England to plan shared core analyses and the required research and computing infrastructure to support the proposed research. They will also form the basis of assessment by the Project’s Access Review Committee, to permit access to data. -
Exploring the Structure and Function Paradigm Oliver C Redfern, Benoit Dessailly and Christine a Orengo
Available online at www.sciencedirect.com Exploring the structure and function paradigm Oliver C Redfern, Benoit Dessailly and Christine A Orengo Advances in protein structure determination, led by the Figure 1 shows that as the international genomics initiat- structural genomics initiatives have increased the proportion of ives gather pace, both the number of sequences and novel folds deposited in the Protein Data Bank. However, these protein families are still growing at an exponential rate, structures are often not accompanied by functional annotations although the rate of expansion of protein families is with experimental confirmation. In this review, we reassess the substantially less. This trend is also observed among meaning of structural novelty and examine its relevance domain families, which are 10-fold fewer (<10 000) than to the complexity of the structure-function paradigm. the number of protein families. By targeting these, the Recent advances in the prediction of protein function from structural genomics initiatives can aim to characterise the structure are discussed, as well as new sequence-based major building blocks of whole proteins and since these methods for partitioning large, diverse superfamilies into domains recur in different combination in the genomes, it biologically meaningful clusters. Obtaining structural data for will be an important step towards understanding the these functionally coherent groups of proteins will allow us to complete structural repertoire in nature. Furthermore, better understand the relationship between structure and the complement of molecular functions found within function. an organism is likely to be even fewer. For example, 97% of proteins in yeast can be assigned one or more of Address 4000 unique GO terms. -
Ontology-Based Methods for Analyzing Life Science Data
Habilitation a` Diriger des Recherches pr´esent´ee par Olivier Dameron Ontology-based methods for analyzing life science data Soutenue publiquement le 11 janvier 2016 devant le jury compos´ede Anita Burgun Professeur, Universit´eRen´eDescartes Paris Examinatrice Marie-Dominique Devignes Charg´eede recherches CNRS, LORIA Nancy Examinatrice Michel Dumontier Associate professor, Stanford University USA Rapporteur Christine Froidevaux Professeur, Universit´eParis Sud Rapporteure Fabien Gandon Directeur de recherches, Inria Sophia-Antipolis Rapporteur Anne Siegel Directrice de recherches CNRS, IRISA Rennes Examinatrice Alexandre Termier Professeur, Universit´ede Rennes 1 Examinateur 2 Contents 1 Introduction 9 1.1 Context ......................................... 10 1.2 Challenges . 11 1.3 Summary of the contributions . 14 1.4 Organization of the manuscript . 18 2 Reasoning based on hierarchies 21 2.1 Principle......................................... 21 2.1.1 RDF for describing data . 21 2.1.2 RDFS for describing types . 24 2.1.3 RDFS entailments . 26 2.1.4 Typical uses of RDFS entailments in life science . 26 2.1.5 Synthesis . 30 2.2 Case study: integrating diseases and pathways . 31 2.2.1 Context . 31 2.2.2 Objective . 32 2.2.3 Linking pathways and diseases using GO, KO and SNOMED-CT . 32 2.2.4 Querying associated diseases and pathways . 33 2.3 Methodology: Web services composition . 39 2.3.1 Context . 39 2.3.2 Objective . 40 2.3.3 Semantic compatibility of services parameters . 40 2.3.4 Algorithm for pairing services parameters . 40 2.4 Application: ontology-based query expansion with GO2PUB . 43 2.4.1 Context . 43 2.4.2 Objective . -
Ploidetect Enables Pan-Cancer Analysis of the Causes and Impacts of Chromosomal Instability
bioRxiv preprint doi: https://doi.org/10.1101/2021.08.06.455329; this version posted August 8, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Ploidetect enables pan-cancer analysis of the causes and impacts of chromosomal instability Luka Culibrk1,2, Jasleen K. Grewal1,2, Erin D. Pleasance1, Laura Williamson1, Karen Mungall1, Janessa Laskin3, Marco A. Marra1,4, and Steven J.M. Jones1,4, 1Canada’s Michael Smith Genome Sciences Center at BC Cancer, Vancouver, British Columbia, Canada 2Bioinformatics training program, University of British Columbia, Vancouver, British Columbia, Canada 3Department of Medical Oncology, BC Cancer, Vancouver, British Columbia, Canada 4Department of Medical Genetics, Faculty of Medicine, Vancouver, British Columbia, Canada Cancers routinely exhibit chromosomal instability, resulting in tumors mutate, these variants are considerably more difficult the accumulation of changes in the abundance of genomic ma- to detect accurately compared to other types of mutations terial, known as copy number variants (CNVs). Unfortunately, and consequently they may represent an under-explored the detection of these variants in cancer genomes is difficult. We facet of tumor biology. 20 developed Ploidetect, a software package that effectively iden- While small mutations can be determined through base tifies CNVs within whole-genome sequenced tumors. Ploidetect changes embedded within aligned sequence reads, CNVs was more sensitive to CNVs in cancer related genes within ad- are variations in DNA quantity and are typically determined vanced, pre-treated metastatic cancers than other tools, while also segmenting the most contiguously. -
BIOINFORMATICS APPLICATIONS NOTE Pages 380-381
Vol. 14 no. 4 1998 BIOINFORMATICS APPLICATIONS NOTE Pages 380-381 MView: a web-compatible database search or multiple alignment viewer NigelP. Brown, ChristopheLeroy and Chris Sander European Bioinformatics Institute (EMBLĆEBI), Wellcome Genome Campus, CambridgeCB10 1SD, UK Received on December 10, 1997; revised and accepted on January 15, 1998 Abstract may be hyperlinked to the SRS system (Etzold et al., 1996), a text field, a field of scoring information from searches, and Summary: MView is a tool for converting the results of a a field reporting the per cent identity of each sequence with sequence database search into the form of a coloured multiple respect to a preferred sequence in the alignment, usually the alignment of hits stacked against the query. Alternatively, an query in the case of a search. existing multiple alignment can be processed. In either case, Multiple alignments require minimal parsing and are the output is simply HTML, so the result is platform independent and does not require a separate application or subjected only to formatting stages. Search hits are first applet to be loaded. stacked against the ungapped query sequence and require Availability: Free from http://www.sander.ebi.ac.uk/mview/ special processing. Ungapped search (e.g. BLAST) hit subject to copyright restrictions. fragments are assembled into a single string by overlaying Contact: [email protected] them preferentially by score onto a template string, while gapped search (e.g. FASTA) hits have columns corresponding Often when running FASTA (Pearson, 1990) or BLAST to query gaps excised. Consequently, the stacked alignment is (Altschul et al., 1990), it is desired to visualize the database a patchwork of reconstituted sequences that nevertheless is hits stacked against the query sequence. -
EMBO Facts & Figures
excellence in life sciences Reykjavik Helsinki Oslo Stockholm Tallinn EMBO facts & figures & EMBO facts Copenhagen Dublin Amsterdam Berlin Warsaw London Brussels Prague Luxembourg Paris Vienna Bratislava Budapest Bern Ljubljana Zagreb Rome Madrid Ankara Lisbon Athens Jerusalem EMBO facts & figures HIGHLIGHTS CONTACT EMBO & EMBC EMBO Long-Term Fellowships Five Advanced Fellows are selected (page ). Long-Term and Short-Term Fellowships are awarded. The Fellows’ EMBO Young Investigators Meeting is held in Heidelberg in June . EMBO Installation Grants New EMBO Members & EMBO elects new members (page ), selects Young EMBO Women in Science Young Investigators Investigators (page ) and eight Installation Grantees Gerlind Wallon EMBO Scientific Publications (page ). Programme Manager Bernd Pulverer S Maria Leptin Deputy Director Head A EMBO Science Policy Issues report on quotas in academia to assure gender balance. R EMBO Director + + A Conducts workshops on emerging biotechnologies and on H T cognitive genomics. Gives invited talks at US National Academy E IC of Sciences, International Summit on Human Genome Editing, I H 5 D MAN 201 O N Washington, DC.; World Congress on Research Integrity, Rio de A M Janeiro; International Scienti c Advisory Board for the Centre for Eilish Craddock IT 2 015 Mammalian Synthetic Biology, Edinburgh. Personal Assistant to EMBO Fellowships EMBO Scientific Publications EMBO Gold Medal Sarah Teichmann and Ido Amit receive the EMBO Gold the EMBO Director David del Álamo Thomas Lemberger Medal (page ). + Programme Manager Deputy Head EMBO Global Activities India and Singapore sign agreements to become EMBC Associate + + Member States. EMBO Courses & Workshops More than , participants from countries attend 6th scienti c events (page ); participants attend EMBO Laboratory Management Courses (page ); rst online course EMBO Courses & Workshops recorded in collaboration with iBiology. -
Are You an Invited Speaker? a Bibliometric Analysis of Elite Groups for Scholarly Events in Bioinformatics
Are You an Invited Speaker? A Bibliometric Analysis of Elite Groups for Scholarly Events in Bioinformatics Senator Jeong, Sungin Lee, and Hong-Gee Kim Biomedical Knowledge Engineering Laboratory, Seoul National University, 28–22 YeonGeon Dong, Jongno Gu, Seoul 110–749, Korea. E-mail: {senator, sunginlee, hgkim}@snu.ac.kr Participating in scholarly events (e.g., conferences, work- evaluation, but it would be hard to claim that they have pro- shops, etc.) as an elite-group member such as an orga- vided comprehensive lists of evaluation measurements. This nizing committee chair or member, program committee article aims not to provide such lists but to add to the current chair or member, session chair, invited speaker, or award winner is beneficial to a researcher’s career develop- practices an alternative metric that complements existing per- ment.The objective of this study is to investigate whether formance measures to give a more comprehensive picture of elite-group membership for scholarly events is represen- scholars’ performance. tative of scholars’ prominence, and which elite group is By one definition (Jeong, 2008), a scholarly event is the most prestigious. We collected data about 15 global “a sequentially and spatially organized collection of schol- (excluding regional) bioinformatics scholarly events held in 2007. We sampled (via stratified random sampling) ars’ interactions with the intention of delivering and shar- participants from elite groups in each event. Then, bib- ing knowledge, exchanging research ideas, and performing liometric indicators (total citations and h index) of seven related activities.” As such, scholarly events are communica- elite groups and a non-elite group, consisting of authors tion channels from which our new evaluation tool can draw who submitted at least one paper to an event but were its supporting evidence. -
Full List of PCAWG Consortium Working Groups and Writing
Supplementary information to: Genomics: data sharing needs an international code of conduct To accompany a Comment published in Nature 578, 31–33 (2020) https://www.nature.com/articles/d41586-020-00082-9 By Mark Phillips, Fruzsina Molnár-Gábor, Jan O. Korbel, Adrian Thorogood, Yann Joly, Don Chalmers, David Townend & Bartha M. Knoppers for the PCAWG Consortium. SUPPLEMENTARY INFORMATION | NATURE | 1 The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium Working Groups PCAWG Steering committee 1,2 3,4,5,6 7,8 9,10 Peter J Campbell# , Gad Getz# , Jan O Korbel# , Lincoln D Stein# and Joshua M Stuart#11,12 PCAWG Head of project management Jennifer L Jennings13 PCAWG Executive committee 14 15 16 17 18 Sultan T Al-Sedairy , Axel Aretz , Cindy Bell , Miguel Betancourt , Christiane Buchholz , 19 20 21 22 23 Fabien Calvo , Christine Chomienne , Michael Dunn , Stuart Edmonds , Eric Green , Shailja 24 23 25 13 26 Gupta , Carolyn M Hutter , Karine Jegalian , Jennifer L Jennings , Nic Jones , Hyung-Lae 27 28,29,30 31 32 32 26 Kim , Youyong Lu , Hitoshi Nakagama , Gerd Nettekoven , Laura Planko , David Scott , 3 3,34 35 9,10 1 35 Tatsuhiro Shibata , Kiyo Shimizu , Lincoln D Stein# , Michael R Stratton , Takashi Yugawa , 36,37 24 38 39 Giampaolo Tortora , K VijayRaghavan , Huanming Yang and Jean C Zenklusen PCAWG Ethics and Legal Working Group 40 41 41 42 41 Don Chalmers# , Yann Joly , Bartha M Knoppers# , Fruzsina -
Biological Pathways Exchange Language Level 3, Release Version 1 Documentation
BioPAX – Biological Pathways Exchange Language Level 3, Release Version 1 Documentation BioPAX Release, July 2010. The BioPAX data exchange format is the joint work of the BioPAX workgroup and Level 3 builds on the work of Level 2 and Level 1. BioPAX Level 3 input from: Mirit Aladjem, Ozgun Babur, Gary D. Bader, Michael Blinov, Burk Braun, Michelle Carrillo, Michael P. Cary, Kei-Hoi Cheung, Julio Collado-Vides, Dan Corwin, Emek Demir, Peter D'Eustachio, Ken Fukuda, Marc Gillespie, Li Gong, Gopal Gopinathrao, Nan Guo, Peter Hornbeck, Michael Hucka, Olivier Hubaut, Geeta Joshi- Tope, Peter Karp, Shiva Krupa, Christian Lemer, Joanne Luciano, Irma Martinez-Flores, Zheng Li, David Merberg, Huaiyu Mi, Ion Moraru, Nicolas Le Novere, Elgar Pichler, Suzanne Paley, Monica Penaloza- Spinola, Victoria Petri, Elgar Pichler, Alex Pico, Harsha Rajasimha, Ranjani Ramakrishnan, Dean Ravenscroft, Jonathan Rees, Liya Ren, Oliver Ruebenacker, Alan Ruttenberg, Matthias Samwald, Chris Sander, Frank Schacherer, Carl Schaefer, James Schaff, Nigam Shah, Andrea Splendiani, Paul Thomas, Imre Vastrik, Ryan Whaley, Edgar Wingender, Guanming Wu, Jeremy Zucker BioPAX Level 2 input from: Mirit Aladjem, Gary D. Bader, Ewan Birney, Michael P. Cary, Dan Corwin, Kam Dahlquist, Emek Demir, Peter D'Eustachio, Ken Fukuda, Frank Gibbons, Marc Gillespie, Michael Hucka, Geeta Joshi-Tope, David Kane, Peter Karp, Christian Lemer, Joanne Luciano, Elgar Pichler, Eric Neumann, Suzanne Paley, Harsha Rajasimha, Jonathan Rees, Alan Ruttenberg, Andrey Rzhetsky, Chris Sander, Frank Schacherer, -
Systems Biology Graphical Notation: Process Description Language Level 1
Erschienen in: Journal of integrative bioinformatics ; 16 (2019), 2. - 20190022 https://dx.doi.org/10.1515/jib-2019-0022 DE GRUYTER Journal of Integrative Bioinformatics. 2019; 20190022 Adrien Rougny1,2 / Vasundra Touré3 / Stuart Moodie4 / Irina Balaur5 / Tobias Czauderna 6 / Hanna Borlinghaus7 / Ugur Dogrusoz8,9 / Alexander Mazein5,10,11 / Andreas Dräger12,13,14 / Michael L. Blinov15 / Alice Villéger16 / Robin Haw17 / Emek Demir18,19 / Huaiyu Mi20 / Anatoly Sorokin11 / Falk Schreiber6,7 / Augustin Luna21,22 Systems Biology Graphical Notation: Process Description language Level 1 Version 2.0 1 Biotechnology Research Institute for Drug Discovery, AIST, Tokyo135-0064, Japan, E-mail: [email protected]. https://orcid.org/0000-0002-2118-035X. 2 Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), AIST, Tokyo 169-8555, Japan, E-mail: [email protected]. https://orcid.org/0000-0002-2118-035X. 3 Department of Biology, Norwegian University of Science and Technology (NTNU), Trondheim, Norway. https://orcid.org/0000-0003-4639-4431. 4 Eight Pillars Ltd, 19 Redford Walk, Edinburgh EH13 0AG, UK 5 European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Av- enue Tony Garnier, 69007 Lyon, France. https://orcid.org/0000-0002-3671-895X, https://orcid.org/0000-0001-7137-4171. 6 Faculty of Information Technology, Monash University, Melbourne, Australia. https://orcid.org/0000-0002-1788-9593. 7 Department of Computer and Information Science, University of Konstanz, Konstanz, Germany. https://orcid.org/0000-0002-5410-6877. 8 Computer Engineering Department, Bilkent University, Ankara 06800, Turkey. https://orcid.org/0000-0002-7153-0784. 9 i-Vis Research Lab, Bilkent University, Ankara 06800, Turkey. -
Statistical and Computational Methods for Analyzing High-Throughout Genomic Data
Statistical and Computational Methods for Analyzing High-Throughout Genomic Data by Jingyi Li A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Biostatistics and the Designated Emphasis in Computational and Genomic Biology in the Graduate Division of the University of California, Berkeley Committee in charge: Professor Peter J. Bickel, Chair Professor Haiyan Huang Professor Sandrine Dudoit Professor Steven E. Brenner Spring 2013 Statistical and Computational Methods for Analyzing High-Throughput Genomic Data Copyright 2013 by Jingyi Li 1 Abstract Statistical and Computational Methods for Analyzing High-Throughput Genomic Data by Jingyi Li Doctor of Philosophy in Biostatistics and the Designated Emphasis in Computational and Genomic Biology University of California, Berkeley Professor Peter J. Bickel, Chair In the burgeoning field of genomics, high-throughput technologies (e.g. microarrays, next-generation sequencing and label-free mass spectrometry) have enabled biologists to perform global analysis on thousands of genes, mRNAs and proteins simultaneously. Ex- tracting useful information from enormous amounts of high-throughput genomic data is an increasingly pressing challenge to statistical and computational science. In this thesis, I will address three problems in which statistical and computational methods were used to analyze high-throughput genomic data to answer important biological questions. The first part of this thesis focuses on addressing an important question in genomics: how to identify and quantify mRNA products of gene transcription (i.e., isoforms) from next- generation mRNA sequencing (RNA-Seq) data? We developed a statistical method called Sparse Linear modeling of RNA-Seq data for Isoform Discovery and abundance Estimation (SLIDE) that employs probabilistic modeling and L1 sparse estimation to answer this ques- tion.