The Proteomics Identifications database (PRIDE, http://www.ebi.ac.uk/pride) at the European Bioinformatics Institute has become one of the main repositories of mass spectrometry-derived proteomics data. For the last 2 years, PRIDE data holdings have grown substantially, comprising 60 different species, more than 2.5 million protein identifications, 11.5 million peptides and over 50 million spectra by September 2009. We here describe several new and improved features in PRIDE, including the revised submission process, which now includes direct submission of fragment ion annotations. Correspondingly, it is now possible to visualize spectrum fragmentation annotations on tandem mass spectra, a key feature for compliance with journal data submission requirements. We also describe recent developments in the PRIDE BioMart interface, which now allows integrative queries that can join PRIDE data to a growing number of biological resources such as Reactome, Ensembl, InterPro and UniProt. This ability to perform extremely powerful across-domain queries will certainly be a cornerstone of future bioinformatics analyses. Finally, we highlight the importance of data sharing in the proteomics field, and the corresponding integration of PRIDE with other databases in the ProteomExchange consortium. ; European Union (ProDaC grant LSHG-CT-2006-036814) ; Burroughs Wellcome Fund (Grant WT085949MA)
Clinical case reports (CCRs) provide an important means of sharing clinical experiences about atypical disease phenotypes and new therapies. However, published case reports contain largely unstructured and heterogeneous clinical data, posing a challenge to mining relevant information. Current indexing approaches generally concern document-level features and have not been specifically designed for CCRs. To address this disparity, we developed a standardized metadata template and identified text corresponding to medical concepts within 3,100 curated CCRs spanning 15 disease groups and more than 750 reports of rare diseases. We also prepared a subset of metadata on reports on selected mitochondrial diseases and assigned ICD-10 diagnostic codes to each. The resulting resource, Metadata Acquired from Clinical Case Reports (MACCRs), contains text associated with high-level clinical concepts, including demographics, disease presentation, treatments, and outcomes for each report. Our template and MACCR set render CCRs more findable, accessible, interoperable, and reusable (FAIR) while serving as valuable resources for key user groups, including researchers, physician investigators, clinicians, data scientists, and those shaping government policies for clinical trials.
Clinical case reports (CCRs) provide an important means of sharing clinical experiences about atypical disease phenotypes and new therapies. However, published case reports contain largely unstructured and heterogeneous clinical data, posing a challenge to mining relevant information. Current indexing approaches generally concern document-level features and have not been specifically designed for CCRs. To address this disparity, we developed a standardized metadata template and identified text corresponding to medical concepts within 3,100 curated CCRs spanning 15 disease groups and more than 750 reports of rare diseases. We also prepared a subset of metadata on reports on selected mitochondrial diseases and assigned ICD-10 diagnostic codes to each. The resulting resource, Metadata Acquired from Clinical Case Reports (MACCRs), contains text associated with high-level clinical concepts, including demographics, disease presentation, treatments, and outcomes for each report. Our template and MACCR set render CCRs more findable, accessible, interoperable, and reusable (FAIR) while serving as valuable resources for key user groups, including researchers, physician investigators, clinicians, data scientists, and those shaping government policies for clinical trials.
Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developedSBMLLevel 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades ofSBMLand a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and howSBMLLevel 3 provides the foundation needed to support this evolution. ; National Institute of General Medical Sciences (NIGMS, US) [R01-GM070923]; Bundesministerium fur Bildung und Forschung (BMBF, DE)Federal Ministry of Education & Research (BMBF) [031L0104A]; NIH (US)United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [P41-GM103313, R01-GM095485, 5R35-GM119770-03, GM57089]; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)German Research Foundation (DFG) [EXC 2124]; German Center for Infection Research (DZIF); JSPS KAKENHIMinistry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI) [21700328]; National Institutes of Health (NIH, US)United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [P41-GM103712]; Biotechnology and Biological Sciences Research Council (BBSRC, UK) "MultiMod" projectBiotechnology and Biological Sciences Research Council (BBSRC) [BB/N019482/1]; NIGMS (US)United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of General Medical Sciences (NIGMS) [P41-GM103313, R01-GM080219, R01-GM123032]; Federal Ministry of Education and Research (BMBF, DE), research network Systems Medicine of the Liver (LiSyM), Humboldt-University BerlinFederal Ministry of Education & Research (BMBF) [031L0054]; BBSRC (UK)Biotechnology and Biological Sciences Research Council (BBSRC); National Science Foundation (NSF, USA)National Science Foundation (NSF) [CCF-1748200, CCF-1856740]; NIHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [P41-GM103313, P41-EB023912]; Department of Biotechnology, Government of IndiaDepartment of Biotechnology (DBT) India [BT/PR4949/BRB/10/1048/2012]; Intramural Research Program of NIAID, NIH (US); BBSRC (UK) "MultiMod" project [BB/N019482/1]; Novo Nordisk Foundation Grant [NNF10CC1016517]; National Institute of Biomedical Imaging and Bioengineering (NIBIB, US) [P41-EB023912]; DDMoRe program (EU), Innovative Medicines Initiative Joint Undertaking [115156]; BBSRC (UK) grant "Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM) " [BB/M017702/1] ; The principal authors thank many funding agencies for their support of this work. F.B., A.D., M.H., T.M.H., S.M.K., B.O., and L.S., as well as SBML.org and its online resources, were supported by the National Institute of General Medical Sciences (NIGMS, US), grant R01-GM070923 (PI: Hucka). In addition, F.B. has been supported by the Bundesministerium fur Bildung und Forschung (BMBF, DE), grant de.NBI ModSim1, 031L0104A (PI: Ursula Kummer). M.L.B. has been supported by NIH (US) grant P41-GM103313 and R01-GM095485. A.D. has been supported by infrastructural funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Cluster of Excellence EXC 2124 Controlling Microbes to Fight Infections, and by the German Center for Infection Research (DZIF). A.F. was supported by the Grant-in-Aid for Young Scientists (B), grant 21700328 from JSPS KAKENHI (JP) to Keio University. J.F. was supported by National Institutes of Health (NIH, US) grant P41-GM103712 to the National Center for Multiscale Modeling of Biological Systems (MMBioS). H.H. was supported by the Biotechnology and Biological Sciences Research Council (BBSRC, UK) "MultiMod" project (grant BB/N019482/1). T.H. was supported by NIH (US) grant 5R35-GM119770-03 to the University of Nebraska-Lincoln. S.H. was supported by NIGMS (US) grant R01-GM080219. M.K. was supported by the Federal Ministry of Education and Research (BMBF, DE), research network Systems Medicine of the Liver (LiSyM), grant 031L0054, Humboldt-University Berlin (PI: Konig). A.L. was supported by the BBSRC (UK) while working at the Centre for Integrated Systems Biology of Ageing and Nutrition (CISBAN), Newcastle University. C.M. was supported by the National Science Foundation (NSF, USA) under grant CCF-1748200 and CCF-1856740. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. I.M. was supported by NIH grant P41-EB023912 and P41-GM103313. K.R. was supported by the Department of Biotechnology, Government of India (grant BT/PR4949/BRB/10/1048/2012). M.M.-S. was supported by the Intramural Research Program of NIAID, NIH (US). R.M.-S. was supported by the BBSRC (UK) "MultiMod" project (grant BB/N019482/1). B.P.'s was supported by NIH (US) grant GM57089 to the University of California, San Diego, and by the Novo Nordisk Foundation Grant NNF10CC1016517. H.M.S. was supported by NIGMS (US) grant R01-GM123032 (PI: Sauro) and by the National Institute of Biomedical Imaging and Bioengineering (NIBIB, US) grant P41-EB023912 (PI: Sauro). J.C.S. was supported by NIGMS (US) grant P41-GM103313. M.S. was supported by the DDMoRe program (EU), Innovative Medicines Initiative Joint Undertaking under grant agreement 115156. N.S. was supported by BBSRC (UK) grant "Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM) ", grant BB/M017702/1 (PI: Nigel S. Scrutton). F.Z. was supported by the Intramural Research Program of NIAID, NIH (US).
