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Published on January 4, 2017
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Improvements to PATRIC, the all-bacterial Bioinformatics Database and Analysis Resource Center.

Authors: Wattam AR, Davis JJ, Assaf R, Boisvert S, Brettin T, Bun C, Conrad N, Dietrich EM, Disz T, Gabbard JL, Gerdes S, Henry CS, Kenyon RW, Machi D, Mao C, Nordberg EK, Olsen GJ, Murphy-Olson DE, Olson R, Overbeek R, Parrello B, Pusch GD, Shukla M, Vonstein V, Warren A, Xia F, Yoo H, Stevens RL

Abstract: The Pathosystems Resource Integration Center (PATRIC) is the bacterial Bioinformatics Resource Center (https://www.patricbrc.org). Recent changes to PATRIC include a redesign of the web interface and some new services that provide users with a platform that takes them from raw reads to an integrated analysis experience. The redesigned interface allows researchers direct access to tools and data, and the emphasis has changed to user-created genome-groups, with detailed summaries and views of the data that researchers have selected. Perhaps the biggest change has been the enhanced capability for researchers to analyze their private data and compare it to the available public data. Researchers can assemble their raw sequence reads and annotate the contigs using RASTtk. PATRIC also provides services for RNA-Seq, variation, model reconstruction and differential expression analysis, all delivered through an updated private workspace. Private data can be compared by 'virtual integration' to any of PATRIC's public data. The number of genomes available for comparison in PATRIC has expanded to over 80 000, with a special emphasis on genomes with antimicrobial resistance data. PATRIC uses this data to improve both subsystem annotation and k-mer classification, and tags new genomes as having signatures that indicate susceptibility or resistance to specific antibiotics.
Published on January 4, 2017
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DrugCentral: online drug compendium.

Authors: Ursu O, Holmes J, Knockel J, Bologa CG, Yang JJ, Mathias SL, Nelson SJ, Oprea TI

Abstract: DrugCentral (http://drugcentral.org) is an open-access online drug compendium. DrugCentral integrates structure, bioactivity, regulatory, pharmacologic actions and indications for active pharmaceutical ingredients approved by FDA and other regulatory agencies. Monitoring of regulatory agencies for new drugs approvals ensures the resource is up-to-date. DrugCentral integrates content for active ingredients with pharmaceutical formulations, indexing drugs and drug label annotations, complementing similar resources available online. Its complementarity with other online resources is facilitated by cross referencing to external resources. At the molecular level, DrugCentral bridges drug-target interactions with pharmacological action and indications. The integration with FDA drug labels enables text mining applications for drug adverse events and clinical trial information. Chemical structure overlap between DrugCentral and five online drug resources, and the overlap between DrugCentral FDA-approved drugs and their presence in four different chemical collections, are discussed. DrugCentral can be accessed via the web application or downloaded in relational database format.
Published on January 4, 2017
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dbSAP: single amino-acid polymorphism database for protein variation detection.

Authors: Cao R, Shi Y, Chen S, Ma Y, Chen J, Yang J, Chen G, Shi T

Abstract: Millions of human single nucleotide polymorphisms (SNPs) or mutations have been identified so far, and these variants could be strongly correlated with phenotypic variations of traits/diseases. Among these variants, non-synonymous ones can result in amino-acid changes that are called single amino-acid polymorphisms (SAPs). Although some studies have tried to investigate the SAPs, only a small fraction of SAPs have been identified due to inadequately inferred protein variation database and the low coverage of mass spectrometry (MS) experiments. Here, we present the dbSAP database for conveniently accessing the comprehensive information and relationships of spectra, peptides and proteins of SAPs, as well as related genes, pathways, diseases and drug targets. In order to fully explore human SAPs, we built a customized protein database that contained comprehensive variant proteins by integrating and annotating the human SNPs and mutations from eight distinct databases (UniProt, Protein Mutation Database, HPMD, MSIPI, MS-CanProVar, dbSNP, Ensembl and COSMIC). After a series of quality controls, a total of 16 854 SAP peptides involving in 439 537 spectra were identified with large scale MS datasets from various human tissues and cell lines. dbSAP is freely available at http://www.megabionet.org/dbSAP/index.html.
Published on January 4, 2017
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Open Targets: a platform for therapeutic target identification and validation.

Authors: Koscielny G, An P, Carvalho-Silva D, Cham JA, Fumis L, Gasparyan R, Hasan S, Karamanis N, Maguire M, Papa E, Pierleoni A, Pignatelli M, Platt T, Rowland F, Wankar P, Bento AP, Burdett T, Fabregat A, Forbes S, Gaulton A, Gonzalez CY, Hermjakob H, Hersey A, Jupe S, Kafkas S, Keays M, Leroy C, Lopez FJ, Magarinos MP, Malone J, McEntyre J, Munoz-Pomer Fuentes A, O'Donovan C, Papatheodorou I, Parkinson H, Palka B, Paschall J, Petryszak R, Pratanwanich N, Sarntivijal S, Saunders G, Sidiropoulos K, Smith T, Sondka Z, Stegle O, Tang YA, Turner E, Vaughan B, Vrousgou O, Watkins X, Martin MJ, Sanseau P, Vamathevan J, Birney E, Barrett J, Dunham I

Abstract: We have designed and developed a data integration and visualization platform that provides evidence about the association of known and potential drug targets with diseases. The platform is designed to support identification and prioritization of biological targets for follow-up. Each drug target is linked to a disease using integrated genome-wide data from a broad range of data sources. The platform provides either a target-centric workflow to identify diseases that may be associated with a specific target, or a disease-centric workflow to identify targets that may be associated with a specific disease. Users can easily transition between these target- and disease-centric workflows. The Open Targets Validation Platform is accessible at https://www.targetvalidation.org.
Published on January 3, 2017
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Current status and perspectives of interventional clinical trials for glioblastoma - analysis of ClinicalTrials.gov.

Authors: Cihoric N, Tsikkinis A, Minniti G, Lagerwaard FJ, Herrlinger U, Mathier E, Soldatovic I, Jeremic B, Ghadjar P, Elicin O, Lossl K, Aebersold DM, Belka C, Herrmann E, Niyazi M

Abstract: The records of 208.777 (100%) clinical trials registered at ClinicalTrials.gov were downloaded on the 19th of February 2016. Phase II and III trials including patients with glioblastoma were selected for further classification and analysis. Based on the disease settings, trials were classified into three groups: newly diagnosed glioblastoma, recurrent disease and trials with no differentiation according to disease setting. Furthermore, we categorized trials according to the experimental interventions, the primary sponsor, the source of financial support and trial design elements. Trends were evaluated using the autoregressive integrated moving average model. Two hundred sixteen (0.1%) trials were selected for further analysis. Academic centers (investigator initiated trials) were recorded as primary sponsors in 56.9% of trials, followed by industry 25.9%. Industry was the leading source of monetary support for the selected trials in 44.4%, followed by 25% of trials with primarily academic financial support. The number of newly initiated trials between 2005 and 2015 shows a positive trend, mainly through an increase in phase II trials, whereas phase III trials show a negative trend. The vast majority of trials evaluate forms of different systemic treatments (91.2%). In total, one hundred different molecular entities or biologicals were identified. Of those, 60% were involving drugs specifically designed for central nervous system malignancies. Trials that specifically address radiotherapy, surgery, imaging and other therapeutic or diagnostic methods appear to be rare. Current research in glioblastoma is mainly driven or sponsored by industry, academic medical oncologists and neuro-oncologists, with the majority of trials evaluating forms of systemic therapies. Few trials reach phase III. Imaging, radiation therapy and surgical procedures are underrepresented in current trials portfolios. Optimization in research portfolio for glioblastoma is needed.
Published on January 1, 2017
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NRDTD: a database for clinically or experimentally supported non-coding RNAs and drug targets associations.

Authors: Chen X, Sun YZ, Zhang DH, Li JQ, Yan GY, An JY, You ZH

Abstract: Database URL: http://chengroup.cumt.edu.cn/NRDTD.
Published on January 1, 2017
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TIBLE: a web-based, freely accessible resource for small-molecule binding data for mycobacterial species.

Authors: Malhotra S, Mugumbate G, Blundell TL, Higueruelo AP

Abstract: Database URL: http://www-cryst.bioc.cam.ac.uk/tible/.
Published on January 1, 2017
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HisgAtlas 1.0: a human immunosuppression gene database.

Authors: Liu Y, He M, Wang D, Diao L, Liu J, Tang L, Guo S, He F, Li D

Abstract: Immunosuppression is body's state in which the activation or efficacy of immune system is weakened. It is associated with a wide spectrum of human diseases. In the last two decades, tremendous efforts have been made to elucidate the mechanism of hundreds of immunosuppression genes. Immunosuppression genes could be valuable drug targets or biomarkers for the immunotherapeutic treatment of different diseases. However, the information of all previously identified immunosuppression genes is dispersed in thousands of publications. Here, we provide the HisgAtlas database that collects 995 previously identified human immunosuppression genes using text mining and manual curation. We believe HisgAtlas will be a valuable resource to search human immunosuppression genes as well as to investigate their functions in further research. Database URL: http://biokb.ncpsb.org/HisgAtlas/.
Published on January 1, 2017
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DLREFD: a database providing associations of long non-coding RNAs, environmental factors and phenotypes.

Authors: Sun YZ, Zhang DH, Ming Z, Li JQ, Chen X

Abstract: Database URL: http://chengroup.cumt.edu.cn/DLREFD.
Published on January 1, 2017
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NutriChem 2.0: exploring the effect of plant-based foods on human health and drug efficacy.

Authors: Ni Y, Jensen K, Kouskoumvekaki I, Panagiotou G

Abstract: Database URL: http://sbb.hku.hk/services/NutriChem-2.0/.