7 research outputs found

    New resources for functional analysis of omics data for the genus Aspergillus

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    <p>Abstract</p> <p>Background</p> <p>Detailed and comprehensive genome annotation can be considered a prerequisite for effective analysis and interpretation of omics data. As such, Gene Ontology (GO) annotation has become a well accepted framework for functional annotation. The genus <it>Aspergillus </it>comprises fungal species that are important model organisms, plant and human pathogens as well as industrial workhorses. However, GO annotation based on both computational predictions and extended manual curation has so far only been available for one of its species, namely <it>A. nidulans</it>.</p> <p>Results</p> <p>Based on protein homology, we mapped 97% of the 3,498 GO annotated <it>A. nidulans </it>genes to at least one of seven other <it>Aspergillus </it>species: <it>A. niger</it>, <it>A. fumigatus</it>, <it>A. flavus</it>, <it>A. clavatus</it>, <it>A. terreus</it>, <it>A. oryzae </it>and <it>Neosartorya fischeri</it>. GO annotation files compatible with diverse publicly available tools have been generated and deposited online. To further improve their accessibility, we developed a web application for GO enrichment analysis named FetGOat and integrated GO annotations for all <it>Aspergillus </it>species with public genome sequences. Both the annotation files and the web application FetGOat are accessible via the Broad Institute's website (<url>http://www.broadinstitute.org/fetgoat/index.html</url>). To demonstrate the value of those new resources for functional analysis of omics data for the genus <it>Aspergillus</it>, we performed two case studies analyzing microarray data recently published for <it>A. nidulans</it>, <it>A. niger </it>and <it>A. oryzae</it>.</p> <p>Conclusions</p> <p>We mapped <it>A. nidulans </it>GO annotation to seven other <it>Aspergilli</it>. By depositing the newly mapped GO annotation online as well as integrating it into the web tool FetGOat, we provide new, valuable and easily accessible resources for omics data analysis and interpretation for the genus <it>Aspergillus</it>. Furthermore, we have given a general example of how a well annotated genome can help improving GO annotation of related species to subsequently facilitate the interpretation of omics data.</p

    A familial risk enriched cohort as a platform for testing early interventions to prevent severe mental illness

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    Modelling human choices: MADeM and decision‑making

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    Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

    Clinical and biological significance of CXCR5 expressed by prostate cancer specimens and cell lines

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    DEGs detected with absolute FC > 2 and adjusted p  < 0.01 in response to feeding of grapevine-adapted and non-adapted mite strains. (XLSX 1803 kb

    The value of open-source clinical science in pandemic response: lessons from ISARIC

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    The value of open-source clinical science in pandemic response: lessons from ISARIC

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    ISARIC-COVID-19 dataset: A Prospective, Standardized, Global Dataset of Patients Hospitalized with COVID-19

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    The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use
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