msBiodat analysis tool, big data analysis for high-throughput experiments

Background Mass spectrometry (MS) are a group of a high-throughput techniques used to increase knowledge about biomolecules. They produce a large amount of data which is presented as a list of hundreds or thousands of proteins. Filtering those data efficiently is the first step for extracting biologically relevant information. The filtering may increase interest by merging previous data with the data obtained from public databases, resulting in an accurate list of proteins which meet the predetermined conditions. Results In this article we present msBiodat Analysis Tool, a web-based application thought to approach proteomics to the big data analysis. With this tool, researchers can easily select the most relevant information from their MS experiments using an easy-to-use web interface. An interesting feature of msBiodat analysis tool is the possibility of selecting proteins by its annotation on Gene Ontology using its Gene Id, ensembl or UniProt codes. Conclusion The msBiodat analysis tool is a web-based application that allows researchers with any programming experience to deal with efficient database querying advantages. Its versatility and user-friendly interface makes easy to perform fast and accurate data screening by using complex queries. Once the analysis is finished, the result is delivered by e-mail. msBiodat analysis tool is freely available at http://msbiodata.irb.hr Electronic supplementary material The online version of this article (doi:10.1186/s13040-016-0104-6) contains supplementary material, which is available to authorized users.

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PID https://www.doi.org/10.1186/s13040-016-0104-6
PID pmc:PMC4990837
PID pmid:27547241
URL http://fulir.irb.hr/4007/1/document.pdf
URL http://fulir.irb.hr/4007/
URL https://core.ac.uk/display/81622866
URL https://dx.doi.org/10.1186/s13040-016-0104-6
URL https://www.bib.irb.hr/906503
URL https://zenodo.org/record/60782
URL https://link.springer.com/article/10.1186/s13040-016-0104-6
URL https://dblp.uni-trier.de/db/journals/biodatamining/biodatamining9.html#Munoz-TorresRBG16
URL https://biodatamining.biomedcentral.com/articles/10.1186/s13040-016-0104-6
URL https://doi.org/10.1186/s13040-016-0104-6
URL http://link.springer.com/content/pdf/10.1186/s13040-016-0104-6.pdf
URL http://link.springer.com/content/pdf/10.1186/s13040-016-0104-6
URL https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4990837/
URL http://dx.doi.org/10.1186/s13040-016-0104-6
URL https://academic.microsoft.com/#/detail/2511792030
URL http://europepmc.org/articles/PMC4990837
URL http://link.springer.com/article/10.1186/s13040-016-0104-6/fulltext.html
URL https://biodatamining.biomedcentral.com/track/pdf/10.1186/s13040-016-0104-6
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Access Right Open Access
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Author Ivana Grbesa, 0000-0002-4579-9149
Author Pau Marc Muñoz-Torres, 0000-0002-0168-3224
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Collected From Europe PubMed Central; ZENODO; PubMed Central; ORCID; Datacite; UnpayWall; Crossref; Microsoft Academic Graph; Full-text Institutional Repository of the Ruđer Bošković Institute; CORE (RIOXX-UK Aggregator)
Hosted By Europe PubMed Central; SpringerOpen; ZENODO; Full-text Institutional Repository of the Ruđer Bošković Institute; BioData Mining
Publication Date 2016-08-19
Publisher Springer Science and Business Media LLC
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Country Croatia
Format application/pdf
Language UNKNOWN
Resource Type Other literature type; Article; UNKNOWN
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Source https://science-innovation-policy.openaire.eu/search/publication?articleId=dedup_wf_001::ad1ed31e82d79c267b0de124ec37d05b
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Last Updated 26 December 2020, 06:43 (CET)
Created 26 December 2020, 06:43 (CET)