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

Abstract

BackgroundMass 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.ResultsIn 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.ConclusionThe 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.hrElectronic supplementary materialThe online version of this article (doi:10.1186/s13040-016-0104-6) contains supplementary material, which is available to authorized users.

Structural Bioinformatics and Big Data Analytics: A Mini-Review

“…Availability of such tools, although specific to their applications, will help in transcending the barrier from user point of view. For example, msBiodat is a web based tool that merges the data available in public databases with that of mass spectrometry results generated via high-throughput methods, resulting in much more precise list of proteins based on their annotations and conditions and help in pin pointing their biological processes [9].…”

Regularization and grouping -omics data by GCA method: A transcriptomic case

“…Classical statistical methods are adapted to process large volumes of data, or data may be preprocessed–with the use of biological knowledge–as a preliminary step in statistical processing pipelines [ 2 ] [ 3 ] [ 4 ]. It is also becoming more and more common to adopt an integrative approach based on specialied databases, with various configurations of analytical methods facilitating simple and efficient extraction of relevant information using custom analysis platforms [ 5 ] [ 6 ]. Nevertheless, in spite of the dynamic evolution of data analytics, the capabilities of existing IT frameworks lag behind the sheer volume of data sets produced by modern research tools.…”