Evidence map›Paper›PMID 27547241›Full record

ArticleBioData mining2016

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

Pau M Muñoz-Torres, Filip Rokć, Robert Belužic, Ivana Grbeša, Oliver Vugrek

Abstract read
In one paragraph

Article in BioData mining, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Pau M Muñoz-TorresTranslational Medicine Group, Institut Rudjer Bošković, Division of Molecular Medicine, Bijenička Cesta 54, Zagreb, 10000 Croatia.ORCID 0000-0002-0168-3224
Filip RokćTranslational Medicine Group, Institut Rudjer Bošković, Division of Molecular Medicine, Bijenička Cesta 54, Zagreb, 10000 Croatia.
Robert BelužicTranslational Medicine Group, Institut Rudjer Bošković, Division of Molecular Medicine, Bijenička Cesta 54, Zagreb, 10000 Croatia.
Ivana GrbešaTranslational Medicine Group, Institut Rudjer Bošković, Division of Molecular Medicine, Bijenička Cesta 54, Zagreb, 10000 Croatia.
Oliver VugrekTranslational Medicine Group, Institut Rudjer Bošković, Division of Molecular Medicine, Bijenička Cesta 54, Zagreb, 10000 Croatia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.hr.

Indexed as

BioinformaticsData analysisData miningHigh-throughput analysisMass spectrometryProteomics

Identifiers

PMID27547241
PMCPMC4990837

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.