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J Biotechnol. 2017 Nov 10;261:126-130. doi: 10.1016/j.jbiotec.2017.06.1199. Epub 2017 Jul 1.

Computational proteomics tools for identification and quality control.

Author information

1
Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany.
2
Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany; College of Physical Sciences, University of Aberdeen, Meston Building, Old Aberdeen, United Kingdom; Medizinische Fakultät, Ruhr-Universität Bochum, Bochum, Germany.
3
Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany. Electronic address: robert.ahrends@isas.de.

Abstract

Computational proteomics is a constantly growing field to support end users with powerful and reliable tools for performing several computational steps within an analytics workflow for proteomics experiments. Typically, after capturing with a mass spectrometer, the proteins have to be identified and quantified. After certain follow-up analyses, an optional targeted approach is suitable for validating the results. The de.NBI (German network for bioinformatics infrastructure) service center in Dortmund provides several software applications and platforms as services to meet these demands. In this work, we present our tools and services, which is the combination of SearchGUI and PeptideShaker. SearchGUI is a managing tool for several search engines to find peptide spectra matches for one or more complex MS2 measurements. PeptideShaker combines all matches and creates a consensus list of identified proteins providing statistical confidence measures. In a next step, we are planning to release a web service for protein identification containing both tools. This system will be designed for high scalability and distributed computing using solutions like the Docker container system among others. As an additional service, we offer a web service oriented database providing all necessary high-quality and high-resolution data for starting targeted proteomics analyses. The user can easily select proteins of interest, review the according spectra and download both protein sequences and spectral library. All systems are designed to be intuitively and user-friendly operable.

KEYWORDS:

Computational proteomics; Peptide identification; Peptide spectra library database; Protein inference

PMID:
28676234
DOI:
10.1016/j.jbiotec.2017.06.1199
[Indexed for MEDLINE]

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