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BY-NC-ND 4.0 license Open Access Published by De Gruyter April 20, 2017

PGMiner reloaded, fully automated proteogenomic annotation tool linking genomes to proteomes

  • Canan Has , Sergey A. Lashin , Alexey Kochetov and Jens Allmer


Improvements in genome sequencing technology increased the availability of full genomes and transcriptomes of many organisms. However, the major benefit of massive parallel sequencing is to better understand the organization and function of genes which then lead to understanding of phenotypes. In order to interpret genomic data with automated gene annotation studies, several tools are currently available. Even though the accuracy of computational gene annotation is increasing, a combination of multiple lines of experimental evidences should be gathered. Mass spectrometry allows the identification and sequencing of proteins as major gene products; and it is only these proteins that conclusively show whether a part of a genome is a coding region or not to result in phenotypes. Therefore, in the field of proteogenomics, the validation of computational methods is done by exploiting mass spectrometric data. As a result, identification of novel protein coding regions, validation of current gene models, and determination of upstream and downstream regions of genes can be achieved. In this paper, we present new functionality for our proteogenomic tool, PGMiner which performs all proteogenomic steps like acquisition of mass spectrometric data, peptide identification against preprocessed sequence databases, assignment of statistical confidence to identified peptides, mapping confident peptides to gene models, and result visualization. The extensions cover determining proteotypic peptides and thus unambiguous protein identification. Furthermore, peptides conflicting with gene models can now automatically assessed within the context of predicted alternative open reading frames.

Published Online: 2017-4-20
Published in Print: 2016-10-1

© 2016 The Author(s). Published by Journal of Integrative Bioinformatics.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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