Molecular Biology and Genetics / Moleküler Biyoloji ve Genetik
Permanent URI for this collectionhttps://hdl.handle.net/11147/9
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Conference Object Preparing Sequence Databases for Application in Proteogenomics(Springer, 2016) Has, Canan; Mungan, Mehmet Direnç; Çiftçi, Cansu; Allmer, JensProteomics involves the identification of proteins from complex mixtures which is performed using mass spectrometry (MS) followed by computational data analysis. MS/MS spectra can either be sequenced de novo if no sequence is available for the proteins in the mixture, or by using database search algorithms such as OMSSA, X!Tandem, and MSGF+.Conference Object Database Normalization Is Crucial for Reliable Protein Identification in Mass Spectrometry-Based Proteomics(Springer, 2016) Has, Canan; Mungan, Mehmet Direnç; Çiftçi, Cansu; Allmer, JensResearch in proteomics is driven by mass spectrometry, especially the identification of proteins from complex samples. Computational analysis of the resulting data determines the peptide sequences of the recorded spectra and integrates identifications into proteins. For this, database search algorithms can be employed, but they need a list of amino acid sequences that are expected to exist in the sample. Many algorithms have been proposed and consensus scoring has been performed. While the comparison/integration among results from different algorithms is important, there has been no attempt to integrate the results from searching multiple databases. This is, however, important since it poses technical problems when all databases, needed for a study, are simply concatenated. Unfortunately, it has been shown that databases of different size influence scoring and prohibit the direct comparison of results.Article Citation - WoS: 4Citation - Scopus: 9Pgminer Reloaded, Fully Automated Proteogenomic Annotation Tool Linking Genomes To Proteomes(Informationsmanagement in der Biotechnologie e.V. (IMBio e.V.), 2016) Has, Canan; Lashin, Sergey A.; Kochetov, Alexey; Allmer, JensImprovements 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.