WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7150

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Author: search.filters.author.Allmer, JensSubject: search.filters.subject.Mass spectrometry

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Now showing 1 - 3 of 3
  • Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Dnmso; an Ontology for Representing De Novo Sequencing Results From Tandem-Ms Data
    (PeerJ Inc., 2020) Takan, Savaş; Allmer, Jens; Allmer, Jens; Takan, Savaş; 04.03. Department of Molecular Biology and Genetics; 03.04. Department of Computer Engineering; 03. Faculty of Engineering; 04. Faculty of Science; 01. Izmir Institute of Technology
    For the identification and sequencing of proteins, mass spectrometry (MS) has become the tool of choice and, as such, drives proteomics. MS/MS spectra need to be assigned a peptide sequence for which two strategies exist. Either database search or de novo sequencing can be employed to establish peptide spectrum matches. For database search, mzIdentML is the current community standard for data representation. There is no community standard for representing de novo sequencing results, but we previously proposed the de novo markup language (DNML). At the moment, each de novo sequencing solution uses different data representation, complicating downstream data integration, which is crucial since ensemble predictions may be more useful than predictions of a single tool. We here propose the de novo MS Ontology (DNMSO), which can, for example, provide many-to-many mappings between spectra and peptide predictions. Additionally, an application programming interface (API) that supports any file operation necessary for de novo sequencing from spectra input to reading, writing, creating, of the DNMSO format, as well as conversion from many other file formats, has been implemented. This API removes all overhead from the production of de novo sequencing tools and allows developers to concentrate on algorithm development completely. We make the API and formal descriptions of the format freely available at https://github.com/savastakan/dnmso.
  • Article
    Citation - WoS: 2
    Citation - Scopus: 5
    Pgminer: Complete Proteogenomics Workflow; From Data Acquisition To Result Visualization
    (Elsevier Ltd., 2017) Has, Canan; Allmer, Jens; Allmer, Jens; 04.03. Department of Molecular Biology and Genetics; 04. Faculty of Science; 01. Izmir Institute of Technology
    In parallel with the development of nucleotide sequencing an equally important interest in further describing the sequence in terms of function arose and the latter represents the current bottleneck in the overall research question. Sequencing the transcriptome allows determination of expressed nucleotide sequences and using mass spectrometry allows sequencing on the protein level. Both approaches can only sequence a subset of the existing transcripts. Moreover, for example post translational modification events can only be determined on the proteomics level. Therefore, it is essential to combine proteomics and genomics. For that purpose, proteogenomics data analysis pipelines have been described. Here, we describe a novel proteogenomics workflow which encompasses everything from the acquisition of data to result visualization in the Konstanz Information Miner (KNIME), a state of the art workflow management and data analytics platform. We amended KNIME with a number of processes like peptide consensus prediction, peptide mapping, and database equalizing, as well as result visualization. This enabled construction of our new workflow, entitled PGMiner, which not only includes all data analysis steps, but is highly customizable which is rather cumbersome for most existing pipelines. Furthermore, no burdensome installation processes have to be performed making PGMiner the most user friendly tool available.
  • Article
    Citation - WoS: 91
    Citation - Scopus: 106
    Algorithms for the De Novo Sequencing of Peptides From Tandem Mass Spectra
    (Taylor & Francis, 2011) Allmer, Jens; Allmer, Jens; 04.03. Department of Molecular Biology and Genetics; 04. Faculty of Science; 01. Izmir Institute of Technology
    Proteomics is the study of proteins, their time- and location-dependent expression profiles, as well as their modifications and interactions. Mass spectrometry is useful to investigate many of the questions asked in proteomics. Database search methods are typically employed to identify proteins from complex mixtures. However, databases are not often available or, despite their availability, some sequences are not readily found therein. To overcome this problem, de novo sequencing can be used to directly assign a peptide sequence to a tandem mass spectrometry spectrum. Many algorithms have been proposed for de novo sequencing and a selection of them are detailed in this article. Although a standard accuracy measure has not been agreed upon in the field, relative algorithm performance is discussed. The current state of the de novo sequencing is assessed thereafter and, finally, examples are used to construct possible future perspectives of the field. © 2011 Expert Reviews Ltd.