A distributed algorithm for protein identification from tandem mass spectrometry data

• Autorzy: Orzechowska Katarzyna , Rubel Tymon , Kurjata Robert , Zaremba Krzysztof

Identyfikatory

DOI

10.35784/acs-2022-10

• Języki publikacji: EN

Abstrakty

EN

Tandem mass spectrometry is an analytical technique widely used in proteomics for the high-throughput characterization of proteins in biological samples. Modern in-depth proteomic studies require the collection of even millions of mass spectra representing short protein fragments (peptides). In order to identify the peptides, the measured spectra are most often scored against a database of amino acid sequences of known proteins. Due to the volume of input data and the sizes of proteomic databases, this is a resource-intensive task, which requires an efficient and scalable computational strategy. Here, we present SparkMS, an algorithm for peptide and protein identification from mass spectrometry data explicitly designed to work in a distributed computational environment. To achieve the required performance and scalability, we use Apache Spark, a modern framework that is becoming increasingly popular not only in the field of “big data” analysis but also in bioinformatics. This paper describes the algorithm in detail and demonstrates its performance on a large proteomic dataset. Experimental results indicate that SparkMS scales with the number of worker nodes and the increas-ing complexity of the search task. Furthermore, it exhibits a protein identification efficiency comparable to X!Tandem, a widely-used proteomic search engine.

Słowa kluczowe

EN

proteomics; mass spectrometry; distributed computing; Apache Spark

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2022
• Tom: Vol. 18, no 2
• Strony: 16--27
• Opis fizyczny: Bibliogr. 25 poz., fig., tab.

Twórcy

autor

Orzechowska Katarzyna

• Warsaw University of Technology, Institute of Radioelectronics and Multimedia Technology, Poland

• https://orcid.org/0000-0001-9797-1142

autor

Rubel Tymon

• Warsaw University of Technology, Institute of Radioelectronics and Multimedia Technology, Poland

• https://orcid.org/0000-0002-1342-414X

autor

Kurjata Robert

• Warsaw University of Technology, Institute of Radioelectronics and Multimedia Technology, Poland

• https://orcid.org/0000-0001-8547-910X

autor

Zaremba Krzysztof

• Warsaw University of Technology, Institute of Radioelectronics and Multimedia Technology, Poland

• https://orcid.org/0000-0002-4036-6459

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).