Repository: Freie Universit├Ąt Berlin, Math Department

Mistle: bringing spectral library predictions to metaproteomics with an efficient search index

Nowatzky, Yannek and Benner, Philipp and Reinert, Knut and Muth, Thilo (2022) Mistle: bringing spectral library predictions to metaproteomics with an efficient search index. bioRxiv .

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Official URL: https://doi.org/10.1101/2022.09.09.507252

Abstract

Motivation: Deep learning has moved to the forefront of tandem mass spectrometry-driven proteomics and authentic prediction for peptide fragmentation is more feasible than ever. Still, at this point spectral prediction is mainly used to validate database search results or used for confined search spaces. Fully predicted spectral libraries have not yet been efficiently adapted to large search space problems that often occur in metaproteomics or proteogenomics. Results: In this study, we showcase a workflow that uses Prosit for spectral library predictions on two common metaproteomes and implement an indexing and search algorithm, Mistle, to efficiently identify experimental mass spectra within the library. Hence, the workflow emulates a classic protein sequence database search with protein digestion but builds a searchable index from spectral predictions as an in-between step. We compare Mistle to popular search engines, both on a spectral and database search level, and provide evidence that this approach is more accurate than a database search using MSFragger. Mistle outperforms other spectral library search engines in terms of run time and proves to be extremely memory efficient with an 8 to 22-fold decrease in RAM usage. This makes Mistle universally applicable to large search spaces, e.g. covering comprehensive sequence databases of diverse microbiomes. Availability: Mistle is freely available on GitHub at https://github.com/BAMeScience/Mistle.

Item Type:Article
Subjects:Mathematical and Computer Sciences > Computer Science
Divisions:Department of Mathematics and Computer Science > Institute of Computer Science > Algorithmic Bioinformatics Group
ID Code:2946
Deposited By: Anja Kasseckert
Deposited On:19 Apr 2023 12:51
Last Modified:19 Apr 2023 12:51

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