Authors:	Bob, Konstantin Teschner, David Kemmer, Thomas Gomez-Zepeda, David Tenzer, Stefan Schmidt, Bertil Hildebrandt, Andreas
Title:	Locality-sensitive hashing enables efficient and scalable signal classification in high-throughput mass spectrometry raw data
Online publication date:	30-Nov-2022
Year of first publication:	2022
Language:	english
Abstract:	Background: Mass spectrometry is an important experimental technique in the field of proteomics. However, analysis of certain mass spectrometry data faces a combination of two challenges: first, even a single experiment produces a large amount of multi-dimensional raw data and, second, signals of interest are not single peaks but patterns of peaks that span along the different dimensions. The rapidly growing amount of mass spectrometry data increases the demand for scalable solutions. Furthermore, existing approaches for signal detection usually rely on strong assumptions concerning the signals properties. Results: In this study, it is shown that locality-sensitive hashing enables signal classification in mass spectrometry raw data at scale. Through appropriate choice of algorithm parameters it is possible to balance false-positive and false-negative rates. On synthetic data, a superior performance compared to an intensity thresholding approach was achieved. Real data could be strongly reduced without losing relevant information. Our implementation scaled out up to 32 threads and supports acceleration by GPUs. Conclusions: Locality-sensitive hashing is a desirable approach for signal classification in mass spectrometry raw data.
DDC:	004 Informatik 004 Data processing
Institution:	Johannes Gutenberg-Universität Mainz
Department:	FB 08 Physik, Mathematik u. Informatik
Place:	Mainz
ROR:	https://ror.org/023b0x485
DOI:	http://doi.org/10.25358/openscience-8439
Version:	Published version
Publication type:	Zeitschriftenaufsatz
Document type specification:	Scientific article
License:	CC BY
Information on rights of use:	https://creativecommons.org/licenses/by/4.0/
Journal:	BMC bioinformatics 23
Pages or article number:	287
Publisher:	Springer Nature
Publisher place:	London
Issue date:	2022
ISSN:	1471-2105
Publisher DOI:	10.1186/s12859-022-04833-5
Appears in collections:	DFG-491381577-G