Gutenberg Open Science: Locality-sensitive hashing enables efficient and scalable signal classification in high-throughput mass spectrometry raw data

Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-8439

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DC Field	Value	Language
dc.contributor.author	Bob, Konstantin	-
dc.contributor.author	Teschner, David	-
dc.contributor.author	Kemmer, Thomas	-
dc.contributor.author	Gomez-Zepeda, David	-
dc.contributor.author	Tenzer, Stefan	-
dc.contributor.author	Schmidt, Bertil	-
dc.contributor.author	Hildebrandt, Andreas	-
dc.date.accessioned	2022-11-30T10:02:06Z	-
dc.date.available	2022-11-30T10:02:06Z	-
dc.date.issued	2022	-
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/8455	-
dc.description.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.	en_GB
dc.description.sponsorship	Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 491381577	de
dc.language.iso	eng	de
dc.rights	CC BY	*
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	*
dc.subject.ddc	004 Informatik	de_DE
dc.subject.ddc	004 Data processing	en_GB
dc.title	Locality-sensitive hashing enables efficient and scalable signal classification in high-throughput mass spectrometry raw data	en_GB
dc.type	Zeitschriftenaufsatz	de
dc.identifier.doi	http://doi.org/10.25358/openscience-8439	-
jgu.type.contenttype	Scientific article	de
jgu.type.dinitype	article	en_GB
jgu.type.version	Published version	de
jgu.type.resource	Text	de
jgu.organisation.department	FB 08 Physik, Mathematik u. Informatik	de
jgu.organisation.number	7940	-
jgu.organisation.name	Johannes Gutenberg-Universität Mainz	-
jgu.rights.accessrights	openAccess	-
jgu.journal.title	BMC bioinformatics	de
jgu.journal.volume	23	de
jgu.pages.alternative	287	de
jgu.publisher.year	2022	-
jgu.publisher.name	Springer Nature	de
jgu.publisher.place	London	de
jgu.publisher.issn	1471-2105	de
jgu.organisation.place	Mainz	-
jgu.subject.ddccode	004	de
jgu.publisher.doi	10.1186/s12859-022-04833-5	de
jgu.organisation.ror	https://ror.org/023b0x485	-
jgu.subject.dfg	Ingenieurwissenschaften	de
Appears in collections:	DFG-491381577-G

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