Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-8439
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dc.contributor.authorBob, Konstantin-
dc.contributor.authorTeschner, David-
dc.contributor.authorKemmer, Thomas-
dc.contributor.authorGomez-Zepeda, David-
dc.contributor.authorTenzer, Stefan-
dc.contributor.authorSchmidt, Bertil-
dc.contributor.authorHildebrandt, Andreas-
dc.date.accessioned2022-11-30T10:02:06Z-
dc.date.available2022-11-30T10:02:06Z-
dc.date.issued2022-
dc.identifier.urihttps://openscience.ub.uni-mainz.de/handle/20.500.12030/8455-
dc.description.abstractBackground: 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.sponsorshipGefördert durch die Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 491381577de
dc.language.isoengde
dc.rightsCC BY*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subject.ddc004 Informatikde_DE
dc.subject.ddc004 Data processingen_GB
dc.titleLocality-sensitive hashing enables efficient and scalable signal classification in high-throughput mass spectrometry raw dataen_GB
dc.typeZeitschriftenaufsatzde
dc.identifier.doihttp://doi.org/10.25358/openscience-8439-
jgu.type.contenttypeScientific articlede
jgu.type.dinitypearticleen_GB
jgu.type.versionPublished versionde
jgu.type.resourceTextde
jgu.organisation.departmentFB 08 Physik, Mathematik u. Informatikde
jgu.organisation.number7940-
jgu.organisation.nameJohannes Gutenberg-Universität Mainz-
jgu.rights.accessrightsopenAccess-
jgu.journal.titleBMC bioinformaticsde
jgu.journal.volume23de
jgu.pages.alternative287de
jgu.publisher.year2022-
jgu.publisher.nameSpringer Naturede
jgu.publisher.placeLondonde
jgu.publisher.issn1471-2105de
jgu.organisation.placeMainz-
jgu.subject.ddccode004de
jgu.publisher.doi10.1186/s12859-022-04833-5de
jgu.organisation.rorhttps://ror.org/023b0x485-
jgu.subject.dfgIngenieurwissenschaftende
Appears in collections:DFG-491381577-G

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