Please use this identifier to cite or link to this item:
http://doi.org/10.25358/openscience-8439
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 |
Files in This Item:
File | Description | Size | Format | ||
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![]() | localitysensitive_hashing_ena-20221129143959201.pdf | 2.16 MB | Adobe PDF | View/Open |