Locality-sensitive hashing enables efficient and scalable signal classification in high-throughput mass spectrometry raw data
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, signa...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-07-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-022-04833-5 |
| _version_ | 1811287446779854848 |
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| author | Konstantin Bob David Teschner Thomas Kemmer David Gomez-Zepeda Stefan Tenzer Bertil Schmidt Andreas Hildebrandt |
| author_facet | Konstantin Bob David Teschner Thomas Kemmer David Gomez-Zepeda Stefan Tenzer Bertil Schmidt Andreas Hildebrandt |
| author_sort | Konstantin Bob |
| collection | DOAJ |
| 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. Availability Generated data and code are available at https://github.com/hildebrandtlab/mzBucket . Raw data is available at https://zenodo.org/record/5036526 . |
| first_indexed | 2024-04-13T03:18:30Z |
| format | Article |
| id | doaj.art-7fbe9bcd7cb8405d93beb2c2c61ca1d5 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-13T03:18:30Z |
| publishDate | 2022-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-7fbe9bcd7cb8405d93beb2c2c61ca1d52022-12-22T03:04:50ZengBMCBMC Bioinformatics1471-21052022-07-0123111610.1186/s12859-022-04833-5Locality-sensitive hashing enables efficient and scalable signal classification in high-throughput mass spectrometry raw dataKonstantin Bob0David Teschner1Thomas Kemmer2David Gomez-Zepeda3Stefan Tenzer4Bertil Schmidt5Andreas Hildebrandt6Institute of Computer Science, Johannes Gutenberg University MainzInstitute of Computer Science, Johannes Gutenberg University MainzInstitute of Computer Science, Johannes Gutenberg University MainzInstitute for Immunology, University Medical Center of the Johannes Gutenberg University MainzInstitute for Immunology, University Medical Center of the Johannes Gutenberg University MainzInstitute of Computer Science, Johannes Gutenberg University MainzInstitute of Computer Science, Johannes Gutenberg University MainzAbstract 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. Availability Generated data and code are available at https://github.com/hildebrandtlab/mzBucket . Raw data is available at https://zenodo.org/record/5036526 .https://doi.org/10.1186/s12859-022-04833-5Mass spectrometryLocality-sensitive hashingSignal processing |
| spellingShingle | Konstantin Bob David Teschner Thomas Kemmer David Gomez-Zepeda Stefan Tenzer Bertil Schmidt Andreas Hildebrandt Locality-sensitive hashing enables efficient and scalable signal classification in high-throughput mass spectrometry raw data BMC Bioinformatics Mass spectrometry Locality-sensitive hashing Signal processing |
| title | Locality-sensitive hashing enables efficient and scalable signal classification in high-throughput mass spectrometry raw data |
| title_full | Locality-sensitive hashing enables efficient and scalable signal classification in high-throughput mass spectrometry raw data |
| title_fullStr | Locality-sensitive hashing enables efficient and scalable signal classification in high-throughput mass spectrometry raw data |
| title_full_unstemmed | Locality-sensitive hashing enables efficient and scalable signal classification in high-throughput mass spectrometry raw data |
| title_short | Locality-sensitive hashing enables efficient and scalable signal classification in high-throughput mass spectrometry raw data |
| title_sort | locality sensitive hashing enables efficient and scalable signal classification in high throughput mass spectrometry raw data |
| topic | Mass spectrometry Locality-sensitive hashing Signal processing |
| url | https://doi.org/10.1186/s12859-022-04833-5 |
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