The Age of Data‐Driven Proteomics: How Machine Learning Enables Novel Workflows

Bouwmeester, Robbin; Gabriels, Ralf; Bossche, Tim Van Den; Martens, Lennart; Degroeve, Sven

doi:10.1002/pmic.201900351

Cited by 44 publications

(49 citation statements)

References 75 publications

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“…To cope with all the resulting data, the automation of data analysis in a clinically applicable way will still require some efforts. However, with recent advances in machine learning in the proteomics field, we are optimistic that a community effort in data analysis can move forward very quickly (49). This includes automated diagnosis based on the MRM data.…”

Section: Making Data Analysis Clinically Applicablementioning

confidence: 99%

Cov-MS: a community-based template assay for clinical MS-based protein detection in SARS-CoV-2 patients

Puyvelde¹,

Uytfanghe²,

Tytgat³

et al. 2020

Preprint

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Rising population density and global mobility are among the reasons why pathogens such as SARS-CoV-2, the virus that causes COVID-19, spread so rapidly across the globe. The policy response to such pandemics will always have to include accurate monitoring of the spread, as this provides one of the few alternatives to total lockdown. However, COVID-19 diagnosis is currently performed almost exclusively by Reverse Transcription Polymerase Chain Reaction (RT-PCR). Although this is efficient, automatable and acceptably cheap, reliance on one type of technology comes with serious caveats, as illustrated by recurring reagent and test shortages. We therefore developed an alternative diagnostic test that detects proteolytically digested SARS-CoV-2 proteins using Mass Spectrometry (MS). We established the Cov-MS consortium, consisting of fifteen academic labs and several industrial partners to increase applicability, accessibility, sensitivity and robustness of this kind of SARS-CoV-2 detection. This in turn gave rise to the Cov-MS Digital Incubator that allows other labs to join the effort, navigate and share their optimizations, and translate the assay into their clinic. As this test relies on viral proteins instead of RNA, it provides an orthogonal and complementary approach to RT-PCR, using other reagents that are relatively inexpensive and widely available, as well as orthogonally skilled personnel and different instruments. Data are available via ProteomeXchange with identifier PXD022550.

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Section: Making Data Analysis Clinically Applicablementioning

confidence: 99%

Cov-MS: a community-based template assay for clinical MS-based protein detection in SARS-CoV-2 patients

Puyvelde¹,

Uytfanghe²,

Tytgat³

et al. 2020

Preprint

Self Cite

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“…While open search lags behind the proteogenomics approach for the moment, it has promise. Algorithms are being continuously improved to better differentiate signal from noise, which will reduce the false positives and false negatives in variant peptide detection 57 preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for this this version posted December 11, 2020. ; https://doi.org/10.1101/2020.12.11.419523 doi: bioRxiv preprint 61 . Using methods of machine learning along with orthogonal information such as peptide retention time should result in significant improvements in open search 62 .…”

Section: Discussionmentioning

confidence: 99%

The Personalized Proteome: Comparing Proteogenomics and Open Variant Search Approaches for Single Amino Acid Variant Detection

Salz

Bouwmeester

Gabriels

et al. 2020

Preprint

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Discovery of variant peptides such as single amino acid variant (SAAV) in shotgun proteomics data is essential for personalized proteomics. Both the resolution of shotgun proteomics methods and the search engines have improved dramatically, allowing for confident identification of SAAV peptides. However, it is not yet known if these methods are truly successful in accurately identifying SAAV peptides without prior genomic information in the search database. We studied this in unprecedented detail by exploiting publicly available long-read RNA seq and shotgun proteomics data from the gold standard reference cell line NA12878. Searching spectra from this cell line with the state-of-the-art open modification search engine ionbot against carefully curated search databases resulted in 96.7% false positive SAAVs and an 85% lower true positive rate than searching with peptide search databases that incorporate prior genetic information. While adding genetic variants to the search database remains indispensable for correct peptide identification, inclusion of long-read RNA sequences in the search database contributes only 0.3% new peptide identifications. These findings reveal the differences in SAAV detection that result from various approaches, providing guidance to researchers studying SAAV peptides and developers of peptide spectrum identification tools.

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“…MS 2 CNN is based on CNN rather than RNN (LSTM or GRU). A single CNN model based on the network structure of LeNet-5 [71] is constructed to predict MS/MS spectra for peptides of a specific length and precursor charge state.…”

Section: Deep Learning For Ms/ms Spectrum Predictionmentioning

confidence: 99%

“…Due to these computational requirements, machine learning methods have been widely used in many aspects of proteomics data analysis. [1][2][3] Deep learning is a sub-discipline of machine learning. It has advanced rapidly during the last two decades and has demonstrated superior performance in various fields including computer vision, speech recognition, natural-language processing, bioinformatics, and medical image analysis.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning in Proteomics

et al. 2020

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Proteomics, the study of all the proteins in biological systems, is becoming a data-rich science. Protein sequences and structures are comprehensively catalogued in online databases. With recent advancements in tandem mass spectrometry (MS) technology, protein expression and post-translational modifications (PTMs) can be studied in a variety of biological systems at the global scale. Sophisticated computational algorithms are needed to translate the vast amount of data into novel biological insights. Deep learning automatically extracts data representations at high levels of abstraction from data, and it thrives in data-rich scientific research domains. Here, a comprehensive overview of deep learning applications in proteomics, including retention time prediction, MS/MS spectrum prediction, de novo peptide sequencing, PTM prediction, major histocompatibility complex-peptide binding prediction, and protein structure prediction, is provided. Limitations and the future directions of deep learning in proteomics are also discussed. This review will provide readers an overview of deep learning and how it can be used to analyze proteomics data.

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The Age of Data‐Driven Proteomics: How Machine Learning Enables Novel Workflows

Cited by 44 publications

References 75 publications

Cov-MS: a community-based template assay for clinical MS-based protein detection in SARS-CoV-2 patients

Cov-MS: a community-based template assay for clinical MS-based protein detection in SARS-CoV-2 patients

The Personalized Proteome: Comparing Proteogenomics and Open Variant Search Approaches for Single Amino Acid Variant Detection

Deep Learning in Proteomics

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