HeapMS : An Automatic Peak-Picking Pipeline for Targeted Proteomic Data Powered by 2D Heatmap Transformation and Convolutional Neural Networks
The process of peak picking and quality assessment for multiple reaction monitoring (MRM) data demands significant human effort, especially for signals with low abundance and high interference. Although multiple peak-picking software packages are available, they often fail to detect peaks with low quality and do not report cases with low confidence. Furthermore, visual examination of all chromatograms is still necessary to identify uncertain or erroneous cases. This study introduces HeapMS, a web service that uses artificial intelligence to assist with peak picking and the quality assessment of MRM chromatograms. HeapMS applies a rule-based filter to remove chromatograms with low interference and high-confidence peak boundaries detected by Skyline. Additionally, it transforms two histograms (representing light and heavy peptides) into a single encoded heatmap and performs a two-step evaluation (quality detection and peak picking) using image convolutional neural networks. HeapMS offers three categories of peak picking: uncertain peak picking that requires manual inspection, deletion peak picking that requires removal or manual re-examination, and automatic peak picking. HeapMS acquires the chromatogram and peak-picking boundaries directly from Skyline output. The output results are imported back into Skyline for further manual inspection, facilitating integration with Skyline. HeapMS offers the benefit of detecting chromatograms that should be deleted or require human inspection. Based on defined categories, it can significantly reduce human workload and provide consistent results. Furthermore, by using heatmaps instead of histograms, HeapMS can adapt to future updates in image recognition models. The HeapMS is available at: https://github.com/ccllabe/HeapMS.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:95 |
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| Enthalten in: |
Analytical chemistry - 95(2023), 42 vom: 24. Okt., Seite 15486-15496 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Lee, Chi-Ching [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 30.10.2023 Date Revised 04.11.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1021/acs.analchem.3c01011 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM363133798 |
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| 520 | |a The process of peak picking and quality assessment for multiple reaction monitoring (MRM) data demands significant human effort, especially for signals with low abundance and high interference. Although multiple peak-picking software packages are available, they often fail to detect peaks with low quality and do not report cases with low confidence. Furthermore, visual examination of all chromatograms is still necessary to identify uncertain or erroneous cases. This study introduces HeapMS, a web service that uses artificial intelligence to assist with peak picking and the quality assessment of MRM chromatograms. HeapMS applies a rule-based filter to remove chromatograms with low interference and high-confidence peak boundaries detected by Skyline. Additionally, it transforms two histograms (representing light and heavy peptides) into a single encoded heatmap and performs a two-step evaluation (quality detection and peak picking) using image convolutional neural networks. HeapMS offers three categories of peak picking: uncertain peak picking that requires manual inspection, deletion peak picking that requires removal or manual re-examination, and automatic peak picking. HeapMS acquires the chromatogram and peak-picking boundaries directly from Skyline output. The output results are imported back into Skyline for further manual inspection, facilitating integration with Skyline. HeapMS offers the benefit of detecting chromatograms that should be deleted or require human inspection. Based on defined categories, it can significantly reduce human workload and provide consistent results. Furthermore, by using heatmaps instead of histograms, HeapMS can adapt to future updates in image recognition models. The HeapMS is available at: https://github.com/ccllabe/HeapMS | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 700 | 1 | |a Lin, Yu-Chieh |e verfasserin |4 aut | |
| 700 | 1 | |a Pan, Teng Yu |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Cheng Hann |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Pei-Hsuan |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Sin You |e verfasserin |4 aut | |
| 700 | 1 | |a Gao, Jhih Jie |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Chi |e verfasserin |4 aut | |
| 700 | 1 | |a Chu, Lichieh Julie |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Po-Jung |e verfasserin |4 aut | |
| 700 | 1 | |a Yeh, Yuan-Ming |e verfasserin |4 aut | |
| 700 | 1 | |a Tang, Petrus |e verfasserin |4 aut | |
| 700 | 1 | |a Chang, Yu-Sun |e verfasserin |4 aut | |
| 700 | 1 | |a Yu, Jau-Song |e verfasserin |4 aut | |
| 700 | 1 | |a Hsiao, Yung-Chin |e verfasserin |4 aut | |
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