HeapMS: An Automatic Peak-Picking Pipeline for Targeted Proteomic Data Powered by 2D Heatmap Transformation and Convolutional Neural Networks

• Published in: Analytical chemistry (Washington) Vol. 95; no. 42; pp. 15486 - 15496
• Main Authors: Lee, Chi-Ching, Lin, Yu-Chieh, Pan, Teng Yu, Yang, Cheng Hann, Li, Pei-Hsuan, Chen, Sin You, Gao, Jhih Jie, Yang, Chi, Chu, Lichieh Julie, Huang, Po-Jung, Yeh, Yuan-Ming, Tang, Petrus, Chang, Yu-Sun, Yu, Jau-Song, Hsiao, Yung-Chin
• Format: Journal Article
• Language: English
• Published: Washington American Chemical Society 24.10.2023
• Subjects: analytical chemistry; Artificial intelligence; Artificial neural networks; Boundaries; computer software; Histograms; humans; Image quality; Inspection; Interference; Internet; Neural networks; Peptides; Proteomics; Quality assessment; Quality control; Web services

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 q...