DeepSCP: utilizing deep learning to boost single-cell proteome coverage

被引：12

作者：

Wang, Bing ^{[1
,2
]}

Wang, Yue ^{[3
]}

Chen, Yu ^{[3
]}

Gao, Mengmeng ^{[3
]}

Ren, Jie ^{[3
]}

Guo, Yueshuai ^{[4
]}

Situ, Chenghao ^{[3
]}

Qi, Yaling ^{[3
]}

Zhu, Hui ^{[3
]}

Li, Yan ^{[5
]}

Guo, Xuejiang ^{[1
,3
]}

机构：

[1] Southeast Univ, Sch Med, Nanjing 210009, Peoples R China

[2] Dept Hist & Embryol, State Key Reprod Med, Nanjing, Peoples R China

[3] Nanjing Med Univ, Dept Histol & Embryol, State Key Lab Reprod Med, Nanjing, Peoples R China

[4] Nanjing Med Univ, State Key Lab Reprod Med, Nanjing 211166, Peoples R China

[5] Nanjing Med Univ, Sir Run Run Hosp, Dept Clin Lab, Nanjing 211100, Peoples R China

来源：

BRIEFINGS IN BIOINFORMATICS | 2022年 / 23卷 / 04期

基金：

国家重点研发计划;

关键词：

single-cell proteomics; peptide-spectrum matches; retention time; fragment ion intensity; deep learning; LightGBM; PEPTIDE IDENTIFICATION; QUANTIFICATION; CHROMATOGRAPHY; SEQUENCE;

D O I：

10.1093/bib/bbac214

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

Multiplexed single-cell proteomes (SCPs) quantification by mass spectrometry greatly improves the SCP coverage. However, it still suffers from a low number of protein identifications and there is much room to boost proteins identification by computational methods. In this study, we present a novel framework DeepSCP, utilizing deep learning to boost SCP coverage. DeepSCP constructs a series of features of peptide-spectrum matches (PSMs) by predicting the retention time based on the multiple SCP sample sets and fragment ion intensities based on deep learning, and predicts PSM labels with an optimized-ensemble learning model. Evaluation of DeepSCP on public and in-house SCP datasets showed superior performances compared with other state-of-the-art methods. DeepSCP identified more confident peptides and proteins by controlling q-value at 0.01 using target-decoy competition method. As a convenient and low-cost computing framework, DeepSCP will help boost single-cell proteome identification and facilitate the future development and application of single-cell proteomics.

引用

页数：15

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