pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework

doi:10.1093/bioinformatics/btz366

	pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework
	Yang, Hao 1,2; Chi, Hao 1,2; Zeng, Wen-Feng 1,2; Zhou, Wen-Jing 1,2; He, Si-Min 1,2
	2019-07-15
发表期刊	BIOINFORMATICS
ISSN	1367-4803
卷号	35 期号:14 页码:I183-I190
摘要	Motivation De novo peptide sequencing based on tandem mass spectrometry data is the key technology of shotgun proteomics for identifying peptides without any database and assembling unknown proteins. However, owing to the low ion coverage in tandem mass spectra, the order of certain consecutive amino acids cannot be determined if all of their supporting fragment ions are missing, which results in the low precision of de novo sequencing. Results In order to solve this problem, we developed pNovo 3, which used a learning-to-rank framework to distinguish similar peptide candidates for each spectrum. Three metrics for measuring the similarity between each experimental spectrum and its corresponding theoretical spectrum were used as important features, in which the theoretical spectra can be precisely predicted by the pDeep algorithm using deep learning. On seven benchmark datasets from six diverse species, pNovo 3 recalled 29-102% more correct spectra, and the precision was 11-89% higher than three other state-of-the-art de novo sequencing algorithms. Furthermore, compared with the newly developed DeepNovo, which also used the deep learning approach, pNovo 3 still identified 21-50% more spectra on the nine datasets used in the study of DeepNovo. In summary, the deep learning and learning-to-rank techniques implemented in pNovo 3 significantly improve the precision of de novo sequencing, and such machine learning framework is worth extending to other related research fields to distinguish the similar sequences. Availability and implementation pNovo 3 can be freely downloaded from http://pfind.ict.ac.cn/software/pNovo/index.html. Supplementary information Supplementary data are available at Bioinformatics online.
DOI	10.1093/bioinformatics/btz366
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2016YFA0501300] ; National Natural Science Foundation of China[31470805] ; Youth Innovation Promotion Association CAS[2014091] ; National High Technology Research and Development Program of China (863)[2014AA020902] ; National High Technology Research and Development Program of China (863)[2014AA020901]
WOS研究方向	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
WOS类目	Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability
WOS记录号	WOS:000477703600021
出版者	OXFORD UNIV PRESS
引用统计	被引频次：54[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/4520
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Chi, Hao; He, Si-Min
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Yang, Hao,Chi, Hao,Zeng, Wen-Feng,et al. pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework[J]. BIOINFORMATICS,2019,35(14):I183-I190.
APA	Yang, Hao,Chi, Hao,Zeng, Wen-Feng,Zhou, Wen-Jing,&He, Si-Min.(2019).pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework.BIOINFORMATICS,35(14),I183-I190.
MLA	Yang, Hao,et al."pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework".BIOINFORMATICS 35.14(2019):I183-I190.