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

doi:10.1093/bioinformatics/btz366

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	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
刊名	BIOINFORMATICS
	2019-07-15
卷号	35 期号:14 页码:I183-I190
ISSN号	1367-4803
DOI	10.1093/bioinformatics/btz366
英文摘要	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.
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
语种	英语
出版者	OXFORD UNIV PRESS
WOS记录号	WOS:000477703600021
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/4519]
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.