Using the entrapment sequence method as a standard to evaluate key steps of proteomics data analysis process

BMC Genomics

Research

Kun-xian Shu¹  Xiao-dong Feng²  Li-wei Li³  Jian-hong Zhang³  Cheng Chang³  Yun-ping Zhu³  Jie Ma³ 

[1] Chongqing University of Posts and Telecommunications, 2 Chong Wen Road of Nan’an District, 400065, Chongqing, China;Chongqing University of Posts and Telecommunications, 2 Chong Wen Road of Nan’an District, 400065, Chongqing, China;Department of Bioinformatics, State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Engineering Research Center for Protein Drugs, National Center for Protein Sciences (Beijing), Beijing Institute of Radiation Medicine, 38 Life Science Park Road, 102206, Beijing, China;Department of Bioinformatics, State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Engineering Research Center for Protein Drugs, National Center for Protein Sciences (Beijing), Beijing Institute of Radiation Medicine, 38 Life Science Park Road, 102206, Beijing, China;

关键词: Proteomics;    Tandem mass spectrometry;    Entrapment sequence method;    Target-decoy search;    Quality control;   

DOI  :  10.1186/s12864-017-3491-2

来源: Springer

【 摘 要 】

BackgroundThe mass spectrometry based technical pipeline has provided a high-throughput, high-sensitivity and high-resolution platform for post-genomic biology. Varied models and algorithms are implemented by different tools to improve proteomics data analysis. The target-decoy searching strategy has become the most popular strategy to control false identification in peptide and protein identifications. While this strategy can estimate the false discovery rate (FDR) within a dataset, it cannot directly evaluate the false positive matches in target identifications.ResultsAs a supplement to target-decoy strategy, the entrapment sequence method was introduced to assess the key steps of mass spectrometry data analysis process, database search engines and quality control methods. Using the entrapment sequences as the standard, we evaluated five database search engines for both the origanal scores and reprocessed scores, as well as four quality control methods in term of quantity and quality aspects. Our results showed that the latest developed search engine MS-GF+ and percolator-embeded quality control method PepDistiller performed best in all tools respectively. Combined with efficient quality control methods, the search engines can improve the low sensitivity of their original scores. Moreover, based on the entrapment sequence method, we proved that filtering the identifications separately could increase the number of identified peptides while improving the confidence level.ConclusionIn this study, we have proved that the entrapment sequence method could be an useful strategy to assess the key steps of the mass spectrometry data analysis process. Its applications can be extended to all steps of the common workflow, such as the protein assembling methods and data integration methods.

【 授权许可】

CC BY   
© The Author(s). 2017