{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T17:57:07Z","timestamp":1791050227310,"version":"4.1.0"},"reference-count":74,"publisher":"Oxford University Press (OUP)","issue":"14","license":[{"start":{"date-parts":[[2020,6,11]],"date-time":"2020-06-11T00:00:00Z","timestamp":1591833600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81871475"],"award-info":[{"award-number":["81871475"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013365","name":"West China Hospital, Sichuan University","doi-asserted-by":"publisher","award":["ZYGD18014"],"award-info":[{"award-number":["ZYGD18014"]}],"id":[{"id":"10.13039\/501100013365","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100005326","name":"Yale University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100005326","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,8,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Mass spectrometry (MS)-based quantitative proteomics experiments frequently generate data with missing values, which may profoundly affect downstream analyses. A wide variety of imputation methods have been established to deal with the missing-value issue. To date, however, there is a scarcity of efficient, systematic, and easy-to-handle tools that are tailored for proteomics community. Herein, we developed a user-friendly and powerful stand-alone software, NAguideR, to enable implementation and evaluation of different missing value methods offered by 23 widely used missing-value imputation algorithms. NAguideR further evaluates data imputation results through classic computational criteria and, unprecedentedly, proteomic empirical criteria, such as quantitative consistency between different charge-states of the same peptide, different peptides belonging to the same proteins, and individual proteins participating protein complexes and functional interactions. We applied NAguideR into three label-free proteomic datasets featuring peptide-level, protein-level, and phosphoproteomic variables respectively, all generated by data independent acquisition mass spectrometry (DIA-MS) with substantial biological replicates. The results indicate that NAguideR is able to discriminate the optimal imputation methods that are facilitating DIA-MS experiments over those sub-optimal and low-performance algorithms. NAguideR further provides downloadable tables and figures supporting flexible data analysis and interpretation. NAguideR is freely available at http:\/\/www.omicsolution.org\/wukong\/NAguideR\/ and the source code: https:\/\/github.com\/wangshisheng\/NAguideR\/.<\/jats:p>","DOI":"10.1093\/nar\/gkaa498","type":"journal-article","created":{"date-parts":[[2020,6,8]],"date-time":"2020-06-08T15:11:27Z","timestamp":1591629087000},"page":"e83-e83","source":"Crossref","is-referenced-by-count":168,"title":["NAguideR: performing and prioritizing missing value imputations for consistent bottom-up proteomic analyses"],"prefix":"10.1093","volume":"48","author":[{"given":"Shisheng","family":"Wang","sequence":"first","affiliation":[{"name":"West China-Washington Mitochondria and Metabolism Research Center; Key Lab of Transplant Engineering and Immunology, MOH,\u00a0Regenerative Medicine Research Center,\u00a0West China Hospital, Sichuan University, Chengdu\u00a0610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenxue","family":"Li","sequence":"additional","affiliation":[{"name":"Yale Cancer Biology Institute, Yale University, West Haven, CT\u00a006516, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liqiang","family":"Hu","sequence":"additional","affiliation":[{"name":"West China-Washington Mitochondria and Metabolism Research Center; Key Lab of Transplant Engineering and Immunology, MOH,\u00a0Regenerative Medicine Research Center,\u00a0West China Hospital, Sichuan University, Chengdu\u00a0610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingqiu","family":"Cheng","sequence":"additional","affiliation":[{"name":"West China-Washington Mitochondria and Metabolism Research Center; Key Lab of Transplant Engineering and Immunology, MOH,\u00a0Regenerative Medicine Research Center,\u00a0West China Hospital, Sichuan University, Chengdu\u00a0610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Yang","sequence":"additional","affiliation":[{"name":"West China-Washington Mitochondria and Metabolism Research Center; Key Lab of Transplant Engineering and Immunology, MOH,\u00a0Regenerative Medicine Research Center,\u00a0West China Hospital, Sichuan University, Chengdu\u00a0610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2626-3912","authenticated-orcid":false,"given":"Yansheng","family":"Liu","sequence":"additional","affiliation":[{"name":"Yale Cancer Biology Institute, Yale University, West Haven, CT\u00a006516, USA"},{"name":"Department of Pharmacology, Yale University School of Medicine, New Haven, CT\u00a006520, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,6,11]]},"reference":[{"key":"2020110412410729100_B1","doi-asserted-by":"crossref","first-page":"964","DOI":"10.1016\/j.cell.2019.10.007","article-title":"Integrated proteogenomic characterization of clear cell renal cell carcinoma","volume":"179","author":"Clark","year":"2019","journal-title":"Cell"},{"key":"2020110412410729100_B2","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1016\/j.cell.2019.08.052","article-title":"Integrated proteogenomic characterization of HBV-related hepatocellular carcinoma","volume":"179","author":"Gao","year":"2019","journal-title":"Cell"},{"key":"2020110412410729100_B3","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1038\/s41586-019-0987-8","article-title":"Proteomics identifies new therapeutic targets of early-stage hepatocellular carcinoma","volume":"567","author":"Jiang","year":"2019","journal-title":"Nature"},{"key":"2020110412410729100_B4","doi-asserted-by":"crossref","first-page":"18","DOI":"10.2174\/1574893608999140109120957","article-title":"A review on missing value imputation algorithms for microarray gene expression data","volume":"9","author":"Moorthy","year":"2014","journal-title":"Curr. 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