{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T04:41:53Z","timestamp":1715056913522},"reference-count":62,"publisher":"The Royal Society of Chemistry","isbn-type":[{"value":"9781782624288","type":"print"},{"value":"9781782624288","type":"print"},{"value":"9781782629573","type":"electronic"},{"value":"9781782626732","type":"electronic"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,11,23]]},"abstract":"<jats:p>Mass spectrometry-based proteomics using soft ionization techniques has been used successfully to identify large numbers of proteins from complex biological samples. However, reproducible quantification across a large number of samples is still highly challenging with commonly used \u201cshotgun proteomics\u201d which uses stochastic sampling of the peptide analytes (data dependent acquisition; DDA) to analyze samples. Recently, data independent acquisition (DIA) methods have been investigated for their potential for reproducible protein quantification, since they deterministically sample all peptide analytes in every single run. This increases reproducibility and sensitivity, reduces the number of missing values and removes stochasticity from the acquisition process. However, one of the major challenges for wider adoption of DIA has been data analysis. In this chapter we will introduce the five most well-known of these techniques, as well as their data analysis methods, classified either as targeted or untargeted; then, we will discuss briefly the meaning of the false discovery rate (FDR) in DIA experiments, to finally close the chapter with a review of the current challenges in this subject.<\/jats:p>","DOI":"10.1039\/9781782626732-00200","type":"book-chapter","created":{"date-parts":[[2016,11,15]],"date-time":"2016-11-15T11:00:18Z","timestamp":1479207618000},"page":"200-228","source":"Crossref","is-referenced-by-count":0,"title":["Data Analysis for Data Independent Acquisition"],"prefix":"10.1039","author":[{"given":"Pedro","family":"Navarro","sequence":"first","affiliation":[{"name":"Institute for Immunology, University Medical Centre of the Johannes Gutenberg University Mainz \u2008 55131 Mainz \u2008 Germany \u2008 pnavarro@uni-mainz.de"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marco","family":"Trevisan-Herraz","sequence":"additional","affiliation":[{"name":"Centro Nacional de Investigaciones Cardiovasculares \u2008 28029 Madrid \u2008 Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hannes L.","family":"R\u00f6st","sequence":"additional","affiliation":[{"name":"Department of Genetics, Stanford University \u2008 Stanford \u2008 CA 94305 \u2008 USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"292","reference":[{"key":"2023051920233370200_BK9781782624288-00200-cit1","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1038\/nmeth.3234","volume":"12","author":"Doerr","year":"2014","journal-title":"Nat. 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