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Processing strategies and software solutions for data‐independent acquisition in mass spectrometry
Author(s) -
Bilbao Aivett,
Varesio Emmanuel,
Luban Jeremy,
StrambioDeCastillia Caterina,
Hopfgartner Gérard,
Müller Markus,
Lisacek Frédérique
Publication year - 2015
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.201400323
Subject(s) - computer science , workflow , software , usability , data mining , data acquisition , data processing , exploit , identification (biology) , database , programming language , botany , computer security , biology , human–computer interaction
Data‐independent acquisition (DIA) offers several advantages over data‐dependent acquisition (DDA) schemes for characterizing complex protein digests analyzed by LC‐MS/MS. In contrast to the sequential detection, selection, and analysis of individual ions during DDA, DIA systematically parallelizes the fragmentation of all detectable ions within a wide m/z range regardless of intensity, thereby providing broader dynamic range of detected signals, improved reproducibility for identification, better sensitivity, and accuracy for quantification, and, potentially, enhanced proteome coverage. To fully exploit these advantages, composite or multiplexed fragment ion spectra generated by DIA require more elaborate processing algorithms compared to DDA. This review examines different DIA schemes and, in particular, discusses the concepts applied to and related to data processing. Available software implementations for identification and quantification are presented as comprehensively as possible and examples of software usage are cited. Processing workflows, including complete proprietary frameworks or combinations of modules from different open source data processing packages are described and compared in terms of software availability and usability, programming language, operating system support, input/output data formats, as well as the main principles employed in the algorithms used for identification and quantification. This comparative study concludes with further discussion of current limitations and expectable improvements in the short‐ and midterm future.