Jeff A. Bilmes's Publications

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DIAmeter: Matching peptides to data-independent acquisition mass spectrometry data

Yang Young Lu, Jeff Bilmes, Ricard A Rodriguez-Mias, Judit Villén, and William Stafford Noble. DIAmeter: Matching peptides to data-independent acquisition mass spectrometry data. bioRxiv, Cold Spring Harbor Laboratory, 2021.

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Abstract

Tandem mass spectrometry data acquired using data independent acquisition (DIA) is challenging to interpret because the data exhibits complex structure along both the mass-to-charge (m/z) and time axes. The most common approach to analyzing this type of data makes use of a library of previously observed DIA data patterns (a \textquotedblleftspectral library\textquotedblright), but this approach is expensive because the libraries do not typically generalize well across laboratories. Here we propose DIAmeter, a search engine that detects peptides in DIA data using only a peptide sequence database. Unlike other library-free DIA analysis methods, DIAmeter supports data generated using both wide and narrow isolation windows, can readily detect peptides containing post-translational modifications, can analyze data from a variety of instrument platforms, and is capable of detecting peptides even in the absence of detectable signal in the survey (MS1) scan.Competing Interest StatementThe authors have declared no competing interest.

BibTeX

@Article{Lu2021.01.29.428872,
	author = {Lu, Yang Young and Bilmes, Jeff and Rodriguez-Mias, Ricard A and Vill{\'e}n, Judit and Noble, William Stafford},
	title = {DIAmeter: Matching peptides to data-independent acquisition mass spectrometry data},
	elocation-id = {2021.01.29.428872},
	year = {2021},
	doi = {10.1101/2021.01.29.428872},
	publisher = {Cold Spring Harbor Laboratory},
	abstract = {Tandem mass spectrometry data acquired using data independent acquisition (DIA) is challenging to interpret because the data exhibits complex structure along both the mass-to-charge (m/z) and time axes. The most common approach to analyzing this type of data makes use of a library of previously observed DIA data patterns (a {\textquotedblleft}spectral library{\textquotedblright}), but this approach is expensive because the libraries do not typically generalize well across laboratories. Here we propose DIAmeter, a search engine that detects peptides in DIA data using only a peptide sequence database. Unlike other library-free DIA analysis methods, DIAmeter supports data generated using both wide and narrow isolation windows, can readily detect peptides containing post-translational modifications, can analyze data from a variety of instrument platforms, and is capable of detecting peptides even in the absence of detectable signal in the survey (MS1) scan.Competing Interest StatementThe authors have declared no competing interest.},
	URL = {https://www.biorxiv.org/content/early/2021/01/31/2021.01.29.428872},
	eprint = {https://www.biorxiv.org/content/early/2021/01/31/2021.01.29.428872.full.pdf},
	journal = {bioRxiv},
}

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