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.

@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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