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Improvements in Mass Spectrometry Assay Library Generation for Targeted Proteomics

Teleman, Johan LU ; Hauri, Simon LU and Malmström, Johan LU (2017) In Journal of Proteome Research 16(7). p.2384-2392
Abstract

In data-independent acquisition mass spectrometry (DIA-MS), targeted extraction of peptide signals in silico using mass spectrometry assay libraries is a successful method for the identification and quantification of proteins. However, it remains unclear if high quality assay libraries with more accurate peptide ion coordinates can improve peptide target identification rates in DIA analysis. In this study, we systematically improved and evaluated the common algorithmic steps for assay library generation and demonstrate that increased assay quality results in substantially higher identification rates of peptide targets from mouse organ protein lysates measured by DIA-MS. The introduced changes are (1) a new spectrum interpretation... (More)

In data-independent acquisition mass spectrometry (DIA-MS), targeted extraction of peptide signals in silico using mass spectrometry assay libraries is a successful method for the identification and quantification of proteins. However, it remains unclear if high quality assay libraries with more accurate peptide ion coordinates can improve peptide target identification rates in DIA analysis. In this study, we systematically improved and evaluated the common algorithmic steps for assay library generation and demonstrate that increased assay quality results in substantially higher identification rates of peptide targets from mouse organ protein lysates measured by DIA-MS. The introduced changes are (1) a new spectrum interpretation algorithm, (2) reapplication of segmented retention time normalization, (3) a ppm fragment mass error matching threshold, (4) usage of internal peptide fragments, and (5) a multilevel false discovery rate calculation. Taken together, these changes yielded 14-36% more identified peptide targets at 1% assay false discovery rate and are implemented in three new open source tools, Fraggle, Tramler, and Franklin, available at https://github.com/fickludd/eviltools . The improved algorithms provide ways to better utilize discovery MS data, translating to substantially increased DIA performance and ultimately better foundations for drawing biological conclusions in DIA-based experiments.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Journal of Proteome Research
volume
16
issue
7
pages
9 pages
publisher
The American Chemical Society
external identifiers
  • scopus:85023172993
  • wos:000405358500006
ISSN
1535-3893
DOI
10.1021/acs.jproteome.6b00928
language
English
LU publication?
yes
id
ac9e9e2e-05a0-4db9-84f0-a89055dcde39
date added to LUP
2017-09-04 17:19:27
date last changed
2017-09-18 11:38:51
@article{ac9e9e2e-05a0-4db9-84f0-a89055dcde39,
  abstract     = {<p>In data-independent acquisition mass spectrometry (DIA-MS), targeted extraction of peptide signals in silico using mass spectrometry assay libraries is a successful method for the identification and quantification of proteins. However, it remains unclear if high quality assay libraries with more accurate peptide ion coordinates can improve peptide target identification rates in DIA analysis. In this study, we systematically improved and evaluated the common algorithmic steps for assay library generation and demonstrate that increased assay quality results in substantially higher identification rates of peptide targets from mouse organ protein lysates measured by DIA-MS. The introduced changes are (1) a new spectrum interpretation algorithm, (2) reapplication of segmented retention time normalization, (3) a ppm fragment mass error matching threshold, (4) usage of internal peptide fragments, and (5) a multilevel false discovery rate calculation. Taken together, these changes yielded 14-36% more identified peptide targets at 1% assay false discovery rate and are implemented in three new open source tools, Fraggle, Tramler, and Franklin, available at https://github.com/fickludd/eviltools . The improved algorithms provide ways to better utilize discovery MS data, translating to substantially increased DIA performance and ultimately better foundations for drawing biological conclusions in DIA-based experiments.</p>},
  author       = {Teleman, Johan and Hauri, Simon and Malmström, Johan},
  issn         = {1535-3893},
  language     = {eng},
  month        = {07},
  number       = {7},
  pages        = {2384--2392},
  publisher    = {The American Chemical Society},
  series       = {Journal of Proteome Research},
  title        = {Improvements in Mass Spectrometry Assay Library Generation for Targeted Proteomics},
  url          = {http://dx.doi.org/10.1021/acs.jproteome.6b00928},
  volume       = {16},
  year         = {2017},
}