Dinosaur : A Refined Open-Source Peptide MS Feature Detector
Teleman, Johan LU ; Chawade, Aakash LU ; Sandin, Marianne LU ; Levander, Fredrik LU
and Malmström, Johan
LU
(2016)
In Journal of Proteome Research
15(7).
p.2143-2151
- Abstract
In bottom-up mass spectrometry (MS)-based proteomics, peptide isotopic and chromatographic traces (features) are frequently used for label-free quantification in data-dependent acquisition MS but can also be used for the improved identification of chimeric spectra or sample complexity characterization. Feature detection is difficult because of the high complexity of MS proteomics data from biological samples, which frequently causes features to intermingle. In addition, existing feature detection algorithms commonly suffer from compatibility issues, long computation times, or poor performance on high-resolution data. Because of these limitations, we developed a new tool, Dinosaur, with increased speed and versatility. Dinosaur has the... (More)
In bottom-up mass spectrometry (MS)-based proteomics, peptide isotopic and chromatographic traces (features) are frequently used for label-free quantification in data-dependent acquisition MS but can also be used for the improved identification of chimeric spectra or sample complexity characterization. Feature detection is difficult because of the high complexity of MS proteomics data from biological samples, which frequently causes features to intermingle. In addition, existing feature detection algorithms commonly suffer from compatibility issues, long computation times, or poor performance on high-resolution data. Because of these limitations, we developed a new tool, Dinosaur, with increased speed and versatility. Dinosaur has the functionality to sample algorithm computations through quality-control plots, which we call a plot trail. From the evaluation of this plot trail, we introduce several algorithmic improvements to further improve the robustness and performance of Dinosaur, with the detection of features for 98% of MS/MS identifications in a benchmark data set, and no other algorithm tested in this study passed 96% feature detection. We finally used Dinosaur to reimplement a published workflow for peptide identification in chimeric spectra, increasing chimeric identification from 26% to 32% over the standard workflow. Dinosaur is operating-system-independent and is freely available as open source on https://github.com/fickludd/dinosaur.
(Less)
- author
- Teleman, Johan
LU
; Chawade, Aakash
LU
; Sandin, Marianne
LU
; Levander, Fredrik
LU
and Malmström, Johan
LU
- organization
- publishing date
- 2016-07-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- algorithm, chimeric spectra, electrospray ionization, feature detection, mass spectrometry, proteomics, software
- in
- Journal of Proteome Research
- volume
- 15
- issue
- 7
- pages
- 9 pages
- publisher
- The American Chemical Society (ACS)
- external identifiers
-
- pmid:27224449
- wos:000379456400006
- scopus:84977108911
- ISSN
- 1535-3893
- DOI
- 10.1021/acs.jproteome.6b00016
- language
- English
- LU publication?
- yes
- id
- 6729fd50-c47d-410e-abac-85f8475dd3f7
- date added to LUP
- 2016-07-18 10:01:35
- date last changed
- 2025-01-12 09:00:17
@article{6729fd50-c47d-410e-abac-85f8475dd3f7,
abstract = {{<p>In bottom-up mass spectrometry (MS)-based proteomics, peptide isotopic and chromatographic traces (features) are frequently used for label-free quantification in data-dependent acquisition MS but can also be used for the improved identification of chimeric spectra or sample complexity characterization. Feature detection is difficult because of the high complexity of MS proteomics data from biological samples, which frequently causes features to intermingle. In addition, existing feature detection algorithms commonly suffer from compatibility issues, long computation times, or poor performance on high-resolution data. Because of these limitations, we developed a new tool, Dinosaur, with increased speed and versatility. Dinosaur has the functionality to sample algorithm computations through quality-control plots, which we call a plot trail. From the evaluation of this plot trail, we introduce several algorithmic improvements to further improve the robustness and performance of Dinosaur, with the detection of features for 98% of MS/MS identifications in a benchmark data set, and no other algorithm tested in this study passed 96% feature detection. We finally used Dinosaur to reimplement a published workflow for peptide identification in chimeric spectra, increasing chimeric identification from 26% to 32% over the standard workflow. Dinosaur is operating-system-independent and is freely available as open source on https://github.com/fickludd/dinosaur.</p>}},
author = {{Teleman, Johan and Chawade, Aakash and Sandin, Marianne and Levander, Fredrik and Malmström, Johan}},
issn = {{1535-3893}},
keywords = {{algorithm; chimeric spectra; electrospray ionization; feature detection; mass spectrometry; proteomics; software}},
language = {{eng}},
month = {{07}},
number = {{7}},
pages = {{2143--2151}},
publisher = {{The American Chemical Society (ACS)}},
series = {{Journal of Proteome Research}},
title = {{Dinosaur : A Refined Open-Source Peptide MS Feature Detector}},
url = {{http://dx.doi.org/10.1021/acs.jproteome.6b00016}},
doi = {{10.1021/acs.jproteome.6b00016}},
volume = {{15}},
year = {{2016}},
}