An adaptive alignment algorithm for quality-controlled label-free LC-MS.
(2013) In Molecular & Cellular Proteomics 12(5). p.1407-1420- Abstract
- Label-free quantification using precursor-based intensities is a versatile workflow for large-scale proteomics studies. The method requires extensive computational analysis and is therefore in need of robust quality control during the data mining stage. We present a new label-free data analysis workflow integrated into a multi-user software platform. A novel adaptive alignment algorithm has been developed to minimize the possible systematic bias introduced into the analysis. Parameters are estimated on the fly from the data at hand, producing a user-friendly analysis suite. Quality metrics are output in every step of the analysis as well as actively incorporated into the parameter estimation. We furthermore show the improvement of this... (More)
- Label-free quantification using precursor-based intensities is a versatile workflow for large-scale proteomics studies. The method requires extensive computational analysis and is therefore in need of robust quality control during the data mining stage. We present a new label-free data analysis workflow integrated into a multi-user software platform. A novel adaptive alignment algorithm has been developed to minimize the possible systematic bias introduced into the analysis. Parameters are estimated on the fly from the data at hand, producing a user-friendly analysis suite. Quality metrics are output in every step of the analysis as well as actively incorporated into the parameter estimation. We furthermore show the improvement of this system by comprehensive comparison to classical label-free analysis methodology as well as current state-of-the-art software. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3438856
- author
- Sandin, Marianne LU ; Ali, Ashfaq ; Hansson, Karin LU ; Månsson, Olle LU ; Andreasson, Erik ; Resjö, Svante and Levander, Fredrik LU
- organization
- publishing date
- 2013
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Molecular & Cellular Proteomics
- volume
- 12
- issue
- 5
- pages
- 1407 - 1420
- publisher
- American Society for Biochemistry and Molecular Biology
- external identifiers
-
- wos:000319705100029
- pmid:23306530
- scopus:84877610614
- pmid:23306530
- ISSN
- 1535-9484
- DOI
- 10.1074/mcp.O112.021907
- language
- English
- LU publication?
- yes
- id
- b48d02bb-c7ec-4376-9cc7-59b4b50050e3 (old id 3438856)
- alternative location
- http://www.ncbi.nlm.nih.gov/pubmed/23306530?dopt=Abstract
- date added to LUP
- 2016-04-01 10:23:59
- date last changed
- 2024-04-07 08:57:25
@article{b48d02bb-c7ec-4376-9cc7-59b4b50050e3,
abstract = {{Label-free quantification using precursor-based intensities is a versatile workflow for large-scale proteomics studies. The method requires extensive computational analysis and is therefore in need of robust quality control during the data mining stage. We present a new label-free data analysis workflow integrated into a multi-user software platform. A novel adaptive alignment algorithm has been developed to minimize the possible systematic bias introduced into the analysis. Parameters are estimated on the fly from the data at hand, producing a user-friendly analysis suite. Quality metrics are output in every step of the analysis as well as actively incorporated into the parameter estimation. We furthermore show the improvement of this system by comprehensive comparison to classical label-free analysis methodology as well as current state-of-the-art software.}},
author = {{Sandin, Marianne and Ali, Ashfaq and Hansson, Karin and Månsson, Olle and Andreasson, Erik and Resjö, Svante and Levander, Fredrik}},
issn = {{1535-9484}},
language = {{eng}},
number = {{5}},
pages = {{1407--1420}},
publisher = {{American Society for Biochemistry and Molecular Biology}},
series = {{Molecular & Cellular Proteomics}},
title = {{An adaptive alignment algorithm for quality-controlled label-free LC-MS.}},
url = {{http://dx.doi.org/10.1074/mcp.O112.021907}},
doi = {{10.1074/mcp.O112.021907}},
volume = {{12}},
year = {{2013}},
}