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Improved label-free LC-MS analysis by wavelet-based noise rejection

Cappadona, Salvatore; Nanni, Paolo; Benevento, Marco; Levander, Fredrik LU ; Versura, Pierra; Roda, Aldo; Cerutti, Sergio and Pattini, Linda (2010) In Journal of Biomedicine and Biotechnology 2010.
Abstract
Label-free LC-MS analysis allows determining the differential expression level of proteins in multiple samples, without the use of stable isotopes. This technique is based on the direct comparison of multiple runs, obtained by continuous detection in MS mode. Only differentially expressed peptides are selected for further fragmentation, thus avoiding the bias toward abundant peptides typical of data-dependent tandem MS. The computational framework includes detection, alignment, normalization and matching of peaks across multiple sets, and several software packages are available to address these processing steps. Yet, more care should be taken to improve the quality of the LC-MS maps entering the pipeline, as this parameter severely affects... (More)
Label-free LC-MS analysis allows determining the differential expression level of proteins in multiple samples, without the use of stable isotopes. This technique is based on the direct comparison of multiple runs, obtained by continuous detection in MS mode. Only differentially expressed peptides are selected for further fragmentation, thus avoiding the bias toward abundant peptides typical of data-dependent tandem MS. The computational framework includes detection, alignment, normalization and matching of peaks across multiple sets, and several software packages are available to address these processing steps. Yet, more care should be taken to improve the quality of the LC-MS maps entering the pipeline, as this parameter severely affects the results of all downstream analyses. In this paper we show how the inclusion of a preprocessing step of background subtraction in a common laboratory pipeline can lead to an enhanced inclusion list of peptides selected for fragmentation and consequently to better protein identification. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Journal of Biomedicine and Biotechnology
volume
2010
publisher
Hindawi Publishing Corporation
external identifiers
  • wos:000275078500001
  • scopus:77951919202
ISSN
1110-7251
DOI
10.1155/2010/131505
language
English
LU publication?
yes
id
4c0ad617-c688-4304-9908-6d2fc7a3ed54 (old id 1566951)
date added to LUP
2010-03-12 13:08:20
date last changed
2018-05-29 09:28:14
@article{4c0ad617-c688-4304-9908-6d2fc7a3ed54,
  abstract     = {Label-free LC-MS analysis allows determining the differential expression level of proteins in multiple samples, without the use of stable isotopes. This technique is based on the direct comparison of multiple runs, obtained by continuous detection in MS mode. Only differentially expressed peptides are selected for further fragmentation, thus avoiding the bias toward abundant peptides typical of data-dependent tandem MS. The computational framework includes detection, alignment, normalization and matching of peaks across multiple sets, and several software packages are available to address these processing steps. Yet, more care should be taken to improve the quality of the LC-MS maps entering the pipeline, as this parameter severely affects the results of all downstream analyses. In this paper we show how the inclusion of a preprocessing step of background subtraction in a common laboratory pipeline can lead to an enhanced inclusion list of peptides selected for fragmentation and consequently to better protein identification.},
  author       = {Cappadona, Salvatore and Nanni, Paolo and Benevento, Marco and Levander, Fredrik and Versura, Pierra and Roda, Aldo and Cerutti, Sergio and Pattini, Linda},
  issn         = {1110-7251},
  language     = {eng},
  publisher    = {Hindawi Publishing Corporation},
  series       = {Journal of Biomedicine and Biotechnology},
  title        = {Improved label-free LC-MS analysis by wavelet-based noise rejection},
  url          = {http://dx.doi.org/10.1155/2010/131505},
  volume       = {2010},
  year         = {2010},
}