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Quantitative analysis of mass spectrometry proteomics data : Software for improved life science

Teleman, Johan LU (2016)
Abstract
The rapid advances in life science, including the sequencing of the human genome and numerous other techiques, has given an extraordinary ability to aquire data on biological systems and human disease. Even so, drug development costs are higher than ever, while the rate of new approved treatments is historically low. A potential explanation to this discrepancy might be the difficulty of understanding the biology underlying the acquired data; the difficulty to refine the data to useful knowledge through interpretation. In this thesis the refinement of the complex data from mass spectrometry proteomics is studied. A number of new algorithms and programs are presented and demonstrated to provide increased analytical ability over previously... (More)
The rapid advances in life science, including the sequencing of the human genome and numerous other techiques, has given an extraordinary ability to aquire data on biological systems and human disease. Even so, drug development costs are higher than ever, while the rate of new approved treatments is historically low. A potential explanation to this discrepancy might be the difficulty of understanding the biology underlying the acquired data; the difficulty to refine the data to useful knowledge through interpretation. In this thesis the refinement of the complex data from mass spectrometry proteomics is studied. A number of new algorithms and programs are presented and demonstrated to provide increased analytical ability over previously suggested alternatives. With the higher goal of increasing the mass spectrometry laboratory scientific output, pragmatic studies were also performed, to create new set on compression algorithms for reduced storage requirement of mass spectrometry data, and also to characterize instrument stability. The final components of this thesis are the discussion of the technical and instrumental weaknesses associated with the currently employed mass spectrometry proteomics methodology, and the discussion of current lacking academical software quality and the reasons thereof. As a whole, the primary algorithms, the enabling technology, and the weakness discussions all aim to improve the current capability to perform mass spectrometry proteomics. As this technology is crucial to understand the main functional components of biology, proteins, this quest should allow better and higher quality life science data, and ultimately increase the chances of developing new treatments or diagnostics. (Less)
Please use this url to cite or link to this publication:
author
supervisor
opponent
  • Professor MacCoss, Michael, University of Washington, USA
organization
publishing date
type
Thesis
publication status
published
subject
keywords
computational proteomics, bioinformatics, life science, data complexity, algorithms, mass spectrometry
edition
200
pages
184 pages
publisher
Lund University Press, Lund, Sweden
defense location
Lundmarksalen, Astronomicentrum, Sölvegatan 27, Lund University, Faculty of Engineering LTH, Lund
defense date
2016-05-27 9:15
external identifiers
  • Scopus:84863624355
ISBN
978-91-7623-818-9
DOI
10.1021/pr300256xhttp://dx.doi.org/10.1016/j.jprot.2013.03.029http://dx.doi.org/10.1074/mcp.O114.037879http://dx.doi.org/10.1093/bioinformatics/btu686
language
English
LU publication?
yes
id
0b76cce2-be76-4599-81c7-b5fd4e1860c2 (old id 8872028)
date added to LUP
2016-05-02 09:15:16
date last changed
2016-10-23 04:42:42
@misc{0b76cce2-be76-4599-81c7-b5fd4e1860c2,
  abstract     = {The rapid advances in life science, including the sequencing of the human genome and numerous other techiques, has given an extraordinary ability to aquire data on biological systems and human disease. Even so, drug development costs are higher than ever, while the rate of new approved treatments is historically low. A potential explanation to this discrepancy might be the difficulty of understanding the biology underlying the acquired data; the difficulty to refine the data to useful knowledge through interpretation. In this thesis the refinement of the complex data from mass spectrometry proteomics is studied. A number of new algorithms and programs are presented and demonstrated to provide increased analytical ability over previously suggested alternatives. With the higher goal of increasing the mass spectrometry laboratory scientific output, pragmatic studies were also performed, to create new set on compression algorithms for reduced storage requirement of mass spectrometry data, and also to characterize instrument stability. The final components of this thesis are the discussion of the technical and instrumental weaknesses associated with the currently employed mass spectrometry proteomics methodology, and the discussion of current lacking academical software quality and the reasons thereof. As a whole, the primary algorithms, the enabling technology, and the weakness discussions all aim to improve the current capability to perform mass spectrometry proteomics. As this technology is crucial to understand the main functional components of biology, proteins, this quest should allow better and higher quality life science data, and ultimately increase the chances of developing new treatments or diagnostics.},
  author       = {Teleman, Johan},
  isbn         = {978-91-7623-818-9},
  keyword      = {computational proteomics,bioinformatics,life science,data complexity,algorithms,mass spectrometry},
  language     = {eng},
  pages        = {184},
  publisher    = {ARRAY(0x8019030)},
  title        = {Quantitative analysis of mass spectrometry proteomics data : Software for improved life science},
  url          = {http://dx.doi.org/10.1021/pr300256xhttp://dx.doi.org/10.1016/j.jprot.2013.03.029http://dx.doi.org/10.1074/mcp.O114.037879http://dx.doi.org/10.1093/bioinformatics/btu686},
  year         = {2016},
}