Quantitative analysis of mass spectrometry proteomics data : Software for improved life science
(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:
https://lup.lub.lu.se/record/0b76cce2-be76-4599-81c7-b5fd4e1860c2
- author
- Teleman, Johan LU
- supervisor
- opponent
-
- Professor MacCoss, Michael, University of Washington, USA
- organization
- publishing date
- 2016-05
- 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
- defense location
- Lundmarksalen, Astronomicentrum, Sölvegatan 27, Lund University, Faculty of Engineering LTH, Lund
- defense date
- 2016-05-27 9:15:00
- ISBN
- 978-91-7623-818-9
- 978-91-7623-819-6
- language
- English
- LU publication?
- yes
- id
- 0b76cce2-be76-4599-81c7-b5fd4e1860c2
- date added to LUP
- 2016-05-02 09:15:16
- date last changed
- 2019-12-03 09:41:33
@phdthesis{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}},
keywords = {{computational proteomics; bioinformatics; life science; data complexity; algorithms; mass spectrometry}},
language = {{eng}},
publisher = {{Lund University Press}},
school = {{Lund University}},
title = {{Quantitative analysis of mass spectrometry proteomics data : Software for improved life science}},
url = {{https://lup.lub.lu.se/search/files/7557268/Paper_IV_Dinosaur.pdf}},
year = {{2016}},
}