Peak Detection in Data Independent Acquisition Analysis
(2016) In LU-CS-EX 2016-20 EDA920 20151Department of Computer Science
- Abstract
- The analysis of peptides using mass spectrometry produces signals, where
intact and fragmented peptides are observable as specific signal peaks.
This report investigates how signal processing and supervised learning,
along with specialized peak detection algorithms, can improve the analysis of
such peptide signals in a mass spectrometry technique called Data Independent
Acquisition. To make this improvement possible, 1120 different possible
approaches were investigated and evaluated, and finally combined into an ensemble
system.
In this ensemble system, the best fit approach for data currently being processed
is chosen based on linear regression. The final system can identify all
the correct peaks from a manually-annotated test... (More) - The analysis of peptides using mass spectrometry produces signals, where
intact and fragmented peptides are observable as specific signal peaks.
This report investigates how signal processing and supervised learning,
along with specialized peak detection algorithms, can improve the analysis of
such peptide signals in a mass spectrometry technique called Data Independent
Acquisition. To make this improvement possible, 1120 different possible
approaches were investigated and evaluated, and finally combined into an ensemble
system.
In this ensemble system, the best fit approach for data currently being processed
is chosen based on linear regression. The final system can identify all
the correct peaks from a manually-annotated test set. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8884133
- author
- Stymne, Viktor LU
- supervisor
-
- Pierre Nugues LU
- Johan Teleman LU
- Fredrik Levander LU
- organization
- course
- EDA920 20151
- year
- 2016
- type
- H3 - Professional qualifications (4 Years - )
- subject
- keywords
- proteomics, signal processing, machine learning, peak detection
- publication/series
- LU-CS-EX 2016-20
- report number
- LU-CS-EX 2016-20
- ISSN
- 1650-2884
- language
- English
- id
- 8884133
- date added to LUP
- 2016-06-22 12:49:57
- date last changed
- 2016-06-22 12:49:57
@misc{8884133, abstract = {{The analysis of peptides using mass spectrometry produces signals, where intact and fragmented peptides are observable as specific signal peaks. This report investigates how signal processing and supervised learning, along with specialized peak detection algorithms, can improve the analysis of such peptide signals in a mass spectrometry technique called Data Independent Acquisition. To make this improvement possible, 1120 different possible approaches were investigated and evaluated, and finally combined into an ensemble system. In this ensemble system, the best fit approach for data currently being processed is chosen based on linear regression. The final system can identify all the correct peaks from a manually-annotated test set.}}, author = {{Stymne, Viktor}}, issn = {{1650-2884}}, language = {{eng}}, note = {{Student Paper}}, series = {{LU-CS-EX 2016-20}}, title = {{Peak Detection in Data Independent Acquisition Analysis}}, year = {{2016}}, }