Full metadata
Title
Improving Peptide Identification in Shotgun Proteomics Using Deep Neural Networks
Description
In shotgun proteomics, liquid chromatography coupled to tandem mass spectrometry
(LC-MS/MS) is used to identify and quantify peptides and proteins. LC-MS/MS produces mass spectra, which must be searched by one or more engines, which employ
algorithms to match spectra to theoretical spectra derived from a reference database.
These engines identify and characterize proteins and their component peptides. By
training a convolutional neural network on a dataset of over 6 million MS/MS spectra
derived from human proteins, we aim to create a tool that can quickly and effectively
identify spectra as peptides prior to database searching. This can significantly reduce search space and thus run time for database searches, thereby accelerating LCMS/MS-based proteomics data acquisition. Additionally, by training neural networks
on labels derived from the search results of three different database search engines, we
aim to examine and compare which features are best identified by individual search
engines, a neural network, or a combination of these.
(LC-MS/MS) is used to identify and quantify peptides and proteins. LC-MS/MS produces mass spectra, which must be searched by one or more engines, which employ
algorithms to match spectra to theoretical spectra derived from a reference database.
These engines identify and characterize proteins and their component peptides. By
training a convolutional neural network on a dataset of over 6 million MS/MS spectra
derived from human proteins, we aim to create a tool that can quickly and effectively
identify spectra as peptides prior to database searching. This can significantly reduce search space and thus run time for database searches, thereby accelerating LCMS/MS-based proteomics data acquisition. Additionally, by training neural networks
on labels derived from the search results of three different database search engines, we
aim to examine and compare which features are best identified by individual search
engines, a neural network, or a combination of these.
Date Created
2020-05
Contributors
- Whyte, Cameron Stafford (Author)
- Suren, Jayasuriya (Thesis director)
- Gil, Speyer (Committee member)
- Patrick, Pirrotte (Committee member)
- School of Mathematical and Statistical Sciences (Contributor, Contributor)
- Barrett, The Honors College (Contributor)
Topical Subject
Resource Type
Extent
50 pages
Language
Copyright Statement
In Copyright
Primary Member of
Series
Academic Year 2019-2020
Handle
https://hdl.handle.net/2286/R.I.56514
Level of coding
minimal
Cataloging Standards
System Created
- 2020-04-19 12:00:05
System Modified
- 2021-08-11 04:09:57
- 2 years 8 months ago
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