METHODS FOR COMPARING METAPROTEOMIC DATA IN THE ABSENCE OF METAGENOMIC INFORMATION

Brian Louis LaMarche

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METHODS FOR COMPARING METAPROTEOMIC DATA IN THE ABSENCE OF METAGENOMIC INFORMATION

Dissertation

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METHODS FOR COMPARING METAPROTEOMIC DATA IN THE ABSENCE OF METAGENOMIC INFORMATION

Brian Louis LaMarche

Doctor of Philosophy (PhD), Washington State University

01/2016

Handle:

https://hdl.handle.net/2376/117791

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Abstract

Alignment

Bioreactor

Microbial Community

Quality Assessment

Spectral Similarity

Bioinformatics

Mass Spectrometry

This dissertation describes research for the improvement of algorithms for the use in comparing metaproteomic datasets analyzed by liquid chromatography mass spectrometry in the absence of a metagenome. Existing methods only leverage tandem mass spectra for the purpose of building spectral networks, clustering of tandem spectra, or inference of a peptide sequence using de novo sequencing. Many important unattributed data in the precursor space are thus left unanalyzed. Alternative methods exist to compare these data but require the alignment of data to adjust for systematic and random variations often due to instrumentation and measurement error. Existing alignment strategies combine correspondence, the process of finding similar features, with transformation. These approaches also lack any methodology to assess an alignment. The presented method decouples the alignment myth into three components: correspondence, alignment, and assessment. The benefits of separating the overall alignment strategy are realized in both the assessment of alignments using statistical hypothesis testing, and in the analysis of a dataset collected from a bioreactor containing a microbial community capable of cellulose degradation.

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Details

Title: METHODS FOR COMPARING METAPROTEOMIC DATA IN THE ABSENCE OF METAGENOMIC INFORMATION
Creators: Brian Louis LaMarche
Contributors: Robert R Lewis (Advisor)
John H Miller (Advisor)
Ananth Kalyanaraman (Committee Member)
Samuel H Payne (Committee Member)
Awarding Institution: Washington State University
Academic Unit: Electrical Engineering and Computer Science, School of
Theses and Dissertations: Doctor of Philosophy (PhD), Washington State University
Number of pages: 113
Identifiers: 99900581636301842
Language: English
Resource Type: Dissertation