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Advances in computational mass spect...

University of California, San Diego., Bioinformatics.

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Advances in computational mass spectrometry: Phosphoproteomics and proteogenomics.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Advances in computational mass spectrometry: Phosphoproteomics and proteogenomics./
Author:	Payne, Samuel Harris.
Description:	133 p.
Notes:	Advisers: Vineet Bafna; William F. Loomis.
Contained By:	Dissertation Abstracts International69-08B.
Subject:	Biology, Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3316208
ISBN:	9780549688457

Advances in computational mass spectrometry: Phosphoproteomics and proteogenomics.
Payne, Samuel Harris.

Advances in computational mass spectrometry: Phosphoproteomics and proteogenomics. - 133 p.

Advisers: Vineet Bafna; William F. Loomis.

Thesis (Ph.D.)--University of California, San Diego, 2008.

The proteome is a dynamic group of proteins, interacting with and modifying each other in response to the environment. Tandem mass spectrometry has become the most convenient and high-throughput means of assaying the proteome. Modern instruments are capable of generating data for tens of thousands of peptides from thousands of proteins in a single experiment. In this work we present two important applications on proteomics: phosphoproteomics and proteogenomics.

ISBN: 9780549688457Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Advances in computational mass spectrometry: Phosphoproteomics and proteogenomics.
LDR:03927nam 2200337 a 45 001 861227
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008 100719s2008 ||||||||||||||||| ||eng d
020 $a 9780549688457
035 $a (UMI)AAI3316208
035 $a AAI3316208
040 $a UMI $c UMI
100 1 $a Payne, Samuel Harris. $3 1028901
245 1 0 $a Advances in computational mass spectrometry: Phosphoproteomics and proteogenomics.
300 $a 133 p.
500 $a Advisers: Vineet Bafna; William F. Loomis.
500 $a Source: Dissertation Abstracts International, Volume: 69-08, Section: B, page: 4507.
502 $a Thesis (Ph.D.)--University of California, San Diego, 2008.
520 $a The proteome is a dynamic group of proteins, interacting with and modifying each other in response to the environment. Tandem mass spectrometry has become the most convenient and high-throughput means of assaying the proteome. Modern instruments are capable of generating data for tens of thousands of peptides from thousands of proteins in a single experiment. In this work we present two important applications on proteomics: phosphoproteomics and proteogenomics.
520 $a Protein signaling is dominated by reversible phosphorylation. Understanding which proteins are phosphorylated, when, where, and by whom is key to understanding most cellular signaling. A variety of obstacles make assaying phosphopeptides with tandem mass spectrometry a difficult task. First, phosphorylation is reversible and transitory. Therefore, although many proteins can be phosphorylated, very few are phosphorylated at any given time. Moreover, the phosphorylation event may be sub-stoichiometric. Thus a small fraction of peptides in a proteomic sample are phosphorylated. Experimental mass spectrometrists have overcome this with the adoption of phosphopeptide enrichment protocols. A sample containing perhaps 1% phosphopeptides can be purified to over 90% phosphopeptides. However, even with a high concentration of phosphorylated peptides, phosphoproteomics suffers from a second challenge, poor spectral quality. Spectra generated by phosphopeptides have low information content and are difficult to interpret. We present an approach for learning the features of phosphopeptide spectra, and model these features in a Bayesian network. This probability model, when applied to the scoring function of Inspect, achieves a dramatic increase in sensitivity versus other peptide identification software.
520 $a The second field of study presented in proteogenomics. The task of annotating the genome for protein coding genes is difficult, and requires substantial effort. Yet this is the arguably the most important outcome of the genomic era. Most annotation pipelines utilize nucleotide centric information, such as cDNA or homology to known genes, to refine their computational predictions. Unfortunately error rates are still suspected to be high, both in terms of genes which are mispredicted and genes which are wholly missing from the annotation. We present our work on utilizing peptides obtained from mass spectrometry to reannotate the genome. We collect a large corpus of MS/MS spectra from Arabidopsis thaliana and annotate spectra from 18,024 peptides which are not currently in the proteome. Using these peptides we present gene models for 778 genes missing from the current annotation, and refine or correct an additional 695 loci, showing that proteogenomics can dramatically improve the quality of a genome annotation.
590 $a School code: 0033.
650 4 $a Biology, Bioinformatics. $3 1018415
690 $a 0715
710 2 $a University of California, San Diego. $b Bioinformatics. $3 1025305
773 0 $t Dissertation Abstracts International $g 69-08B.
790 $a 0033
790 1 0 $a Bafna, Vineet, $e advisor
790 1 0 $a Briggs, Steven P. $e committee member
790 1 0 $a Hwa, Terrence $e committee member
790 1 0 $a Komives, Elizabeth A. $e committee member
790 1 0 $a Loomis, William F., $e advisor
791 $a Ph.D.
792 $a 2008
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3316208