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Proteogenomics

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Attention for Chapter 7: Proteogenomic Analysis of Single Amino Acid Polymorphisms in Cancer Research.
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Chapter title
Proteogenomic Analysis of Single Amino Acid Polymorphisms in Cancer Research.
Chapter number 7
Book title
Proteogenomics
Published in
Advances in experimental medicine and biology, September 2016
DOI 10.1007/978-3-319-42316-6_7
Pubmed ID
Book ISBNs
978-3-31-942314-2, 978-3-31-942316-6
Authors

Alba Garin-Muga, Fernando J. Corrales, Victor Segura

Editors

Ákos Végvári

Abstract

The integration of genomics and proteomics has led to the emergence of proteogenomics, a field of research successfully applied to the characterization of cancer samples. The diagnosis, prognosis and response to therapy of cancer patients will largely benefit from the identification of mutations present in their genome. The current state of the art of high throughput experiments for genome-wide detection of somatic mutations in cancer samples has allowed the development of projects such as the TCGA, in which hundreds of cancer genomes have been sequenced. This huge amount of data can be used to generate protein sequence databases in which each entry corresponds to a mutated peptide associated with certain cancer types. In this chapter, we describe a bioinformatics workflow for creating these databases and detecting mutated peptides in cancer samples from proteomic shotgun experiments. The performance of the proposed method has been evaluated using publicly available datasets from four cancer cell lines.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 29%
Researcher 3 21%
Unspecified 1 7%
Professor 1 7%
Student > Doctoral Student 1 7%
Other 2 14%
Unknown 2 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 4 29%
Biochemistry, Genetics and Molecular Biology 2 14%
Nursing and Health Professions 1 7%
Unspecified 1 7%
Computer Science 1 7%
Other 1 7%
Unknown 4 29%