Chapter title |
Proteogenomics: Key Driver for Clinical Discovery and Personalized Medicine
|
---|---|
Chapter number | 3 |
Book title |
Proteogenomics
|
Published in |
Advances in experimental medicine and biology, January 2016
|
DOI | 10.1007/978-3-319-42316-6_3 |
Pubmed ID | |
Book ISBNs |
978-3-31-942314-2, 978-3-31-942316-6
|
Authors |
Ruggero Barbieri, Victor Guryev, Corry-Anke Brandsma, Frank Suits, Rainer Bischoff, Peter Horvatovich, Barbieri, Ruggero, Guryev, Victor, Brandsma, Corry-Anke, Suits, Frank, Bischoff, Rainer, Horvatovich, Peter |
Editors |
Ákos Végvári |
Abstract |
Proteogenomics is a multi-omics research field that has the aim to efficiently integrate genomics, transcriptomics and proteomics. With this approach it is possible to identify new patient-specific proteoforms that may have implications in disease development, specifically in cancer. Understanding the impact of a large number of mutations detected at the genomics level is needed to assess the effects at the proteome level. Proteogenomics data integration would help in identifying molecular changes that are persistent across multiple molecular layers and enable better interpretation of molecular mechanisms of disease, such as the causal relationship between single nucleotide polymorphisms (SNPs) and the expression of transcripts and translation of proteins compared to mainstream proteomics approaches. Identifying patient-specific protein forms and getting a better picture of molecular mechanisms of disease opens the avenue for precision and personalized medicine. Proteogenomics is, however, a challenging interdisciplinary science that requires the understanding of sample preparation, data acquisition and processing for genomics, transcriptomics and proteomics. This chapter aims to guide the reader through the technology and bioinformatics aspects of these multi-omics approaches, illustrated with proteogenomics applications having clinical or biological relevance. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United Kingdom | 1 | 33% |
Unknown | 2 | 67% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 2 | 67% |
Scientists | 1 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 56 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 10 | 18% |
Researcher | 7 | 13% |
Student > Ph. D. Student | 6 | 11% |
Student > Bachelor | 4 | 7% |
Other | 3 | 5% |
Other | 9 | 16% |
Unknown | 17 | 30% |
Readers by discipline | Count | As % |
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Biochemistry, Genetics and Molecular Biology | 21 | 38% |
Medicine and Dentistry | 6 | 11% |
Computer Science | 3 | 5% |
Agricultural and Biological Sciences | 2 | 4% |
Pharmacology, Toxicology and Pharmaceutical Science | 1 | 2% |
Other | 4 | 7% |
Unknown | 19 | 34% |