Chapter title |
Proteogenomic Tools and Approaches to Explore Protein Coding Landscapes of Eukaryotic Genomes.
|
---|---|
Chapter number | 1 |
Book title |
Proteogenomics
|
Published in |
Advances in experimental medicine and biology, September 2016
|
DOI | 10.1007/978-3-319-42316-6_1 |
Pubmed ID | |
Book ISBNs |
978-3-31-942314-2, 978-3-31-942316-6
|
Authors |
Dhirendra Kumar, Debasis Dash |
Editors |
Ákos Végvári |
Abstract |
Proteogenomic strategies aim to refine genome-wide annotations of protein coding features by using actual protein level observations. Most of the currently applied proteogenomic approaches include integrative analysis of multiple types of high-throughput omics data, e.g., genomics, transcriptomics, proteomics, etc. Recent efforts towards creating a human proteome map were primarily targeted to experimentally detect at least one protein product for each gene in the genome and extensively utilized proteogenomic approaches. The 14 year long wait to get a draft human proteome map, after completion of similar efforts to sequence the genome, explains the huge complexity and technical hurdles of such efforts. Further, the integrative analysis of large-scale multi-omics datasets inherent to these studies becomes a major bottleneck to their success. However, recent developments of various analysis tools and pipelines dedicated to proteogenomics reduce both the time and complexity of such analysis. Here, we summarize notable approaches, studies, software developments and their potential applications towards eukaryotic genome annotation and clinical proteogenomics. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 33 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 6 | 18% |
Student > Ph. D. Student | 6 | 18% |
Other | 4 | 12% |
Student > Bachelor | 3 | 9% |
Student > Doctoral Student | 2 | 6% |
Other | 4 | 12% |
Unknown | 8 | 24% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 13 | 39% |
Agricultural and Biological Sciences | 4 | 12% |
Chemistry | 2 | 6% |
Nursing and Health Professions | 1 | 3% |
Medicine and Dentistry | 1 | 3% |
Other | 1 | 3% |
Unknown | 11 | 33% |