Data Mining Techniques for the Life Sciences:
High throughput sequencing (HTS) technologies have conquered the genomics and epigenomics worlds. The applications of HTS methods are wide, and can be used to sequence everything from whole or partial genomes, transcriptomes, non-coding RNAs, ribosome profiling, to single-cell sequencing. Having suc...
Gespeichert in:
| Weitere Verfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer New York
2016
|
| Ausgabe: | 2nd ed. 2016 |
| Schriftenreihe: | Methods in Molecular Biology
1415 |
| Schlagworte: | |
| Online-Zugang: | UBR01 TUM01 URL des Erstveröffentlichers |
| Zusammenfassung: | High throughput sequencing (HTS) technologies have conquered the genomics and epigenomics worlds. The applications of HTS methods are wide, and can be used to sequence everything from whole or partial genomes, transcriptomes, non-coding RNAs, ribosome profiling, to single-cell sequencing. Having such diversity of alternatives, there is a demand for information by research scientists without experience in HTS that need to choose the most suitable methodology or combination of platforms and to define their experimental designs to achieve their specific objectives. Field Guidelines for Genetic Experimental Designs in High-Throughput Sequencing |
| Beschreibung: | 1 Online-Ressource (XIII, 552 p. 97 illus., 84 illus. in color) |
| ISBN: | 9781493935727 |
| DOI: | 10.1007/978-1-4939-3572-7 |
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| spelling | Data Mining Techniques for the Life Sciences edited by Oliviero Carugo, Frank Eisenhaber 2nd ed. 2016 New York, NY Springer New York 2016 1 Online-Ressource (XIII, 552 p. 97 illus., 84 illus. in color) txt rdacontent c rdamedia cr rdacarrier Methods in Molecular Biology 1415 High throughput sequencing (HTS) technologies have conquered the genomics and epigenomics worlds. The applications of HTS methods are wide, and can be used to sequence everything from whole or partial genomes, transcriptomes, non-coding RNAs, ribosome profiling, to single-cell sequencing. Having such diversity of alternatives, there is a demand for information by research scientists without experience in HTS that need to choose the most suitable methodology or combination of platforms and to define their experimental designs to achieve their specific objectives. Field Guidelines for Genetic Experimental Designs in High-Throughput Sequencing Life Sciences Bioinformatics Life sciences Data Mining (DE-588)4428654-5 gnd rswk-swf Biowissenschaften (DE-588)4129772-6 gnd rswk-swf 1\p (DE-588)4143413-4 Aufsatzsammlung gnd-content Biowissenschaften (DE-588)4129772-6 s Data Mining (DE-588)4428654-5 s 2\p DE-604 Carugo, Oliviero edt Eisenhaber, Frank edt Erscheint auch als Druck-Ausgabe 9781493935703 https://doi.org/10.1007/978-1-4939-3572-7 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Data Mining Techniques for the Life Sciences Life Sciences Bioinformatics Life sciences Data Mining (DE-588)4428654-5 gnd Biowissenschaften (DE-588)4129772-6 gnd |
| subject_GND | (DE-588)4428654-5 (DE-588)4129772-6 (DE-588)4143413-4 |
| title | Data Mining Techniques for the Life Sciences |
| title_auth | Data Mining Techniques for the Life Sciences |
| title_exact_search | Data Mining Techniques for the Life Sciences |
| title_full | Data Mining Techniques for the Life Sciences edited by Oliviero Carugo, Frank Eisenhaber |
| title_fullStr | Data Mining Techniques for the Life Sciences edited by Oliviero Carugo, Frank Eisenhaber |
| title_full_unstemmed | Data Mining Techniques for the Life Sciences edited by Oliviero Carugo, Frank Eisenhaber |
| title_short | Data Mining Techniques for the Life Sciences |
| title_sort | data mining techniques for the life sciences |
| topic | Life Sciences Bioinformatics Life sciences Data Mining (DE-588)4428654-5 gnd Biowissenschaften (DE-588)4129772-6 gnd |
| topic_facet | Life Sciences Bioinformatics Life sciences Data Mining Biowissenschaften Aufsatzsammlung |
| url | https://doi.org/10.1007/978-1-4939-3572-7 |
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