An integrative approach for a network based meta-analysis of viral RNAi screens
Big data is becoming ubiquitous in biology, and poses significant challenges in data analysis and interpretation. RNAi screening has become a workhorse of functional genomics, and has been applied, for example, to identify host factors involved in infection for a panel of different viruses. However,...
Gespeichert in:
| Hauptverfasser: | , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
13 February 2015
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| In: |
Algorithms for molecular biology
Year: 2015, Jahrgang: 10 |
| ISSN: | 1748-7188 |
| DOI: | 10.1186/s13015-015-0035-7 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1186/s13015-015-0035-7
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| Verfasserangaben: | Sandeep S. Amberkar, Lars Kaderali |
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| 520 | |a Big data is becoming ubiquitous in biology, and poses significant challenges in data analysis and interpretation. RNAi screening has become a workhorse of functional genomics, and has been applied, for example, to identify host factors involved in infection for a panel of different viruses. However, the analysis of data resulting from such screens is difficult, with often low overlap between hit lists, even when comparing screens targeting the same virus. This makes it a major challenge to select interesting candidates for further detailed, mechanistic experimental characterization. | ||
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