Cogito: automated and generic comparison of annotated genomic intervals
Genetic and epigenetic biological studies often combine different types of experiments and multiple conditions. While the corresponding raw and processed data are made available through specialized public databases, the processed files are usually limited to a specific research question. Hence, they...
Gespeichert in:
| Hauptverfasser: | , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
04 August 2022
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| In: |
BMC bioinformatics
Year: 2022, Jahrgang: 23, Pages: 1-16 |
| ISSN: | 1471-2105 |
| DOI: | 10.1186/s12859-022-04853-1 |
| Online-Zugang: | Resolving-System, kostenfrei, Volltext: https://doi.org/10.1186/s12859-022-04853-1 Verlag, kostenfrei, Volltext: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-04853-1 
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| Verfasserangaben: | Annika Bürger, Martin Dugas |
MARC
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| 520 | |a Genetic and epigenetic biological studies often combine different types of experiments and multiple conditions. While the corresponding raw and processed data are made available through specialized public databases, the processed files are usually limited to a specific research question. Hence, they are unsuitable for an unbiased, systematic overview of a complex dataset. However, possible combinations of different sample types and conditions grow exponentially with the amount of sample types and conditions. Therefore the risk to miss a correlation or to overrate an identified correlation should be mitigated in a complex dataset. Since reanalysis of a full study is rarely a viable option, new methods are needed to address these issues systematically, reliably, reproducibly and efficiently. | ||
| 650 | 4 | |a Correlation | |
| 650 | 4 | |a Data integration | |
| 650 | 4 | |a Genomic interval | |
| 650 | 4 | |a Reproducible data analysis | |
| 650 | 4 | |a Statistics | |
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