Cogito: automated and generic comparison of annotated genomic intervals

Background: 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....

Verfasser: Bürger, Annika
Dugas, Martin
FB/Einrichtung:FB 05: Medizinische Fakultät
Dokumenttypen:Artikel
Medientypen:Text
Erscheinungsdatum:2022
Publikation in MIAMI:13.10.2023
Datum der letzten Änderung:13.10.2023
Angaben zur Ausgabe:[Electronic ed.]
Quelle:BMC Bioinformatics 23 (2022), 315, 1-16
Schlagwörter:Correlation; Statistics; Genomic interval; Reproducible data analysis; Data integration
Fachgebiet (DDC):610: Medizin und Gesundheit
Lizenz:CC BY 4.0
Sprache:English
Förderung:Finanziert über die DEAL-Vereinbarung mit Wiley 2019-2022.
Format:PDF-Dokument
URN:urn:nbn:de:hbz:6-48988611808
Weitere Identifikatoren:DOI: 10.17879/58988618656
Permalink:https://nbn-resolving.de/urn:nbn:de:hbz:6-48988611808
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  • Onlinezugriff:10.1186_s12859-022-04853-1.pdf
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    520 3 |a Background: 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. Results: Cogito "COmpare annotated Genomic Intervals TOol" provides a workflow for an unbiased, structured overview and systematic analysis of complex genomic datasets consisting of different data types (e.g. RNA-seq, ChIP-seq) and conditions. Cogito is able to visualize valuable key information of genomic or epigenomic interval-based data, thereby providing a straightforward analysis approach for comparing different conditions. It supports getting an unbiased impression of a dataset and developing an appropriate analysis strategy for it. In addition to a text-based report, Cogito offers a fully customizable report as a starting point for further in-depth investigation. Conclusions: Cogito implements a novel approach to facilitate high-level overview analyses of complex datasets, and offers additional insights into the data without the need for a full, time-consuming reanalysis. The R/Bioconductor package is freely available at https://bioconductor.org/packages/release/bioc/html/Cogito.html, a comprehensive documentation with detailed descriptions and reproducible examples is included. 
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