Cogito: automated and generic comparison of annotated genomic intervals
Abstract 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 q...
| 出版年: | BMC Bioinformatics |
|---|---|
| 主要な著者: | , |
| フォーマット: | 論文 |
| 言語: | 英語 |
| 出版事項: |
BMC
2022-08-01
|
| 主題: | |
| オンライン・アクセス: | https://doi.org/10.1186/s12859-022-04853-1 |
| _version_ | 1852726012291317760 |
|---|---|
| author | Annika Bürger Martin Dugas |
| author_facet | Annika Bürger Martin Dugas |
| author_sort | Annika Bürger |
| collection | DOAJ |
| container_title | BMC Bioinformatics |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-e14e18fceebc43a4a451b7daeb5f136c |
| institution | Directory of Open Access Journals |
| issn | 1471-2105 |
| language | English |
| publishDate | 2022-08-01 |
| publisher | BMC |
| record_format | Article |
| spelling | doaj-art-e14e18fceebc43a4a451b7daeb5f136c2025-08-19T21:10:30ZengBMCBMC Bioinformatics1471-21052022-08-0123111610.1186/s12859-022-04853-1Cogito: automated and generic comparison of annotated genomic intervalsAnnika Bürger0Martin Dugas1Institute of Medical Informatics, Westfälische Wilhelms-Universität MünsterInstitute of Medical Informatics, Heidelberg University HospitalAbstract 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.https://doi.org/10.1186/s12859-022-04853-1CorrelationStatisticsGenomic intervalReproducible data analysisData integration |
| spellingShingle | Annika Bürger Martin Dugas Cogito: automated and generic comparison of annotated genomic intervals Correlation Statistics Genomic interval Reproducible data analysis Data integration |
| title | Cogito: automated and generic comparison of annotated genomic intervals |
| title_full | Cogito: automated and generic comparison of annotated genomic intervals |
| title_fullStr | Cogito: automated and generic comparison of annotated genomic intervals |
| title_full_unstemmed | Cogito: automated and generic comparison of annotated genomic intervals |
| title_short | Cogito: automated and generic comparison of annotated genomic intervals |
| title_sort | cogito automated and generic comparison of annotated genomic intervals |
| topic | Correlation Statistics Genomic interval Reproducible data analysis Data integration |
| url | https://doi.org/10.1186/s12859-022-04853-1 |
| work_keys_str_mv | AT annikaburger cogitoautomatedandgenericcomparisonofannotatedgenomicintervals AT martindugas cogitoautomatedandgenericcomparisonofannotatedgenomicintervals |
