Network- and enrichment-based inference of phenotypes and targets from large-scale disease maps
Abstract Complex diseases are inherently multifaceted, and the associated data are often heterogeneous, making linking interactions across genes, metabolites, RNA, proteins, cellular functions, and clinically relevant phenotypes a high-priority challenge. Disease maps have emerged as knowledge bases...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-04-01
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| Series: | npj Systems Biology and Applications |
| Online Access: | https://doi.org/10.1038/s41540-022-00222-z |
| _version_ | 1811289504730841088 |
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| author | Matti Hoch Suchi Smita Konstantin Cesnulevicius David Lescheid Myron Schultz Olaf Wolkenhauer Shailendra Gupta |
| author_facet | Matti Hoch Suchi Smita Konstantin Cesnulevicius David Lescheid Myron Schultz Olaf Wolkenhauer Shailendra Gupta |
| author_sort | Matti Hoch |
| collection | DOAJ |
| description | Abstract Complex diseases are inherently multifaceted, and the associated data are often heterogeneous, making linking interactions across genes, metabolites, RNA, proteins, cellular functions, and clinically relevant phenotypes a high-priority challenge. Disease maps have emerged as knowledge bases that capture molecular interactions, disease-related processes, and disease phenotypes with standardized representations in large-scale molecular interaction maps. Various tools are available for disease map analysis, but an intuitive solution to perform in silico experiments on the maps in a wide range of contexts and analyze high-dimensional data is currently missing. To this end, we introduce a two-dimensional enrichment analysis (2DEA) approach to infer downstream and upstream elements through the statistical association of network topology parameters and fold changes from molecular perturbations. We implemented our approach in a plugin suite for the MINERVA platform, providing an environment where experimental data can be mapped onto a disease map and predict potential regulatory interactions through an intuitive graphical user interface. We show several workflows using this approach and analyze two RNA-seq datasets in the Atlas of Inflammation Resolution (AIR) to identify enriched downstream processes and upstream transcription factors. Our work improves the usability of disease maps and increases their functionality by facilitating multi-omics data integration and exploration. |
| first_indexed | 2024-04-13T03:55:59Z |
| format | Article |
| id | doaj.art-d72d43239f934401bc964e306368da3f |
| institution | Directory Open Access Journal |
| issn | 2056-7189 |
| language | English |
| last_indexed | 2024-04-13T03:55:59Z |
| publishDate | 2022-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Systems Biology and Applications |
| spelling | doaj.art-d72d43239f934401bc964e306368da3f2022-12-22T03:03:39ZengNature Portfolionpj Systems Biology and Applications2056-71892022-04-018111110.1038/s41540-022-00222-zNetwork- and enrichment-based inference of phenotypes and targets from large-scale disease mapsMatti Hoch0Suchi Smita1Konstantin Cesnulevicius2David Lescheid3Myron Schultz4Olaf Wolkenhauer5Shailendra Gupta6Department of Systems Biology and Bioinformatics, University of RostockDepartment of Systems Biology and Bioinformatics, University of RostockHeel GmbHHeel GmbHHeel GmbHDepartment of Systems Biology and Bioinformatics, University of RostockDepartment of Systems Biology and Bioinformatics, University of RostockAbstract Complex diseases are inherently multifaceted, and the associated data are often heterogeneous, making linking interactions across genes, metabolites, RNA, proteins, cellular functions, and clinically relevant phenotypes a high-priority challenge. Disease maps have emerged as knowledge bases that capture molecular interactions, disease-related processes, and disease phenotypes with standardized representations in large-scale molecular interaction maps. Various tools are available for disease map analysis, but an intuitive solution to perform in silico experiments on the maps in a wide range of contexts and analyze high-dimensional data is currently missing. To this end, we introduce a two-dimensional enrichment analysis (2DEA) approach to infer downstream and upstream elements through the statistical association of network topology parameters and fold changes from molecular perturbations. We implemented our approach in a plugin suite for the MINERVA platform, providing an environment where experimental data can be mapped onto a disease map and predict potential regulatory interactions through an intuitive graphical user interface. We show several workflows using this approach and analyze two RNA-seq datasets in the Atlas of Inflammation Resolution (AIR) to identify enriched downstream processes and upstream transcription factors. Our work improves the usability of disease maps and increases their functionality by facilitating multi-omics data integration and exploration.https://doi.org/10.1038/s41540-022-00222-z |
| spellingShingle | Matti Hoch Suchi Smita Konstantin Cesnulevicius David Lescheid Myron Schultz Olaf Wolkenhauer Shailendra Gupta Network- and enrichment-based inference of phenotypes and targets from large-scale disease maps npj Systems Biology and Applications |
| title | Network- and enrichment-based inference of phenotypes and targets from large-scale disease maps |
| title_full | Network- and enrichment-based inference of phenotypes and targets from large-scale disease maps |
| title_fullStr | Network- and enrichment-based inference of phenotypes and targets from large-scale disease maps |
| title_full_unstemmed | Network- and enrichment-based inference of phenotypes and targets from large-scale disease maps |
| title_short | Network- and enrichment-based inference of phenotypes and targets from large-scale disease maps |
| title_sort | network and enrichment based inference of phenotypes and targets from large scale disease maps |
| url | https://doi.org/10.1038/s41540-022-00222-z |
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