Journal Article DKFZ-2021-02567

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Integrative pathway enrichment analysis of multivariate omics data.

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2020
Nature Publishing Group UK [London]

Nature Communications 11(1), 735 () [10.1038/s41467-019-13983-9]
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Abstract: Multi-omics datasets represent distinct aspects of the central dogma of molecular biology. Such high-dimensional molecular profiles pose challenges to data interpretation and hypothesis generation. ActivePathways is an integrative method that discovers significantly enriched pathways across multiple datasets using statistical data fusion, rationalizes contributing evidence and highlights associated genes. As part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumor types, we integrated genes with coding and non-coding mutations and revealed frequently mutated pathways and additional cancer genes with infrequent mutations. We also analyzed prognostic molecular pathways by integrating genomic and transcriptomic features of 1780 breast cancers and highlighted associations with immune response and anti-apoptotic signaling. Integration of ChIP-seq and RNA-seq data for master regulators of the Hippo pathway across normal human tissues identified processes of tissue regeneration and stem cell regulation. ActivePathways is a versatile method that improves systems-level understanding of cellular organization in health and disease through integration of multiple molecular datasets and pathway annotations.

Keyword(s): Adenocarcinoma: genetics (MeSH) ; Adenocarcinoma: metabolism (MeSH) ; Apoptosis: genetics (MeSH) ; Breast Neoplasms: genetics (MeSH) ; Breast Neoplasms: immunology (MeSH) ; Breast Neoplasms: metabolism (MeSH) ; Breast Neoplasms: mortality (MeSH) ; Chromatin Immunoprecipitation (MeSH) ; Computational Biology: methods (MeSH) ; Databases, Factual (MeSH) ; Female (MeSH) ; Gene Dosage (MeSH) ; Gene Expression Profiling (MeSH) ; Gene Regulatory Networks (MeSH) ; Genomics: methods (MeSH) ; Humans (MeSH) ; Metabolic Networks and Pathways: genetics (MeSH) ; Mutation (MeSH) ; Neoplasms: genetics (MeSH) ; Neoplasms: metabolism (MeSH) ; Prognosis (MeSH) ; Protein-Serine-Threonine Kinases: genetics (MeSH) ; Protein-Serine-Threonine Kinases: metabolism (MeSH) ; RNA, Messenger: genetics (MeSH) ; Sequence Analysis, RNA (MeSH) ; Signal Transduction (MeSH) ; RNA, Messenger ; Hippo protein, human ; Protein-Serine-Threonine Kinases

Classification:

Note: siehe Correction: DKFZ Autoren affiliiert im PCAWG Consortium: https://inrepo02.dkfz.de/record/212438 / https://doi.org/10.1038/s41467-022-32342-9

Contributing Institute(s):
  1. Theoretische Bioinformatik (B080)
  2. Bioinformatik und Omics Data Analytics (B240)
  3. Epigenomik (B370)
  4. Angewandte Bioinformatik (B330)
  5. DKTK HD zentral (HD01)
  6. B060 Molekulare Genetik (B060)
  7. Pädiatrische Gliomforschung (B360)
  8. DKTK Koordinierungsstelle Berlin (BE01)
  9. B062 Pädiatrische Neuroonkologie (B062)
  10. B066 Chromatin-Netzwerke (B066)
  11. B063 Krebsgenomforschung (B063)
  12. Hochdurchsatz-Sequenzierung (W190)
  13. B260 Bioinformatik der Genomik und Systemgenetik (B260)
  14. Core Facility Omics IT (W610)
  15. B087 Neuroblastom Genomik (B087)
Research Program(s):
  1. 312 - Functional and structural genomics (POF3-312) (POF3-312)

Appears in the scientific report 2020
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 Record created 2021-11-17, last modified 2024-03-20


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