FIREWORKS: a bottom-up approach to integrative coessentiality network analysis.
David R AmiciJasen M JacksonMihai Ioan TruicaRoger S SmithSarki A AbdulkadirMarc L MendilloPublished in: Life science alliance (2020)
Genetic coessentiality analysis, a computational approach which identifies genes sharing a common effect on cell fitness across large-scale screening datasets, has emerged as a powerful tool to identify functional relationships between human genes. However, widespread implementation of coessentiality to study individual genes and pathways is limited by systematic biases in existing coessentiality approaches and accessibility barriers for investigators without computational expertise. We created FIREWORKS, a method and interactive tool for the construction and statistical analysis of coessentiality networks centered around gene(s) provided by the user. FIREWORKS incorporates a novel bias reduction approach to reduce false discoveries, enables restriction of coessentiality analyses to custom subsets of cell lines, and integrates multiomic and drug-gene interaction datasets to investigate and target contextual gene essentiality. We demonstrate the broad utility of FIREWORKS through case vignettes investigating gene function and specialization, indirect therapeutic targeting of "undruggable" proteins, and context-specific rewiring of genetic networks.
Keyphrases
- genome wide
- genome wide identification
- copy number
- dna methylation
- network analysis
- genome wide analysis
- healthcare
- endothelial cells
- body composition
- transcription factor
- physical activity
- rna seq
- gene expression
- stem cells
- emergency department
- cancer therapy
- cell therapy
- social media
- single cell
- mesenchymal stem cells
- peripheral blood
- bioinformatics analysis
- adverse drug