ArticleBioinformatics advances2026
Transcriptomic buffering detection: a computational approach to identifying essential gene dependencies in pancreatic cancer.
Article in Bioinformatics advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
6 authors.
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Abstract
Motivation: Loss-of-function (LOF) mutations in essential genes (Goners) should be lethal, yet some tumours survive through compensatory buffering mechanisms. Existing computational methods for identifying synthetic lethal (SL) interactions focus on known tumour suppressor losses, missing essential gene dependencies. We developed a computational pipeline to identify SL buffering mechanisms by correlating Goner LOF burden with transcriptomic compensation. Results: Applying our pipeline to pancreatic adenocarcinoma (PAAD), we integrated CRISPR essentiality screens (DepMap), mutation data, and RNA expression profiles from 165 TCGA-PAAD cases. This PAAD analysis supports the feasibility of the pipeline but does not nominate a transcriptomic buffer; applicability to other cancer types remains to be tested. Availability and implementation: Analysis code and documentation are available at https://github.com/d3dd/goblet. Implemented in Python 3.9+ using pandas, scipy, and gseapy. All data are publicly available from TCGA and DepMap.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.