Accurate and efficient detection of gene fusions from RNA sequencing data
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
No Canadian affiliation. An affiliation-only frame — the usual design — would never have seen this work. It is one of the works that make the case for inverting the frame.
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
- Teacher spread
- 0.183 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
The identification of gene fusions from RNA sequencing data is a routine task in cancer research and precision oncology. However, despite the availability of many computational tools, fusion detection remains challenging. Existing methods suffer from poor prediction accuracy and are computationally demanding. We developed Arriba, a novel fusion detection algorithm with high sensitivity and short runtime. When applied to a large collection of published pancreatic cancer samples ( n = 803), Arriba identified a variety of driver fusions, many of which affected druggable proteins, including ALK, BRAF, FGFR2, NRG1, NTRK1, NTRK3, RET, and ROS1. The fusions were significantly associated with KRAS wild-type tumors and involved proteins stimulating the MAPK signaling pathway, suggesting that they substitute for activating mutations in KRAS . In addition, we confirmed the transforming potential of two novel fusions, RRBP1 - RAF1 and RASGRP1 - ATP1A1 , in cellular assays. These results show Arriba's utility in both basic cancer research and clinical translation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- Genome Research
- Topic
- Pancreatic and Hepatic Oncology Research
- Field
- Medicine
- Canadian institutions
- —
- Funders
- Heidelberger Zentrum für Personalisierte Onkologie Deutsches Krebsforschungszentrum In Der Helmholtz-GemeinschaftNationales Centrum für Tumorerkrankungen HeidelbergOntario Institute for Cancer ResearchGovernment of OntarioDeutsches Krebsforschungszentrum
- Keywords
- KRASBiologyFusion geneROS1Computational biologyDruggabilityGeneCancerIdentification (biology)Cancer researchBioinformaticsMutationGeneticsAdenocarcinoma
- Has abstract in OpenAlex
- yes