.png){kind=icon}
Return
araCNA: somatic copy number profiling using long-range sequence models
.png){kind=icon}
OA
AI
DOI：10.1093/nargab/lqaf124.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Somatic copy number alterations (CNAs) are hallmarks of cancer. Current algorithms that call CNAs from whole-genome sequenced (WGS) data have not exploited deep learning methods owing to computational scaling limitations. Here, we present a novel deep-learning approach, araCNA, trained only on simulated data that can accurately predict CNAs in real WGS cancer genomes. araCNA uses novel transformer alternatives (e.g. Mamba) to handle genomic-scale sequence lengths (similar to 1M) and learn long-range interactions. Results are extremely accurate on simulated data, and this zero-shot approach is on par with existing methods when applied to 50 WGS samples from the Cancer Genome Atlas. Notably, our approach requires only a tumour sample and not a matched normal sample, has fewer markers of overfitting, and performs inference in only a few minutes. araCNA demonstrates how domain knowledge can be used to simulate training sets that harness the power of modern machine learning in biological applications.
Keywords:
CANCER
POPULATIONS
INFERENCE
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
N
IF:
2.8
Papers:
264
Citations:
0
Organization
Cited Papers
Chromothripsis followed by circular recombination drives oncogene amplification in human cancer
PREAI
ShareSaveHATCHet2: clone- and haplotype-specific copy number inference from bulk tumor sequencing data.png)
GENOME BIOLOGYOAAI
GENOME BIOLOGYIF9.4
OAAIShareSaveShareSaveTITAN: inference of copy number architectures, in clonal cell populations from tumor whole-genome sequence dataGENOME RESEARCHOAAI
GENOME RESEARCHIF5.5
OAAIShareSaveDistinct Classes of Complex Structural Variation Uncovered across Thousands of Cancer Genome GraphsCellOAAI
CellIF0
OAAIShareSaveShareSaveShareSaveECOLE: Learning to call copy number variants on whole exome sequencing dataNATURE COMMUNICATIONSOAAI
NATURE COMMUNICATIONSIF15.7
OAAIShareSaveShareSave
View more