Machine learning-based detection of insertions and deletions in the human genome
Curnin, C.; Goldfeder, R. L.; Marwaha, S.; Bonner, D.; Waggott, D.; Undiagnosed Diseases Network, ; Wheeler, M. T.; Ashley, E. A.
Show abstract
Insertions and deletions (indels) make a critical contribution to human genetic variation. While indel calling has improved significantly, it lags dramatically in performance relative to single-nucleotide variant calling, something of particular concern for clinical genomics where larger scale disruption of the open reading frame can commonly cause disease. Here, we present a machine learning-based approach to the detection of indel breakpoints called Scotch. This novel approach improves sensitivity to larger variants dramatically by leveraging sequencing metrics and signatures of poor read alignment. We also introduce a meta-analytic indel caller, called Metal, that performs a "smart intersection" of Scotch and currently available tools to be maximally sensitive to large variants. We use new benchmark datasets and Sanger sequencing to compare Scotch and Metal to current gold standard indel callers, achieving unprecedented levels of precision and recall. We demonstrate the impact of these improvements by applying this tool to a cohort of patients with undiagnosed disease, generating plausible novel candidates in 21 out of 26 undiagnosed cases. We highlight the diagnosis of one patient with a 498-bp deletion in HNRNPA1 missed by traditional indel-detection tools.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Lancet2: Improved and accelerated somatic variant calling with joint multi-sample local assembly graph 95%
- DoBSeqWF: A framework for sensitive detection of individual genetic variation in pooled sequencing data 95%
- Needlestack: an ultra-sensitive variant caller for multi-sample next generation sequencing data 95%
Similar papers in this journal
- An Algorithm for Sequence Location Approximation using Nuclear Families (ASLAN) Validates Regions of the Telomere-to-Telomere Assembly and Identifies New Hotspots for Genetic Diversity 96%
- A Complete Pedigree-Based Graph Workflow for Rare Candidate Variant Analysis 96%
- Whole-genome long-read sequencing downsampling and its effect on variant calling precision and recall 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.