Whole Genome De Novo Variant Identification with FreeBayes and Neural Network Approaches
Richter, F.; Morton, S.; Qi, H.; Kitaygorodksy, A.; Wang, J.; Homsy, J.; DePalma, S.; Patel, N.; Gelb, B. D.; Seidman, J. G.; Seidman, C. E.; Shen, Y.
Show abstract
MotivationDe novo variant (DNV) calling typically relies on heuristic filters intrinsic to specific platforms and variant calling algorithms. FreeBayes and neural network approaches have overcome this limitation for variant calling, and we implemented a similar approach for DNV identification. ResultsWe developed a DNV calling framework that uses Genome Analysis Toolkit (GATK), FreeBayes and a neural network trained on Integrative Genomics Viewer pile-up plots (IGV-bot). We identified DNVs in 2,390 WGS trios and benchmarked results against heuristics based on GATK parameters. Results were validated in silico and with Sanger sequencing, with the latter showing true positive rates of 98.4% and 97.3% for SNVs and indels, respectively. Taken together we describe a scalable framework for DNV identification based on both FreeBayes and neural network methods. AvailabilitySource code and documentation are available at https://github.com/ShenLab/igv-classifier and https://github.com/frichter/dnv_pipeline under the MIT license. Contactys2411@cumc.columbia.edu
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Reducing Sanger Confirmation Testing through False Positive Prediction Algorithms 95%
- The Importance of Automation in Genetic Diagnosis: Lessons from Analyzing an Inherited Retinal Degeneration Cohort with the Mendelian Analysis Toolkit (MATK) 94%
- A gene pathogenicity tool 'GenePy' identifies missed biallelic diagnoses in the 100,000 Genomes Project 94%
Similar papers in this journal
- Evidence-based calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for clinical use of PP3/BP4 criteria 94%
- HiFi long-read genomes for difficult-to-detect clinically relevant variants 93%
- Advanced variant classification framework reduces the false positive rate of predicted loss of function (pLoF) variants in population sequencing data 93%
Similar papers in this journal
Similar papers in this journal
- Systematic benchmark of state-of-the-art variant calling pipelines identifies major factors affecting accuracy of coding sequence variant discovery 94%
- Complex structural variant visualization with SVTopo 94%
- Towards a better understanding of the low recall of insertion variants with short-read based variant callers 93%
Similar papers in this journal
- Phasing of de novo mutations using a scaled-up multiple amplicon long-read sequencing approach 95%
- GeneBreaker: Variant simulation to improve the diagnosis of Mendelian rare genetic diseases 94%
- STRipy: a graphical application for enhanced genotyping of pathogenic short tandem repeats in sequencing data 93%
"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.