Biology subjects

Aldana, R.

Publications and source records attributed to Aldana, R..

4 recordsLinked to original sources

bioRxiv2022

DNAscope: High accuracy small variant calling using machine learning

We present DNAscope, an accurate and efficient germline small-variant caller. DNAscope combines the robust and well-established preprocessing and assembly mathematics of the GATKs HaplotypeCaller with a machine-learned genotyping model. Benchmarks of DNAscope and DNAseq (Sentieons GATK-matching germline variant calling pipeline) demonstrate that DNAscope achieves superior SNP and insertion/deletion accuracy with reduced computational cost.

bioinformatics↗

bioRxiv2020

Mis-annotated multi nucleotide variants in public cancer genomics datasets can lead to inaccurate mutation calls with significant implications

BackgroundNext generation sequencing is widely used in cancer to profile tumors and detect variants. Most somatic variant callers used in these pipelines identify variants at the lowest possible granularity - single nucleotide variants (SNVs). As a result, multiple adjacent SNVs are called individually instead of as a multi-nucleotide variant (MNV). The problem with this level of granularity is that the amino acid change from the individual SNVs within a codon could be different from the amino acid change based on the MNV that results from combining the SNVs. Most variant annotation tools do not account for this, leading to incorrect conclusions about the downstream effects of the variants. MethodHere, we used Variant Call Files (VCFs) from the TCGA Mutect2 caller, and developed a solution to merge SNVs to MNVs. Our custom script takes the phasing information from the SNV VCFs and based on a gene model, determines if SNVs are at the same codon and need to be merged into a MNV prior to variant annotation. ResultsWe analyzed 10,383 VCFs from TCGA and found 12,141 MNVs that were incorrectly annotated. Strikingly, the analysis of seven commonly mutated genes from 178 studies from cBioPortal revealed that MNVs were consistently missed in 20 of these studies, while they were correctly annotated in 15 more recent studies. The best and most common example of MNVs was found at the BRAF V600 locus, where several public datasets reported separate BRAF V600E and BRAF V600M variants, instead of a single merged V600K variant. ConclusionWhile some datasets merged MNVs correctly, many public datasets have not been corrected for this problem. As a best practice for variant calling, we recommend that MNVs be accounted for in NGS processing pipelines, thus improving analyses on the impact of somatic variants in cancer genomics.

genomics↗

bioRxiv2018

TNscope: Accurate Detection of Somatic Mutations with Haplotype-based Variant Candidate Detection and Machine Learning Filtering

Detection of somatic mutations in tumor samples is important in the clinic, where treatment decisions are increasingly based upon molecular diagnostics. However, accurate detection of these mutations is difficult, due in part to intra-tumor heterogeneity, contamination of the tumor sample with normal tissue and pervasive structural variation. Here, we describe Sentieon TNscope, a haplotype-based somatic variant caller with increased accuracy relative to existing methods. An early engineering version of TNscope was used in our submission to the most recent ICGC-DREAM Somatic Mutation calling challenge. In that challenge, TNscope is the leader in accuracy for SNVs, indels and SVs. To further improve variant calling accuracy, we combined the improvements in the variant caller with machine learning. We benchmarked TNscope using in-silico mixtures of well-characterized Genome in a Bottle (GIAB) samples. TNscope displays higher accuracy than the other benchmarked tools and the accuracy is substantially improved by the machine learning model.

bioinformatics↗

bioRxiv2017

The Sentieon Genomics Tools - A fast and accurate solution to variant calling from next-generation sequence data

In the past six years worldwide capacity for human genome sequencing has grown by more than five orders of magnitude, with costs falling by nearly two orders of magnitude over the same period [1], [2]. The rapid expansion in the production of next-generation sequence data and the use of these data in a wide range of new applications has created a need for improved computational tools for data processing. The Sentieon Genomics tools provide an optimized reimplementation of the most accurate pipelines for calling variants from next-generation sequence data, resulting in more than a 10-fold increase in processing speed while providing identical results to best practices pipelines. Here we demonstrate the consistency and improved performance of Sentieons tools relative to BWA, GATK, MuTect, and MuTect2 through analysis of publicly available human exome, low-coverage genome, and high-depth genome sequence data.

bioinformatics↗