BOL: Related items

DAVI: Deep learning-based tool for alignment and single nucleotide variant identification

Jit — Tue, 16 Mar 2021 05:41:33 -0500

DAVI consists of models for both global and local alignment and for variant calling. We have evaluated the performance of DAVI against existing state-of-the-art tool sets and found that its accuracy and performance is comparable to existing tools used for bench-marking. We further demonstrate that while existing tools are based on data generated from a specific sequencing technology, the models proposed in DAVI are generic and can be used across different NGS technologies as well as across different species

https://iopscience.iop.org/article/10.1088/2632-2153/ab7e19/pdf

Address of the bookmark: https://github.com/gguptaiitd/NEAT

VG: variation graph data structures, interchange formats, alignment, genotyping, and variant calling methods

Jit — Tue, 28 Jan 2020 03:53:24 -0600

Variation graphs provide a succinct encoding of the sequences of many genomes. A variation graph (in particular as implemented in vg) is composed of:

nodes, which are labeled by sequences and ids
edges, which connect two nodes via either of their respective ends
paths, describe genomes, sequence alignments, and annotations (such as gene models and transcripts) as walks through nodes connected by edges

Address of the bookmark: https://github.com/vgteam/vg

Webinar on Fast and Accurate DNA Variant Calling on 26 Apr 2017

Yeshodari — Fri, 21 Apr 2017 06:14:37 -0500

Continuing our DNA-Seq webinar series, we'll present Strand NGS v3.0 best-practices: a workflow that identifies highly accurate variants from raw reads. Our best practices workflow is twice as fast as its GATK counterpart, and results in precision/recall rates of up to 99%/98% on whole exome and whole genome samples. We'll also speak briefly about some of the other features in v3.0 including one-shot pipelines, TSS plots, RNA-Seq performance improvements, and, for the first time, HGVS notations for SNP effect analysis.