Biology subjects

Chaisson, M.

Publications and source records attributed to Chaisson, M..

2 recordsLinked to original sources

bioRxiv2021

TT-Mars: Structural Variants Assessment Based on Haplotype-resolved Assemblies

Variant benchmarking is a critical component of method development and evaluating the accuracy of studies of genetic variation. Currently, the best approach to evaluate the accuracy of a callset is the comparison against a well curated gold standard. In repetitive regions of the genome it may be difficult to establish what is the truth for a call, for example when different alignment scoring metrics provide equally supported but different variant calls in on the same data. Here we provide an alternative approach, TT-Mars, that takes advantage of the recent production of high-quality haplotype-resolved genome assemblies by evaluating variant calls based on how well their call reflects the content of the assembly, rather than comparing calls themselves. We used TT-Mars to assess callsets from different SV discovery methods on multiple human genome samples and demonstrated that it is capable at accurately classifying true positive and false positive SVs. On the HG002 personal genome, TT-Mars recapitulates 96.0%-99.6% of the validations made using the Genome in a Bottle gold standard callset evaluated by truvari, and evaluates an additional 121-10,966 variants across different callsets. Furthermore, with a group of high-quality assemblies, TT-Mars can evaluate performance of SV calling algorithms as a distribution rather than a point estimate. We also compare TT-Mars against the long-read based validation tool, VaPoR, and when assembly-based variant calls produced by dipcall are used as a gold standard. Compared with VaPoR, TT-Mars analyzes more calls on a long read callset by assessing more short variant calls (< 100 bases), while requiring smaller input. Compared with validation using dipcall variants, TT-Mars analyzes 1,497-2,229 more calls on long read callsets and has favorable results when candidate calls are fragmented into multiple calls in alignments. TT-Mars is available at https://github.com/ChaissonLab/TT-Mars.git with accompanying assembly data and corresponding liftover files.

genomics↗

bioRxiv2020

lra: the Long Read Aligner for Sequences and Contigs

MotivationIt is computationally challenging to detect variation by aligning long reads from single-molecule sequencing (SMS) instruments, or megabase-scale contigs from SMS assemblies. One approach to efficiently align long sequences is sparse dynamic programming (SDP), where exact matches are found between the sequence and the genome, and optimal chains of matches are found representing a rough alignment. Sequence variation is more accurately modeled when alignments are scored with a gap penalty that is a convex function of the gap length. Because previous implementations of SDP used a linear-cost gap function that does not accurately model variation, and implementations of alignment that have a convex gap penalty are either inefficient or use heuristics, we developed a method, lra, that uses SDP with a convex-cost gap penalty. We use lra to align long-read sequences from PacBio and Oxford Nanopore (ONT) instruments as well as de novo assembly contigs. ResultsAcross all data types, the runtime of lra is between 52-168% of the state of the art aligner minimap2 when generating SAM alignment, and 9-15% of an alternative method, ngmlr. This alignment approach may be used to provide additional evidence of SV calls in PacBio datasets, and an increase in sensitivity and specificity on ONT data with current SV detection algorithms. The number of calls discovered using pbsv with lra alignments are within 98.3-98.6% of calls made from minimap2 alignments on the same data, and give a nominal 0.2-0.4% increase in F1 score by Truvari analysis. On ONT data with SV called using Sniffles, the number of calls made from lra alignments is 3% greater than minimap2-based calls, and 30% greater than ngmlr based calls, with a 4.6-5.5% increase in Truvari F1 score. When applied to calling variation from de novo assembly contigs, there is a 5.8% increase in SV calls compared to minimap2+paftools, with a 4.3% increase in Truvari F1 score. Availability and implementationAvailable in bioconda: https://anaconda.org/bioconda/lra and github: https://github.com/ChaissonLab/LRA Contactmchaisso@usc.edu, jingwenr@usc.edu

bioinformatics↗