@article {867, title = {Parliament2: Accurate structural variant calling at scale.}, journal = {Gigascience}, volume = {9}, year = {2020}, month = {2020 12 21}, abstract = {

BACKGROUND: Structural variants (SVs) are critical contributors to genetic diversity and genomic disease. To predict the phenotypic impact of SVs, there is a need for better estimates of both the occurrence and frequency of SVs, preferably from large, ethnically diverse cohorts. Thus, the current standard approach requires the use of short paired-end reads, which remain challenging to detect, especially at the scale of hundreds to thousands of samples.

FINDINGS: We present Parliament2, a consensus SV framework that leverages multiple best-in-class methods to identify high-quality SVs from short-read DNA sequence data at scale. Parliament2 incorporates pre-installed SV callers that are optimized for efficient execution in parallel to reduce the overall runtime and costs. We demonstrate the accuracy of Parliament2 when applied to data from NovaSeq and HiSeq X platforms with the Genome in a Bottle (GIAB) SV call set across all size classes. The reported quality score per SV is calibrated across different SV types and size classes. Parliament2 has the highest F1 score (74.27\%) measured across the independent gold standard from GIAB. We illustrate the compute performance by processing all 1000 Genomes samples (2,691 samples) in <1 day on GRCH38. Parliament2 improves the runtime performance of individual methods and is open source (https://github.com/slzarate/parliament2), and a Docker image, as well as a WDL implementation, is available.

CONCLUSION: Parliament2 provides both a highly accurate single-sample SV call set from short-read DNA sequence data and enables cost-efficient application over cloud or cluster environments, processing thousands of samples.

}, issn = {2047-217X}, doi = {10.1093/gigascience/giaa145}, author = {Zarate, Samantha and Carroll, Andrew and Mahmoud, Medhat and Krasheninina, Olga and Jun, Goo and Salerno, William J and Schatz, Michael C and Boerwinkle, Eric and Gibbs, Richard A and Sedlazeck, Fritz J} } @article {874, title = {PhaseME: Automatic rapid assessment of phasing quality and phasing improvement.}, journal = {Gigascience}, volume = {9}, year = {2020}, month = {2020 07 01}, abstract = {

BACKGROUND: The detection of which mutations are occurring on the same DNA molecule is essential to predict their consequences. This can be achieved by phasing the genomic variations. Nevertheless, state-of-the-art haplotype phasing is currently a black box in which the accuracy and quality of the reconstructed haplotypes are hard to assess.

FINDINGS: Here we present PhaseME, a versatile method to provide insights into and improvement of sample phasing results based on linkage data. We showcase the performance and the importance of PhaseME by comparing phasing information obtained from Pacific Biosciences including both continuous long reads and high-quality consensus reads, Oxford Nanopore Technologies, 10x Genomics, and Illumina sequencing technologies. We found that 10x Genomics and Oxford Nanopore phasing can be significantly improved while retaining a high N50 and completeness of phase blocks. PhaseME generates reports and summary plots to provide insights into phasing performance and correctness. We observed unique phasing issues for each of the sequencing technologies, highlighting the necessity of quality assessments. PhaseME is able to decrease the Hamming error rate significantly by 22.4\% on average across all 5 technologies. Additionally, a significant improvement is obtained in the reduction of long switch errors. Especially for high-quality consensus reads, the improvement is 54.6\% in return for only a 5\% decrease in phase block N50 length.

CONCLUSIONS: PhaseME is a universal method to assess the phasing quality and accuracy and improves the quality of phasing using linkage information. The package is freely available at https://github.com/smajidian/phaseme.

}, issn = {2047-217X}, doi = {10.1093/gigascience/giaa078}, author = {Majidian, Sina and Sedlazeck, Fritz J} }