SVDF: enhancing structural variation detect from long-read sequencing via automatic filtering strategies.

Structural variation (SV) is an important form of genomic variation that influences gene function and expression by altering the structure of the genome. Although long-read data have been proven to better characterize SVs, SVs detected from noisy long-read data still include a considerable portion of false-positive calls. To accurately detect SVs in long-read data, we present SVDF, a method that employs a learning-based noise filtering strategy and an SV signature-adaptive clustering algorithm, for effectively reducing the likelihood of false-positive events. Benchmarking results from multiple orthogonal experiments demonstrate that, across different sequencing platforms and depths, SVDF achieves higher calling accuracy for each sample compared to several existing general SV calling tools. We believe that, with its meticulous and sensitive SV detection capability, SVDF can bring new opportunities and advancements to cutting-edge genomic research.

Hu H ，Gao R ，Gao W ，Gao B ，Jiang Z ，Zhou M ，Wang G ，Jiang T ... -  《-》

Comparison of multiple algorithms to reliably detect structural variants in pears.

Liu Y ，Zhang M ，Sun J ，Chang W ，Sun M ，Zhang S ，Wu J ... -  《BMC GENOMICS》

A Comparison of Structural Variant Calling from Short-Read and Nanopore-Based Whole-Genome Sequencing Using Optical Genome Mapping as a Benchmark.

Pei Y ，Tanguy M ，Giess A ，Dixit A ，Wilson LC ，Gibbons RJ ，Twigg SRF ，Elgar G ，Wilkie AOM ... -  《Genes》

rMFilter: acceleration of long read-based structure variation calling by chimeric read filtering.

Long read sequencing technologies provide new opportunities to investigate genome structural variations (SVs) more accurately. However, the state-of-the-art SV calling pipelines are computational intensive and the applications of long reads are restricted. We propose a local region match-based filter (rMFilter) to efficiently nail down chimeric noisy long reads based on short token matches within local genomic regions. rMFilter is able to substantially accelerate long read-based SV calling pipelines without loss of effectiveness. It can be easily integrated into current long read-based pipelines to facilitate SV studies. The C ++ source code of rMFilter is available at https://github.com/hitbc/rMFilter . ydwang@hit.edu.cn. Supplementary data are available at Bioinformatics online.

Liu B ，Jiang T ，Yiu SM ，Li J ，Wang Y ... -  《-》

The impact of FASTQ and alignment read order on structural variant calling from long-read sequencing data.

Structural variant (SV) calling from DNA sequencing data has been challenging due to several factors, including the ambiguity of short-read alignments, multiple complex SVs in the same genomic region, and the lack of "truth" datasets for benchmarking. Additionally, caller choice, parameter settings, and alignment method are known to affect SV calling. However, the impact of FASTQ read order on SV calling has not been explored for long-read data. Here, we used PacBio DNA sequencing data from 15 Caenorhabditis elegans strains and four Arabidopsis thaliana ecotypes to evaluate the sensitivity of different SV callers on FASTQ read order. Comparisons of variant call format files generated from the original and permutated FASTQ files demonstrated that the order of input data affected the SVs predicted by each caller. In particular, pbsv was highly sensitive to the order of the input data, especially at the highest depths where over 70% of the SV calls generated from pairs of differently ordered FASTQ files were in disagreement. These demonstrate that read order sensitivity is a complex, multifactorial process, as the differences observed both within and between species varied considerably according to the specific combination of aligner, SV caller, and sequencing depth. In addition to the SV callers being sensitive to the input data order, the SAMtools alignment sorting algorithm was identified as a source of variability following read order randomization. The results of this study highlight the sensitivity of SV calling on the order of reads encoded in FASTQ files, which has not been recognized in long-read approaches. These findings have implications for the replication of SV studies and the development of consistent SV calling protocols. Our study suggests that researchers should pay attention to the input order sensitivity of read alignment sorting methods when analyzing long-read sequencing data for SV calling, as mitigating a source of variability could facilitate future replication work. These results also raise important questions surrounding the relationship between SV caller read order sensitivity and tool performance. Therefore, tool developers should also consider input order sensitivity as a potential source of variability during the development and benchmarking of new and improved methods for SV calling.

Lesack KJ ，Wasmuth JD 《PeerJ》