Article ; Online: Aquila_stLFR: diploid genome assembly based structural variant calling package for stLFR linked-reads.
Bioinformatics advances
2021 Volume 1, Issue 1, Page(s) vbab007
Abstract: Motivation: Identifying structural variants (SVs) is critical in health and disease, however, detecting them remains a challenge. Several linked-read sequencing technologies, including 10X Genomics, TELL-Seq and single tube long fragment read (stLFR), ... ...
| Abstract | Motivation: Identifying structural variants (SVs) is critical in health and disease, however, detecting them remains a challenge. Several linked-read sequencing technologies, including 10X Genomics, TELL-Seq and single tube long fragment read (stLFR), have been recently developed as cost-effective approaches to reconstruct multi-megabase haplotypes (phase blocks) from sequence data of a single sample. These technologies provide an optimal sequencing platform to characterize SVs, though few computational algorithms can utilize them. Thus, we developed Aquila_stLFR, an approach that resolves SVs through haplotype-based assembly of stLFR linked-reads. Results: Aquila_stLFR first partitions long fragment reads into two haplotype-specific blocks with the assistance of the high-quality reference genome, by taking advantage of the potential phasing ability of the linked-read itself. Each haplotype is then assembled independently, to achieve a complete diploid assembly to finally reconstruct the genome-wide SVs. We benchmarked Aquila_stLFR on a well-studied sample, NA24385, and showed Aquila_stLFR can detect medium to large size deletions (50 bp-10 kb) with high sensitivity and medium-size insertions (50 bp-1 kb) with high specificity. Availability and implementation: Source code and documentation are available on https://github.com/maiziex/Aquila_stLFR. Supplementary information: Supplementary data are available at |
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| Language | English |
| Publishing date | 2021-06-16 |
| Publishing country | England |
| Document type | Journal Article |
| ISSN | 2635-0041 |
| ISSN (online) | 2635-0041 |
| DOI | 10.1093/bioadv/vbab007 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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