{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Liu YH"],"funding":["Joint Initiative for Metrology in Biology (JIMB; National Institute of Standards and Technology) and Research Grant Council Early Career Scheme","Vanderbilt University Development Funds"],"pagination":["vbab007"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9710574"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["1(1)"],"pubmed_abstract":["<h4>Motivation</h4>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.<h4>Results</h4>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.<h4>Availability and implementation</h4>Source code and documentation are available on https://github.com/maiziex/Aquila_stLFR.<h4>Supplementary information</h4>Supplementary data are available at <i>Bioinformatics Advances</i> online."],"journal":["Bioinformatics advances"],"pubmed_title":["Aquila_stLFR: diploid genome assembly based structural variant calling package for stLFR linked-reads."],"pmcid":["PMC9710574"],"funding_grant_id":["FF_300033","HKBU 22201419"],"pubmed_authors":["Sidow A","Fang X","Grubbs GL","Liu YH","Zhang L","Dill DL","Zhou X"],"additional_accession":[]},"is_claimable":false,"name":"Aquila_stLFR: diploid genome assembly based structural variant calling package for stLFR linked-reads.","description":"<h4>Motivation</h4>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.<h4>Results</h4>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.<h4>Availability and implementation</h4>Source code and documentation are available on https://github.com/maiziex/Aquila_stLFR.<h4>Supplementary information</h4>Supplementary data are available at <i>Bioinformatics Advances</i> online.","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021","modification":"2024-12-03T16:00:00.534Z","creation":"2024-12-03T16:00:00.534Z"},"accession":"S-EPMC9710574","cross_references":{"pubmed":["36700103"],"doi":["10.1093/bioadv/vbab007"]}}