{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["11(11)"],"submitter":["Ogasawara T"],"pubmed_abstract":["This paper introduces a high-throughput software tool framework called sam2bam that enables users to significantly speed up pre-processing for next-generation sequencing data. The sam2bam is especially efficient on single-node multi-core large-memory systems. It can reduce the runtime of data pre-processing in marking duplicate reads on a single node system by 156-186x compared with de facto standard tools. The sam2bam consists of parallel software components that can fully utilize multiple processors, available memory, high-bandwidth storage, and hardware compression accelerators, if available. The sam2bam provides file format conversion between well-known genome file formats, from SAM to BAM, as a basic feature. Additional features such as analyzing, filtering, and converting input data are provided by using plug-in tools, e.g., duplicate marking, which can be attached to sam2bam at runtime. We demonstrated that sam2bam could significantly reduce the runtime of next generation sequencing (NGS) data pre-processing from about two hours to about one minute for a whole-exome data set on a 16-core single-node system using up to 130 GB of memory. The sam2bam could reduce the runtime of NGS data pre-processing from about 20 hours to about nine minutes for a whole-genome sequencing data set on the same system using up to 711 GB of memory."],"journal":["PloS one"],"pagination":["e0167100"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC5115855"],"repository":["biostudies-literature"],"pubmed_title":["Sam2bam: High-Performance Framework for NGS Data Preprocessing Tools."],"pmcid":["PMC5115855"],"pubmed_authors":["Ogasawara T","Cheng Y","Tzeng TK"],"additional_accession":[]},"is_claimable":false,"name":"Sam2bam: High-Performance Framework for NGS Data Preprocessing Tools.","description":"This paper introduces a high-throughput software tool framework called sam2bam that enables users to significantly speed up pre-processing for next-generation sequencing data. The sam2bam is especially efficient on single-node multi-core large-memory systems. It can reduce the runtime of data pre-processing in marking duplicate reads on a single node system by 156-186x compared with de facto standard tools. The sam2bam consists of parallel software components that can fully utilize multiple processors, available memory, high-bandwidth storage, and hardware compression accelerators, if available. The sam2bam provides file format conversion between well-known genome file formats, from SAM to BAM, as a basic feature. Additional features such as analyzing, filtering, and converting input data are provided by using plug-in tools, e.g., duplicate marking, which can be attached to sam2bam at runtime. We demonstrated that sam2bam could significantly reduce the runtime of next generation sequencing (NGS) data pre-processing from about two hours to about one minute for a whole-exome data set on a 16-core single-node system using up to 130 GB of memory. The sam2bam could reduce the runtime of NGS data pre-processing from about 20 hours to about nine minutes for a whole-genome sequencing data set on the same system using up to 711 GB of memory.","dates":{"release":"2016-01-01T00:00:00Z","publication":"2016","modification":"2025-04-25T19:22:19.797Z","creation":"2019-03-26T22:48:52Z"},"accession":"S-EPMC5115855","cross_references":{"pubmed":["27861637"],"doi":["10.1371/journal.pone.0167100"]}}