Sam2bam: High-Performance Framework for NGS Data Preprocessing Tools
  This paper introduces a high-throughput software tool framework called {\it sam2bam} that enables users to significantly speedup 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 the multiple processors, available memory, high-bandwidth of 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 the input data are provided by {\it 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 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 for whole-genome sequencing data from about 20 hours to about nine minutes on the same system using up to 711 GB of memory. 
Takeshi Ogasawara, Yinhe Cheng, Tzy-Hwa Kathy Tzeng
q-bio.GN