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DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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Updated: Mar 11, 2026

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance
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Sam2bam: High-Performance Framework for NGS Data Preprocessing Tools.

Takeshi Ogasawara1, Yinhe Cheng2, Tzy-Hwa Kathy Tzeng3

  • 1IBM Research-Tokyo, Tokyo, Japan.

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|November 19, 2016
PubMed
Summary
This summary is machine-generated.

The sam2bam software tool significantly accelerates next-generation sequencing (NGS) data pre-processing, reducing runtimes by up to 186x. This high-throughput framework efficiently handles large datasets on single-node systems.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Next-generation sequencing (NGS) generates vast amounts of data, requiring efficient pre-processing.
  • Current pre-processing tools can be time-consuming, especially for large datasets like whole-genome sequencing.
  • Optimizing data handling is crucial for accelerating genomic research.

Purpose of the Study:

  • To introduce sam2bam, a high-throughput software tool framework for accelerating NGS data pre-processing.
  • To demonstrate the efficiency of sam2bam on single-node, multi-core, large-memory systems.
  • To showcase the speedup achieved in tasks like duplicate read marking.

Main Methods:

  • Developed sam2bam as a parallel software component framework.
  • Utilized multi-processor, memory, high-bandwidth storage, and compression accelerators.
  • Integrated plug-in tools for functionalities like duplicate marking, filtering, and format conversion (SAM to BAM).

Main Results:

  • Achieved 156-186x reduction in pre-processing runtime for duplicate marking compared to standard tools.
  • Reduced whole-exome data pre-processing time from ~2 hours to ~1 minute on a 16-core system.
  • Reduced whole-genome data pre-processing time from ~20 hours to ~9 minutes on the same system.

Conclusions:

  • sam2bam significantly enhances the speed of NGS data pre-processing.
  • The framework is highly efficient on optimized single-node systems.
  • sam2bam offers a scalable solution for handling large-scale genomic datasets.