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Removing duplicate reads using graphics processing units.

Andrea Manconi1, Marco Moscatelli2, Giuliano Armano3

  • 1Institute for Biomedical Technologies, National Research Council, Via Fratelli Cervi, 93, Segrate (Mi), 20090, Italy. andrea.manconi@itb.cnr.it.

BMC Bioinformatics
|February 11, 2017
PubMed
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This summary is machine-generated.

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This study introduces GPU-DupRemoval, a faster method for removing duplicate DNA sequences from sequencing data. It improves accuracy and handles paired-end libraries, essential for reliable genomic analysis.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Polymerase chain reaction (PCR) during DNA library preparation generates duplicate sequences.
  • Duplicate reads can lead to misinterpretation of sequencing data.
  • Existing methods for removing identical and nearly-identical duplicates have limitations.

Purpose of the Study:

  • To develop an improved GPU-accelerated method for removing duplicate sequencing reads.
  • To overcome limitations of previous duplicate removal techniques, including prefix length constraints and paired-end read support.

Main Methods:

  • Developed GPU-DupRemoval, a novel implementation utilizing graphics processing units (GPUs) for parallel processing.
  • Implemented prefix-suffix comparison for identifying potential duplicates.
Keywords:
CUDADuplicate readsGraphics processing unitsNext generation sequencing

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  • Enhanced clustering algorithms to handle reads without prefix length constraints and analyze large duplicate clusters.

    • Main Results:

    • GPU-DupRemoval supports both single- and paired-end read libraries.
    • The method effectively clusters reads without maximum prefix length limitations.
    • Analyzes large clusters of potential duplicates, improving accuracy.

    Conclusions:

    • GPU-DupRemoval offers faster duplicate read removal compared to existing solutions.
    • The method demonstrates superior performance in the quantity of duplicates removed.
    • Leverages massive parallelization on GPUs for efficient genomic data processing.