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elPrep: High-Performance Preparation of Sequence Alignment/Map Files for Variant Calling.

Charlotte Herzeel1, Pascal Costanza2, Dries Decap3

  • 1Imec, Leuven, Belgium; ExaScience Life Lab, Leuven, Belgium.

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|July 17, 2015
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Summary
This summary is machine-generated.

elPrep significantly accelerates variant calling preparation by processing sequence alignment/map files in a single pass. This high-performance tool reduces computational time from hours to minutes, lowering analysis costs for large sequencing studies.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Variant calling in sequencing pipelines requires extensive data preparation.
  • Existing tools like SAMtools and Picard involve multiple passes and repeated file I/O, leading to long execution times.
  • Efficient preparation of sequence alignment/map (BAM) files is crucial for timely and cost-effective genomic analysis.

Purpose of the Study:

  • To introduce elPrep, a novel high-performance tool for preparing sequence alignment/map files.
  • To demonstrate elPrep's ability to replace traditional tools like SAMtools and Picard for essential preparation steps.
  • To highlight elPrep's unique single-pass architecture for accelerated data processing.

Main Methods:

  • elPrep is designed as a multithreaded, in-memory application.
  • It merges multiple preparation steps (filtering, sorting, duplicate marking, etc.) into a single data pass.
  • The software architecture avoids repeated file input/output operations.

Main Results:

  • elPrep reduces preparation time for a five-step pipeline on whole-exome data (NA12878) from 1:40 hours to 15 minutes.
  • For whole-genome data (NA12878), the same pipeline runtime is decreased from 24 hours to under 5 hours.
  • These speedups are achieved using comparable server resources (e.g., 48 threads, 23GB RAM).

Conclusions:

  • elPrep offers a substantial speedup for sequence data preparation, significantly reducing analysis time and costs.
  • Its single-pass, in-memory, and merged-computation approach makes it highly efficient for large-scale sequencing projects.
  • elPrep provides identical results to SAMtools and Picard while drastically improving performance.