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DNAp: A Pipeline for DNA-seq Data Analysis.

Jason L Causey1, Cody Ashby2, Karl Walker3

  • 1Department of Computer Science, Arkansas State University, Jonesboro, Arkansas, 72467, United States of America.

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|May 3, 2018
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Summary
This summary is machine-generated.

DNAp is an open-source pipeline for analyzing whole exome and whole genome sequencing data to detect genetic mutations. This containerized tool ensures reproducible and comparable results for disease research.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Next-generation sequencing (NGS) technologies accelerate genetic disease research but present significant data analysis challenges.
  • Efficient and effective analysis of whole exome sequencing (WES) and whole genome sequencing (WGS) data is crucial for mutation detection.

Purpose of the Study:

  • To develop and introduce DNAp, a user-friendly, containerized bioinformatics pipeline for analyzing WES and WGS data.
  • To provide an open-source solution for mutation detection in disease samples, enhancing accessibility and customization.

Main Methods:

  • Development of DNAp as a fully automated process within a Docker container, ensuring cross-platform compatibility.
  • Implementation of user intervention points for easy customization, including reference file and software version updates.
  • Testing the pipeline with human and mouse sequencing datasets to validate its performance.

Main Results:

  • DNAp successfully generated mutation detection results comparable to published data for tested human and mouse datasets.
  • The pipeline demonstrated reproducibility across heterogeneous hardware platforms, highlighting its robustness.
  • The containerized nature ensures ease of use and system independence.

Conclusions:

  • DNAp offers a robust, reproducible, and customizable solution for analyzing NGS data in genetic disease research.
  • The open-source release of DNAp facilitates wider adoption and contribution within the bioinformatics community.
  • This tool supports the critical need for efficient mutation detection in WES and WGS data analysis.