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Related Experiment Videos

Viral deep sequencing needs an adaptive approach: IRMA, the iterative refinement meta-assembler.

Samuel S Shepard1, Sarah Meno2, Justin Bahl3

  • 1Influenza Division, Centers for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA, 30329, USA. vfn4@cdc.gov.

BMC Genomics
|September 7, 2016
PubMed
Summary

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The iterative refinement meta-assembler (IRMA) pipeline enhances viral genome assembly from deep sequencing data. It accurately detects and phases minor viral variants, improving analysis of genetic diversity.

Area of Science:

  • Genomics
  • Bioinformatics
  • Virology

Background:

  • Deep sequencing enables sensitive detection of low-frequency viral variants.
  • Analyzing multiplexed viral sequencing data is complex, with existing tools struggling with high viral genome variability.
  • Reference-based and de novo assemblers have limitations for viral genome assembly.

Purpose of the Study:

  • To develop a robust pipeline for viral genome assembly that addresses challenges posed by high genetic diversity.
  • To improve the accuracy and sensitivity of detecting and analyzing low-frequency viral variants.
  • To provide a customized solution for variant calling, phasing, and quality control in viral genomic sequencing.

Main Methods:

  • Implemented the iterative refinement meta-assembler (IRMA) pipeline for optimized read gathering and assembly.
Keywords:
Deep sequencingEbolaHigh throughputInfluenzaNGSPublic healthSurveillance

Related Experiment Videos

  • Incorporated on-the-fly reference editing, correction, and elongation to increase read depth and breadth.
  • Developed specialized modules for influenza and ebolavirus, focusing on quality control, error correction, indel reporting, variant calling, and phasing.

    • Main Results:

    • IRMA effectively handles viral variation through iterative optimization of assembly processes.
    • The pipeline enhances read depth and breadth by allowing on-the-fly reference manipulation.
    • IRMA demonstrates significant capability in detecting and phasing minor viral variants, crucial for understanding viral evolution.

    Conclusions:

    • IRMA offers a robust solution for next-generation sequencing assembly tailored to viral genomes.
    • The software effectively addresses challenges of viral genetic diversity, offering customized variant analysis and quality control.
    • IRMA is freely available, parallelized for high-throughput computing, and supports Linux and Mac OS X.