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

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About: Viral deep sequencing needs an adaptive approach: IRMA, the iterative refinement meta-assembler Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Viral deep sequencing needs an adaptive approach: IRMA, the iterative refinement meta-assembler
Creator	Barnes, John Bahl, Justin Neuhaus, Elizabeth Meno, Sarah Shepard, Samuel Wilson, Malania
Source	Medline; PMC
abstract	BACKGROUND: Deep sequencing makes it possible to observe low-frequency viral variants and sub-populations with greater accuracy and sensitivity than ever before. Existing platforms can be used to multiplex a large number of samples; however, analysis of the resulting data is complex and involves separating barcoded samples and various read manipulation processes ending in final assembly. Many assembly tools were designed with larger genomes and higher fidelity polymerases in mind and do not perform well with reads derived from highly variable viral genomes. Reference-based assemblers may leave gaps in viral assemblies while de novo assemblers may struggle to assemble unique genomes. RESULTS: The IRMA (iterative refinement meta-assembler) pipeline solves the problem of viral variation by the iterative optimization of read gathering and assembly. As with all reference-based assembly, reads are included in assembly when they match consensus template sets; however, IRMA provides for on-the-fly reference editing, correction, and optional elongation without the need for additional reference selection. This increases both read depth and breadth. IRMA also focuses on quality control, error correction, indel reporting, variant calling and variant phasing. In fact, IRMA’s ability to detect and phase minor variants is one of its most distinguishing features. We have built modules for influenza and ebolavirus. We demonstrate usage and provide calibration data from mixture experiments. Methods for variant calling, phasing, and error estimation/correction have been redesigned to meet the needs of viral genomic sequencing. CONCLUSION: IRMA provides a robust next-generation sequencing assembly solution that is adapted to the needs and characteristics of viral genomes. The software solves issues related to the genetic diversity of viruses while providing customized variant calling, phasing, and quality control. IRMA is freely available for non-commercial use on Linux and Mac OS X and has been parallelized for high-throughput computing. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-016-3030-6) contains supplementary material, which is available to authorized users.
has issue date	2016-09-05(xsd:dateTime)
bibo:doi	10.1186/s12864-016-3030-6
bibo:pmid	27595578
has license	cc-by
sha1sum (hex)	940de307debf3a9042f73ef7cd17fa7a2464d408
schema:url	https://doi.org/10.1186/s12864-016-3030-6
resource representing a document's title	Viral deep sequencing needs an adaptive approach: IRMA, the iterative refinement meta-assembler
has PubMed Central identifier	PMC5011931
has PubMed identifier	27595578
schema:publication	BMC Genomics
resource representing a document's body	covid:940de307debf3a9042f73ef7cd17fa7a2464d408#body_text
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