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

Abstract

BackgroundDeep 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.ResultsThe 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.ConclusionIRMA 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 materialThe online version of this article (doi:10.1186/s12864-016-3030-6) contains supplementary material, which is available to authorized users.

Non-mumps Viral Parotitis During the 2014–2015 Influenza Season in the United States

“…Further, detection of mumps virus by RT-PCR decreases >2 days following onset of swelling independently of the vaccination status. In one study, the sensitivity of RT-PCR for mumps virus detection decreased from 87% among oral samples collected during day 1 of swelling and 78% among samples collected during day 2 to 41% among samples collected during day 3 [22]. To enhance detection and diagnostic accuracy among patients with NMP, public health laboratories should consider additional respiratory virus panel and herpes family viral testing if resources permit.…”

“…RT-PCR assays to detect influenza viral RNA were performed as previously described [21] or next-generation sequencing was conducted using a MiSeq platform and the Iterative Refinement Meta Assembler [22]. Study sequences were compared to viral reference sequences and sequences from other circulating viruses.…”

An influenza A virus (H7N9) anti-neuraminidase monoclonal antibody protects mice from morbidity without interfering with the development of protective immunity to subsequent homologous challenge

“…Genome assemblies were generated and minor variant frequency detection was performed using IRMA software as described (Shepard et al, 2016). Post-assembly thresholds of 100× average coverage depth were applied and full-length reading frames were verified.…”

Integrating Advanced Molecular Technologies into Public Health

“…This approach was particularly helpful in 2014, when new H3N2 influenza virus subtypes were found to have drifted; notable changes in the hemagglutinin structures of these viruses made them difficult to characterize antigenically. The CDC's Influenza Division performed deep sequencing of these viruses using a new NGS pipeline that employs multisegment reverse transcription-PCR and an iterative refinement metaassembler (IRMA) (15). Analysis of the sequence data confirmed the expansion of cocirculating variant H3N2 viruses in clades 3C.2a and 3C.3a; the data also allowed inference of antigenicity, improving the knowledge base for upcoming vaccine recommendations for the Southern Hemisphere.…”