RT Journal Article SR Electronic T1 High-throughput pipeline for de-novo assembly and drug resistance mutations identification from Next-Generation Sequencing viral data of residual diagnostic samples JF bioRxiv FD Cold Spring Harbor Laboratory SP 035154 DO 10.1101/035154 A1 Tiziano Gallo Cassarino A1 Daniel Frampton A1 Robert Sugar A1 Elijah Charles A1 Zisis Kozlakidis A1 Paul Kellam YR 2015 UL http://biorxiv.org/content/early/2015/12/24/035154.abstract AB Motivation Viral infections represent one of the most serious challenges to public health; the high genomic variation expressed by the viral population within an individual patient can lead the drug therapy to failure. Next-generation sequencing enables to identify viral quasi-species and to quantify the minority variants present in clinical samples; therefore it can be of direct benefit in terms of devising optimal treatment strategies for individual patients.Method Within the ICONIC (InfeCtion respONse through vIrus genomiCs) project, we developed an automated, portable and customisable high-throughput analysis pipeline to generate denovo viral whole genomes and quantify minority variants from residual diagnostic samples. Our pipeline analyses Illumina short paired reads and can assemble either single or multiple segments viral genomes.Results The ICONIC pipeline was benchmarked on a dedicated High Performance Computing cluster using a pilot set of paired reads from 420 HIV clinical samples not filtered by viral load or amplification quality. The median genome length was 82% respect to the HIV-1 reference sequence (HXB2). The analysis lasted less than 10 hours, each sample took around 4 hours and required 5 GB of memory on average. The pipeline can be ported on a cluster or a single server through either an installation file or a Dockerfile.Conclusions It is technically possible for clinicians to obtain subtype information and a list of relevant Drug Resistance Mutations within three days of sample collection, therefore our pipeline can be used as a decision support tool towards more effective personalised treatments.Availability The pipeline and its documentation can be found on the GitHub repository https://github.com/ICONIC-UCL/pipeline.