Easy and accurate reconstruction of whole HIV genomes from short-read sequence data with shiver.

Chris Wymant,Daniela Bezemer,Oliver Laeyendecker,Marion Cornelissen,Matthew Hall,Paul Kellam,Kholoud Porter,Christophe Fraser,Katrien Fransen,Ben Berkhout,Jan Albert,Matti Ristola,Ard Van Sighem,Margreet Bakker,Oliver Ratmann,Pia Kivelä,M Kate Grabowski,Tanya Golubchik,Norbert Bannert,Annabelle Gourlay,Laurence Meyer,Huldrych F Günthard,François Blanquart,Peter Reiss,Jacques Fellay,Kirsi Liitsola,Swee Hoe Ong,Mariska Hillebregt,Astrid Gall,Roger D Kouyos ,Barbara Gunsenheimer‐Bartmeyer ,Nicholas J Croucher

doi:10.1093/ve/vey007

Abstract

Studying the evolution of viruses and their molecular epidemiology relies on accurate viral sequence data, so that small differences between similar viruses can be meaningfully interpreted. Despite its higher throughput and more detailed minority variant data, next-generation sequencing has yet to be widely adopted for HIV. The difficulty of accurately reconstructing the consensus sequence of a quasispecies from reads (short fragments of DNA) in the presence of large between- and within-host diversity, including frequent indels, may have presented a barrier. In particular, mapping (aligning) reads to a reference sequence leads to biased loss of information; this bias can distort epidemiological and evolutionary conclusions. De novo assembly avoids this bias by aligning the reads to themselves, producing a set of sequences called contigs. However contigs provide only a partial summary of the reads, misassembly may result in their having an incorrect structure, and no information is available at parts of the genome where contigs could not be assembled. To address these problems we developed the tool shiver to pre-process reads for quality and contamination, then map them to a reference tailored to the sample using corrected contigs supplemented with the user’s choice of existing reference sequences. Run with two commands per sample, it can easily be used for large heterogeneous data sets. We used shiver to reconstruct the consensus sequence and minority variant information from paired-end short-read whole-genome data produced with the Illumina platform, for sixty-five existing publicly available samples and fifty new samples. We show the systematic superiority of mapping to shiver’s constructed reference compared with mapping the same reads to the closest of 3,249 real references: median values of 13 bases called differently and more accurately, 0 bases called differently and less accurately, and 205 bases of missing sequence recovered. We also successfully applied shiver to whole-genome samples of Hepatitis C Virus and Respiratory Syncytial Virus. shiver is publicly available from https://github.com/ChrisHIV/shiver.

Highlights

The genetic sequences of pathogens are a rich data source for studying their epidemiology and evolution, and provide information for vaccine and therapeutic design
We used the same mapping parameters, mapped the same set of reads, and called the consensus of the mapped reads in the same way, i.e. we changed only the reference sequence used for mapping
We developed the tool shiver to preprocess and map reads from each sample to a custom reference, constructed using de novo assembled contigs supplemented by existing reference genomes

Summary

Introduction

The genetic sequences of pathogens are a rich data source for studying their epidemiology and evolution, and provide information for vaccine and therapeutic design. On the comprehensive Los Alamos National Laboratory HIV database (http://www.hiv.lanl.gov/ accessed 11 October 2017), of the 147,751 samples with platform information, 90.8% were generated by Sanger sequencing, 6.9% with the Roche 454 platform, 2.2% with Illumina platforms, and 0.02% with the IonTorrent platform. Breakdowns of these numbers by date and sequence length are in Supplementary Section S1. NGS has been hugely successful both for sequencing samples with no within-sample diversity, and at the opposite end of the spectrum, for metagenomic studies. Any apparent within-sample diversity is attributable to sequencing error; in the latter case, there is no presumption that different fragments of sequence in the same sample have the same origin, and so each fragment is checked against large databases to catalogue these diverse origins (Kunin et al 2008; Thomas, Gilbert, and Meyer, 2012)

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Results

Conclusion