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A modified RNA-Seq approach for whole genome sequencing of RNA viruses from faecal and blood samples.

ABSTRACT: To date, very large scale sequencing of many clinically important RNA viruses has been complicated by their high population molecular variation, which creates challenges for polymerase chain reaction and sequencing primer design. Many RNA viruses are also difficult or currently not possible to culture, severely limiting the amount and purity of available starting material. Here, we describe a simple, novel, high-throughput approach to Norovirus and Hepatitis C virus whole genome sequence determination based on RNA shotgun sequencing (also known as RNA-Seq). We demonstrate the effectiveness of this method by sequencing three Norovirus samples from faeces and two Hepatitis C virus samples from blood, on an Illumina MiSeq benchtop sequencer. More than 97% of reference genomes were recovered. Compared with Sanger sequencing, our method had no nucleotide differences in 14,019 nucleotides (nt) for Noroviruses (from a total of 2 Norovirus genomes obtained with Sanger sequencing), and 8 variants in 9,542 nt for Hepatitis C virus (1 variant per 1,193 nt). The three Norovirus samples had 2, 3, and 2 distinct positions called as heterozygous, while the two Hepatitis C virus samples had 117 and 131 positions called as heterozygous. To confirm that our sample and library preparation could be scaled to true high-throughput, we prepared and sequenced an additional 77 Norovirus samples in a single batch on an Illumina HiSeq 2000 sequencer, recovering >90% of the reference genome in all but one sample. No discrepancies were observed across 118,757 nt compared between Sanger and our custom RNA-Seq method in 16 samples. By generating viral genomic sequences that are not biased by primer-specific amplification or enrichment, this method offers the prospect of large-scale, affordable studies of RNA viruses which could be adapted to routine diagnostic laboratory workflows in the near future, with the potential to directly characterize within-host viral diversity.

SUBMITTER: Batty EM

PROVIDER: S-EPMC3677912 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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A modified RNA-Seq approach for whole genome sequencing of RNA viruses from faecal and blood samples.

Batty Elizabeth M EM Wong T H Nicholas TH Trebes Amy A Argoud Karène K Attar Moustafa M Buck David D Ip Camilla L C CL Golubchik Tanya T Cule Madeleine M Bowden Rory R Manganis Charis C Klenerman Paul P Barnes Eleanor E Walker A Sarah AS Wyllie David H DH Wilson Daniel J DJ Dingle Kate E KE Peto Tim E A TE Crook Derrick W DW Piazza Paolo P

PloS one 20130610 6

To date, very large scale sequencing of many clinically important RNA viruses has been complicated by their high population molecular variation, which creates challenges for polymerase chain reaction and sequencing primer design. Many RNA viruses are also difficult or currently not possible to culture, severely limiting the amount and purity of available starting material. Here, we describe a simple, novel, high-throughput approach to Norovirus and Hepatitis C virus whole genome sequence determi ...[more]

PMID: 23762474

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Project description:RNA viruses are the etiological agents of many infectious diseases. Since RNA viruses are error-prone during genome replication, rapid, accurate and economical whole RNA viral genome sequence determination is highly demanded. Next-generation sequencing (NGS) techniques perform whole viral genome sequencing due to their high-throughput sequencing capacity. However, the NGS techniques involve a significant burden for sample preparation. Since to generate complete viral genome coverage, genomic nucleic acid enrichment is required by reverse transcription PCR using virus-specific primers or by viral particle concentration. Furthermore, conventional NGS techniques cannot determine the 5' and 3' terminal sequences of the RNA viral genome. Therefore, the terminal sequences are determined one by one using rapid amplification of cDNA ends (RACE). However, since some RNA viruses have segmented genomes, the burden of the determination using RACE is proportional to the number of segments. To date, there is only one study attempting whole genome sequencing of multiple RNA viruses without using above mentioned methods, but the generated sequences' accuracy compared to the reference sequences was up to 97% and did not reach 100% due to the low read depth. Hence, we established novel methods, named PCR-NGS and RCA-NGS, that were optimized for an NGS machine, MinION. These methods do not require nucleic acid amplification with virus-specific PCR primers, physical viral particle enrichment, and RACE. These methods enable whole RNA viral genome sequencing by combining the following techniques: (1) removal of unwanted DNA and RNA other than the RNA viral genome by nuclease treatment; (2) the terminal of viral genome sequence determination by barcoded linkers ligation; (3) amplification of the viral genomic cDNA using ligated linker sequences-specific PCR or an isothermal DNA amplification technique, such as rolling circle amplification (RCA). The established method was evaluated using isolated RNA viruses with single-stranded, double-stranded, positive-stranded, negative-stranded, non-segmented or multi-segmented genomes. As a result, all the viral genome sequences could be determined with 100% accuracy, and these mean read depths were greater than 2,500×, at least using either of the methods. This method should allow for easy and economical determination of accurate RNA viral genomes.

Project description:MotivationDespite numerous RNA-seq samples available at large databases, most RNA-seq analysis tools are evaluated on a limited number of RNA-seq samples. This drives a need for methods to select a representative subset from all available RNA-seq samples to facilitate comprehensive, unbiased evaluation of bioinformatics tools. In sequence-based approaches for representative set selection (e.g. a k-mer counting approach that selects a subset based on k-mer similarities between RNA-seq samples), because of the large numbers of available RNA-seq samples and of k-mers/sequences in each sample, computing the full similarity matrix using k-mers/sequences for the entire set of RNA-seq samples in a large database (e.g. the SRA) has memory and runtime challenges; this makes direct representative set selection infeasible with limited computing resources.ResultsWe developed a novel computational method called 'hierarchical representative set selection' to handle this challenge. Hierarchical representative set selection is a divide-and-conquer-like algorithm that breaks representative set selection into sub-selections and hierarchically selects representative samples through multiple levels. We demonstrate that hierarchical representative set selection can achieve summarization quality close to that of direct representative set selection, while largely reducing runtime and memory requirements of computing the full similarity matrix (up to 8.4× runtime reduction and 5.35× memory reduction for 10 000 and 12 000 samples respectively that could be practically run with direct subset selection). We show that hierarchical representative set selection substantially outperforms random sampling on the entire SRA set of RNA-seq samples, making it a practical solution to representative set selection on large databases like the SRA.Availability and implementationThe code is available at https://github.com/Kingsford-Group/hierrepsetselection and https://github.com/Kingsford-Group/jellyfishsim.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

A modified RNA-Seq approach for whole genome sequencing of RNA viruses from faecal and blood samples.

Publications

A modified RNA-Seq approach for whole genome sequencing of RNA viruses from faecal and blood samples.

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