Dataset Information

Ve-SEQ: Robust, unbiased enrichment for streamlined detection and whole-genome sequencing of HCV and other highly diverse pathogens.

ABSTRACT: The routine availability of high-depth virus sequence data would allow the sensitive detection of resistance-associated variants that can jeopardize HIV or hepatitis C virus (HCV) treatment. We introduce ve-SEQ, a high-throughput method for sequence-specific enrichment and characterization of whole-virus genomes at up to 20% divergence from a reference sequence and 1,000-fold greater sensitivity than direct sequencing. The extreme genetic diversity of HCV led us to implement an algorithm for the efficient design of panels of oligonucleotide probes to capture any sequence among a defined set of targets without detectable bias. ve-SEQ enables efficient detection and sequencing of any HCV genome, including mixtures and intra-host variants, in a single experiment, with greater tolerance of sequence diversity than standard amplification methods and greater sensitivity than metagenomic sequencing, features that are directly applicable to other pathogens or arbitrary groups of target organisms, allowing the combination of sensitive detection with sequencing in many settings.

SUBMITTER: Bonsall D

PROVIDER: S-EPMC4821293 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

ve-SEQ: Robust, unbiased enrichment for streamlined detection and whole-genome sequencing of HCV and other highly diverse pathogens.

Bonsall David D Ansari M Azim MA Ip Camilla C Trebes Amy A Brown Anthony A Klenerman Paul P Buck David D Piazza Paolo P Barnes Eleanor E Bowden Rory R

F1000Research 20151013

The routine availability of high-depth virus sequence data would allow the sensitive detection of resistance-associated variants that can jeopardize HIV or hepatitis C virus (HCV) treatment. We introduce ve-SEQ, a high-throughput method for sequence-specific enrichment and characterization of whole-virus genomes at up to 20% divergence from a reference sequence and 1,000-fold greater sensitivity than direct sequencing. The extreme genetic diversity of HCV led us to implement an algorithm for the ...[more]

PMID: 27092241

Similar Datasets

Project description:BackgroundThe organization of eukaryotic DNA into chromatin has a strong influence on the accessibility and regulation of genetic information. The locations and occupancies of a principle component of chromatin, nucleosomes, are typically assayed through use of enzymatic digestion with micrococcal nuclease (MNase). MNase is an endo-exo nuclease that preferentially digests naked DNA and the DNA in linkers between nucleosomes, thus enriching for nucleosome-associated DNA. To determine nucleosome organization genome-wide, DNA remaining from MNase digestion is sequenced using high-throughput sequencing technologies (MNase-seq). Unfortunately, the results of MNase-seq can vary dramatically due to technical differences and this confounds comparisons between MNase-seq experiments, such as examining condition-dependent chromatin organizations.ResultsIn this study we use MNase digestion simulations to demonstrate how MNase-seq signals can vary for different nucleosome configuration when experiments are performed with different extents of MNase digestion. Signal variation in these simulations reveals an important DNA sampling bias that results from a neighborhood effect of MNase digestion techniques. The presence of this neighborhood effect ultimately confounds comparisons between different MNase-seq experiments. To address this issue we present a standardized chromatin preparation which controls for technical variance between MNase-based chromatin preparations and enables the collection of similarly sampled (matched) chromatin populations. Standardized preparation of chromatin includes a normalization step for DNA input into MNase digestions and close matching of the extent of digestion between each chromatin preparation using gel densitometry analysis. The protocol also includes directions for successful pairing with multiplex sequencing reactions.ConclusionsWe validated our method by comparing the experiment-to-experiment variation between biological replicates of chromatin preparations from S. cerevisiae. Results from our matched preparation consistently produced MNase-seq datasets that were more closely correlated than other unstandardized approaches. Additionally, we validated the ability of our approach at enabling accurate downstream comparisons of chromatin structures, by comparing the specificity of detecting Tup1-dependent chromatin remodeling events in comparisons between matched and un-matched wild-type and tup1? MNase-seq datasets. Our matched MNase-seq datasets demonstrated a significant reduction in non-specific (technical) differences between experiments and were able to maximize the detection of biologically-relevant (Tup1-dependent) changes in chromatin structure.

Dataset Information

Ve-SEQ: Robust, unbiased enrichment for streamlined detection and whole-genome sequencing of HCV and other highly diverse pathogens.

Publications

ve-SEQ: Robust, unbiased enrichment for streamlined detection and whole-genome sequencing of HCV and other highly diverse pathogens.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets