QuRe: software for viral quasispecies reconstruction from next-generation sequencing data

Prosperi, Mattia; Salemi, Marco

doi:10.1093/bioinformatics/btr627

Cited by 136 publications

(143 citation statements)

References 21 publications

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“…None of the other tools generated even only one such perfectly matching segment, possibly because they require much higher coverage. This improvement in terms of accuracy may be due to the probabilistic model that treats error Global haplotype assembly comparison of HaploClique with the software packages ShoRAH [33], PredictHaplo [14], and QuRe [16]. We report the estimated variant frequencies and, in parenthesis, the maximal length of the reconstructed haplotypes relative to the genome length, for each of the five variants.…”

Section: Discussionmentioning

confidence: 99%

“…Fourth, we evaluate the quality of the local and global haplotypes that HaploClique predicts. Lastly, we compare HaploClique to stateof-the-art tools ShoRAH [33], PredictHaplo [14], and QuRe [16] in quasispecies reconstruction of a simulated five virus mix of wellknown HIV-1 lab-strains.…”

Section: Simulation Studiesmentioning

confidence: 99%

“…We performed haplotype reconstruction for the lab-mix using the tools ShoRAH [33], PredictHaplo [14], and QuRe [16], and compared the results to those of HaploClique (Table 1). Hapler v1.60 [19] did not accept NGS alignments with insertions, and hence was not applicable to this heterogeneous virus populations.…”

Section: Simulation Studiesmentioning

confidence: 99%

“…For quasispecies assembly, approaches from different domains have been developed: (i) probabilistic mixture models [14], (ii) hidden Markov models [15], (iii) sampling schemes [16], (iv) combinatorial approaches based on analyzing the read overlap graph [8,[17][18][19], (v) coloring of overlap and conflict graphs by constraint programming [20], and (vi) exploiting the ''identical by descent'' information [21] in the HapCompass framework [22], originally designed for diploid single nucleotide polymorphism data.…”

Section: Introductionmentioning

confidence: 99%

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Viral Quasispecies Assembly via Maximal Clique Enumeration

Töpfer

Marschall

Bull

et al. 2014

Lecture Notes in Computer Science

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Virus populations can display high genetic diversity within individual hosts. The intra-host collection of viral haplotypes, called viral quasispecies, is an important determinant of virulence, pathogenesis, and treatment outcome. We present HaploClique, a computational approach to reconstruct the structure of a viral quasispecies from next-generation sequencing data as obtained from bulk sequencing of mixed virus samples. We develop a statistical model for paired-end reads accounting for mutations, insertions, and deletions. Using an iterative maximal clique enumeration approach, read pairs are assembled into haplotypes of increasing length, eventually enabling global haplotype assembly. The performance of our quasispecies assembly method is assessed on simulated data for varying population characteristics and sequencing technology parameters. Owing to its paired-end handling, HaploClique compares favorably to state-of-the-art haplotype inference methods. It can reconstruct error-free full-length haplotypes from low coverage samples and detect large insertions and deletions at low frequencies. We applied HaploClique to sequencing data derived from a clinical hepatitis C virus population of an infected patient and discovered a novel deletion of length 3576167 bp that was validated by two independent long-read sequencing experiments. HaploClique is available at https://github.com/armintoepfer/haploclique. A summary of this

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Section: Discussionmentioning

confidence: 99%

Section: Simulation Studiesmentioning

confidence: 99%

Section: Simulation Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Viral Quasispecies Assembly via Maximal Clique Enumeration

Töpfer

Marschall

Bull

et al. 2014

Lecture Notes in Computer Science

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“…Others [85,86,87] use a reference genome to correct NGS data. Some assemblers [88] have an integrated error correction method, which cannot be used separately.…”

Section: Conditionsmentioning

confidence: 99%

Improved Error Correction of NGS Data

Alic¹

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SummaryThe work done for this doctorate thesis focuses on error correction of Next Generation Sequencing (NGS) data in the context of High Performance Computing (HPC).Due to the reduction in sequencing cost, the increasing output of the sequencers and the advancements in the biological and medical sciences, the amount of NGS data has increased tremendously. Humans alone are not able to keep pace with this explosion of information, therefore computers must assist them to ease the handle of the deluge of information generated by the sequencing machines. Since NGS is no longer just a research topic (used in clinical routine to detect cancer mutations, for instance), requirements in performance and accuracy are more stringent. For sequencing to be useful outside research, the analysis software must work accurately and fast. This is where HPC comes into play. NGS processing tools should leverage the full potential of multi-core and even distributed computing, as those platforms are extensively available. Moreover, as the performance of the individual core has hit a barrier, current computing tendencies focus on adding more cores and explicitly split the computation to take advantage of them.This thesis starts with a deep analysis of all these problems in a general and comprehensive way (to reach out to a very wide audience), in the form of an exhaustive and objective review of the NGS error correction field. We dedicate a chapter to this topic to introduce the reader gradually and gently into the world of sequencing. It presents real problems and applications of NGS that demonstrate the impact this technology has on science. The review results in the following conclusions: the need of understanding of the specificities of NGS data samples (given the high variety of technologies and features) and the need of flexible, efficient and accurate tools for error correction as a preliminary step of any NGS postprocessing.As a result of the explosion of NGS data, we introduce MuffinInfo. It is a piece of software capable of extracting information from the raw data produced by the sequencer to help the user understand the data. MuffinInfo uses HTML5, therefore it runs in almost any software and hardware environment. It supports custom statistics to mould itself to specific requirements. MuffinInfo can reload the results of a run which are stored in JSON format for easier integration with third party applications. Finally, our application uses threads to perform the calculations, to load the data from the disk and to handle the UI.In continuation to our research and as a result of the single core performance limitation, we leverage the power of multi-core computers to develop a new error correction tool. The error correction of the NGS data is normally the first step of any analysis targeting NGS. As we conclude from the review performed within the frame of this thesis, many projects in different real-life applications have opted for this step before further analysis. In this sense, we propose MuffinEC, a multi-technology (Illu...

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