Accurate assembly of minority viral haplotypes from next-generation sequencing through efficient noise reduction.
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ABSTRACT:
SUBMITTER: Knyazev S
PROVIDER: S-EPMC8464054 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:RNA viruses within infected individuals exist as a population of evolutionary-related variants. Owing to evolutionary change affecting the constitution of this population, the frequency and/or occurrence of individual viral variants can show marked or subtle fluctuations. Since the development of massively parallel sequencing platforms, such viral populations can now be investigated to unprecedented resolution. A critical problem with such analyses is the presence of sequencing-related errors that obscure the identification of true biological variants present at low frequency. Here, we report the development and assessment of the Quality Assessment of Short Read (QUASR) Pipeline (http://sourceforge.net/projects/quasr) specific for virus genome short read analysis that minimizes sequencing errors from multiple deep-sequencing platforms, and enables post-mapping analysis of the minority variants within the viral population. QUASR significantly reduces the error-related noise in deep-sequencing datasets, resulting in increased mapping accuracy and reduction of erroneous mutations. Using QUASR, we have determined influenza virus genome dynamics in sequential samples from an in vitro evolution of 2009 pandemic H1N1 (A/H1N1/09) influenza from samples sequenced on both the Roche 454 GSFLX and Illumina GAIIx platforms. Importantly, concordance between the 454 and Illumina sequencing allowed unambiguous minority-variant detection and accurate determination of virus population turnover in vitro.
| S-EPMC3678329 | biostudies-literature
Project description:BackgroundNext-generation sequencing allows the analysis of an unprecedented number of viral sequence variants from infected patients, presenting a novel opportunity for understanding virus evolution, drug resistance and immune escape. However, sequencing in bulk is error prone. Thus, the generated data require error identification and correction. Most error-correction methods to date are not optimized for amplicon analysis and assume that the error rate is randomly distributed. Recent quality assessment of amplicon sequences obtained using 454-sequencing showed that the error rate is strongly linked to the presence and size of homopolymers, position in the sequence and length of the amplicon. All these parameters are strongly sequence specific and should be incorporated into the calibration of error-correction algorithms designed for amplicon sequencing.ResultsIn this paper, we present two new efficient error correction algorithms optimized for viral amplicons: (i) k-mer-based error correction (KEC) and (ii) empirical frequency threshold (ET). Both were compared to a previously published clustering algorithm (SHORAH), in order to evaluate their relative performance on 24 experimental datasets obtained by 454-sequencing of amplicons with known sequences. All three algorithms show similar accuracy in finding true haplotypes. However, KEC and ET were significantly more efficient than SHORAH in removing false haplotypes and estimating the frequency of true ones.ConclusionsBoth algorithms, KEC and ET, are highly suitable for rapid recovery of error-free haplotypes obtained by 454-sequencing of amplicons from heterogeneous viruses.The implementations of the algorithms and data sets used for their testing are available at: http://alan.cs.gsu.edu/NGS/?q=content/pyrosequencing-error-correction-algorithm.
| S-EPMC3382444 | biostudies-literature
Project description:MotivationRNA viruses tend to mutate constantly. While many of the variants are neutral, some can lead to higher transmissibility or virulence. Accurate assembly of complete viral genomes enables the identification of underlying variants, which are essential for studying virus evolution and elucidating the relationship between genotypes and virus properties. Recently, third-generation sequencing platforms such as Nanopore sequencers have been used for real-time virus sequencing for Ebola, Zika, coronavirus disease 2019, etc. However, their high per-base error rate prevents the accurate reconstruction of the viral genome.ResultsIn this work, we introduce a new tool, AccuVIR, for viral genome assembly and polishing using error-prone long reads. It can better distinguish sequencing errors from true variants based on the key observation that sequencing errors can disrupt the gene structures of viruses, which usually have a high density of coding regions. Our experimental results on both simulated and real third-generation sequencing data demonstrated its superior performance on generating more accurate viral genomes than generic assembly or polish tools.Availability and implementationThe source code and the documentation of AccuVIR are available at https://github.com/rainyrubyzhou/AccuVIR.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC9825286 | biostudies-literature
Project description:BackgroundHaplotypes extracted from human DNA can be used for gene mapping and other analysis of genetic patterns within and across populations. A fundamental problem is, however, that current practical laboratory methods do not give haplotype information. Estimation of phased haplotypes of unrelated individuals given their unphased genotypes is known as the haplotype reconstruction or phasing problem.ResultsWe define three novel statistical models and give an efficient algorithm for haplotype reconstruction, jointly called HaploRec. HaploRec is based on exploiting local regularities conserved in haplotypes: it reconstructs haplotypes so that they have maximal local coherence. This approach--not assuming statistical dependence for remotely located markers--has two useful properties: it is well-suited for sparse marker maps, such as those used in gene mapping, and it can actually take advantage of long maps.ConclusionOur experimental results with simulated and real data show that HaploRec is a powerful method for the large scale haplotyping needed in association studies. With sample sizes large enough for gene mapping it appeared to be the best compared to all other tested methods (Phase, fastPhase, PL-EM, Snphap, Gerbil; simulated data), with small samples it was competitive with the best available methods (real data). HaploRec is several orders of magnitude faster than Phase and comparable to the other methods; the running times are roughly linear in the number of subjects and the number of markers. HaploRec is publicly available at http://www.cs.helsinki.fi/group/genetics/haplotyping.html.
| S-EPMC1766938 | biostudies-literature
Project description:As new SARS-CoV-2 variants emerge, there is an urgent need to increase the efficiency and availability of viral genome sequencing, notably to detect the lineage in samples with a low viral load. SARS-CoV-2 genome next-generation sequencing (NGS) was performed retrospectively in a single center on 175 positive samples from individuals. An automated workflow used the Ion AmpliSeq SARS-CoV-2 Insight Research Assay on the Genexus Sequencer. All samples were collected in the metropolitan area of the city of Nice (France) over a period of 32 weeks (from 19 July 2021 to 11 February 2022). In total, 76% of cases were identified with a low viral load (Ct ≥ 32, and ≤200 copies/µL). The NGS analysis was successful in 91% of cases, among which 57% of cases harbored the Delta variant, and 34% the Omicron BA.1.1 variant. Only 9% of cases had unreadable sequences. There was no significant difference in the viral load in patients infected with the Omicron variant compared to the Delta variant (Ct values, p = 0.0507; copy number, p = 0.252). We show that the NGS analysis of the SARS-CoV-2 genome provides reliable detection of the Delta and Omicron SARS-CoV-2 variants in low viral load samples.
| S-EPMC9964843 | biostudies-literature
Project description:Gene assembly, which recovers gene segments from short reads, is an important step in functional analysis of next-generation sequencing data. Lacking quality reference genomes, de novo assembly is commonly used for RNA-Seq data of non-model organisms and metagenomic data. However, heterogeneous sequence coverage caused by heterogeneous expression or species abundance, similarity between isoforms or homologous genes, and large data size all pose challenges to de novo assembly. As a result, existing assembly tools tend to output fragmented contigs or chimeric contigs, or have high memory footprint. In this work, we introduce a targeted gene assembly program SAT-Assembler, which aims to recover gene families of particular interest to biologists. It addresses the above challenges by conducting family-specific homology search, homology-guided overlap graph construction, and careful graph traversal. It can be applied to both RNA-Seq and metagenomic data. Our experimental results on an Arabidopsis RNA-Seq data set and two metagenomic data sets show that SAT-Assembler has smaller memory usage, comparable or better gene coverage, and lower chimera rate for assembling a set of genes from one or multiple pathways compared with other assembly tools. Moreover, the family-specific design and rapid homology search allow SAT-Assembler to be naturally compatible with parallel computing platforms. The source code of SAT-Assembler is available at https://sourceforge.net/projects/sat-assembler/. The data sets and experimental settings can be found in supplementary material.
| S-EPMC4133164 | biostudies-literature
Project description:UnlabelledWe present Quip, a lossless compression algorithm for next-generation sequencing data in the FASTQ and SAM/BAM formats. In addition to implementing reference-based compression, we have developed, to our knowledge, the first assembly-based compressor, using a novel de novo assembly algorithm. A probabilistic data structure is used to dramatically reduce the memory required by traditional de Bruijn graph assemblers, allowing millions of reads to be assembled very efficiently. Read sequences are then stored as positions within the assembled contigs. This is combined with statistical compression of read identifiers, quality scores, alignment information and sequences, effectively collapsing very large data sets to <15% of their original size with no loss of information.AvailabilityQuip is freely available under the 3-clause BSD license from http://cs.washington.edu/homes/dcjones/quip.
| S-EPMC3526293 | biostudies-literature
Project description:During tumor initiation and progression, cancer cells acquire a selective advantage, allowing them to outcompete their normal counterparts. Identification of the genetic changes that underlie these tumor acquired traits can provide deeper insights into the biology of tumorigenesis. Regions of copy number alterations and germline DNA variants are some of the elements subject to selection during tumor evolution. Integrated examination of inherited variation and somatic alterations holds the potential to reveal specific nucleotide alleles that a tumor "prefers" to have amplified. Next-generation sequencing of tumor and matched normal tissues provides a high-resolution platform to identify and analyze such somatic amplicons. Within an amplicon, examination of informative (e.g., heterozygous) sites deviating from a 1:1 ratio may suggest selection of that allele. A naive approach examines the reads for each heterozygous site in isolation; however, this ignores available valuable linkage information across sites. We, therefore, present a novel hidden Markov model-based method-Haplotype Amplification in Tumor Sequences (HATS)-that analyzes tumor and normal sequence data, along with training data for phasing purposes, to infer amplified alleles and haplotypes in regions of copy number gain. Our method is designed to handle rare variants and biases in read data. We assess the performance of HATS using simulated amplified regions generated from varying copy number and coverage levels, followed by amplicons in real data. We demonstrate that HATS infers the amplified alleles more accurately than does the naive approach, especially at low to intermediate coverage levels and in cases (including high coverage) possessing stromal contamination or allelic bias.
| S-EPMC3266043 | biostudies-literature
Project description:The homology, recombination, variation, and repetitive elements in the natural killer-cell immunoglobulin-like receptor (KIR) region has made full haplotype DNA interpretation impossible in a high-throughput workflow. Here, we present a new approach using long-read sequencing to efficiently capture, sequence, and assemble diploid human KIR haplotypes. Probes were designed to capture KIR fragments efficiently by leveraging the repeating homology of the region. IDT xGen® Lockdown probes were used to capture 2-8 kb of sheared DNA fragments followed by sequencing on a PacBio Sequel. The sequences were error corrected, binned, and then assembled using the Canu assembler. The location of genes and their exon/intron boundaries are included in the workflow. The assembly and annotation was evaluated on 16 individuals (8 African American and 8 Europeans) from whom ground truth was known via long-range sequencing with fosmid library preparation. Using only 18 capture probes, the results show that the assemblies cover 97% of the GenBank reference, are 99.97% concordant, and it takes only 1.8 haplotigs to cover 75% of the reference. We also report the first assembly of diploid KIR haplotypes from long-read WGS. Our targeted hybridization probe capture and sequencing approach is the first of its kind to fully sequence and phase all diploid human KIR haplotypes, and it is efficient enough for population-scale studies and clinical use. The open and free software is available at https://github.com/droeatumn/kass and supported by a environment at https://hub.docker.com/repository/docker/droeatumn/kass.
| S-EPMC7581912 | biostudies-literature
Project description:The use of next generation sequencing (NGS) to identify novel viral sequences from eukaryotic tissue samples is challenging. Issues can include the low proportion and copy number of viral reads and the high number of contigs (post-assembly), making subsequent viral analysis difficult. Comparison of assembly algorithms with pre-assembly host-mapping subtraction using a short-read mapping tool, a k-mer frequency based filter and a low complexity filter, has been validated for viral discovery with Illumina data derived from naturally infected liver tissue and simulated data. Assembled contig numbers were significantly reduced (up to 99.97%) by the application of these pre-assembly filtering methods. This approach provides a validated method for maximizing viral contig size as well as reducing the total number of assembled contigs that require down-stream analysis as putative viral nucleic acids.
| S-EPMC4476701 | biostudies-literature