scholarly journals Family reunion via error correction: An efficient analysis of duplex sequencing data

2018 ◽  
Author(s):  
Nicholas Stoler ◽  
Barbara Arbeithuber ◽  
Gundula Povysil ◽  
Monika Heinzl ◽  
Renato Salazar ◽  
...  
Keyword(s):  
Error Correction ◽  
Dynamic Range ◽  
Pcr Amplification ◽  
Cost Effective ◽  
Sequencing Data ◽  
Nucleotide Substitutions ◽  
Low Frequencies ◽  
Family Reunion ◽  
Sequencing Errors ◽  
Duplex Sequencing

AbstractDuplex sequencing is the most accurate approach for identification of sequence variants present at very low frequencies. Its power comes from pooling together multiple descendants of both strands of original DNA molecules, which allows distinguishing true nucleotide substitutions from PCR amplification and sequencing artifacts. This strategy comes at a cost—sequencing the same molecule multiple times increases dynamic range but significantly diminishes coverage, making whole genome duplex sequencing prohibitively expensive. Furthermore, every duplex experiment produces a substantial proportion of singleton reads that cannot be used in the analysis and are, technically, thrown away. In this paper we demonstrate that a significant fraction of these reads contains PCR or sequencing errors within duplex tags. Correction of such errors allows “reuniting” these reads with their respective families increasing the output of the method and making it more cost effective. Additionally, we combine error correction strategy with a number of algorithmic improvements in a new version of the duplex analysis software, Du Novo 2.0, readily available through Galaxy, Bioconda, and as the source code.

scholarly journals Family reunion via error correction: an efficient analysis of duplex sequencing data

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Nicholas Stoler ◽  
Barbara Arbeithuber ◽  
Gundula Povysil ◽  
Monika Heinzl ◽  
Renato Salazar ◽  
...  
Keyword(s):  
Error Correction ◽  
Sequencing Data ◽  
Family Reunion ◽  
Duplex Sequencing

scholarly journals Increased yields of duplex sequencing data by a series of quality control tools

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Gundula Povysil ◽  
Monika Heinzl ◽  
Renato Salazar ◽  
Nicholas Stoler ◽  
Anton Nekrutenko ◽  
...  
Keyword(s):  
Low Frequency ◽  
Variant Calling ◽  
Data Loss ◽  
Sequencing Data ◽  
Bioinformatics Pipeline ◽  
Consensus Sequences ◽  
Sequencing Errors ◽  
Data Output ◽  
Reverse Strand ◽  
Duplex Sequencing

Abstract Duplex sequencing is currently the most reliable method to identify ultra-low frequency DNA variants by grouping sequence reads derived from the same DNA molecule into families with information on the forward and reverse strand. However, only a small proportion of reads are assembled into duplex consensus sequences (DCS), and reads with potentially valuable information are discarded at different steps of the bioinformatics pipeline, especially reads without a family. We developed a bioinformatics toolset that analyses the tag and family composition with the purpose to understand data loss and implement modifications to maximize the data output for the variant calling. Specifically, our tools show that tags contain polymerase chain reaction and sequencing errors that contribute to data loss and lower DCS yields. Our tools also identified chimeras, which likely reflect barcode collisions. Finally, we also developed a tool that re-examines variant calls from raw reads and provides different summary data that categorizes the confidence level of a variant call by a tier-based system. With this tool, we can include reads without a family and check the reliability of the call, that increases substantially the sequencing depth for variant calling, a particular important advantage for low-input samples or low-coverage regions.

scholarly journals A hybrid correcting method considering heterozygous variations by a comprehensive probabilistic model

BMC Genomics ◽  
2020 ◽  
Vol 21 (S10) ◽  
Author(s):  
Jiaqi Liu ◽  
Jiayin Wang ◽  
Xiao Xiao ◽  
Xin Lai ◽  
Daocheng Dai ◽  
...  
Keyword(s):  
Error Correction ◽  
Correction Method ◽  
Reference Sequence ◽  
Third Generation ◽  
Next Generation ◽  
Sequencing Data ◽  
Sequencing Errors ◽  
The Third ◽  
Third Generation Sequencing ◽  
Generation Sequencing

Abstract Background The emergence of the third generation sequencing technology, featuring longer read lengths, has demonstrated great advancement compared to the next generation sequencing technology and greatly promoted the biological research. However, the third generation sequencing data has a high level of the sequencing error rates, which inevitably affects the downstream analysis. Although the issue of sequencing error has been improving these years, large amounts of data were produced at high sequencing errors, and huge waste will be caused if they are discarded. Thus, the error correction for the third generation sequencing data is especially important. The existing error correction methods have poor performances at heterozygous sites, which are ubiquitous in diploid and polyploidy organisms. Therefore, it is a lack of error correction algorithms for the heterozygous loci, especially at low coverages. Results In this article, we propose a error correction method, named QIHC. QIHC is a hybrid correction method, which needs both the next generation and third generation sequencing data. QIHC greatly enhances the sensitivity of identifying the heterozygous sites from sequencing errors, which leads to a high accuracy on error correction. To achieve this, QIHC established a set of probabilistic models based on Bayesian classifier, to estimate the heterozygosity of a site and makes a judgment by calculating the posterior probabilities. The proposed method is consisted of three modules, which respectively generates a pseudo reference sequence, obtains the read alignments, estimates the heterozygosity the sites and corrects the read harboring them. The last module is the core module of QIHC, which is designed to fit for the calculations of multiple cases at a heterozygous site. The other two modules enable the reads mapping to the pseudo reference sequence which somehow overcomes the inefficiency of multiple mappings that adopt by the existing error correction methods. Conclusions To verify the performance of our method, we selected Canu and Jabba to compare with QIHC in several aspects. As a hybrid correction method, we first conducted a groups of experiments under different coverages of the next-generation sequencing data. QIHC is far ahead of Jabba on accuracy. Meanwhile, we varied the coverages of the third generation sequencing data and compared performances again among Canu, Jabba and QIHC. QIHC outperforms the other two methods on accuracy of both correcting the sequencing errors and identifying the heterozygous sites, especially at low coverage. We carried out a comparison analysis between Canu and QIHC on the different error rates of the third generation sequencing data. QIHC still performs better. Therefore, QIHC is superior to the existing error correction methods when heterozygous sites exist.

scholarly journals Handling of targeted amplicon sequencing data focusing on index hopping and demultiplexing using a nested metabarcoding approach in ecology

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yasemin Guenay-Greunke ◽  
David A. Bohan ◽  
Michael Traugott ◽  
Corinna Wallinger
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Cost Effective ◽  
Amplicon Sequencing ◽  
Sequencing Depth ◽  
Sequencing Error ◽  
Sequencing Data ◽  
Large Sample ◽  
Sequencing Errors ◽  
Plant Feeding

AbstractHigh-throughput sequencing platforms are increasingly being used for targeted amplicon sequencing because they enable cost-effective sequencing of large sample sets. For meaningful interpretation of targeted amplicon sequencing data and comparison between studies, it is critical that bioinformatic analyses do not introduce artefacts and rely on detailed protocols to ensure that all methods are properly performed and documented. The analysis of large sample sets and the use of predefined indexes create challenges, such as adjusting the sequencing depth across samples and taking sequencing errors or index hopping into account. However, the potential biases these factors introduce to high-throughput amplicon sequencing data sets and how they may be overcome have rarely been addressed. On the example of a nested metabarcoding analysis of 1920 carabid beetle regurgitates to assess plant feeding, we investigated: (i) the variation in sequencing depth of individually tagged samples and the effect of library preparation on the data output; (ii) the influence of sequencing errors within index regions and its consequences for demultiplexing; and (iii) the effect of index hopping. Our results demonstrate that despite library quantification, large variation in read counts and sequencing depth occurred among samples and that the sequencing error rate in bioinformatic software is essential for accurate adapter/primer trimming and demultiplexing. Moreover, setting an index hopping threshold to avoid incorrect assignment of samples is highly recommended.

scholarly journals Powerful Inference with the D-statistic on Low-Coverage Whole-Genome Data

2017 ◽  
Author(s):  
Samuele Soraggi ◽  
Carsten Wiuf ◽  
Anders Albrechtsen
Keyword(s):  
Error Correction ◽  
Genetic Relationship ◽  
High Throughput Sequencing ◽  
Sequencing Depth ◽  
Human Populations ◽  
Sequencing Data ◽  
Sequencing Errors ◽  
Genome Data ◽  
High Throughput Sequencing Data ◽  
External Population

ABSTRACTThe detection of ancient gene flow between human populations is an important issue in population genetics. A common tool for detecting ancient admixture events is the D-statistic. The D-statistic is based on the hypothesis of a genetic relationship that involves four populations, whose correctness is assessed by evaluating specific coincidences of alleles between the groups.When working with high throughput sequencing data calling genotypes accurately is not always possible, therefore the D-statistic currently samples a single base from the reads of one individual per population. This implies ignoring much of the information in the data, an issue especially striking in the case of ancient genomes.We provide a significant improvement to overcome the problems of the D-statistic by considering all reads from multiple individuals in each population. We also apply type-specific error correction to combat the problems of sequencing errors and show a way to correct for introgression from an external population that is not part of the supposed genetic relationship, and how this leads to an estimate of the admixture rate.We prove that the D-statistic is approximated by a standard normal. Furthermore we show that our method outperforms the traditional D-statistic in detecting admixtures. The power gain is most pronounced for low/medium sequencing depth (1-10X) and performances are as good as with perfectly called genotypes at a sequencing depth of 2X. We show the reliability of error correction on scenarios with simulated errors and ancient data, and correct for introgression in known scenarios to estimate the admixture rates.

Ehapp2: Estimate haplotype frequencies from pooled sequencing data with prior database information

2016 ◽  
Vol 14 (04) ◽  
pp. 1650017
Author(s):  
Chang-Chang Cao ◽  
Xiao Sun
Keyword(s):  
Large Scale ◽  
Linear Equations ◽  
Cost Effective ◽  
Relative Difference ◽  
Sequencing Error ◽  
Sequencing Data ◽  
Sequencing Errors ◽  
Pooled Sequencing ◽  
Haplotype Frequencies ◽  
The Cost

To reduce the cost of large-scale re-sequencing, multiple individuals are pooled together and sequenced called pooled sequencing. Pooled sequencing could provide a cost-effective alternative to sequencing individuals separately. To facilitate the application of pooled sequencing in haplotype-based diseases association analysis, the critical procedure is to accurately estimate haplotype frequencies from pooled samples. Here we present Ehapp2 for estimating haplotype frequencies from pooled sequencing data by utilizing a database which provides prior information of known haplotypes. We first translate the problem of estimating frequency for each haplotype into finding a sparse solution for a system of linear equations, where the NNREG algorithm is employed to achieve the solution. Simulation experiments reveal that Ehapp2 is robust to sequencing errors and able to estimate the frequencies of haplotypes with less than 3% average relative difference for pooled sequencing of mixture of real Drosophila haplotypes with 50× total coverage even when the sequencing error rate is as high as 0.05. Owing to the strategy that proportions for local haplotypes spanning multiple SNPs are accurately calculated first, Ehapp2 retains excellent estimation for recombinant haplotypes resulting from chromosomal crossover. Comparisons with present methods reveal that Ehapp2 is state-of-the-art for many sequencing study designs and more suitable for current massive parallel sequencing.

scholarly journals UMI-VarCal: a new UMI-based variant caller that efficiently improves low-frequency variant detection in paired-end sequencing NGS libraries

2020 ◽  
Vol 36 (9) ◽  
pp. 2718-2724 ◽  
Author(s):  
Vincent Sater ◽  
Pierre-Julien Viailly ◽  
Thierry Lecroq ◽  
Élise Prieur-Gaston ◽  
Élodie Bohers ◽  
...  
Keyword(s):  
Tumor Cells ◽  
Low Frequency ◽  
Variant Calling ◽  
Pcr Amplification ◽  
Targeted Sequencing ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Background Error ◽  
Low Frequencies

Abstract Motivation Next-generation sequencing has become the go-to standard method for the detection of single-nucleotide variants in tumor cells. The use of such technologies requires a PCR amplification step and a sequencing step, steps in which artifacts are introduced at very low frequencies. These artifacts are often confused with true low-frequency variants that can be found in tumor cells and cell-free DNA. The recent use of unique molecular identifiers (UMI) in targeted sequencing protocols has offered a trustworthy approach to filter out artefactual variants and accurately call low-frequency variants. However, the integration of UMI analysis in the variant calling process led to developing tools that are significantly slower and more memory consuming than raw-reads-based variant callers. Results We present UMI-VarCal, a UMI-based variant caller for targeted sequencing data with better sensitivity compared to other variant callers. Being developed with performance in mind, UMI-VarCal stands out from the crowd by being one of the few variant callers that do not rely on SAMtools to do their pileup. Instead, at its core runs an innovative homemade pileup algorithm specifically designed to treat the UMI tags in the reads. After the pileup, a Poisson statistical test is applied at every position to determine if the frequency of the variant is significantly higher than the background error noise. Finally, an analysis of UMI tags is performed, a strand bias and a homopolymer length filter are applied to achieve better accuracy. We illustrate the results obtained using UMI-VarCal through the sequencing of tumor samples and we show how UMI-VarCal is both faster and more sensitive than other publicly available solutions. Availability and implementation The entire pipeline is available at https://gitlab.com/vincent-sater/umi-varcal-master under MIT license. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Increased yields of duplex sequencing data by a series of quality control tools

2019 ◽  
Author(s):  
Gundula Povysil ◽  
Monika Heinzl ◽  
Renato Salazar ◽  
Nicholas Stoler ◽  
Anton Nekrutenko ◽  
...  
Keyword(s):  
Low Frequency ◽  
Variant Calling ◽  
Data Loss ◽  
Sequencing Data ◽  
Bioinformatics Pipeline ◽  
Sequencing Errors ◽  
Data Output ◽  
Reverse Strand ◽  
Tool Set ◽  
Duplex Sequencing

AbstractDuplex sequencing is currently the most reliable method to identify ultra-low frequency DNA variants by grouping sequence reads derived from the same DNA molecule into families with information on the forward and reverse strand. However, only a small proportion of reads are assembled into duplex consensus sequences, and reads with potentially valuable information are discarded at different steps of the bioinformatics pipeline, especially reads without a family. We developed a bioinformatics tool-set that analyses the tag and family composition with the purpose to understand data loss and implement modifications to maximize the data output for the variant calling. Specifically, our tools show that tags contain PCR and sequencing errors that contribute to data loss and lower DCS yields. Our tools also identified chimeras, which result in unpaired families that do not form DCS. Finally, we also developed a tool called Variant Analyzer that re-examines variant calls from raw reads and provides different summary data that categorizes the confidence level of a variant call by a tier-based system. We demonstrate that this tool identified false positive variants tagged by the tier-based classification. Furthermore, with this tool we can include reads without a family and check the reliability of the call, which increases substantially the sequencing depth for variant calling, a particular important advantage for low-input samples or low-coverage regions.

scholarly journals Measuring genetic differentiation from Pool-seq data

2018 ◽  
Author(s):  
Valentin Hivert ◽  
Raphël Leblois ◽  
Eric J. Petit ◽  
Mathieu Gautier ◽  
Renaud Vitalis
Keyword(s):  
Genetic Differentiation ◽  
High Throughput Sequencing ◽  
Model Misspecification ◽  
Cost Effective ◽  
Sequencing Data ◽  
Sequencing Errors ◽  
Cost Effective Alternative ◽  
Method Of Moments Estimator ◽  
Dna Pool

AbstractThe recent advent of high throughput sequencing and genotyping technologies enables the comparison of patterns of polymorphisms at a very large number of markers. While the characterization of genetic structure from individual sequencing data remains expensive for many non-model species, it has been shown that sequencing pools of individual DNAs (Pool-seq) represents an attractive and cost-effective alternative. However, analyzing sequence read counts from a DNA pool instead of individual genotypes raises statistical challenges in deriving correct estimates of genetic differentiation. In this article, we provide a method-of-moments estimator of FST for Pool-seq data, based on an analysis-of-variance framework. We show, by means of simulations, that this new estimator is unbiased, and outperforms previously proposed estimators. We evaluate the robustness of our estimator to model misspecification, such as sequencing errors and uneven contributions of individual DNAs to the pools. Last, by reanalyzing published Pool-seq data of different ecotypes of the prickly sculpin Cottus asper, we show how the use of an unbiased FST estimator may question the interpretation of population structure inferred from previous analyses.

scholarly journals Error correction enables use of Oxford Nanopore technology for reference-free transcriptome analysis

2020 ◽  
Author(s):  
Kristoffer Sahlin ◽  
Botond Sipos ◽  
Phillip L James ◽  
Paul Medvedev
Keyword(s):  
Error Correction ◽  
Transcriptome Analysis ◽  
Transcription Initiation ◽  
Cost Effective ◽  
Error Rates ◽  
Computational Method ◽  
Bias Error ◽  
Sequencing Data ◽  
Oxford Nanopore ◽  
Long Read

AbstractOxford Nanopore (ONT) is a leading long-read technology which has been revolutionizing transcriptome analysis through its capacity to sequence the majority of transcripts from end-to-end. This has greatly increased our ability to study the diversity of transcription mechanisms such as transcription initiation, termination, and alternative splicing. However, ONT still suffers from high error rates which have thus far limited its scope to reference-based analyses. When a reference is not available or is not a viable option due to reference-bias, error correction is a crucial step towards the reconstruction of the sequenced transcripts and downstream sequence analysis of transcripts. In this paper, we present a novel computational method to error correct ONT cDNA sequencing data, called isONcorrect. IsONcorrect is able to jointly use all isoforms from a gene during error correction, thereby allowing it to correct reads at low sequencing depths. We are able to obtain a median accuracy of 98.9-99.6%, demonstrating the feasibility of applying cost-effective cDNA full transcript length sequencing for reference-free transcriptome analysis.

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