scholarly journals TideHunter: efficient and sensitive tandem repeat detection from noisy long-reads using seed-and-chain

2019 ◽  
Vol 35 (14) ◽  
pp. i200-i207 ◽  
Author(s):  
Yan Gao ◽  
Bo Liu ◽  
Yadong Wang ◽  
Yi Xing
Keyword(s):  
Tandem Repeat ◽  
Rolling Circle Amplification ◽  
Consensus Sequence ◽  
Error Rates ◽  
Sequencing Error ◽  
Template Molecule ◽  
Long Reads ◽  
Pattern Size ◽  
Repeat Pattern ◽  
Repeat Detection

Abstract Motivation Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) sequencing technologies can produce long-reads up to tens of kilobases, but with high error rates. In order to reduce sequencing error, Rolling Circle Amplification (RCA) has been used to improve library preparation by amplifying circularized template molecules. Linear products of the RCA contain multiple tandem copies of the template molecule. By integrating additional in silico processing steps, these tandem sequences can be collapsed into a consensus sequence with a higher accuracy than the original raw reads. Existing pipelines using alignment-based methods to discover the tandem repeat patterns from the long-reads are either inefficient or lack sensitivity. Results We present a novel tandem repeat detection and consensus calling tool, TideHunter, to efficiently discover tandem repeat patterns and generate high-quality consensus sequences from amplified tandemly repeated long-read sequencing data. TideHunter works with noisy long-reads (PacBio and ONT) at error rates of up to 20% and does not have any limitation of the maximal repeat pattern size. We benchmarked TideHunter using simulated and real datasets with varying error rates and repeat pattern sizes. TideHunter is tens of times faster than state-of-the-art methods and has a higher sensitivity and accuracy. Availability and implementation TideHunter is written in C, it is open source and is available at https://github.com/yangao07/TideHunter

scholarly journals Hybrid de novo tandem repeat detection using short and long reads

2015 ◽  
Vol 8 (S3) ◽  
Author(s):  
Guillaume Fertin ◽  
Géraldine Jean ◽  
Andreea Radulescu ◽  
Irena Rusu
Keyword(s):  
Tandem Repeat ◽  
De Novo ◽  
Long Reads ◽  
Repeat Detection

scholarly journals Finding Long Tandem Repeats In Long Noisy Reads

2020 ◽  
Author(s):  
Shinichi Morishita ◽  
Kazuki Ichikawa ◽  
Gene Myers
Keyword(s):  
Tandem Repeat ◽  
Error Rate ◽  
Tandem Repeats ◽  
Repeat Unit ◽  
Error Rates ◽  
De Bruijn Graph ◽  
Frequency Distributions ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Repeat Expansions

Abstract Motivation Long tandem repeat expansions of more than 1000 nt have been suggested to be associated with diseases, but remain largely unexplored in individual human genomes because read lengths have been too short. However, new long-read sequencing technologies can produce single reads of 10,000 nt or more that can span such repeat expansions, although these long reads have high error rates, of 10%-20%, which complicates the detection of repetitive elements. Moreover, most traditional algorithms for finding tandem repeats are designed to find short tandem repeats (< 1000 nt) and cannot effectively handle the high error rate of long reads in a reasonable amount of time. Results Here, we report an efficient algorithm for solving this problem that takes advantage of the length of the repeat. Namely, a long tandem repeat has hundreds or thousands of approximate copies of the repeated unit, so despite the error rate, many short k-mers will be error-free in many copies of the unit. We exploited this characteristic to develop a method for first estimating regions that could contain a tandem repeat, by analyzing the k-mer frequency distributions of fixed-size windows across the target read, followed by an algorithm that assembles the k-mers of a putative region into the consensus repeat unit by greedily traversing a de Bruijn graph. Experimental results indicated that the proposed algorithm largely outperformed Tandem Repeats Finder (TRF), a widely used program for finding tandem repeats, in terms of sensitivity. Software availability https://github.com/morisUtokyo/mTR

scholarly journals HECIL: A Hybrid Error Correction Algorithm for Long Reads with Iterative Learning

2017 ◽  
Author(s):  
Olivia Choudhury ◽  
Ankush Chakrabarty ◽  
Scott J. Emrich
Keyword(s):  
Error Correction ◽  
Real Data ◽  
Error Rates ◽  
Iterative Learning ◽  
Sequencing Error ◽  
Full Potential ◽  
Long Reads ◽  
Second Generation Sequencing ◽  
Sequencing Platforms ◽  
Generation Sequencing

AbstractSecond-generation sequencing techniques generate short reads that can result in fragmented genome assemblies. Third-generation sequencing platforms mitigate this limitation by producing longer reads that span across complex and repetitive regions. Currently, the usefulness of such long reads is limited, however, because of high sequencing error rates. To exploit the full potential of these longer reads, it is imperative to correct the underlying errors. We propose HECIL—Hybrid Error Correction with Iterative Learning—a hybrid error correction framework that determines a correction policy for erroneous long reads, based on optimal combinations of decision weights obtained from short read alignments. We demonstrate that HECIL outperforms state-of-the-art error correction algorithms for an overwhelming majority of evaluation metrics on diverse real data sets includingE. coli,S. cerevisiae, and the malaria vector mosquitoA. funestus. We further improve the performance of HECIL by introducing an iterative learning paradigm that improves the correction policy at each iteration by incorporating knowledge gathered from previous iterations via confidence metrics assigned to prior corrections.Availability and Implementationhttps://github.com/NDBL/[email protected]

scholarly journals Long-read amplicon denoising

2018 ◽  
Author(s):  
Venkatesh Kumar ◽  
Thomas Vollbrecht ◽  
Mark Chernyshev ◽  
Sanjay Mohan ◽  
Brian Hanst ◽  
...  
Keyword(s):  
Ground Truth ◽  
Amplicon Sequencing ◽  
Error Rates ◽  
Sequencing Error ◽  
Short Read Sequencing ◽  
Sequencing Technologies ◽  
Medium Length ◽  
Long Reads ◽  
Long Read ◽  
Error Profiles

Long-read next generation amplicon sequencing shows promise for studying complete genes or genomes from complex and diverse populations. Current long-read sequencing technologies have challenging error profiles, hindering data processing and incorporation into downstream analyses. Here we consider the problem of how to reconstruct, free of sequencing error, the true sequence variants and their associated frequencies. Called “amplicon denoising”, this problem has been extensively studied for short-read sequencing technologies, but current solutions do not appear to generalize well to long reads with high indel error rates. We introduce two methods: one that runs nearly instantly and is very accurate for medium length reads (here ~2.6kb) and high template coverage, and another, slower method that is more robust when reads are very long or coverage is lower.On one real dataset with ground truth, and on a number of simulated datasets, we compare our two approaches to each other and to existing algorithms. We outperform all tested methods in accuracy, with competitive run times even for our slower method.Fast Amplicon Denoising (FAD) and Robust Amplicon Denoising (RAD) are implemented purely in the Julia scientific computing language, and are hereby released along with a complete toolkit of functions that allow long-read amplicon sequence analysis pipelines to be constructed in pure Julia. Further, we make available a webserver to dramatically simplify the processing of long-read PacBio sequences.

scholarly journals TRiCoLOR: tandem repeat profiling using whole-genome long-read sequencing data

GigaScience ◽  
2020 ◽  
Vol 9 (10) ◽  
Author(s):  
Davide Bolognini ◽  
Alberto Magi ◽  
Vladimir Benes ◽  
Jan O Korbel ◽  
Tobias Rausch
Keyword(s):  
Tandem Repeat ◽  
Error Rates ◽  
Sequencing Error ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Third Generation ◽  
Sequencing Data ◽  
Sequencing Technologies ◽  
Third Generation Sequencing ◽  
Generation Sequencing

Abstract Background Tandem repeat sequences are widespread in the human genome, and their expansions cause multiple repeat-mediated disorders. Genome-wide discovery approaches are needed to fully elucidate their roles in health and disease, but resolving tandem repeat variation accurately remains a challenging task. While traditional mapping-based approaches using short-read data have severe limitations in the size and type of tandem repeats they can resolve, recent third-generation sequencing technologies exhibit substantially higher sequencing error rates, which complicates repeat resolution. Results We developed TRiCoLOR, a freely available tool for tandem repeat profiling using error-prone long reads from third-generation sequencing technologies. The method can identify repetitive regions in sequencing data without a prior knowledge of their motifs or locations and resolve repeat multiplicity and period size in a haplotype-specific manner. The tool includes methods to interactively visualize the identified repeats and to trace their Mendelian consistency in pedigrees. Conclusions TRiCoLOR demonstrates excellent performance and improved sensitivity and specificity compared with alternative tools on synthetic data. For real human whole-genome sequencing data, TRiCoLOR achieves high validation rates, suggesting its suitability to identify tandem repeat variation in personal genomes.

scholarly journals Haplotype-aware genotyping from noisy long reads

2018 ◽  
Author(s):  
Jana Ebler ◽  
Marina Haukness ◽  
Trevor Pesout ◽  
Tobias Marschall ◽  
Benedict Paten
Keyword(s):  
Error Rates ◽  
Sequencing Error ◽  
Sequencing Data ◽  
Novel Approach ◽  
Long Reads ◽  
Oxford Nanopore ◽  
Linkage Information ◽  
Second Generation Sequencing ◽  
Sequencing Platforms ◽  
Generation Sequencing

MotivationCurrent genotyping approaches for single nucleotide variations (SNVs) rely on short, relatively accurate reads from second generation sequencing devices. Presently, third generation sequencing platforms able to generate much longer reads are becoming more widespread. These platforms come with the significant drawback of higher sequencing error rates, which makes them ill-suited to current genotyping algorithms. However, the longer reads make more of the genome unambiguously mappable and typically provide linkage information between neighboring variants.ResultsIn this paper we introduce a novel approach for haplotype-aware genotyping from noisy long reads. We do this by considering bipartitions of the sequencing reads, corresponding to the two haplotypes. We formalize the computational problem in terms of a Hidden Markov Model and compute posterior genotype probabilities using the forward-backward algorithm. Genotype predictions can then be made by picking the most likely genotype at each site. Our experiments indicate that longer reads allow significantly more of the genome to potentially be accurately genotyped. Further, we are able to use both Oxford Nanopore and Pacific Biosciences sequencing data to independently validate millions of variants previously identified by short-read technologies in the reference NA12878 sample, including hundreds of thousands of variants that were not previously included in the high-confidence reference set.

scholarly journals PB-Motif—A Method for Identifying Gene/Pseudogene Rearrangements With Long Reads: An Application to CYP21A2 Genotyping

2021 ◽  
Vol 12 ◽  
Author(s):  
Zachary Stephens ◽  
Dragana Milosevic ◽  
Benjamin Kipp ◽  
Stefan Grebe ◽  
Ravishankar K. Iyer ◽  
...  
Keyword(s):  
Phase Variation ◽  
Variant Calling ◽  
Error Rates ◽  
Clinical Samples ◽  
Sequencing Error ◽  
Carrier Status ◽  
Sequencing Errors ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Genomic Regions

Long read sequencing technologies have the potential to accurately detect and phase variation in genomic regions that are difficult to fully characterize with conventional short read methods. These difficult to sequence regions include several clinically relevant genes with highly homologous pseudogenes, many of which are prone to gene conversions or other types of complex structural rearrangements. We present PB-Motif, a new method for identifying rearrangements between two highly homologous genomic regions using PacBio long reads. PB-Motif leverages clustering and filtering techniques to efficiently report rearrangements in the presence of sequencing errors and other systematic artifacts. Supporting reads for each high-confidence rearrangement can then be used for copy number estimation and phased variant calling. First, we demonstrate PB-Motif's accuracy with simulated sequence rearrangements of PMS2 and its pseudogene PMS2CL using simulated reads sweeping over a range of sequencing error rates. We then apply PB-Motif to 26 clinical samples, characterizing CYP21A2 and its pseudogene CYP21A1P as part of a diagnostic assay for congenital adrenal hyperplasia. We successfully identify damaging variation and patient carrier status concordant with clinical diagnosis obtained from multiplex ligation-dependent amplification (MLPA) and Sanger sequencing. The source code is available at: github.com/zstephens/pb-motif.

scholarly journals Long-read amplicon denoising

2019 ◽  
Vol 47 (18) ◽  
pp. e104-e104 ◽  
Author(s):  
Venkatesh Kumar ◽  
Thomas Vollbrecht ◽  
Mark Chernyshev ◽  
Sanjay Mohan ◽  
Brian Hanst ◽  
...  
Keyword(s):  
Ground Truth ◽  
Amplicon Sequencing ◽  
Error Rates ◽  
Sequencing Error ◽  
Single Nucleotide ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Long Read ◽  
Error Profiles ◽  
Virus Community

Abstract Long-read next-generation amplicon sequencing shows promise for studying complete genes or genomes from complex and diverse populations. Current long-read sequencing technologies have challenging error profiles, hindering data processing and incorporation into downstream analyses. Here we consider the problem of how to reconstruct, free of sequencing error, the true sequence variants and their associated frequencies from PacBio reads. Called ‘amplicon denoising’, this problem has been extensively studied for short-read sequencing technologies, but current solutions do not always successfully generalize to long reads with high indel error rates. We introduce two methods: one that runs nearly instantly and is very accurate for medium length reads and high template coverage, and another, slower method that is more robust when reads are very long or coverage is lower. On two Mock Virus Community datasets with ground truth, each sequenced on a different PacBio instrument, and on a number of simulated datasets, we compare our two approaches to each other and to existing algorithms. We outperform all tested methods in accuracy, with competitive run times even for our slower method, successfully discriminating templates that differ by a just single nucleotide. Julia implementations of Fast Amplicon Denoising (FAD) and Robust Amplicon Denoising (RAD), and a webserver interface, are freely available.

scholarly journals Estimating sequencing error rates using families

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Kelley Paskov ◽  
Jae-Yoon Jung ◽  
Brianna Chrisman ◽  
Nate T. Stockham ◽  
Peter Washington ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Exome Sequencing ◽  
Genome Sequencing ◽  
Variant Calling ◽  
Error Rates ◽  
Sequencing Error ◽  
Whole Genome ◽  
Sequencing Data ◽  
Sequencing Platform ◽  
Whole Exome

Abstract Background As next-generation sequencing technologies make their way into the clinic, knowledge of their error rates is essential if they are to be used to guide patient care. However, sequencing platforms and variant-calling pipelines are continuously evolving, making it difficult to accurately quantify error rates for the particular combination of assay and software parameters used on each sample. Family data provide a unique opportunity for estimating sequencing error rates since it allows us to observe a fraction of sequencing errors as Mendelian errors in the family, which we can then use to produce genome-wide error estimates for each sample. Results We introduce a method that uses Mendelian errors in sequencing data to make highly granular per-sample estimates of precision and recall for any set of variant calls, regardless of sequencing platform or calling methodology. We validate the accuracy of our estimates using monozygotic twins, and we use a set of monozygotic quadruplets to show that our predictions closely match the consensus method. We demonstrate our method’s versatility by estimating sequencing error rates for whole genome sequencing, whole exome sequencing, and microarray datasets, and we highlight its sensitivity by quantifying performance increases between different versions of the GATK variant-calling pipeline. We then use our method to demonstrate that: 1) Sequencing error rates between samples in the same dataset can vary by over an order of magnitude. 2) Variant calling performance decreases substantially in low-complexity regions of the genome. 3) Variant calling performance in whole exome sequencing data decreases with distance from the nearest target region. 4) Variant calls from lymphoblastoid cell lines can be as accurate as those from whole blood. 5) Whole-genome sequencing can attain microarray-level precision and recall at disease-associated SNV sites. Conclusion Genotype datasets from families are powerful resources that can be used to make fine-grained estimates of sequencing error for any sequencing platform and variant-calling methodology.

scholarly journals A First Genome Survey and Genomic SSR Marker Analysis of Trematomus loennbergii Regan, 1913

Animals ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 3186
Author(s):  
Eunkyung Choi ◽  
Sun Hee Kim ◽  
Seung Jae Lee ◽  
Euna Jo ◽  
Jinmu Kim ◽  
...  
Keyword(s):  
Microsatellite Markers ◽  
Fish Species ◽  
Evolutionary Biology ◽  
Average Rate ◽  
Antarctic Fish ◽  
Error Rates ◽  
Repeat Motif ◽  
Sequencing Error ◽  
Genome Survey ◽  
Next Generation Sequencing Ngs

Trematomus loennbergii Regan, 1913, is an evolutionarily important marine fish species distributed in the Antarctic Ocean. However, its genome has not been studied to date. In the present study, whole genome sequencing was performed using next-generation sequencing (NGS) technology to characterize its genome and develop genomic microsatellite markers. The 25-mer frequency distribution was estimated to be the best, and the genome size was predicted to be 815,042,992 bp. The heterozygosity, average rate of read duplication, and sequencing error rates were 0.536%, 0.724%, and 0.292%, respectively. These data were used to analyze microsatellite markers, and a total of 2,264,647 repeat motifs were identified. The most frequent repeat motif was di-nucleotide with 87.00% frequency, followed by tri-nucleotide (10.45%), tetra-nucleotide (1.94%), penta-nucleotide (0.34%), and hexa-nucleotide (0.27%). The AC repeat motif was the most abundant motif among di-nucleotides and among all repeat motifs. Among microsatellite markers, 181 markers were selected and PCR technology was used to validate several markers. A total of 15 markers produced only one band. In summary, these results provide a good basis for further studies, including evolutionary biology studies and population genetics of Antarctic fish species.

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