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

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.

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RNA Transcriptome Mapping with GraphMap

10.1101/160085 ◽

2017 ◽

Cited By ~ 1

Author(s):

Krešimir Križanović ◽

Ivan Sović ◽

Ivan Krpelnik ◽

Mile Šikić

Keyword(s):

Third Generation ◽

Sequencing Data ◽

Mapping Algorithm ◽

Gene Annotations ◽

Sequencing Technologies ◽

Third Generation Sequencing ◽

Oxford Nanopore ◽

Rna Mapping ◽

Synthetic Datasets ◽

Generation Sequencing

AbstractNext generation sequencing technologies have made RNA sequencing widely accessible and applicable in many areas of research. In recent years, 3rd generation sequencing technologies have matured and are slowly replacing NGS for DNA sequencing. This paper presents a novel tool for RNA mapping guided by gene annotations. The tool is an adapted version of a previously developed DNA mapper – GraphMap, tailored for third generation sequencing data, such as those produced by Pacific Biosciences or Oxford Nanopore Technologies devices. It uses gene annotations to generate a transcriptome, uses a DNA mapping algorithm to map reads to the transcriptome, and finally transforms the mappings back to genome coordinates. Modified version of GraphMap is compared on several synthetic datasets to the state-of-the-art RNAseq mappers enabled to work with third generation sequencing data. The results show that our tool outperforms other tools in general mapping quality.

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Evaluating approaches to find exon chains based on long reads

10.1101/066241 ◽

2016 ◽

Author(s):

Anna Kuosmanen ◽

Veli Mäkinen

Keyword(s):

Second Generation ◽

Simulated Data ◽

Error Rates ◽

Third Generation ◽

Sequencing Technologies ◽

Third Generation Sequencing ◽

Long Reads ◽

Long Read ◽

Second Generation Sequencing ◽

Generation Sequencing

AbstractMotivationTranscript prediction can be modelled as a graph problem where exons are modelled as nodes and reads spanning two or more exons are modelled as exon chains. PacBio third-generation sequencing technology produces significantly longer reads than earlier second-generation sequencing technologies, which gives valuable information about longer exon chains in a graph. However, with the high error rates of third-generation sequencing, aligning long reads correctly around the splice sites is a challenging task. Incorrect alignments lead to spurious nodes and arcs in the graph, which in turn lead to incorrect transcript predictions.ResultsWe survey several approaches to find the exon chains corresponding to long reads in a splicing graph, and experimentally study the performance of these methods using simulated data to allow for sensitivity / precision analysis. Our experiments show that short reads from second-generation sequencing can be used to significantly improve exon chain correctness either by error-correcting the long reads before splicing graph creation, or by using them to create a splicing graph on which the long read alignments are then projected. We also study the memory and time consumption of various modules, and show that accurate exon chains lead to significantly increased transcript prediction accuracy.AvailabilityThe simulated data and in-house scripts used for this article are available at http://cs.helsinki.fi/u/aekuosma/exon_chain_evaluation_publish.tar.gz.

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Latent variable model for aligning barcoded short-reads improves downstream analyses

10.1101/220236 ◽

2017 ◽

Author(s):

Ariya Shajii ◽

Ibrahim Numanagić ◽

Bonnie Berger

Keyword(s):

Long Range ◽

Latent Variable ◽

Error Rates ◽

Latent Variable Model ◽

Third Generation ◽

Sequencing Data ◽

Variable Model ◽

Short Reads ◽

Third Generation Sequencing ◽

Generation Sequencing

AbstractRecent years have seen the emergence of several “third-generation” sequencing platforms, each of which aims to address shortcomings of standard next-generation short-read sequencing by producing data that capture long-range information, thereby allowing us to access regions of the genome that are inaccessible with short-reads alone. These technologies either produce physically longer reads typically with higher error rates or instead capture long-range information at low error rates by virtue of read “barcodes” as in 10x Genomics’ Chromium platform. As with virtually all sequencing data, sequence alignment for third-generation sequencing data is the foundation on which all downstream analyses are based. Here we introduce a latent variable model for improving barcoded read alignment, thereby enabling improved downstream genotyping and phasing. We demonstrate the feasibility of this approach through developing EMerAld— or EMA for short— and testing it on the barcoded short-reads produced by 10x’s sequencing technologies. EMA not only produces more accurate alignments, but unlike other methods also assigns interpretable probabilities to the alignments it generates. We show that genotypes called from EMA’s alignments contain over 30% fewer false positives than those called from Lariat’s (the current 10x alignment tool), with a fewer number of false negatives, on datasets of NA12878 and NA24385 as compared to NIST GIAB gold standard variant calls. Moreover, we demonstrate that EMA is able to effectively resolve alignments in regions containing nearby homologous elements— a particularly challenging problem in read mapping— through the introduction of a novel statistical binning optimization framework, which allows us to find variants in the pharmacogenomically important CYP2D region that go undetected when using Lariat or BWA. Lastly, we show that EMA’s alignments improve phasing performance compared to Lariat’s in both NA12878 and NA24385, producing fewer switch/mismatch errors and larger phase blocks on average.EMA software and datasets used are available at http://ema.csail.mit.edu.

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IsoDetect: Detection of splice isoforms from third generation long reads based on short feature sequences

Current Bioinformatics ◽

10.2174/1574893615666200316101205 ◽

2020 ◽

Vol 15 ◽

Author(s):

Hongdong Li ◽

Wenjing Zhang ◽

Yuwen Luo ◽

Jianxin Wang

Keyword(s):

Sequence Similarity ◽

Detection Methods ◽

Sequence Information ◽

Third Generation ◽

Sequencing Data ◽

Splice Isoforms ◽

Third Generation Sequencing ◽

Long Reads ◽

Feature Sequence ◽

Generation Sequencing

Aims: Accurately detect isoforms from third generation sequencing data. Background: Transcriptome annotation is the basis for the analysis of gene expression and regulation. The transcriptome annotation of many organisms such as humans is far from incomplete, due partly to the challenge in the identification of isoforms that are produced from the same gene through alternative splicing. Third generation sequencing (TGS) reads provide unprecedented opportunity for detecting isoforms due to their long length that exceeds the length of most isoforms. One limitation of current TGS reads-based isoform detection methods is that they are exclusively based on sequence reads, without incorporating the sequence information of known isoforms. Objective: Develop an efficient method for isoform detection. Method: Based on annotated isoforms, we propose a splice isoform detection method called IsoDetect. First, the sequence at exon-exon junction is extracted from annotated isoforms as the “short feature sequence”, which is used to distinguish different splice isoforms. Second, we aligned these feature sequences to long reads and divided long reads into groups that contain the same set of feature sequences, thereby avoiding the pair-wise comparison among the large number of long reads. Third, clustering and consensus generation are carried out based on sequence similarity. For the long reads that do not contain any short feature sequence, clustering analysis based on sequence similarity is performed to identify isoforms. Result: Tested on two datasets from Calypte Anna and Zebra Finch, IsoDetect showed higher speed and compelling accuracy compared with four existing methods. Conclusion: IsoDetect is a promising method for isoform detection. Other: This paper was accepted by the CBC2019 conference.

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Comprehensive identification of transposable element insertions using multiple sequencing technologies

Nature Communications ◽

10.1038/s41467-021-24041-8 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Chong Chu ◽

Rebeca Borges-Monroy ◽

Vinayak V. Viswanadham ◽

Soohyun Lee ◽

Heng Li ◽

...

Keyword(s):

Transposable Element ◽

Structure And Function ◽

Endogenous Retroviruses ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Sequencing Data ◽

Short Read ◽

Sequencing Technologies ◽

Long Read ◽

And Function

AbstractTransposable elements (TEs) help shape the structure and function of the human genome. When inserted into some locations, TEs may disrupt gene regulation and cause diseases. Here, we present xTea (x-Transposable element analyzer), a tool for identifying TE insertions in whole-genome sequencing data. Whereas existing methods are mostly designed for short-read data, xTea can be applied to both short-read and long-read data. Our analysis shows that xTea outperforms other short read-based methods for both germline and somatic TE insertion discovery. With long-read data, we created a catalogue of polymorphic insertions with full assembly and annotation of insertional sequences for various types of retroelements, including pseudogenes and endogenous retroviruses. Notably, we find that individual genomes have an average of nine groups of full-length L1s in centromeres, suggesting that centromeres and other highly repetitive regions such as telomeres are a significant yet unexplored source of active L1s. xTea is available at https://github.com/parklab/xTea.

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Estimating sequencing error rates using families

BioData Mining ◽

10.1186/s13040-021-00259-6 ◽

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.

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Apollo: a sequencing-technology-independent, scalable and accurate assembly polishing algorithm

Bioinformatics ◽

10.1093/bioinformatics/btaa179 ◽

2020 ◽

Vol 36 (12) ◽

pp. 3669-3679 ◽

Cited By ~ 3

Author(s):

Can Firtina ◽

Jeremie S Kim ◽

Mohammed Alser ◽

Damla Senol Cali ◽

A Ercument Cicek ◽

...

Keyword(s):

Genome Analysis ◽

Supplementary Information ◽

Third Generation ◽

Sequencing Technology ◽

Base Pairs ◽

Sequencing Technologies ◽

Third Generation Sequencing ◽

Long Reads ◽

Generation Sequencing ◽

Large Genomes

Abstract Motivation Third-generation sequencing technologies can sequence long reads that contain as many as 2 million base pairs. These long reads are used to construct an assembly (i.e. the subject’s genome), which is further used in downstream genome analysis. Unfortunately, third-generation sequencing technologies have high sequencing error rates and a large proportion of base pairs in these long reads is incorrectly identified. These errors propagate to the assembly and affect the accuracy of genome analysis. Assembly polishing algorithms minimize such error propagation by polishing or fixing errors in the assembly by using information from alignments between reads and the assembly (i.e. read-to-assembly alignment information). However, current assembly polishing algorithms can only polish an assembly using reads from either a certain sequencing technology or a small assembly. Such technology-dependency and assembly-size dependency require researchers to (i) run multiple polishing algorithms and (ii) use small chunks of a large genome to use all available readsets and polish large genomes, respectively. Results We introduce Apollo, a universal assembly polishing algorithm that scales well to polish an assembly of any size (i.e. both large and small genomes) using reads from all sequencing technologies (i.e. second- and third-generation). Our goal is to provide a single algorithm that uses read sets from all available sequencing technologies to improve the accuracy of assembly polishing and that can polish large genomes. Apollo (i) models an assembly as a profile hidden Markov model (pHMM), (ii) uses read-to-assembly alignment to train the pHMM with the Forward–Backward algorithm and (iii) decodes the trained model with the Viterbi algorithm to produce a polished assembly. Our experiments with real readsets demonstrate that Apollo is the only algorithm that (i) uses reads from any sequencing technology within a single run and (ii) scales well to polish large assemblies without splitting the assembly into multiple parts. Availability and implementation Source code is available at https://github.com/CMU-SAFARI/Apollo. Supplementary information Supplementary data are available at Bioinformatics online.

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Quality of Third Generation Sequencing

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.9630 ◽

2020 ◽

Vol 17 (12) ◽

pp. 5205-5209

Author(s):

Ali Elbialy ◽

M. A. El-Dosuky ◽

Ibrahim M. El-Henawy

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

Gc Content ◽

Error Rates ◽

Third Generation ◽

Third Generation Sequencing ◽

Long Reads ◽

Generation Sequencing

Third generation sequencing (TGS) relates to long reads but with relatively high error rates. Quality of TGS is a hot topic, dealing with errors. This paper combines and investigates three quality related metrics. They are basecalling accuracy, Phred Quality Scores, and GC content. For basecalling accuracy, a deep neural network is adopted. The measured loss does not exceed 5.42.

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