scholarly journals Linking De Novo Assembly Results with Long DNA Reads Using the dnaasm-link Application

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Wiktor Kuśmirek ◽  
Wiktor Franus ◽  
Robert Nowak
Keyword(s):  
Dna Sequences ◽  
De Novo ◽  
Computation Time ◽  
Third Generation ◽  
Next Generation ◽  
Sequencing Data ◽  
Third Generation Sequencing ◽  
Combining Data ◽  
Second Generation Sequencing ◽  
Generation Sequencing

Currently, third-generation sequencing techniques, which make it possible to obtain much longer DNA reads compared to the next-generation sequencing technologies, are becoming more and more popular. There are many possibilities for combining data from next-generation and third-generation sequencing. Herein, we present a new application called dnaasm-link for linking contigs, the result of de novo assembly of second-generation sequencing data, with long DNA reads. Our tool includes an integrated module to fill gaps with a suitable fragment of an appropriate long DNA read, which improves the consistency of the resulting DNA sequences. This feature is very important, in particular for complex DNA regions. Our implementation is found to outperform other state-of-the-art tools in terms of speed and memory requirements, which may enable its usage for organisms with a large genome, something which is not possible in existing applications. The presented application has many advantages: (i) it significantly optimizes memory and reduces computation time; (ii) it fills gaps with an appropriate fragment of a specified long DNA read; (iii) it reduces the number of spanned and unspanned gaps in existing genome drafts. The application is freely available to all users under GNU Library or Lesser General Public License version 3.0 (LGPLv3). The demo application, Docker image, and source code can be downloaded from project homepage.

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 Third generation indexing for third generation sequencing

2020 ◽  
Author(s):  
Abdulqader Jighly
Keyword(s):  
Dna Sequences ◽  
De Novo ◽  
Total Error ◽  
Error Rates ◽  
Routine Activity ◽  
Considerable Proportion ◽  
Third Generation ◽  
Structural Variations ◽  
Third Generation Sequencing ◽  
Generation Sequencing

AbstractIndexing of DNA sequences is the art of sorting massive genomic data in a user-friendly structure to enable rapid accessing and comparing of different patterns in the data. Current genome assemblers use general algorithms for string indexing that do not exploit the special structural arrangement of genomes. Here, I am proposing a new algorithm that indexes only the configuration of microsatellite motifs along reads assuming that the order of microsatellites will be the same in overlapped sequences. The index size is >1000 times smaller than currently used indices and it has higher tolerance to the high error rates produced by third generation sequencing platforms. The results showed that the proposed algorithm can rapidly detect overlaps among considerable proportion of uncorrected long reads (~50% of all simulated base pairs with average read size of 8.16 kb and total error rates of 14.4%) to build large initial contigs. Unassembled reads can be then mapped to these contigs or can be assembled with them with currently used algorithms. Thus, the proposed algorithm can efficiently be used as an initial stage to significantly reduce the number of pairwise sequence comparisons among reads and/or references and improve the performance of different software but not replacing them. The algorithm was also useful for comparative genomics and detect large locally colinear blocks and structural variations among ten saccharomyces cerevisiae strains. The proposed algorithm has the power to make de novo assembly of individuals as routine activity which can lead to more accurate variant calling and pan genomics.

scholarly journals Third-generation sequencing and the future of genomics

2016 ◽  
Author(s):  
Hayan Lee ◽  
James Gurtowski ◽  
Shinjae Yoo ◽  
Maria Nattestad ◽  
Shoshana Marcus ◽  
...  
Keyword(s):  
Single Molecule ◽  
Structural Variation ◽  
De Novo ◽  
Third Generation ◽  
Base Pairs ◽  
Haplotype Phasing ◽  
Third Generation Sequencing ◽  
Second Generation Sequencing ◽  
High Quality Genome ◽  
Generation Sequencing

AbstractThird-generation long-range DNA sequencing and mapping technologies are creating a renaissance in high-quality genome sequencing. Unlike second-generation sequencing, which produces short reads a few hundred base-pairs long, third-generation single-molecule technologies generate over 10,000 bp reads or map over 100,000 bp molecules. We analyze how increased read lengths can be used to address longstanding problems in de novo genome assembly, structural variation analysis and haplotype phasing.

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

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.

scholarly journals VikNGS: A C ++ Variant Integration Kit for Next Generation Sequencing Association Analysis

2019 ◽  
Author(s):  
Zeynep Baskurt ◽  
Scott Mastromatteo ◽  
Jiafen Gong ◽  
Richard F Wintle ◽  
Stephen W Scherer ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Genetic Association ◽  
Association Analysis ◽  
Supplementary Information ◽  
Next Generation Sequencing Data ◽  
Data Sets ◽  
Next Generation ◽  
Sequencing Data ◽  
Combining Data ◽  
Generation Sequencing

Abstract Integration of next generation sequencing data (NGS) across different research studies can improve the power of genetic association testing by increasing sample size and can obviate the need for sequencing controls. If differential genotype uncertainty across studies is not accounted for, combining data sets can produce spurious association results. We developed the Variant Integration Kit for NGS (VikNGS), a fast cross-platform software package, to enable aggregation of several data sets for rare and common variant genetic association analysis of quantitative and binary traits with covariate adjustment. VikNGS also includes a graphical user interface, power simulation functionality and data visualization tools. Availability The VikNGS package can be downloaded at http://www.tcag.ca/tools/index.html. Supplementary information Supplementary data are available at Bioinformatics online.

Next generation sequencing allows deeper analysis and understanding of genomes and transcriptomes including aspects to fertility

2011 ◽  
Vol 23 (1) ◽  
pp. 75 ◽  
Author(s):  
Thomas Werner
Keyword(s):  
Next Generation Sequencing ◽  
Transcriptional Control ◽  
Target Genes ◽  
De Novo ◽  
Alternative Promoters ◽  
Next Generation ◽  
Sequencing Data ◽  
Genome Wide ◽  
A Genome ◽  
Generation Sequencing

Reproduction and fertility are controlled by specific events naturally linked to oocytes, testes and early embryonal tissues. A significant part of these events involves gene expression, especially transcriptional control and alternative transcription (alternative promoters and alternative splicing). While methods to analyse such events for carefully predetermined target genes are well established, until recently no methodology existed to extend such analyses into a genome-wide de novo discovery process. With the arrival of next generation sequencing (NGS) it becomes possible to attempt genome-wide discovery in genomic sequences as well as whole transcriptomes at a single nucleotide level. This does not only allow identification of the primary changes (e.g. alternative transcripts) but also helps to elucidate the regulatory context that leads to the induction of transcriptional changes. This review discusses the basics of the new technological and scientific concepts arising from NGS, prominent differences from microarray-based approaches and several aspects of its application to reproduction and fertility research. These concepts will then be illustrated in an application example of NGS sequencing data analysis involving postimplantation endometrium tissue from cows.

scholarly journals RNA Transcriptome Mapping with GraphMap

2017 ◽  
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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