De Novo genome assembly for third generation sequencing data

2018 ◽  
Author(s):  
Robert M. Nowak ◽  
Mateusz Forc ◽  
Wiktor Kuśmirek
Keyword(s):  
Genome Assembly ◽  
De Novo ◽  
Third Generation ◽  
Sequencing Data ◽  
De Novo Genome Assembly ◽  
Third Generation Sequencing ◽  
Generation Sequencing

scholarly journals SPAligner: Alignment of Long Diverged Molecular Sequences to Assembly Graphs

2019 ◽  
Author(s):  
Tatiana Dvorkina ◽  
Dmitry Antipov ◽  
Anton Korobeynikov ◽  
Sergey Nurk
Keyword(s):  
Genome Assembly ◽  
Efficient Solution ◽  
Software Tools ◽  
Third Generation ◽  
Sequencing Data ◽  
Molecular Sequences ◽  
Third Generation Sequencing ◽  
Alignment Problem ◽  
Genome Assemblies ◽  
Generation Sequencing

AbstractBackgroundGraph-based representation of genome assemblies has been recently used in different applications — from gene finding to haplotype separation. While most of these applications are based on the alignment of molecular sequences to assembly graphs, existing software tools for finding such alignments have important limitations.ResultsWe present a novel SPAligner tool for aligning long diverged molecular sequences to assembly graphs and demonstrate that SPAligner is an efficient solution for mapping third generation sequencing data and can also facilitate the identification of known genes in complex metagenomic datasets.ConclusionsOur work will facilitate accelerating the development of graph-based approaches in solving sequence to genome assembly alignment problem. SPAligner is implemented as a part of SPAdes tools library and is available on https://github.com/ablab/spades/archive/spaligner-paper.zip.

scholarly journals Effects of GC Bias in Next-Generation-Sequencing Data on De Novo Genome Assembly

PLoS ONE ◽  
2013 ◽  
Vol 8 (4) ◽  
pp. e62856 ◽  
Author(s):  
Yen-Chun Chen ◽  
Tsunglin Liu ◽  
Chun-Hui Yu ◽  
Tzen-Yuh Chiang ◽  
Chi-Chuan Hwang
Keyword(s):  
Next Generation Sequencing ◽  
Genome Assembly ◽  
De Novo ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
De Novo Genome Assembly ◽  
Gc Bias ◽  
Generation Sequencing

scholarly journals Genome assembly of Vitis rotundifolia Michx. using third-generation sequencing (Oxford Nanopore Technologies)

2021 ◽  
Vol 182 (2) ◽  
pp. 63-71
Author(s):  
M. M. Agakhanov ◽  
E. A. Grigoreva ◽  
E. K. Potokina ◽  
P. S. Ulianich ◽  
Y. V. Ukhatova
Keyword(s):  
Genome Sequence ◽  
Genome Assembly ◽  
De Novo ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Third Generation ◽  
Vitis Rotundifolia ◽  
Third Generation Sequencing ◽  
Genome Sequence Assembly ◽  
Generation Sequencing

The immune North American grapevine species Vitis rotundifolia Michaux (subgen. Muscadinia Planch.) is regarded as a potential donor of disease resistance genes, withstanding such dangerous diseases of grapes as powdery and downy mildews. The cultivar ‘Dixie’ is the only representative of this species preserved ex situ in Russia: it is maintained by the N.I. Vavilov All-Russian Institute of Plant Genetic Resources (VIR) in the orchards of its branch, Krymsk Experiment Breeding Station. Third-generation sequencing on the MinION platform was performed to obtain information on the primary structure of the cultivar’s genomic DNA, employing also the results of Illumina sequencing available in databases. A detailed description of the technique with modifications at various stages is presented, as it was used for grapevine genome sequencing and whole-genome sequence assembly. The modified technique included the main stages of the original protocol recommended by the MinION producer: 1) DNA extraction; 2) preparation of libraries for sequencing; 3) MinION sequencing and bioinformatic data processing; 4) de novo whole-genome sequence assembly using only MinION data or hybrid assembly (MinION+Illumina data); and 5) functional annotation of the whole-genome assembly. Stage 4 included not only de novo sequencing, but also the analysis of the available bioinformatic data, thus minimizing errors and increasing precision during the assembly of the studied genome. The DNA isolated from the leaves of cv. ‘Dixie’ was sequenced using two MinION flow cells (R9.4.1).

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 Third-Generation Sequencing: The Spearhead towards the Radical Transformation of Modern Genomics

Life ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 30
Author(s):  
Konstantina Athanasopoulou ◽  
Michaela A. Boti ◽  
Panagiotis G. Adamopoulos ◽  
Paraskevi C. Skourou ◽  
Andreas Scorilas
Keyword(s):  
De Novo ◽  
Direct Detection ◽  
Transcriptional Profiling ◽  
Third Generation ◽  
De Novo Genome Assembly ◽  
Rna Molecules ◽  
Third Generation Sequencing ◽  
Long Reads ◽  
Long Read ◽  
Generation Sequencing

Although next-generation sequencing (NGS) technology revolutionized sequencing, offering a tremendous sequencing capacity with groundbreaking depth and accuracy, it continues to demonstrate serious limitations. In the early 2010s, the introduction of a novel set of sequencing methodologies, presented by two platforms, Pacific Biosciences (PacBio) and Oxford Nanopore Sequencing (ONT), gave birth to third-generation sequencing (TGS). The innovative long-read technologies turn genome sequencing into an ease-of-handle procedure by greatly reducing the average time of library construction workflows and simplifying the process of de novo genome assembly due to the generation of long reads. Long sequencing reads produced by both TGS methodologies have already facilitated the decipherment of transcriptional profiling since they enable the identification of full-length transcripts without the need for assembly or the use of sophisticated bioinformatics tools. Long-read technologies have also provided new insights into the field of epitranscriptomics, by allowing the direct detection of RNA modifications on native RNA molecules. This review highlights the advantageous features of the newly introduced TGS technologies, discusses their limitations and provides an in-depth comparison regarding their scientific background and available protocols as well as their potential utility in research and clinical applications.

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.

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