scholarly journals TALC: Transcript-level Aware Long-read Correction

2020 ◽  
Vol 36 (20) ◽  
pp. 5000-5006 ◽  
Author(s):  
Lucile Broseus ◽  
Aubin Thomas ◽  
Andrew J Oldfield ◽  
Dany Severac ◽  
Emeric Dubois ◽  
...  
Keyword(s):  
Transcriptome Sequencing ◽  
Transcript Level ◽  
Supplementary Information ◽  
De Bruijn Graph ◽  
Rna Seq ◽  
Sequencing Data ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Long Read ◽  
Rna Transcript

Abstract Motivation Long-read sequencing technologies are invaluable for determining complex RNA transcript architectures but are error-prone. Numerous ‘hybrid correction’ algorithms have been developed for genomic data that correct long reads by exploiting the accuracy and depth of short reads sequenced from the same sample. These algorithms are not suited for correcting more complex transcriptome sequencing data. Results We have created a novel reference-free algorithm called Transcript-level Aware Long-Read Correction (TALC) which models changes in RNA expression and isoform representation in a weighted De Bruijn graph to correct long reads from transcriptome studies. We show that transcript-level aware correction by TALC improves the accuracy of the whole spectrum of downstream RNA-seq applications and is thus necessary for transcriptome analyses that use long read technology. Availability and implementation TALC is implemented in C++ and available at https://github.com/lbroseus/TALC. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals TALC: Transcript-level Aware Long Read Correction

2020 ◽  
Author(s):  
Lucile Broseus ◽  
Aubin Thomas ◽  
Andrew J. Oldfield ◽  
Dany Severac ◽  
Emeric Dubois ◽  
...  
Keyword(s):  
Transcriptome Sequencing ◽  
Transcript Level ◽  
De Bruijn Graph ◽  
Rna Seq ◽  
Sequencing Data ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Long Read ◽  
De Bruijn ◽  
Rna Transcript

ABSTRACTMotivationLong-read sequencing technologies are invaluable for determining complex RNA transcript architectures but are error-prone. Numerous “hybrid correction” algorithms have been developed for genomic data that correct long reads by exploiting the accuracy and depth of short reads sequenced from the same sample. These algorithms are not suited for correcting more complex transcriptome sequencing data.ResultsWe have created a novel reference-free algorithm called TALC (Transcription Aware Long Read Correction) which models changes in RNA expression and isoform representation in a weighted De-Bruijn graph to correct long reads from transcriptome studies. We show that transcription aware correction by TALC improves the accuracy of the whole spectrum of downstream RNA-seq applications and is thus necessary for transcriptome analyses that use long read technology.Availability and ImplementationTALC is implemented in C++ and available at https://gitlab.igh.cnrs.fr/lbroseus/[email protected]

scholarly journals SVJedi: genotyping structural variations with long reads

2020 ◽  
Vol 36 (17) ◽  
pp. 4568-4575
Author(s):  
Lolita Lecompte ◽  
Pierre Peterlongo ◽  
Dominique Lavenier ◽  
Claire Lemaitre
Keyword(s):  
Supplementary Information ◽  
Sequencing Data ◽  
Structural Variations ◽  
Short Read ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Oxford Nanopore ◽  
Clinical Diagnoses ◽  
Long Read ◽  
The One

Abstract Motivation Studies on structural variants (SVs) are expanding rapidly. As a result, and thanks to third generation sequencing technologies, the number of discovered SVs is increasing, especially in the human genome. At the same time, for several applications such as clinical diagnoses, it is important to genotype newly sequenced individuals on well-defined and characterized SVs. Whereas several SV genotypers have been developed for short read data, there is a lack of such dedicated tool to assess whether known SVs are present or not in a new long read sequenced sample, such as the one produced by Pacific Biosciences or Oxford Nanopore Technologies. Results We present a novel method to genotype known SVs from long read sequencing data. The method is based on the generation of a set of representative allele sequences that represent the two alleles of each structural variant. Long reads are aligned to these allele sequences. Alignments are then analyzed and filtered out to keep only informative ones, to quantify and estimate the presence of each SV allele and the allele frequencies. We provide an implementation of the method, SVJedi, to genotype SVs with long reads. The tool has been applied to both simulated and real human datasets and achieves high genotyping accuracy. We show that SVJedi obtains better performances than other existing long read genotyping tools and we also demonstrate that SV genotyping is considerably improved with SVJedi compared to other approaches, namely SV discovery and short read SV genotyping approaches. Availability and implementation https://github.com/llecompte/SVJedi.git Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals LRSDAY: Long-read Sequencing Data Analysis for Yeasts

2017 ◽  
Author(s):  
Jia-Xing Yue ◽  
Gianni Liti
Keyword(s):  
Genome Assembly ◽  
Model Organism ◽  
Sequencing Data ◽  
Protein Coding ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Long Read ◽  
Downstream Analysis ◽  
Eukaryotic Organisms ◽  
Genomic Regions

AbstractLong-read sequencing technologies have become increasingly popular in genome projects due to their strengths in resolving complex genomic regions. As a leading model organism with small genome size and great biotechnological importance, the budding yeast, Saccharomyces cerevisiae, has many isolates currently being sequenced with long reads. However, analyzing long-read sequencing data to produce high-quality genome assembly and annotation remains challenging. Here we present LRSDAY, the first one-stop solution to streamline this process. LRSDAY can produce chromosome-level end-to-end genome assembly and comprehensive annotations for various genomic features (including centromeres, protein-coding genes, tRNAs, transposable elements and telomere-associated elements) that are ready for downstream analysis. Although tailored for S. cerevisiae, we designed LRSDAY to be highly modular and customizable, making it adaptable for virtually any eukaryotic organisms. Applying LRSDAY to a S. cerevisiae strain takes ∼43 hrs to generate a complete and well-annotated genome from ∼100X Pacific Biosciences (PacBio) reads using four threads.

scholarly journals Evaluation of Germline Structural Variant Calling Methods for Nanopore Sequencing Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Davide Bolognini ◽  
Alberto Magi
Keyword(s):  
Variant Calling ◽  
Research Report ◽  
Nanopore Sequencing ◽  
Sequencing Data ◽  
Factors Affecting ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Oxford Nanopore ◽  
Sequencing Studies ◽  
Long Read

Structural variants (SVs) are genomic rearrangements that involve at least 50 nucleotides and are known to have a serious impact on human health. While prior short-read sequencing technologies have often proved inadequate for a comprehensive assessment of structural variation, more recent long reads from Oxford Nanopore Technologies have already been proven invaluable for the discovery of large SVs and hold the potential to facilitate the resolution of the full SV spectrum. With many long-read sequencing studies to follow, it is crucial to assess factors affecting current SV calling pipelines for nanopore sequencing data. In this brief research report, we evaluate and compare the performances of five long-read SV callers across four long-read aligners using both real and synthetic nanopore datasets. In particular, we focus on the effects of read alignment, sequencing coverage, and variant allele depth on the detection and genotyping of SVs of different types and size ranges and provide insights into precision and recall of SV callsets generated by integrating the various long-read aligners and SV callers. The computational pipeline we propose is publicly available at https://github.com/davidebolo1993/EViNCe and can be adjusted to further evaluate future nanopore sequencing datasets.

scholarly journals BleTIES: Annotation of natural genome editing in ciliates using long read sequencing

2021 ◽  
Author(s):  
Brandon K. B. Seah ◽  
Estienne C. Swart
Keyword(s):  
Dna Sequences ◽  
Sequence Data ◽  
Low Complexity ◽  
Supplementary Information ◽  
Neighboring Element ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Oxford Nanopore ◽  
Long Read ◽  
Element Elimination

Ciliates are single-celled eukaryotes that eliminate specific, interspersed DNA sequences (internally eliminated sequences, IESs) from their genomes during development. These are challenging to annotate and assemble because IES-containing sequences are much less abundant in the cell than those without, and IES sequences themselves often contain repetitive and low-complexity sequences. Long read sequencing technologies from Pacific Biosciences and Oxford Nanopore have the potential to reconstruct longer IESs than has been possible with short reads, and also the ability to detect correlations of neighboring element elimination. Here we present BleTIES, a software toolkit for detecting, assembling, and analyzing IESs using mapped long reads. Availability and implementation: BleTIES is implemented in Python 3. Source code is available at https://github.com/Swart-lab/bleties (MIT license), and also distributed via Bioconda. Contact: [email protected] Supplementary information: Benchmarking of BleTIES with published sequence data.

scholarly journals cloudSPAdes: assembly of synthetic long reads using de Bruijn graphs

2019 ◽  
Vol 35 (14) ◽  
pp. i61-i70 ◽  
Author(s):  
Ivan Tolstoganov ◽  
Anton Bankevich ◽  
Zhoutao Chen ◽  
Pavel A Pevzner
Keyword(s):  
Narrow Range ◽  
State Of The Art ◽  
Supplementary Information ◽  
De Bruijn Graph ◽  
Hybrid Assembly ◽  
De Bruijn Graphs ◽  
Long Reads ◽  
Long Read ◽  
De Bruijn ◽  
New Applications

Abstract Motivation The recently developed barcoding-based synthetic long read (SLR) technologies have already found many applications in genome assembly and analysis. However, although some new barcoding protocols are emerging and the range of SLR applications is being expanded, the existing SLR assemblers are optimized for a narrow range of parameters and are not easily extendable to new barcoding technologies and new applications such as metagenomics or hybrid assembly. Results We describe the algorithmic challenge of the SLR assembly and present a cloudSPAdes algorithm for SLR assembly that is based on analyzing the de Bruijn graph of SLRs. We benchmarked cloudSPAdes across various barcoding technologies/applications and demonstrated that it improves on the state-of-the-art SLR assemblers in accuracy and speed. Availability and implementation Source code and installation manual for cloudSPAdes are available at https://github.com/ablab/spades/releases/tag/cloudspades-paper. Supplementary Information Supplementary data are available at Bioinformatics online.

scholarly journals A benchmark of structural variation detection by long reads through a realistic simulated model

2020 ◽  
Author(s):  
Nicolas Dierckxsens ◽  
Tong Li ◽  
Joris R. Vermeesch ◽  
Zhi Xie
Keyword(s):  
Structural Variation ◽  
Rapid Evolution ◽  
Detection Methods ◽  
Sequencing Data ◽  
Simulated Model ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Long Read ◽  
Sequencing Platforms ◽  
The Impact

ABSTRACTDespite the rapid evolution of new sequencing technologies, structural variation detection remains poorly ascertained. The high discrepancy between the results of structural variant analysis programs makes it difficult to assess their performance on real datasets. Accurate simulations of structural variation distributions and sequencing data of the human genome are crucial for the development and benchmarking of new tools. In order to gain a better insight into the detection of structural variation with long sequencing reads, we created a realistic simulated model to thoroughly compare SV detection methods and the impact of the chosen sequencing technology and sequencing depth. To achieve this, we developed Sim-it, a straightforward tool for the simulation of both structural variation and long-read data. These simulations from Sim-it revealed the strengths and weaknesses for current available structural variation callers and long read sequencing platforms. Our findings were also supported by the latest structural variation benchmark set developed by the GIAB Consortium. With these findings, we developed a new method (combiSV) that can combine the results from five different SV callers into a superior call set with increased recall and precision. Both Sim-it and combiSV are open source and can be downloaded at https://github.com/ndierckx/.

scholarly journals SVJedi: Genotyping structural variations with long reads

2019 ◽  
Author(s):  
Lolita Lecompte ◽  
Pierre Peterlongo ◽  
Dominique Lavenier ◽  
Claire Lemaitre
Keyword(s):  
Sequencing Data ◽  
Structural Variations ◽  
Short Read ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Oxford Nanopore ◽  
Clinical Diagnoses ◽  
Long Read ◽  
Reference Sequences ◽  
The One

AbstractMotivationStudies on structural variants (SV) are expanding rapidly. As a result, and thanks to third generation sequencing technologies, the number of discovered SVs is increasing, especially in the human genome. At the same time, for several applications such as clinical diagnoses, it is important to genotype newly sequenced individuals on well defined and characterized SVs. Whereas several SV genotypers have been developed for short read data, there is a lack of such dedicated tool to assess whether known SVs are present or not in a new long read sequenced sample, such as the one produced by Pacific Biosciences or Oxford Nanopore Technologies.ResultsWe present a novel method to genotype known SVs from long read sequencing data. The method is based on the generation of a set of reference sequences that represent the two alleles of each structural variant. Long reads are aligned to these reference sequences. Alignments are then analyzed and filtered out to keep only informative ones, to quantify and estimate the presence of each SV allele and the allele frequencies. We provide an implementation of the method, SVJedi, to genotype insertions and deletions with long reads. The tool has been applied to both simulated and real human datasets and achieves high genotyping accuracy. We also demonstrate that SV genotyping is considerably improved with SVJedi compared to other approaches, namely SV discovery and short read SV genotyping approaches.Availabilityhttps://github.com/llecompte/[email protected]

scholarly journals Accurate Haplotype-Resolved Assembly Reveals The Origin Of Structural Variants For Human Trios

2021 ◽  
Author(s):  
Mengyang Xu ◽  
Lidong Guo ◽  
Xiao Du ◽  
Lei Li ◽  
Brock A Peters ◽  
...  
Keyword(s):  
De Novo ◽  
Substantial Improvement ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Homologous Chromosomes ◽  
Assembly Method ◽  
Long Reads ◽  
Long Read ◽  
Second Generation Sequencing ◽  
Generation Sequencing

Abstract Motivation Achieving a near complete understanding of how the genome of an individual affects the phenotypes of that individual requires deciphering the order of variations along homologous chromosomes in species with diploid genomes. However, true diploid assembly of long-range haplotypes remains challenging. Results To address this, we have developed Haplotype-resolved Assembly for Synthetic long reads using a Trio-binning strategy, or HAST, which uses parental information to classify reads into maternal or paternal. Once sorted, these reads are used to independently de novo assemble the parent-specific haplotypes. We applied HAST to co-barcoded second-generation sequencing data from an Asian individual, resulting in a haplotype assembly covering 94.7% of the reference genome with a scaffold N50 longer than 11 Mb. The high haplotyping precision (∼99.7%) and recall (∼95.9%) represents a substantial improvement over the commonly used tool for assembling co-barcoded reads (Supernova), and is comparable to a trio-binning-based third generation long-read based assembly method (TrioCanu) but with a significantly higher single-base accuracy (up to 99.99997% (Q65)). This makes HAST a superior tool for accurate haplotyping and future haplotype-based studies. Availability The code of the analysis is available at https://github.com/BGI-Qingdao/HAST. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Eoulsan 2: an efficient workflow manager for reproducible bulk, long-read and single-cell transcriptomics analyses

2021 ◽  
Author(s):  
Nathalie Lehmann ◽  
Sandrine Perrin ◽  
Claire Wallon ◽  
Xavier Bauquet ◽  
Vivien Deshaies ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Seq ◽  
Sequencing Data ◽  
Long Reads ◽  
Oxford Nanopore ◽  
The Galaxy ◽  
Long Read ◽  
Hadoop Clusters ◽  
Core Facilities ◽  
Workflow Manager

Motivation: Core sequencing facilities produce huge amounts of sequencing data that need to be analysed with automated workflows to ensure reproducibility and traceability. Eoulsan is a versatile open-source workflow engine meeting the needs of core facilities, by automating the analysis of a large number of samples. Its core design separates the description of the workflow from the actual commands to be run. This originality simplifies its usage as the user does not need to handle code, while ensuring reproducibility. Eoulsan was initially developed for bulk RNA-seq data, but the transcriptomics applications have recently widened with the advent of long-read sequencing and single-cell technologies, calling for the development of new workflows. Result: We present Eoulsan 2, a major update that (i) enhances the workflow manager itself, (ii) facilitates the development of new modules, and (iii) expands its applications to long reads RNA-seq (Oxford Nanopore Technologies) and scRNA-seq (Smart-seq2 and 10x Genomics). The workflow manager has been rewritten, with support for execution on a larger choice of computational infrastructure (workstations, Hadoop clusters, and various job schedulers for cluster usage). Eoulsan now facilitates the development of new modules, by reusing wrappers developed for the Galaxy platform, with support for container images (Docker or Singularity) packaging tools to execute. Finally, Eoulsan natively integrates novel modules for bulk RNA-seq, as well as others specifically designed for processing long read RNA-seq and scRNA-seq. Eoulsan 2 is distributed with ready-to-use workflows and companion tutorials. Availability and implementation: Eoulsan is implemented in Java, supported on Linux systems and distributed under the LGPL and CeCILL-C licenses at: http://outils.genomique.biologie.ens.fr/eoulsan/. The source code and sample workflows are available on GitHub: https://github.com/GenomicParisCentre/eoulsan. A GitHub repository for modules using the Galaxy tool XML syntax is further provided at: https://github.com/GenomicParisCentre/eoulsan-tools

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