scholarly journals ntJoin: Fast and lightweight assembly-guided scaffolding using minimizer graphs

2020 ◽  
Vol 36 (12) ◽  
pp. 3885-3887 ◽  
Author(s):  
Lauren Coombe ◽  
Vladimir Nikolić ◽  
Justin Chu ◽  
Inanc Birol ◽  
René L Warren
Keyword(s):  
Reference Sequence ◽  
Supplementary Information ◽  
Biological Research ◽  
Closely Related Species ◽  
Draft Assembly ◽  
Short Read ◽  
Sequencing Technologies ◽  
Long Read ◽  
Genome Assemblies ◽  
High Quality Genome

Abstract Summary The ability to generate high-quality genome sequences is cornerstone to modern biological research. Even with recent advancements in sequencing technologies, many genome assemblies are still not achieving reference-grade. Here, we introduce ntJoin, a tool that leverages structural synteny between a draft assembly and reference sequence(s) to contiguate and correct the former with respect to the latter. Instead of alignments, ntJoin uses a lightweight mapping approach based on a graph data structure generated from ordered minimizer sketches. The tool can be used in a variety of different applications, including improving a draft assembly with a reference-grade genome, a short-read assembly with a draft long-read assembly and a draft assembly with an assembly from a closely related species. When scaffolding a human short-read assembly using the reference human genome or a long-read assembly, ntJoin improves the NGA50 length 23- and 13-fold, respectively, in under 13 m, using <11 GB of RAM. Compared to existing reference-guided scaffolders, ntJoin generates highly contiguous assemblies faster and using less memory. Availability and implementation ntJoin is written in C++ and Python and is freely available at https://github.com/bcgsc/ntjoin. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals ntJoin: Fast and lightweight assembly-guided scaffolding using minimizer graphs

2020 ◽  
Author(s):  
Lauren Coombe ◽  
Vladimir Nikolić ◽  
Justin Chu ◽  
Inanc Birol ◽  
René L. Warren
Keyword(s):  
Reference Sequence ◽  
Biological Research ◽  
Closely Related Species ◽  
Draft Assembly ◽  
Short Read ◽  
Sequencing Technologies ◽  
Long Read ◽  
Genome Assemblies ◽  
High Quality Genome ◽  
Reference Human Genome

AbstractSummaryThe ability to generate high-quality genome sequences is cornerstone to modern biological research. Even with recent advancements in sequencing technologies, many genome assemblies are still not achieving reference-grade. Here, we introduce ntJoin, a tool that leverages structural synteny between a draft assembly and reference sequence(s) to contiguate and correct the former with respect to the latter. Instead of alignments, ntJoin uses a lightweight mapping approach based on a graph data structure generated from ordered minimizer sketches. The tool can be used in a variety of different applications, including improving a draft assembly with a reference-grade genome, a short read assembly with a draft long read assembly, and a draft assembly with an assembly from a closely-related species. When scaffolding a human short read assembly using the reference human genome or a long read assembly, ntJoin improves the NGA50 length 23- and 13-fold, respectively, in under 13 m, using less than 11 GB of RAM. Compared to existing reference-guided assemblers, ntJoin generates highly contiguous assemblies faster and using less memory.Availability and implementationntJoin is written in C++ and Python, and is freely available at https://github.com/bcgsc/[email protected]

scholarly journals Liftoff: accurate mapping of gene annotations

2020 ◽  
Author(s):  
Alaina Shumate ◽  
Steven L Salzberg
Keyword(s):  
Reference Genome ◽  
Supplementary Information ◽  
Closely Related Species ◽  
Protein Coding ◽  
Human Reference Genome ◽  
Sequence Identity ◽  
Gene Annotations ◽  
Genome Assemblies ◽  
Average Sequence Identity ◽  
High Quality Genome

Abstract Motivation Improvements in DNA sequencing technology and computational methods have led to a substantial increase in the creation of high-quality genome assemblies of many species. To understand the biology of these genomes, annotation of gene features and other functional elements is essential; however for most species, only the reference genome is well-annotated. Results One strategy to annotate new or improved genome assemblies is to map or ‘lift over’ the genes from a previously-annotated reference genome. Here we describe Liftoff, a new genome annotation lift-over tool capable of mapping genes between two assemblies of the same or closely-related species. Liftoff aligns genes from a reference genome to a target genome and finds the mapping that maximizes sequence identity while preserving the structure of each exon, transcript, and gene. We show that Liftoff can accurately map 99.9% of genes between two versions of the human reference genome with an average sequence identity >99.9%. We also show that Liftoff can map genes across species by successfully lifting over 98.3% of human protein-coding genes to a chimpanzee genome assembly with 98.2% sequence identity. Availability and Implementation Liftoff can be installed via bioconda and PyPI. Additionally, the source code for Liftoff is available at https://github.com/agshumate/Liftoff Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Read coverage as an indicator of misassembly in a short-read based genome assembly

2019 ◽  
Author(s):  
Peipei Wang ◽  
Fanrui Meng ◽  
Bethany M. Moore ◽  
Shin-Han Shiu
Keyword(s):  
Machine Learning ◽  
Great Majority ◽  
Significant Advance ◽  
High Coverage ◽  
Short Read ◽  
Sequencing Technologies ◽  
Long Read ◽  
Genomic Regions ◽  
Genome Assemblies ◽  
Simple Sequence

ABSTRACTAvailability of genome sequences has led to significant advance in biology. With few exceptions, the great majority of existing genome assemblies are derived from short read sequencing technologies with highly uneven read coverages indicative of sequencing and assembly issues. In tomato, 0.6% (5.1 Mb) and 9.7% (79.6 Mb) of short-read based assembly had significantly higher and lower coverage compared to background, respectively. We established machine learning models capable of predicting genomic regions with variable coverages and found that high coverage regions tend to have lower simple sequence repeat but higher tandem gene densities compared to background regions. To determine if the high coverage regions were misassembled, we examined a recently available long-read based assembly and found that 27.8% (1.41 Mb) of high coverage regions were potentially mis-assembled of duplicate sequences, compared to 1.4% in background regions. In addition, using a machine learning model that can distinguish correctly and incorrectly assembled high coverage regions, we found that misassembled, high coverage regions tend to be flanked by simple sequence repeats, pseudogenes, and transposon elements. Our study provides insights on the causes of variable coverage regions and a quantitative assessment of factors contributing to misassembly when using short reads.

NextPolish: a fast and efficient genome polishing tool for long-read assembly

2019 ◽  
Vol 36 (7) ◽  
pp. 2253-2255 ◽  
Author(s):  
Jiang Hu ◽  
Junpeng Fan ◽  
Zongyi Sun ◽  
Shanlin Liu
Keyword(s):  
Error Rates ◽  
Supplementary Information ◽  
Sequencing Technologies ◽  
Large Numbers ◽  
Long Reads ◽  
Long Read ◽  
Genome Assemblies ◽  
Polishing Tool ◽  
Sequence Errors ◽  
Plant Arabidopsis Thaliana

Abstract Motivation Although long-read sequencing technologies can produce genomes with long contiguity, they suffer from high error rates. Thus, we developed NextPolish, a tool that efficiently corrects sequence errors in genomes assembled with long reads. This new tool consists of two interlinked modules that are designed to score and count K-mers from high quality short reads, and to polish genome assemblies containing large numbers of base errors. Results When evaluated for the speed and efficiency using human and a plant (Arabidopsis thaliana) genomes, NextPolish outperformed Pilon by correcting sequence errors faster, and with a higher correction accuracy. Availability and implementation NextPolish is implemented in C and Python. The source code is available from https://github.com/Nextomics/NextPolish. Supplementary information Supplementary data are available at Bioinformatics online.

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 Scaffolding and Completing Genome Assemblies in Real-time with Nanopore Sequencing

2016 ◽  
Author(s):  
Minh Duc Cao ◽  
Son Hoang Nguyen ◽  
Devika Ganesamoorthy ◽  
Alysha G. Elliott ◽  
Matthew Cooper ◽  
...  
Keyword(s):  
Real Time ◽  
Sequence Data ◽  
Repetitive Sequences ◽  
Computational Method ◽  
Nanopore Sequencing ◽  
Short Read ◽  
Sequencing Technologies ◽  
Long Read ◽  
Computational Resources ◽  
Genome Assemblies

AbstractGenome assemblies obtained from short read sequencing technologies are often fragmented into many contigs because of the abundance of repetitive sequences. Long read sequencing technologies allow the generation of reads spanning most repeat sequences, providing the opportunity to complete these genome assemblies. However, substantial amounts of sequence data and computational resources are required to overcome the high per-base error rate inherent to these technologies. Furthermore, most existing methods only assemble the genomes after sequencing has completed which could result in either generation of more sequence data at greater cost than required or a low-quality assembly if insufficient data are generated. Here we present the first computational method which utilises real-time nanopore sequencing to scaffold and complete short-read assemblies while the long read sequence data is being generated. The method reports the progress of completing the assembly in real-time so users can terminate the sequencing once an assembly of sufficient quality and completeness is obtained. We use our method to complete four bacterial genomes and one eukaryotic genome, and show that it is able to construct more complete and more accurate assemblies, and at the same time, requires less sequencing data and computational resources than existing pipelines. We also demonstrate that the method can facilitate real-time analyses of positional information such as identification of bacterial genes encoded in plasmids and pathogenicity islands.

scholarly journals Comprehensive identification of transposable element insertions using multiple sequencing technologies

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.

scholarly journals Hapo-G, haplotype-aware polishing of genome assemblies with accurate reads

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Jean-Marc Aury ◽  
Benjamin Istace
Keyword(s):  
Single Molecule ◽  
Direct Consequence ◽  
High Quality ◽  
Sequencing Errors ◽  
Coding Regions ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Oxford Nanopore ◽  
Long Read ◽  
Genome Assemblies

Abstract Single-molecule sequencing technologies have recently been commercialized by Pacific Biosciences and Oxford Nanopore with the promise of sequencing long DNA fragments (kilobases to megabases order) and then, using efficient algorithms, provide high quality assemblies in terms of contiguity and completeness of repetitive regions. However, the error rate of long-read technologies is higher than that of short-read technologies. This has a direct consequence on the base quality of genome assemblies, particularly in coding regions where sequencing errors can disrupt the coding frame of genes. In the case of diploid genomes, the consensus of a given gene can be a mixture between the two haplotypes and can lead to premature stop codons. Several methods have been developed to polish genome assemblies using short reads and generally, they inspect the nucleotide one by one, and provide a correction for each nucleotide of the input assembly. As a result, these algorithms are not able to properly process diploid genomes and they typically switch from one haplotype to another. Herein we proposed Hapo-G (Haplotype-Aware Polishing Of Genomes), a new algorithm capable of incorporating phasing information from high-quality reads (short or long-reads) to polish genome assemblies and in particular assemblies of diploid and heterozygous genomes.

scholarly journals Contiguity: Contig adjacency graph construction and visualisation

2015 ◽  
Author(s):  
Mitchell J Sullivan ◽  
Nouri L Ben Zakour ◽  
Brian M Forde ◽  
Mitchell Stanton-Cook ◽  
Scott A Beatson
Keyword(s):  
De Novo ◽  
Reference Sequence ◽  
De Bruijn Graph ◽  
Interactive Software ◽  
Graph Exploration ◽  
Adjacency Graph ◽  
Highly Sensitive ◽  
Long Read ◽  
Genome Assemblies ◽  
Adjacency Graphs

Contiguity is an interactive software for the visualization and manipulation of de novo genome assemblies. Contiguity creates and displays information on contig adjacency which is contextualized by the simultaneous display of a comparison between assembled contigs and reference sequence. Where scaffolders allow unambiguous connections between contigs to be resolved into a single scaffold, Contiguity allows the user to create all potential scaffolds in ambiguous regions of the genome. This enables the resolution of novel sequence or structural variants from the assembly. In addition, Contiguity provides a sequencing and assembly agnostic approach for the creation of contig adjacency graphs. To maximize the number of contig adjacencies determined, Contiguity combines information from read pair mappings, sequence overlap and De Bruijn graph exploration. We demonstrate how highly sensitive graphs can be achieved using this method. Contig adjacency graphs allow the user to visualize potential arrangements of contigs in unresolvable areas of the genome. By combining adjacency information with comparative genomics, Contiguity provides an intuitive approach for exploring and improving sequence assemblies. It is also useful in guiding manual closure of long read sequence assemblies. Contiguity is an open source application, implemented using Python and the Tkinter GUI package that can run on any Unix, OSX and Windows operating system. It has been designed and optimized for bacterial assemblies. Contiguity is available at http://mjsull.github.io/Contiguity .

scholarly journals SVIM-asm: Structural variant detection from haploid and diploid genome assemblies

2020 ◽  
Author(s):  
David Heller ◽  
Martin Vingron
Keyword(s):  
Genetic Information ◽  
Source Code ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Diploid Genome ◽  
Insertions And Deletions ◽  
Structural Variant ◽  
Sequencing Technologies ◽  
Variant Detection ◽  
Genome Assemblies

AbstractMotivationWith the availability of new sequencing technologies, the generation of haplotype-resolved genome assemblies up to chromosome scale has become feasible. These assemblies capture the complete genetic information of both parental haplotypes, increase structural variant (SV) calling sensitivity and enable direct genotyping and phasing of SVs. Yet, existing SV callers are designed for haploid genome assemblies only, do not support genotyping or detect only a limited set of SV classes.ResultsWe introduce our method SVIM-asm for the detection and genotyping of six common classes of SVs from haploid and diploid genome assemblies. Compared against the only other existing SV caller for diploid assemblies, DipCall, SVIM-asm detects more SV classes and reached higher F1 scores for the detection of insertions and deletions on two recently published assemblies of the HG002 individual.Availability and ImplementationSVIM-asm has been implemented in Python and can be easily installed via bioconda. Its source code is available at github.com/eldariont/[email protected] informationSupplementary data are available online.

Sign in / Sign up

Export Citation Format

Share Document