scholarly journals HINGE: Long-Read Assembly Achieves Optimal Repeat Resolution

2016 ◽  
Author(s):  
Govinda M. Kamath ◽  
Ilan Shomorony ◽  
Fei Xia ◽  
Thomas A. Courtade ◽  
David N. Tse
Keyword(s):  
Gold Standard ◽  
De Novo ◽  
Error Resilience ◽  
De Bruijn Graph ◽  
Sequencing Technologies ◽  
Long Read ◽  
De Bruijn ◽  
Genome Assemblies

ABSTRACTLong-read sequencing technologies have the potential to produce gold-standard de novo genome assemblies, but fully exploiting error-prone reads to resolve repeats remains a challenge. Aggressive approaches to repeat resolution often produce mis-assemblies, and conservative approaches lead to unnecessary fragmentation. We present HINGE, an assembler that seeks to achieve optimal repeat resolution by distinguishing repeats that can be resolved given the data from those that cannot. This is accomplished by adding "hinges" to reads for constructing an overlap graph where only unresolvable repeats are merged. As a result, HINGE combines the error resilience of overlap-based assemblers with repeat-resolution capabilities of de Bruijn graph assemblers. HINGE was evaluated on the long-read bacterial datasets from the NCTC project. HINGE produces more finished assemblies than Miniasm and the manual pipeline of NCTC based on the HGAP assembler and Circlator. HINGE also allows us to identify 40 datasets where unresolvable repeats prevent the reliable construction of a unique finished assembly. In these cases, HINGE outputs a visually interpretable assembly graph that encodes all possible finished assemblies consistent with the reads, while other approaches such as the NCTC pipeline and FALCON either fragment the assembly or resolve the ambiguity arbitrarily.

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 Scalable Genome Assembly through Parallel de Bruijn Graph Construction for Multiple k-mers

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Kanak Mahadik ◽  
Christopher Wright ◽  
Milind Kulkarni ◽  
Saurabh Bagchi ◽  
Somali Chaterji
Keyword(s):  
De Novo ◽  
De Bruijn Graph ◽  
High Quality ◽  
De Bruijn Graphs ◽  
Sequencing Technologies ◽  
De Bruijn ◽  
Similar Accuracy ◽  
Valued Graph ◽  
Assembly Algorithms ◽  
Level Parallelism

Abstract Remarkable advancements in high-throughput gene sequencing technologies have led to an exponential growth in the number of sequenced genomes. However, unavailability of highly parallel and scalable de novo assembly algorithms have hindered biologists attempting to swiftly assemble high-quality complex genomes. Popular de Bruijn graph assemblers, such as IDBA-UD, generate high-quality assemblies by iterating over a set of k-values used in the construction of de Bruijn graphs (DBG). However, this process of sequentially iterating from small to large k-values slows down the process of assembly. In this paper, we propose ScalaDBG, which metamorphoses this sequential process, building DBGs for each distinct k-value in parallel. We develop an innovative mechanism to “patch” a higher k-valued graph with contigs generated from a lower k-valued graph. Moreover, ScalaDBG leverages multi-level parallelism, by both scaling up on all cores of a node, and scaling out to multiple nodes simultaneously. We demonstrate that ScalaDBG completes assembling the genome faster than IDBA-UD, but with similar accuracy on a variety of datasets (6.8X faster for one of the most complex genome in our dataset).

scholarly journals Clover: a clustering-oriented de novo assembler for Illumina sequences

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Ming-Feng Hsieh ◽  
Chin Lung Lu ◽  
Chuan Yi Tang
Keyword(s):  
De Novo Assembly ◽  
De Novo ◽  
Low Cost ◽  
De Bruijn Graph ◽  
Illumina Platform ◽  
Sequencing Errors ◽  
Sequencing Technologies ◽  
String Graph ◽  
Clustering Approach ◽  
De Bruijn

Abstract Background Next-generation sequencing technologies revolutionized genomics by producing high-throughput reads at low cost, and this progress has prompted the recent development of de novo assemblers. Multiple assembly methods based on de Bruijn graph have been shown to be efficient for Illumina reads. However, the sequencing errors generated by the sequencer complicate analysis of de novo assembly and influence the quality of downstream genomic researches. Results In this paper, we develop a de Bruijn assembler, called Clover (clustering-oriented de novo assembler), that utilizes a novel k-mer clustering approach from the overlap-layout-consensus concept to deal with the sequencing errors generated by the Illumina platform. We further evaluate Clover’s performance against several de Bruijn graph assemblers (ABySS, SOAPdenovo, SPAdes and Velvet), overlap-layout-consensus assemblers (Bambus2, CABOG and MSR-CA) and string graph assembler (SGA) on three datasets (Staphylococcus aureus, Rhodobacter sphaeroides and human chromosome 14). The results show that Clover achieves a superior assembly quality in terms of corrected N50 and E-size while remaining a significantly competitive in run time except SOAPdenovo. In addition, Clover was involved in the sequencing projects of bacterial genomes Acinetobacter baumannii TYTH-1 and Morganella morganii KT. Conclusions The marvel clustering-based approach of Clover that integrates the flexibility of the overlap-layout-consensus approach and the efficiency of the de Bruijn graph method has high potential on de novo assembly. Now, Clover is freely available as open source software from https://oz.nthu.edu.tw/~d9562563/src.html.

scholarly journals Comparative Annotation Toolkit (CAT) - simultaneous clade and personal genome annotation

2017 ◽  
Author(s):  
Ian T. Fiddes ◽  
Joel Armstrong ◽  
Mark Diekhans ◽  
Stefanie Nachtweide ◽  
Zev N. Kronenberg ◽  
...  
Keyword(s):  
Genome Annotation ◽  
De Novo ◽  
Low Cost ◽  
Great Apes ◽  
Personal Genome ◽  
Sequencing Technologies ◽  
Human Genomes ◽  
Long Read ◽  
Genome Assemblies ◽  
Rat Genome

ABSTRACTThe recent introductions of low-cost, long-read, and read-cloud sequencing technologies coupled with intense efforts to develop efficient algorithms have made affordable, high-quality de novo sequence assembly a realistic proposition. The result is an explosion of new, ultra-contiguous genome assemblies. To compare these genomes we need robust methods for genome annotation. We describe the fully open source Comparative Annotation Toolkit (CAT), which provides a flexible way to simultaneously annotate entire clades and identify orthology relationships. We show that CAT can be used to improve annotations on the rat genome, annotate the great apes, annotate a diverse set of mammals, and annotate personal, diploid human genomes. We demonstrate the resulting discovery of novel genes, isoforms and structural variants, even in genomes as well studied as rat and the great apes, and how these annotations improve cross-species RNA expression experiments.

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 WENGAN: Efficient and high quality hybrid de novo assembly of human genomes

2019 ◽  
Author(s):  
Alex Di Genova ◽  
Elena Buena-Atienza ◽  
Stephan Ossowski ◽  
Marie-France Sagot
Keyword(s):  
De Novo ◽  
Computational Cost ◽  
Sequence Information ◽  
Sequencing Data ◽  
High Quality ◽  
Sequencing Technologies ◽  
Human Genomes ◽  
Long Reads ◽  
Long Read ◽  
Genome Assemblies

The continuous improvement of long-read sequencing technologies along with the development of ad-doc algorithms has launched a new de novo assembly era that promises high-quality genomes. However, it has proven difficult to use only long reads to generate accurate genome assemblies of large, repeat-rich human genomes. To date, most of the human genomes assembled from long error-prone reads add accurate short reads to further polish the consensus quality. Here, we report the development of a novel algorithm for hybrid assembly, WENGAN, and the de novo assembly of four human genomes using a combination of sequencing data generated on ONT PromethION, PacBio Sequel, Illumina and MGI technology. WENGAN implements efficient algorithms that exploit the sequence information of short and long reads to tackle assembly contiguity as well as consensus quality. The resulting genome assemblies have high contiguity (contig NG50:16.67-62.06 Mb), few assembly errors (contig NGA50:10.9-45.91 Mb), good consensus quality (QV:27.79-33.61), and high gene completeness (BUSCO complete: 94.6-95.1%), while consuming low computational resources (CPU hours:153-1027). In particular, the WENGAN assembly of the haploid CHM13 sample achieved a contig NG50 of 62.06 Mb (NGA50:45.91 Mb), which surpasses the contiguity of the current human reference genome (GRCh38 contig NG50:57.88 Mb). Providing highest quality at low computational cost, WENGAN is an important step towards the democratization of the de novo assembly of human genomes. The WENGAN assembler is available at https://github.com/adigenova/wengan

scholarly journals MBG: Minimizer-based Sparse de Bruijn Graph Construction

2020 ◽  
Author(s):  
Mikko Rautiainen ◽  
Tobias Marschall
Keyword(s):  
Source Code ◽  
Error Rates ◽  
Read Length ◽  
De Bruijn Graph ◽  
De Bruijn Graphs ◽  
E Coli ◽  
Link Type ◽  
Sequencing Technologies ◽  
Long Read ◽  
De Bruijn

MotivationDe Bruijn graphs can be constructed from short reads efficiently and have been used for many purposes. Traditionally long read sequencing technologies have had too high error rates for de Bruijn graph-based methods. Recently, HiFi reads have provided a combination of long read length and low error rate, which enables de Bruijn graphs to be used with HiFi reads.ResultsWe have implemented MBG, a tool for building sparse de Bruijn graphs from HiFi reads. MBG outperforms existing tools for building dense de Bruijn graphs, and can build a graph of 50x coverage whole human genome HiFi reads in four hours on a single core. MBG also assembles the bacterial E. coli genome into a single contig in 8 seconds.AvailabilityPackage manager: https://anaconda.org/bioconda/mbg and source code: https://github.com/maickrau/MBG

scholarly journals Deep repeat resolution—the assembly of the Drosophila Histone Complex

2018 ◽  
Vol 47 (3) ◽  
pp. e18-e18 ◽  
Author(s):  
Philipp Bongartz ◽  
Siegfried Schloissnig
Keyword(s):  
De Novo ◽  
Machine Learning Algorithms ◽  
Single Nucleotide Variants ◽  
Major Step ◽  
Base Pairs ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Wide Range ◽  
Long Read ◽  
Genome Assemblies

Abstract Though the advent of long-read sequencing technologies has led to a leap in contiguity of de novo genome assemblies, current reference genomes of higher organisms still do not provide unbroken sequences of complete chromosomes. Despite reads in excess of 30 000 base pairs, there are still repetitive structures that cannot be resolved by current state-of-the-art assemblers. The most challenging of these structures are tandemly arrayed repeats, which occur in the genomes of all eukaryotes. Untangling tandem repeat clusters is exceptionally difficult, since the rare differences between repeat copies are obscured by the high error rate of long reads. Solving this problem would constitute a major step towards computing fully assembled genomes. Here, we demonstrate by example of the Drosophila Histone Complex that via machine learning algorithms, it is possible to exploit the underlying distinguishing patterns of single nucleotide variants of repeats from very noisy data to resolve a large and highly conserved repeat cluster. The ideas explored in this paper are a first step towards the automated assembly of complex repeat structures and promise to be applicable to a wide range of eukaryotic genomes.

Population-scale detection of non-reference sequence variants using colored de Bruijn Graphs

2021 ◽  
Author(s):  
Thomas Krannich ◽  
Walton Timothy James White ◽  
Sebastian Niehus ◽  
Guillaume Holley ◽  
Bjarni Halldorsson ◽  
...  
Keyword(s):  
De Novo ◽  
Sequence Data ◽  
Simulated Data ◽  
Reference Sequence ◽  
De Bruijn Graph ◽  
High Coverage ◽  
De Bruijn Graphs ◽  
Sequencing Technologies ◽  
De Bruijn ◽  
Population Scale

With the increasing throughput of sequencing technologies, structural variant (SV) detection has become possible across ten of thousands of genomes. Non-reference sequence (NRS) variants have drawn less attention compared to other types of SVs due to the computational complexity of detecting them. When using short-read data the detection of NRS variants inevitably involves a de novo assembly which requires high-quality sequence data at high coverage. Previous studies have demonstrated how sequence data of multiple genomes can be combined for the reliable detection of NRS variants. However, the algorithms proposed in these studies have limited scalability to larger sets of genomes. We introduce PopIns2, a tool to discover and characterize NRS variants in many genomes, which scales to considerably larger numbers of genomes than its predecessor PopIns. In this article, we briefly outline the workflow of PopIns and highlight the novel algorithmic contributions. We developed an entirely new approach for merging contig assemblies of unaligned reads from many genomes into a single set of NRS using a colored de Bruijn graph. Our tests on simulated data indicate that the new merging algorithm ranks among the best approaches in terms of quality and reliability and that PopIns2 shows the best precision for a growing number of genomes processed. Results on the Polaris Diversity Cohort and a set of 1000 Icelandic human genomes demonstrate unmatched scalability for the application on population-scale datasets.

scholarly journals Higher quality de novo genome assemblies from degraded museum specimens: a linked-read approach to museomics

2019 ◽  
Author(s):  
Jocelyn P. Colella ◽  
Anna Tigano ◽  
Matthew D. MacManes
Keyword(s):  
High Throughput Sequencing ◽  
De Novo ◽  
Deer Mouse ◽  
Cost Effective ◽  
Molecular Data ◽  
Degraded Dna ◽  
Museum Specimens ◽  
Sequencing Technologies ◽  
Long Read ◽  
Genome Assemblies

AbstractHigh-throughput sequencing technologies are a proposed solution for accessing the molecular data in historic specimens. However, degraded DNA combined with the computational demands of short-read assemblies has posed significant laboratory and bioinformatics challenges. Linked-read or ‘synthetic long-read’ sequencing technologies, such as 10X Genomics, may provide a cost-effective alternative solution to assemble higher quality de novo genomes from degraded specimens. Here, we compare assembly quality (e.g., genome contiguity and completeness, presence of orthogroups) between four published genomes assembled from a single shotgun library and four deer mouse (Peromyscus spp.) genomes assembled using 10X Genomics technology. At a similar price-point, these approaches produce vastly different assemblies, with linked-read assemblies having overall higher quality, measured by larger N50 values and greater gene content. Although not without caveats, our results suggest that linked-read sequencing technologies may represent a viable option to build de novo genomes from historic museum specimens, which may prove particularly valuable for extinct, rare, or difficult to collect taxa.

Sign in / Sign up

Export Citation Format

Share Document