scholarly journals NxRepair: Error correction in de novo sequence assembly using Nextera mate pairs

2014 ◽  
Author(s):  
Rebecca R Murphy ◽  
Jared M O'Connell ◽  
Anthony J Cox ◽  
Ole B Schulz-Trieglaff
Keyword(s):  
Error Correction ◽  
Large Scale ◽  
De Novo ◽  
Reference Sequence ◽  
De Bruijn Graph ◽  
Sequencing Data ◽  
Additional Information ◽  
Mate Pair ◽  
De Bruijn ◽  
De Novo Sequence Assembly

Scaffolding errors and incorrect traversals of the de Bruijn graph during de novo assembly can result in large scale misassemblies in draft genomes. Nextera mate pair sequencing data provide additional information to resolve assembly ambiguities during scaffolding. Here, we introduce NxRepair, an open source toolkit for error correction in de novo assemblies that uses Nextera mate pair libraries to identify and correct large-scale errors. We show that NxRepair can identify and correct large scaffolding errors, without use of a reference sequence, resulting in quantitative improvements in the assembly quality. NxRepair can be downloaded from GitHub; a tutorial and user documentation are also available.

scholarly journals NxRepair: Error correction in de novo sequence assembly using Nextera mate pairs

2014 ◽  
Author(s):  
Rebecca R Murphy ◽  
Jared M O'Connell ◽  
Anthony J Cox ◽  
Ole B Schulz-Trieglaff
Keyword(s):  
Error Correction ◽  
Large Scale ◽  
De Novo ◽  
Reference Sequence ◽  
De Bruijn Graph ◽  
Sequencing Data ◽  
Additional Information ◽  
Mate Pair ◽  
De Bruijn ◽  
De Novo Sequence Assembly

Scaffolding errors and incorrect traversals of the de Bruijn graph during de novo assembly can result in large scale misassemblies in draft genomes. Nextera mate pair sequencing data provide additional information to resolve assembly ambiguities during scaffolding. Here, we introduce NxRepair, an open source toolkit for error correction in de novo assemblies that uses Nextera mate pair libraries to identify and correct large-scale errors. We show that NxRepair can identify and correct large scaffolding errors, without use of a reference sequence, resulting in quantitative improvements in the assembly quality. NxRepair can be downloaded from GitHub; a tutorial and user documentation are also available.

Inference of viral quasispecies with a paired de Bruijn graph

2020 ◽  
Author(s):  
Borja Freire ◽  
Susana Ladra ◽  
Jose R Paramá ◽  
Leena Salmela
Keyword(s):  
High Throughput Sequencing ◽  
De Novo ◽  
Supplementary Information ◽  
De Bruijn Graph ◽  
Viral Quasispecies ◽  
Sequencing Data ◽  
De Bruijn Graphs ◽  
Sequencing Errors ◽  
High Throughput Sequencing Data ◽  
De Bruijn

Abstract Motivation RNA viruses exhibit a high mutation rate and thus they exist in infected cells as a population of closely related strains called viral quasispecies. The viral quasispecies assembly problem asks to characterize the quasispecies present in a sample from high-throughput sequencing data. We study the de novo version of the problem, where reference sequences of the quasispecies are not available. Current methods for assembling viral quasispecies are either based on overlap graphs or on de Bruijn graphs. Overlap graph-based methods tend to be accurate but slow, whereas de Bruijn graph-based methods are fast but less accurate. Results We present viaDBG, which is a fast and accurate de Bruijn graph-based tool for de novo assembly of viral quasispecies. We first iteratively correct sequencing errors in the reads, which allows us to use large k-mers in the de Bruijn graph. To incorporate the paired-end information in the graph, we also adapt the paired de Bruijn graph for viral quasispecies assembly. These features enable the use of long-range information in contig construction without compromising the speed of de Bruijn graph-based approaches. Our experimental results show that viaDBG is both accurate and fast, whereas previous methods are either fast or accurate but not both. In particular, viaDBG has comparable or better accuracy than SAVAGE, while being at least nine times faster. Furthermore, the speed of viaDBG is comparable to PEHaplo but viaDBG is able to retrieve also low abundance quasispecies, which are often missed by PEHaplo. Availability and implementation viaDBG is implemented in C++ and it is publicly available at https://bitbucket.org/bfreirec1/viadbg. All datasets used in this article are publicly available at https://bitbucket.org/bfreirec1/data-viadbg/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals An Efficient, Scalable and Exact Representation of High-Dimensional Color Information Enabled via de Bruijn Graph Search

2018 ◽  
Author(s):  
Fatemeh Almodaresi ◽  
Prashant Pandey ◽  
Michael Ferdman ◽  
Rob Johnson ◽  
Rob Patro
Keyword(s):  
Large Scale ◽  
Population Level ◽  
Reference Sequence ◽  
De Bruijn Graph ◽  
High Dimensional ◽  
Level Variation ◽  
Color Information ◽  
Sequence Search ◽  
De Bruijn ◽  
Scale Sequence

AbstractThe colored de Bruijn graph (cdbg) and its variants have become an important combinatorial structure used in numerous areas in genomics, such as population-level variation detection in metagenomic samples, large scale sequence search, and cdbg-based reference sequence indices. As samples or genomes are added to the cdbg, the color information comes to dominate the space required to represent this data structure.In this paper, we show how to represent the color information efficiently by adopting a hierarchical encoding that exploits correlations among color classes — patterns of color occurrence — present in the de Bruijn graph (dbg). A major challenge in deriving an efficient encoding of the color information that takes advantage of such correlations is determining which color classes are close to each other in the high-dimensional space of possible color patterns. We demonstrate that the dbg itself can be used as an efficient mechanism to search for approximate nearest neighbors in this space. While our approach reduces the encoding size of the color information even for relatively small cdbgs (hundreds of experiments), the gains are particularly consequential as the number of potential colors (i.e. samples or references) grows to thousands of experiments.We apply this encoding in the context of two different applications; the implicit cdbg used for a large-scale sequence search index, Mantis, as well as the encoding of color information used in population-level variation detection by tools such as Vari and Rainbowfish. Our results show significant improvements in the overall size and scalability of representation of the color information. In our experiment on 10,000 samples, we achieved more than 11× better compression compared to RRR.

scholarly journals Succinct Dynamic de Bruijn Graphs

2020 ◽  
Author(s):  
Bahar Alipanahi ◽  
Alan Kuhnle ◽  
Simon J. Puglisi ◽  
Leena Salmela ◽  
Christina Boucher
Keyword(s):  
Data Structures ◽  
Large Scale ◽  
High Throughput Sequencing ◽  
De Bruijn Graph ◽  
Sequencing Data ◽  
Efficient Manner ◽  
De Bruijn Graphs ◽  
High Throughput Sequencing Data ◽  
Efficient Data ◽  
De Bruijn

AbstractMotivationThe de Bruijn graph is one of the fundamental data structures for analysis of high throughput sequencing data. In order to be applicable to population-scale studies, it is essential to build and store the graph in a space- and time-efficient manner. In addition, due to the ever-changing nature of population studies, it has become essential to update the graph after construction e.g. add and remove nodes and edges. Although there has been substantial effort on making the construction and storage of the graph efficient, there is a limited amount of work in building the graph in an efficient and mutable manner. Hence, most space efficient data structures require complete reconstruction of the graph in order to add or remove edges or nodes.ResultsIn this paper we present DynamicBOSS, a succinct representation of the de Bruijn graph that allows for an unlimited number of additions and deletions of nodes and edges. We compare our method with other competing methods and demonstrate that DynamicBOSS is the only method that supports both addition and deletion and is applicable to very large samples (e.g. greater than 15 billion k-mers). Competing dynamic methods e.g., FDBG (Crawford et al., 2018) cannot be constructed on large scale datasets, or cannot support both addition and deletion e.g., BiFrost (Holley and Melsted, 2019).AvailabilityDynamicBOSS is publicly available at https://github.com/baharpan/[email protected]

scholarly journals Succinct Dynamic de Bruijn Graphs

2020 ◽  
Author(s):  
Bahar Alipanahi ◽  
Alan Kuhnle ◽  
Simon J Puglisi ◽  
Leena Salmela ◽  
Christina Boucher
Keyword(s):  
Data Structures ◽  
Large Scale ◽  
High Throughput Sequencing ◽  
Supplementary Information ◽  
De Bruijn Graph ◽  
Sequencing Data ◽  
Efficient Manner ◽  
De Bruijn Graphs ◽  
High Throughput Sequencing Data ◽  
De Bruijn

Abstract Motivation The de Bruijn graph is one of the fundamental data structures for analysis of high throughput sequencing data. In order to be applicable to population-scale studies, it is essential to build and store the graph in a space- and time- efficient manner. In addition, due to the ever-changing nature of population studies, it has become essential to update the graph after construction e.g. add and remove nodes and edges. Although there has been substantial effort on making the construction and storage of the graph efficient, there is a limited amount of work in building the graph in an efficient and mutable manner. Hence, most space efficient data structures require complete reconstruction of the graph in order to add or remove edges or nodes. Results In this paper we present DynamicBOSS, a succinct representation of the de Bruijn graph that allows for an unlimited number of additions and deletions of nodes and edges. We compare our method with other competing methods and demonstrate that DynamicBOSS is the only method that supports both addition and deletion and is applicable to very large samples (e.g. greater than 15 billion k-mers). Competing dynamic methods e.g., FDBG (Crawford et al., 2018) cannot be constructed on large scale datasets, or cannot support both addition and deletion e.g., BiFrost (Holley and Melsted, 2019). Availability DynamicBOSS is publicly available at https://github.com/baharpan/dynboss. Supplementary information Supplementary data are available at Bioinformatics online.

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 deGSM: memory scalable construction of large scale de Bruijn Graph

2018 ◽  
Author(s):  
Hongzhe Guo ◽  
Yilei Fu ◽  
Yan Gao ◽  
Junyi Li ◽  
Yadong Wang ◽  
...  
Keyword(s):  
Genome Sequence ◽  
Large Scale ◽  
High Throughput Sequencing ◽  
De Novo ◽  
Rapid Development ◽  
Main Idea ◽  
Supplementary Information ◽  
De Bruijn Graph ◽  
External Sorting ◽  
De Bruijn

AbstractMotivationDe Bruijn graph, a fundamental data structure to represent and organize genome sequence, plays important roles in various kinds of sequence analysis tasks such as de novo assembly, high-throughput sequencing (HTS) read alignment, pan-genome analysis, metagenomics analysis, HTS read correction, etc. With the rapid development of HTS data and ever-increasing number of assembled genomes, there is a high demand to construct de Bruijn graph for sequences up to Tera-base-pair level. It is non-trivial since the size of the graph to be constructed could be very large and each graph consists of hundreds of billions of vertices and edges. Current existing approaches may have unaffordable memory footprints to handle such a large de Bruijn graph. Moreover, it also requires the construction approach to handle very large dataset efficiently, even if in a relatively small RAM space.ResultsWe propose a lightweight parallel de Bruijn graph construction approach, de Bruijn Graph Constructor in Scalable Memory (deGSM). The main idea of deGSM is to efficiently construct the Bur-rows-Wheeler Transformation (BWT) of the unipaths of de Bruijn graph in constant RAM space and transform the BWT into the original unitigs. It is mainly implemented by a fast parallel external sorting of k-mers, which allows only a part of k-mers kept in RAM by a novel organization of the k-mers. The experimental results demonstrate that, just with a commonly used machine, deGSM is able to handle very large genome sequence(s), e.g., the contigs (305 Gbp) and scaffolds (1.1 Tbp) recorded in Gen-Bank database and Picea abies HTS dataset (9.7 Tbp). Moreover, deGSM also has faster or comparable construction speed compared with state-of-the-art approaches. With its high scalability and efficiency, deGSM has enormous potentials in many large scale genomics studies.Availabilityhttps://github.com/hitbc/[email protected] (YW) and [email protected] (BL)Supplementary informationSupplementary data are available online.

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 Implications of Genetic Distance to Reference and De Novo Genome Assembly for Clinical Genomics in Africans

2020 ◽  
Author(s):  
Daniel Shriner ◽  
Adebowale Adeyemo ◽  
Charles Rotimi
Keyword(s):  
Genetic Distance ◽  
De Novo ◽  
Reference Sequence ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
De Novo Genome Assembly ◽  
Single Nucleotide ◽  
Clinical Genomics ◽  
Advantages And Disadvantages ◽  
False Discovery

In clinical genomics, variant calling from short-read sequencing data typically relies on a pan-genomic, universal human reference sequence. A major limitation of this approach is that the number of reads that incorrectly map or fail to map increase as the reads diverge from the reference sequence. In the context of genome sequencing of genetically diverse Africans, we investigate the advantages and disadvantages of using a de novo assembly of the read data as the reference sequence in single sample calling. Conditional on sufficient read depth, the alignment-based and assembly-based approaches yielded comparable sensitivity and false discovery rates for single nucleotide variants when benchmarked against a gold standard call set. The alignment-based approach yielded coverage of an additional 270.8 Mb over which sensitivity was lower and the false discovery rate was higher. Although both approaches detected and missed clinically relevant variants, the assembly-based approach identified more such variants than the alignment-based approach. Of particular relevance to individuals of African descent, the assembly-based approach identified four heterozygous genotypes containing the sickle allele whereas the alignment-based approach identified no occurrences of the sickle allele. Variant annotation using dbSNP and gnomAD identified systematic biases in these databases due to underrepresentation of Africans. Using the counts of homozygous alternate genotypes from the alignment-based approach as a measure of genetic distance to the reference sequence GRCh38.p12, we found that the numbers of misassemblies, total variant sites, potentially novel single nucleotide variants (SNVs), and certain variant classes (e.g., splice acceptor variants, stop loss variants, missense variants, synonymous variants, and variants absent from gnomAD) were significantly correlated with genetic distance. In contrast, genomic coverage and other variant classes (e.g., ClinVar pathogenic or likely pathogenic variants, start loss variants, stop gain variants, splice donor variants, incomplete terminal codons, variants with CADD score ≥20) were not correlated with genetic distance. With improvement in coverage, the assembly-based approach can offer a viable alternative to the alignment-based approach, with the advantage that it can obviate the need to generate diverse human reference sequences or collections of alternate scaffolds.

scholarly journals ABySS 2.0: Resource-Efficient Assembly of Large Genomes using a Bloom Filter

2016 ◽  
Author(s):  
Shaun D Jackman ◽  
Benjamin P Vandervalk ◽  
Hamid Mohamadi ◽  
Justin Chu ◽  
Sarah Yeo ◽  
...  
Keyword(s):  
Human Genome ◽  
Dna Sequences ◽  
Message Passing ◽  
Large Scale ◽  
De Novo ◽  
Bloom Filter ◽  
Genomic Variation ◽  
De Bruijn Graph ◽  
Single Individual ◽  
Probabilistic Data Structure

AbstractThe assembly of DNA sequences de novo is fundamental to genomics research. It is the first of many steps towards elucidating and characterizing whole genomes. Downstream applications, including analysis of genomic variation between species, between or within individuals critically depends on robustly assembled sequences. In the span of a single decade, the sequence throughput of leading DNA sequencing instruments has increased drastically, and coupled with established and planned large-scale, personalized medicine initiatives to sequence genomes in the thousands and even millions, the development of efficient, scalable and accurate bioinformatics tools for producing high-quality reference draft genomes is timely.With ABySS 1.0, we originally showed that assembling the human genome using short 50 bp sequencing reads was possible by aggregating the half terabyte of compute memory needed over several computers using a standardized message-passing system (MPI). We present here its re-design, which departs from MPI and instead implements algorithms that employ a Bloom filter, a probabilistic data structure, to represent a de Bruijn graph and reduce memory requirements.We present assembly benchmarks of human Genome in a Bottle 250 bp Illumina paired-end and 6 kbp mate-pair libraries from a single individual, yielding a NG50 (NGA50) scaffold contiguity of 3.5 (3.0) Mbp using less than 35 GB of RAM, a modest memory requirement by today’s standard that is often available on a single computer. We also investigate the use of BioNano Genomics and 10x Genomics’ Chromium data to further improve the scaffold contiguity of this assembly to 42 (15) Mbp.

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