Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Ariadne: synthetic long read deconvolution using assembly graphs.

ABSTRACT: Synthetic long read sequencing techniques such as UST's TELL-Seq and Loop Genomics' LoopSeq combine 3[Formula: see text] barcoding with standard short-read sequencing to expand the range of linkage resolution from hundreds to tens of thousands of base-pairs. However, the lack of a 1:1 correspondence between a long fragment and a 3[Formula: see text] unique molecular identifier confounds the assignment of linkage between short reads. We introduce Ariadne, a novel assembly graph-based synthetic long read deconvolution algorithm, that can be used to extract single-species read-clouds from synthetic long read datasets to improve the taxonomic classification and de novo assembly of complex populations, such as metagenomes.

SUBMITTER: Mak L

PROVIDER: S-EPMC10463629 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Ariadne: synthetic long read deconvolution using assembly graphs.

Mak Lauren L Meleshko Dmitry D Danko David C DC Barakzai Waris N WN Maharjan Salil S Belchikov Natan N Hajirasouliha Iman I

Genome biology 20230828 1

Synthetic long read sequencing techniques such as UST's TELL-Seq and Loop Genomics' LoopSeq combine 3[Formula: see text] barcoding with standard short-read sequencing to expand the range of linkage resolution from hundreds to tens of thousands of base-pairs. However, the lack of a 1:1 correspondence between a long fragment and a 3[Formula: see text] unique molecular identifier confounds the assignment of linkage between short reads. We introduce Ariadne, a novel assembly graph-based synthetic lo ...[more]

PMID: 37641111

Similar Datasets

metaFlye: scalable long-read metagenome assembly using repeat graphs.

Project description:Long-read sequencing technologies have substantially improved the assemblies of many isolate bacterial genomes as compared to fragmented short-read assemblies. However, assembling complex metagenomic datasets remains difficult even for state-of-the-art long-read assemblers. Here we present metaFlye, which addresses important long-read metagenomic assembly challenges, such as uneven bacterial composition and intra-species heterogeneity. First, we benchmarked metaFlye using simulated and mock bacterial communities and show that it consistently produces assemblies with better completeness and contiguity than state-of-the-art long-read assemblers. Second, we performed long-read sequencing of the sheep microbiome and applied metaFlye to reconstruct 63 complete or nearly complete bacterial genomes within single contigs. Finally, we show that long-read assembly of human microbiomes enables the discovery of full-length biosynthetic gene clusters that encode biomedically important natural products.

| S-EPMC10699202 | biostudies-literature

cloudSPAdes: assembly of synthetic long reads using de Bruijn graphs.

Project description:MotivationThe 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.ResultsWe 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 implementationSource code and installation manual for cloudSPAdes are available at https://github.com/ablab/spades/releases/tag/cloudspades-paper.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC6612831 | biostudies-literature

Coverage-preserving sparsification of overlap graphs for long-read assembly.

Project description:MotivationRead-overlap-based graph data structures play a central role in computing de novo genome assembly. Most long-read assemblers use Myers's string graph model to sparsify overlap graphs. Graph sparsification improves assembly contiguity by removing spurious and redundant connections. However, a graph model must be coverage-preserving, i.e. it must ensure that there exist walks in the graph that spell all chromosomes, given sufficient sequencing coverage. This property becomes even more important for diploid genomes, polyploid genomes, and metagenomes where there is a risk of losing haplotype-specific information.ResultsWe develop a novel theoretical framework under which the coverage-preserving properties of a graph model can be analyzed. We first prove that de Bruijn graph and overlap graph models are guaranteed to be coverage-preserving. We next show that the standard string graph model lacks this guarantee. The latter result is consistent with prior work suggesting that removal of contained reads, i.e. the reads that are substrings of other reads, can lead to coverage gaps during string graph construction. Our experiments done using simulated long reads from HG002 human diploid genome show that 50 coverage gaps are introduced on average by ignoring contained reads from nanopore datasets. To remedy this, we propose practical heuristics that are well-supported by our theoretical results and are useful to decide which contained reads should be retained to avoid coverage gaps. Our method retains a small fraction of contained reads (1-2%) and closes majority of the coverage gaps.Availability and implementationSource code is available through GitHub (https://github.com/at-cg/ContainX) and Zenodo with doi: 10.5281/zenodo.7687543.

| S-EPMC10132763 | biostudies-literature

Multiplexed Assembly and Annotation of Synthetic Biology Constructs Using Long-Read Nanopore Sequencing.

Project description:Recombinant DNA is a fundamental tool in biotechnology and medicine. These DNA sequences are often built, replicated, and delivered in the form of plasmids. Validation of these plasmid sequences is a critical and time-consuming step, which has been dominated for the last 35 years by Sanger sequencing. As plasmid sequences grow more complex with new DNA synthesis and cloning techniques, we need new approaches that address the corresponding validation challenges at scale. Here we prototype a high-throughput plasmid sequencing approach using DNA transposition and Oxford Nanopore sequencing. Our method, Circuit-seq, creates robust, full-length, and accurate plasmid assemblies without prior knowledge of the underlying sequence. We demonstrate the power of Circuit-seq across a wide range of plasmid sizes and complexities, generating full-length, contiguous plasmid maps. We then leverage our long-read data to characterize epigenetic marks and estimate plasmid contamination levels. Circuit-seq scales to large numbers of samples at a lower per-sample cost than commercial Sanger sequencing, accelerating a key step in synthetic biology, while low equipment costs make it practical for individual laboratories.

| S-EPMC9295152 | biostudies-literature

Parallelized short read assembly of large genomes using de Bruijn graphs.

Project description:BackgroundNext-generation sequencing technologies have given rise to the explosive increase in DNA sequencing throughput, and have promoted the recent development of de novo short read assemblers. However, existing assemblers require high execution times and a large amount of compute resources to assemble large genomes from quantities of short reads.ResultsWe present PASHA, a parallelized short read assembler using de Bruijn graphs, which takes advantage of hybrid computing architectures consisting of both shared-memory multi-core CPUs and distributed-memory compute clusters to gain efficiency and scalability. Evaluation using three small-scale real paired-end datasets shows that PASHA is able to produce more contiguous high-quality assemblies in shorter time compared to three leading assemblers: Velvet, ABySS and SOAPdenovo. PASHA's scalability for large genome datasets is demonstrated with human genome assembly. Compared to ABySS, PASHA achieves competitive assembly quality with faster execution speed on the same compute resources, yielding an NG50 contig size of 503 with the longest correct contig size of 18,252, and an NG50 scaffold size of 2,294. Moreover, the human assembly is completed in about 21 hours with only modest compute resources.ConclusionsDeveloping parallel assemblers for large genomes has been garnering significant research efforts due to the explosive size growth of high-throughput short read datasets. By employing hybrid parallelism consisting of multi-threading on multi-core CPUs and message passing on compute clusters, PASHA is able to assemble the human genome with high quality and in reasonable time using modest compute resources.

| S-EPMC3167803 | biostudies-literature

Velvet: algorithms for de novo short read assembly using de Bruijn graphs.

Project description:We have developed a new set of algorithms, collectively called "Velvet," to manipulate de Bruijn graphs for genomic sequence assembly. A de Bruijn graph is a compact representation based on short words (k-mers) that is ideal for high coverage, very short read (25-50 bp) data sets. Applying Velvet to very short reads and paired-ends information only, one can produce contigs of significant length, up to 50-kb N50 length in simulations of prokaryotic data and 3-kb N50 on simulated mammalian BACs. When applied to real Solexa data sets without read pairs, Velvet generated contigs of approximately 8 kb in a prokaryote and 2 kb in a mammalian BAC, in close agreement with our simulated results without read-pair information. Velvet represents a new approach to assembly that can leverage very short reads in combination with read pairs to produce useful assemblies.

| S-EPMC2336801 | biostudies-literature

Plastid Genome Assembly Using Long-read data.

Project description:Although plastid genome (plastome) structure is highly conserved across most seed plants, investigations during the past two decades have revealed several disparately related lineages that experienced substantial rearrangements. Most plastomes contain a large inverted repeat and two single-copy regions, and a few dispersed repeats; however, the plastomes of some taxa harbour long repeat sequences (>300 bp). These long repeats make it challenging to assemble complete plastomes using short-read data, leading to misassemblies and consensus sequences with spurious rearrangements. Single-molecule, long-read sequencing has the potential to overcome these challenges, yet there is no consensus on the most effective method for accurately assembling plastomes using long-read data. We generated a pipeline, plastid Genome Assembly Using Long-read data (ptGAUL), to address the problem of plastome assembly using long-read data from Oxford Nanopore Technologies (ONT) or Pacific Biosciences platforms. We demonstrated the efficacy of the ptGAUL pipeline using 16 published long-read data sets. We showed that ptGAUL quickly produces accurate and unbiased assemblies using only ~50× coverage of plastome data. Additionally, we deployed ptGAUL to assemble four new Juncus (Juncaceae) plastomes using ONT long reads. Our results revealed many long repeats and rearrangements in Juncus plastomes compared with basal lineages of Poales. The ptGAUL pipeline is available on GitHub: https://github.com/Bean061/ptgaul.

| S-EPMC10354735 | biostudies-literature

Assembly of long error-prone reads using de Bruijn graphs.

Project description:The recent breakthroughs in assembling long error-prone reads were based on the overlap-layout-consensus (OLC) approach and did not utilize the strengths of the alternative de Bruijn graph approach to genome assembly. Moreover, these studies often assume that applications of the de Bruijn graph approach are limited to short and accurate reads and that the OLC approach is the only practical paradigm for assembling long error-prone reads. We show how to generalize de Bruijn graphs for assembling long error-prone reads and describe the ABruijn assembler, which combines the de Bruijn graph and the OLC approaches and results in accurate genome reconstructions.

| S-EPMC5206522 | biostudies-literature

Gapless assembly of maize chromosomes using long-read technologies.

Project description:Creating gapless telomere-to-telomere assemblies of complex genomes is one of the ultimate challenges in genomics. We use two independent assemblies and an optical map-based merging pipeline to produce a maize genome (B73-Ab10) composed of 63 contigs and a contig N50 of 162 Mb. This genome includes gapless assemblies of chromosome 3 (236 Mb) and chromosome 9 (162 Mb), and 53 Mb of the Ab10 meiotic drive haplotype. The data also reveal the internal structure of seven centromeres and five heterochromatic knobs, showing that the major tandem repeat arrays (CentC, knob180, and TR-1) are discontinuous and frequently interspersed with retroelements.

| S-EPMC7238635 | biostudies-literature

Compact representation of k-mer de Bruijn graphs for genome read assembly.

Project description:BackgroundProcessing of reads from high throughput sequencing is often done in terms of edges in the de Bruijn graph representing all k-mers from the reads. The memory requirements for storing all k-mers in a lookup table can be demanding, even after removal of read errors, but can be alleviated by using a memory efficient data structure.ResultsThe FM-index, which is based on the Burrows-Wheeler transform, provides an efficient data structure providing a searchable index of all substrings from a set of strings, and is used to compactly represent full genomes for use in mapping reads to a genome: the memory required to store this is in the same order of magnitude as the strings themselves. However, reads from high throughput sequences mostly have high coverage and so contain the same substrings multiple times from different reads. I here present a modification of the FM-index, which I call the kFM-index, for indexing the set of k-mers from the reads. For DNA sequences, this requires 5 bit of information for each vertex of the corresponding de Bruijn subgraph, i.e. for each different k-1-mer, plus some additional overhead, typically 0.5 to 1 bit per vertex, for storing the equivalent of the FM-index for walking the underlying de Bruijn graph and reproducing the actual k-mers efficiently.ConclusionsThe kFM-index could replace more memory demanding data structures for storing the de Bruijn k-mer graph representation of sequence reads. A Java implementation with additional technical documentation is provided which demonstrates the applicability of the data structure (http://folk.uio.no/einarro/Projects/KFM-index/).

| S-EPMC4015147 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data