scholarly journals Ariadne: Barcoded Linked-Read Deconvolution Using de Bruijn Graphs

2021 ◽  
Author(s):  
Lauren Mak ◽  
Dmitry Meleshko ◽  
David C Danko ◽  
Waris N Barakzai ◽  
Natan Belchikov ◽  
...  
Keyword(s):  
De Novo Assembly ◽  
Large Scale ◽  
De Novo ◽  
Single Species ◽  
Computationally Efficient ◽  
Base Pairs ◽  
De Bruijn Graphs ◽  
Complex Samples ◽  
Single Fragment

Background: De novo assemblies are critical for capturing the genetic composition of complex samples. Linked-read sequencing techniques such as 10x Genomics' Linked-Reads, UST's TELL-Seq, Loop Genomics' LoopSeq, and BGI's Long Fragment Read combines 30 barcoding with standard short-read sequencing to expand the range of linkage resolution from hundreds to tens of thousands of base-pairs. The application of linked-read sequencing to genome assembly has demonstrated that barcoding-based technologies balance the ffs between long-range linkage, per-base coverage, and costs. Linkedreads come with their own challenges, chief among them the association of multiple long fragments with the same 30 barcode. The lack of a unique correspondence between a long fragment and a barcode, in conjunction with low sequencing depth, confounds the assignment of linkage between short-reads. Results: We introduce Ariadne, a novel linked-read deconvolution algorithm based on assembly graphs, that can be used to extract single-species read-sets from a large linked-read dataset. Ariadne deconvolution of linked-read clouds increases the proportion of read clouds containing only reads from a single fragment by up to 37.5-fold. Using these enhanced read clouds in de novo assembly significantly improves assembly contiguity and the size of the largest aligned blocks in comparison to the non-deconvolved read clouds. Integrating barcode deconvolution tools, such as Ariadne, into the postprocessing pipeline for linked-read technologies increases the quality of de novo assembly for complex populations, such as microbiomes. Ariadne is intuitive, computationally efficient, and scalable to other large-scale linked-read problems, such as human genome phasing.

scholarly journals Accurate long-read de novo assembly evaluation with Inspector

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yu Chen ◽  
Yixin Zhang ◽  
Amy Y. Wang ◽  
Min Gao ◽  
Zechen Chong
Keyword(s):  
Genome Assembly ◽  
De Novo Assembly ◽  
In Silico ◽  
Large Scale ◽  
De Novo ◽  
Small Scale ◽  
De Novo Genome Assembly ◽  
Consensus Sequences ◽  
Assembly Evaluation ◽  
Long Read

AbstractLong-read de novo genome assembly continues to advance rapidly. However, there is a lack of effective tools to accurately evaluate the assembly results, especially for structural errors. We present Inspector, a reference-free long-read de novo assembly evaluator which faithfully reports types of errors and their precise locations. Notably, Inspector can correct the assembly errors based on consensus sequences derived from raw reads covering erroneous regions. Based on in silico and long-read assembly results from multiple long-read data and assemblers, we demonstrate that in addition to providing generic metrics, Inspector can accurately identify both large-scale and small-scale assembly errors.

scholarly journals De novo assembly of a Tibetan genome and identification of novel structural variants associated with high-altitude adaptation

2019 ◽  
Vol 7 (2) ◽  
pp. 391-402 ◽  
Author(s):  
Yaoxi He ◽  
Haiyi Lou ◽  
Chaoying Cui ◽  
Lian Deng ◽  
Yang Gao ◽  
...  
Keyword(s):  
High Altitude ◽  
De Novo Assembly ◽  
De Novo ◽  
Population Analysis ◽  
Extreme Environments ◽  
Structural Variants ◽  
Base Pairs ◽  
High Quality ◽  
Long Read ◽  
Altitude Adaptation

Abstract Structural variants (SVs) may play important roles in human adaptation to extreme environments such as high altitude but have been under-investigated. Here, combining long-read sequencing with multiple scaffolding techniques, we assembled a high-quality Tibetan genome (ZF1), with a contig N50 length of 24.57 mega-base pairs (Mb) and a scaffold N50 length of 58.80 Mb. The ZF1 assembly filled 80 remaining N-gaps (0.25 Mb in total length) in the reference human genome (GRCh38). Markedly, we detected 17 900 SVs, among which the ZF1-specific SVs are enriched in GTPase activity that is required for activation of the hypoxic pathway. Further population analysis uncovered a 163-bp intronic deletion in the MKL1 gene showing large divergence between highland Tibetans and lowland Han Chinese. This deletion is significantly associated with lower systolic pulmonary arterial pressure, one of the key adaptive physiological traits in Tibetans. Moreover, with the use of the high-quality de novo assembly, we observed a much higher rate of genome-wide archaic hominid (Altai Neanderthal and Denisovan) shared non-reference sequences in ZF1 (1.32%–1.53%) compared to other East Asian genomes (0.70%–0.98%), reflecting a unique genomic composition of Tibetans. One such archaic hominid shared sequence—a 662-bp intronic insertion in the SCUBE2 gene—is enriched and associated with better lung function (the FEV1/FVC ratio) in Tibetans. Collectively, we generated the first high-resolution Tibetan reference genome, and the identified SVs may serve as valuable resources for future evolutionary and medical studies.

scholarly journals When Less is More: "Slicing" Sequencing Data Improves Read Decoding Accuracy and De Novo Assembly Quality

2015 ◽  
Author(s):  
Stefano Lonardi ◽  
Hamid Mirebrahim ◽  
Steve Wanamaker ◽  
Matthew Alpert ◽  
Gianfranco Ciardo ◽  
...  
Keyword(s):  
Deep Sequencing ◽  
De Novo Assembly ◽  
De Novo ◽  
Optimal Size ◽  
Sequencing Data ◽  
Less Is More ◽  
Bac Clones ◽  
Deep Sequencing Data ◽  
First Time

Since the invention of DNA sequencing in the seventies, computational biologists have had to deal with the problem de novo genome assembly with limited (or insufficient) depth of sequencing. In this work, for the first time we investigate the opposite problem, that is, the challenge of dealing with excessive depth of sequencing. Specifically, we explore the effect of ultra-deep sequencing data in two domains: (i) the problem of decoding reads to BAC clones (in the context of the combinatorial pooling design proposed by our group), and (ii) the problem of de novo assembly of BAC clones. Using real ultra-deep sequencing data, we show that when the depth of sequencing increases over a certain threshold, sequencing errors make these two problems harder and harder (instead of easier, as one would expect with error-free data), and as a consequence the quality of the solution degrades with more and more data. For the first problem, we propose an effective solution based on "divide and conquer": we "slice" a large dataset into smaller samples of optimal size, decode each slice independently, then merge the results. Experimental results on over 15,000 barley BACs and over 4,000 cowpea BACs demonstrate a significant improvement in the quality of the decoding and the final assembly. For the second problem, we show for the first time that modern de novo assemblers cannot take advantage of ultra-deep sequencing data.

scholarly journals Evaluation of hybrid and non-hybrid methods for de novo assembly of nanopore reads

2015 ◽  
Author(s):  
Ivan Sovic ◽  
Kresimir Krizanovic ◽  
Karolj Skala ◽  
Mile Sikic
Keyword(s):  
De Novo Assembly ◽  
De Novo ◽  
Hybrid Methods ◽  
Bacterial Genome ◽  
Error Rates ◽  
Sequencing Data ◽  
E Coli ◽  
Recent Emergence ◽  
K 12

Recent emergence of nanopore sequencing technology set a challenge for the established assembly methods not optimized for the combination of read lengths and high error rates of nanopore reads. In this work we assessed how existing de novo assembly methods perform on these reads. We benchmarked three non-hybrid (in terms of both error correction and scaffolding) assembly pipelines as well as two hybrid assemblers which use third generation sequencing data to scaffold Illumina assemblies. Tests were performed on several publicly available MinION and Illumina datasets of E. coli K-12, using several sequencing coverages of nanopore data (20x, 30x, 40x and 50x). We attempted to assess the quality of assembly at each of these coverages, to estimate the requirements for closed bacterial genome assembly. Results show that hybrid methods are highly dependent on the quality of NGS data, but much less on the quality and coverage of nanopore data and perform relatively well on lower nanopore coverages. Furthermore, when coverage is above 40x, all non-hybrid methods correctly assemble the E. coli genome, even a non-hybrid method tailored for Pacific Bioscience reads. While it requires higher coverage compared to a method designed particularly for nanopore reads, its running time is significantly lower.

scholarly journals SimkaMin: fast and resource frugal de novo comparative metagenomics

2019 ◽  
Author(s):  
Gaëtan Benoit ◽  
Mahendra Mariadassou ◽  
Stéphane Robin ◽  
Sophie Schbath ◽  
Pierre Peterlongo ◽  
...  
Keyword(s):  
Large Scale ◽  
De Novo ◽  
Supplementary Information ◽  
Metagenomic Data ◽  
Supplementary Data ◽  
Comparative Metagenomics ◽  
Large Sets ◽  
Efficient Data ◽  
Genomic Similarity

Abstract Motivation De novo comparative metagenomics is one of the most straightforward ways to analyze large sets of metagenomic data. Latest methods use the fraction of shared k-mers to estimate genomic similarity between read sets. However, those methods, while extremely efficient, are still limited by computational needs for practical usage outside of large computing facilities. Results We present SimkaMin, a quick comparative metagenomics tool with low disk and memory footprints, thanks to an efficient data subsampling scheme used to estimate Bray-Curtis and Jaccard dissimilarities. One billion metagenomic reads can be analyzed in <3 min, with tiny memory (1.09 GB) and disk (≈0.3 GB) requirements and without altering the quality of the downstream comparative analyses, making of SimkaMin a tool perfectly tailored for very large-scale metagenomic projects. Availability and implementation https://github.com/GATB/simka. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

Hardware accelerated novel optical de novo assembly for large-scale genomes

2014 ◽  
Author(s):  
Pingfan Meng ◽  
Matthew Jacobsen ◽  
Motoki Kimura ◽  
Vladimir Dergachev ◽  
Thomas Anantharaman ◽  
...  
Keyword(s):  
De Novo Assembly ◽  
Large Scale ◽  
De Novo

scholarly journals Compression of short-read sequences using path encoding

2014 ◽  
Author(s):  
Carl Kingsford ◽  
Rob Patro
Keyword(s):  
Large Scale ◽  
De Novo ◽  
Biological Sequence ◽  
Rna Seq ◽  
De Bruijn Graphs ◽  
Common Task ◽  
Meta Analyses ◽  
Sequence Compression ◽  
Good Compression ◽  
Generation Sequencing

Storing, transmitting, and archiving the amount of data produced by next generation sequencing is becoming a significant computational burden. For example, large-scale RNA-seq meta-analyses may now routinely process tens of terabytes of sequence. We present here an approach to biological sequence compression that reduces the difficulty associated with managing the data produced by large-scale transcriptome sequencing. Our approach offers a new direction by sitting between pure reference-based compression and reference-free compression and combines much of the benefit of reference-based approaches with the flexibility of de novo encoding. Our method, called path encoding, draws a connection between storing paths in de Bruijn graphs --- a common task in genome assembly --- and context-dependent arithmetic coding. Supporting this method is a system, called a bit tree, to compactly store sets of kmers that is of independent interest. Using these techniques, we are able to encode RNA-seq reads using 3% -- 11% of the space of the sequence in raw FASTA files, which is on average more than 34% smaller than recent competing approaches. We also show that even if the reference is very poorly matched to the reads that are being encoded, good compression can still be achieved.

scholarly journals GPU acceleration of Darwin read overlapper for de novo assembly of long DNA reads

2020 ◽  
Vol 21 (S13) ◽  
Author(s):  
Nauman Ahmed ◽  
Tong Dong Qiu ◽  
Koen Bertels ◽  
Zaid Al-Ars
Keyword(s):  
De Novo Assembly ◽  
Large Scale ◽  
De Novo ◽  
State Of The Art ◽  
Gpu Acceleration ◽  
Dna Assembly ◽  
Scoring Model ◽  
Assembly Pipeline ◽  
Scoring Schemes ◽  
Gpu Implementation

Abstract Background In Overlap-Layout-Consensus (OLC) based de novo assembly, all reads must be compared with every other read to find overlaps. This makes the process rather slow and limits the practicality of using de novo assembly methods at a large scale in the field. Darwin is a fast and accurate read overlapper that can be used for de novo assembly of state-of-the-art third generation long DNA reads. Darwin is designed to be hardware-friendly and can be accelerated on specialized computer system hardware to achieve higher performance. Results This work accelerates Darwin on GPUs. Using real Pacbio data, our GPU implementation on Tesla K40 has shown a speedup of 109x vs 8 CPU threads of an Intel Xeon machine and 24x vs 64 threads of IBM Power8 machine. The GPU implementation supports both linear and affine gap, scoring model. The results show that the GPU implementation can achieve the same high speedup for different scoring schemes. Conclusions The GPU implementation proposed in this work shows significant improvement in performance compared to the CPU version, thereby making it accessible for utilization as a practical read overlapper in a DNA assembly pipeline. Furthermore, our GPU acceleration can also be used for performing fast Smith-Waterman alignment between long DNA reads. GPU hardware has become commonly available in the field today, making the proposed acceleration accessible to a larger public. The implementation is available at https://github.com/Tongdongq/darwin-gpu.

scholarly journals Hamming-shifting graph of genomic short reads: Efficient construction and its application for compression

2021 ◽  
Vol 17 (7) ◽  
pp. e1009229
Author(s):  
Yuansheng Liu ◽  
Jinyan Li
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Genomic Library ◽  
De Novo ◽  
Hamming Distance ◽  
Sequencing Data ◽  
De Novo Genome Assembly ◽  
De Bruijn Graphs ◽  
Short Reads ◽  
Future Work

Graphs such as de Bruijn graphs and OLC (overlap-layout-consensus) graphs have been widely adopted for the de novo assembly of genomic short reads. This work studies another important problem in the field: how graphs can be used for high-performance compression of the large-scale sequencing data. We present a novel graph definition named Hamming-Shifting graph to address this problem. The definition originates from the technological characteristics of next-generation sequencing machines, aiming to link all pairs of distinct reads that have a small Hamming distance or a small shifting offset or both. We compute multiple lexicographically minimal k-mers to index the reads for an efficient search of the weight-lightest edges, and we prove a very high probability of successfully detecting these edges. The resulted graph creates a full mutual reference of the reads to cascade a code-minimized transfer of every child-read for an optimal compression. We conducted compression experiments on the minimum spanning forest of this extremely sparse graph, and achieved a 10 − 30% more file size reduction compared to the best compression results using existing algorithms. As future work, the separation and connectivity degrees of these giant graphs can be used as economical measurements or protocols for quick quality assessment of wet-lab machines, for sufficiency control of genomic library preparation, and for accurate de novo genome assembly.

scholarly journals De Novo Assembly and Comparative Transcriptome Profiling of Anser anser and Anser cygnoides Geese Species’ Embryonic Skin Feather Follicles

Genes ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 351 ◽  
Author(s):  
Cornelius Tlotliso Sello ◽  
Chang Liu ◽  
Yongfeng Sun ◽  
Petunia Msuthwana ◽  
Jingtao Hu ◽  
...  
Keyword(s):  
De Novo ◽  
Average Length ◽  
Gene Families ◽  
Follicle Development ◽  
Sequencing Analysis ◽  
Anser Anser ◽  
Base Pairs ◽  
Feather Follicle ◽  
Embryonic Skin

Geese feather production and the quality of downy feathers are additional economically important traits in the geese industry. However, little information is available about the molecular mechanisms fundamental to feather formation and the quality of feathers in geese. This study conducted de novo transcriptome sequencing analysis of two related geese species using the Illumina 4000 platform to determine the genes involved in embryonic skin feather follicle development. A total of 165,564,278 for Anser anser and 144,595,262 for Anser cygnoides clean reads were generated, which were further assembled into 77,134 unigenes with an average length of 906 base pairs in Anser anser and 66,041 unigenes with an average length of 922 base pairs in Anser cygnoides. To recognize the potential regulatory roles of differentially expressed genes (DEGs) during geese embryonic skin feather follicle development, the obtained unigenes were annotated to Gene Ontology (GO), Eukaryotic Orthologous Groups (KOG), and Kyoto Encyclopedia of Genes and Genomes (KEGG) for functional analysis. In both species, GO and KOG had shown similar distribution patterns during functional annotation except for KEGG, which showed significant variation in signaling enrichment. Anser asnser was significantly enriched in the calcium signaling pathway, whereas Anser cygnoides was significantly enriched with glycerolipid metabolism. Further analysis indicated that 14,227 gene families were conserved between the species, among which a total of 20,715 specific gene families were identified. Comparative RNA-Seq data analysis may reveal inclusive knowledge to assist in the identification of genetic regulators at a molecular level to improve feather quality production in geese and other poultry species.

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