De novo assembly methods for next generation sequencing data

He, Yiming; Zhang, Zhen; Peng, Xiaoqing; Wu, Fang‐Xiang; Wang, Jianxin

doi:10.1109/tst.2013.6616523

Cited by 17 publications

(9 citation statements)

References 89 publications

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“…Although it is not a fool-proof process, it can solve some significant problems in de novo assembly. However, in certain cases where errors in reads and repeat structures are present still it can make the graphs more complex, inefficient and high memory consumable [14].…”

Section: Current Methods In Genome Assemblymentioning

confidence: 99%

A Crowdsourced Gameplay for Whole-Genome Assembly via Short Reads

Gamage¹,

Perera²,

Meedeniya³

et al. 2020

Int. J. Onl. Eng.

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Next-generation sequencing has revolutionized the field of genomics by producing accurate, rapid and cost-effective genome analysis with the use of high throughput sequencing technologies. This has intensified the need for accurate and performance efficient genome assemblers to assemble a large set of short reads produced by next-generation sequencing technology. Genome assembly is an NP-hard problem that is computationally challenging. Therefore, the current methods that rely on heuristic and approximation algorithms to assemble genomes prevent them from arriving at the most accurate solution. This paper presents a novel approach by gamifying whole-genome shotgun assembly from next-generation sequencing data; we present "Geno", a human-computing game designed with the aim of improving the accuracy of whole-genome shotgun assembly. We evaluate the feasibility of crowdsourcing the problem of whole-genome shotgun assembly by breaking the problem into small subtasks. The evaluation results, for single-cell Escherichia coli K-12 substr. MG1655 with a read length of 25 bp that produced 144,867 game instances of mean 25 sequences per instance at 40x coverage indicate the feasibility of sub-tasking the problem of genome assembly to be solved using crowdsourcing.<br /><br />

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Section: Current Methods In Genome Assemblymentioning

confidence: 99%

A Crowdsourced Gameplay for Whole-Genome Assembly via Short Reads

Gamage¹,

Perera²,

Meedeniya³

et al. 2020

Int. J. Onl. Eng.

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“…In genetics, genotype-phenotype association studies have established that single nucleotide polymorphisms (SNPs) [2], one type of genetic variants, are associated with a variety of diseases [3]. However, the primary analysis paradigm for GWAS is dominated by the analysis on susceptibility of individual SNPs, which accordingly can only explain a small part of genetic causal effects for complex diseases [4].…”

Section: Introductionmentioning

confidence: 99%

“…Genome-wide association study (GWAS) has been proved to be a powerful genomic and statistical inference tool, and its goal is to identify genetic susceptibility through statistical tests on associations between a trait of interests and the genetic information of unrelated individuals [ 1 ]. In genetics, genotype-phenotype association studies have established that single nucleotide polymorphisms (SNPs) [ 2 ], one type of genetic variants, are associated with a variety of diseases [ 3 ]. However, the primary analysis paradigm for GWAS is dominated by the analysis on susceptibility of individual SNPs, which accordingly can only explain a small part of genetic causal effects for complex diseases [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering

Guo

et al. 2014

BMC Bioinformatics

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BackgroudTaking the advan tage of high-throughput single nucleotide polymorphism (SNP) genotyping technology, large genome-wide association studies (GWASs) have been considered to hold promise for unravelling complex relationships between genotype and phenotype. At present, traditional single-locus-based methods are insufficient to detect interactions consisting of multiple-locus, which are broadly existing in complex traits. In addition, statistic tests for high order epistatic interactions with more than 2 SNPs propose computational and analytical challenges because the computation increases exponentially as the cardinality of SNPs combinations gets larger.ResultsIn this paper, we provide a simple, fast and powerful method using dynamic clustering and cloud computing to detect genome-wide multi-locus epistatic interactions. We have constructed systematic experiments to compare powers performance against some recently proposed algorithms, including TEAM, SNPRuler, EDCF and BOOST. Furthermore, we have applied our method on two real GWAS datasets, Age-related macular degeneration (AMD) and Rheumatoid arthritis (RA) datasets, where we find some novel potential disease-related genetic factors which are not shown up in detections of 2-loci epistatic interactions.ConclusionsExperimental results on simulated data demonstrate that our method is more powerful than some recently proposed methods on both two- and three-locus disease models. Our method has discovered many novel high-order associations that are significantly enriched in cases from two real GWAS datasets. Moreover, the running time of the cloud implementation for our method on AMD dataset and RA dataset are roughly 2 hours and 50 hours on a cluster with forty small virtual machines for detecting two-locus interactions, respectively. Therefore, we believe that our method is suitable and effective for the full-scale analysis of multiple-locus epistatic interactions in GWAS.

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“…genetics, many genotype-phenotype association studies have established that Single Nucleotide Polymorphisms (SNPs) [3] , a common type of genetic variants, are associated with a variety of diseases [4] . In a case-control study, an SNP is said to be associated with a disease if the genotype distributions at that SNP in cases and controls are different.…”

mentioning

confidence: 99%

BAM: A block-based Bayesian method for detecting genome-wide associations with multiple diseases

Guo

2020

Tinshhua Sci. Technol.

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Many human diseases involve multiple genes in complex interactions. Large Genome-Wide Association Studies (GWASs) have been considered to hold promise for unraveling such interactions. However, statistic tests for high-order epistatic interactions (2 Single Nucleotide Polymorphisms (SNPs)) raise enormous computational and analytical challenges. It is well known that the block-wise structure exists in the human genome due to Linkage Disequilibrium (LD) between adjacent SNPs. In this paper, we propose a novel Bayesian method, named BAM, for simultaneously partitioning SNPs into LD-blocks and detecting genome-wide multi-locus epistatic interactions that are associated with multiple diseases. Experimental results on the simulated datasets demonstrate that BAM is powerful and efficient. We also applied BAM on two GWAS datasets from WTCCC, i.e., Rheumatoid Arthritis and Type 1 Diabetes, and accurately recovered the LD-block structure. Therefore, we believe that BAM is suitable and efficient for the full-scale analysis of multi-disease-related interactions in GWASs.

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De novo assembly methods for next generation sequencing data

Cited by 17 publications

References 89 publications

A Crowdsourced Gameplay for Whole-Genome Assembly via Short Reads

A Crowdsourced Gameplay for Whole-Genome Assembly via Short Reads

Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering

BAM: A block-based Bayesian method for detecting genome-wide associations with multiple diseases

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