scholarly journals SLR-superscaffolder: a de novo scaffolding tool for synthetic long reads using a top-to-bottom scheme

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Lidong Guo ◽  
Mengyang Xu ◽  
Wenchao Wang ◽  
Shengqiang Gu ◽  
Xia Zhao ◽  
...  
Keyword(s):  
High Efficiency ◽  
De Novo ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Draft Assembly ◽  
Screening Algorithm ◽  
Long Reads ◽  
Hybrid Genome ◽  
Genomics Research ◽  
Negative Effect

Abstract Background Synthetic long reads (SLR) with long-range co-barcoding information are now widely applied in genomics research. Although several tools have been developed for each specific SLR technique, a robust standalone scaffolder with high efficiency is warranted for hybrid genome assembly. Results In this work, we developed a standalone scaffolding tool, SLR-superscaffolder, to link together contigs in draft assemblies using co-barcoding and paired-end read information. Our top-to-bottom scheme first builds a global scaffold graph based on Jaccard Similarity to determine the order and orientation of contigs, and then locally improves the scaffolds with the aid of paired-end information. We also exploited a screening algorithm to reduce the negative effect of misassembled contigs in the input assembly. We applied SLR-superscaffolder to a human single tube long fragment read sequencing dataset and increased the scaffold NG50 of its corresponding draft assembly 1349 fold. Moreover, benchmarking on different input contigs showed that this approach overall outperformed existing SLR scaffolders, providing longer contiguity and fewer misassemblies, especially for short contigs assembled by next-generation sequencing data. The open-source code of SLR-superscaffolder is available at https://github.com/BGI-Qingdao/SLR-superscaffolder. Conclusions SLR-superscaffolder can dramatically improve the contiguity of a draft assembly by integrating a hybrid assembly strategy.

scholarly journals Systematic Comparison of the Performances of De Novo Genome Assemblers for Oxford Nanopore Technology Reads From Piroplasm

2021 ◽  
Vol 11 ◽  
Author(s):  
Jinming Wang ◽  
Kai Chen ◽  
Qiaoyun Ren ◽  
Ying Zhang ◽  
Junlong Liu ◽  
...  
Keyword(s):  
De Novo ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Coverage Depth ◽  
Sequence Coverage ◽  
Long Reads ◽  
Oxford Nanopore ◽  
Generation Sequencing ◽  
Easy Operation

BackgroundEmerging long reads sequencing technology has greatly changed the landscape of whole-genome sequencing, enabling scientists to contribute to decoding the genetic information of non-model species. The sequences generated by PacBio or Oxford Nanopore Technology (ONT) be assembled de novo before further analyses. Some genome de novo assemblers have been developed to assemble long reads generated by ONT. The performance of these assemblers has not been completely investigated. However, genome assembly is still a challenging task.Methods and ResultsWe systematically evaluated the performance of nine de novo assemblers for ONT on different coverage depth datasets. Several metrics were measured to determine the performance of these tools, including N50 length, sequence coverage, runtime, easy operation, accuracy of genome and genomic completeness in varying depths of coverage. Based on the results of our assessments, the performances of these tools are summarized as follows: 1) Coverage depth has a significant effect on genome quality; 2) The level of contiguity of the assembled genome varies dramatically among different de novo tools; 3) The correctness of an assembled genome is closely related to the completeness of the genome. More than 30× nanopore data can be assembled into a relatively complete genome, the quality of which is highly dependent on the polishing using next generation sequencing data.ConclusionConsidering the results of our investigation, the advantage and disadvantage of each tool are summarized and guidelines of selecting assembly tools are provided under specific conditions.

scholarly journals METAMVGL: a multi-view graph-based metagenomic contig binning algorithm by integrating assembly and paired-end graphs

2021 ◽  
Vol 22 (S10) ◽  
Author(s):  
Zhenmiao Zhang ◽  
Lu Zhang
Keyword(s):  
De Novo ◽  
Label Propagation ◽  
Next Generation Sequencing Data ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Fecal Samples ◽  
Microbial Genomes ◽  
Metagenome Assembly ◽  
High Chance ◽  
Mock Communities

Abstract Background Due to the complexity of microbial communities, de novo assembly on next generation sequencing data is commonly unable to produce complete microbial genomes. Metagenome assembly binning becomes an essential step that could group the fragmented contigs into clusters to represent microbial genomes based on contigs’ nucleotide compositions and read depths. These features work well on the long contigs, but are not stable for the short ones. Contigs can be linked by sequence overlap (assembly graph) or by the paired-end reads aligned to them (PE graph), where the linked contigs have high chance to be derived from the same clusters. Results We developed METAMVGL, a multi-view graph-based metagenomic contig binning algorithm by integrating both assembly and PE graphs. It could strikingly rescue the short contigs and correct the binning errors from dead ends. METAMVGL learns the two graphs’ weights automatically and predicts the contig labels in a uniform multi-view label propagation framework. In experiments, we observed METAMVGL made use of significantly more high-confidence edges from the combined graph and linked dead ends to the main graph. It also outperformed many state-of-the-art contig binning algorithms, including MaxBin2, MetaBAT2, MyCC, CONCOCT, SolidBin and GraphBin on the metagenomic sequencing data from simulation, two mock communities and Sharon infant fecal samples. Conclusions Our findings demonstrate METAMVGL outstandingly improves the short contig binning and outperforms the other existing contig binning tools on the metagenomic sequencing data from simulation, mock communities and infant fecal samples.

scholarly journals Inherited variants in CHD3 demonstrate variable expressivity in Snijders Blok-Campeau syndrome

2021 ◽  
Author(s):  
Jet van der Spek ◽  
Joery den Hoed ◽  
Lot Snijders Blok ◽  
Alexander J. M. Dingemans ◽  
Dick Schijven ◽  
...  
Keyword(s):  
De Novo ◽  
Neurodevelopmental Disorder ◽  
Underlying Mechanism ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Human Phenotype ◽  
Variable Expressivity ◽  
Pathogenic Variants ◽  
Reduced Penetrance ◽  
Coding Variants

Interpretation of next-generation sequencing data of individuals with an apparent sporadic neurodevelopmental disorder (NDD) often focusses on pathogenic variants in genes associated with NDD, assuming full clinical penetrance with limited variable expressivity. Consequently, inherited variants in genes associated with dominant disorders may be overlooked when the transmitting parent is clinically unaffected. While de novo variants explain a substantial proportion of cases with NDDs, a significant number remains undiagnosed possibly explained by coding variants associated with reduced penetrance and variable expressivity. We characterized twenty families with inherited heterozygous missense or protein-truncating variants (PTVs) in CHD3, a gene in which de novo variants cause Snijders Blok-Campeau syndrome, characterized by intellectual disability, speech delay and recognizable facial features (SNIBCPS). Notably, the majority of the inherited CHD3 variants were maternally transmitted. Computational facial and human phenotype ontology-based comparisons demonstrated that the phenotypic features of probands with inherited CHD3 variants overlap with the phenotype previously associated with de novo variants in the gene, while carrier parents are mildly or not affected, suggesting variable expressivity. Additionally, similarly reduced expression levels of CHD3 protein in cells of an affected proband and of related healthy carriers with a CHD3 PTV, suggested that compensation of expression from the wildtype allele is unlikely to be an underlying mechanism. Our results point to a significant role of inherited variation in SNIBCPS, a finding that is critical for correct variant interpretation and genetic counseling and warrants further investigation towards understanding the broader contributions of such variation to the landscape of human disease.

Computational Approaches for Transcriptome Assembly Based on Sequencing Technologies

2020 ◽  
Vol 15 (1) ◽  
pp. 2-16
Author(s):  
Yuwen Luo ◽  
Xingyu Liao ◽  
Fang-Xiang Wu ◽  
Jianxin Wang
Keyword(s):  
De Novo ◽  
Transcriptome Assembly ◽  
Critical Role ◽  
High Sensitivity ◽  
Biological Properties ◽  
Sequencing Data ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Massive Sequencing ◽  
Generation Sequencing

Transcriptome assembly plays a critical role in studying biological properties and examining the expression levels of genomes in specific cells. It is also the basis of many downstream analyses. With the increase of speed and the decrease in cost, massive sequencing data continues to accumulate. A large number of assembly strategies based on different computational methods and experiments have been developed. How to efficiently perform transcriptome assembly with high sensitivity and accuracy becomes a key issue. In this work, the issues with transcriptome assembly are explored based on different sequencing technologies. Specifically, transcriptome assemblies with next-generation sequencing reads are divided into reference-based assemblies and de novo assemblies. The examples of different species are used to illustrate that long reads produced by the third-generation sequencing technologies can cover fulllength transcripts without assemblies. In addition, different transcriptome assemblies using the Hybrid-seq methods and other tools are also summarized. Finally, we discuss the future directions of transcriptome assemblies.

scholarly journals SLR-superscaffolder: a de novo scaffolding tool for synthetic long reads using a top-to-bottom scheme

2019 ◽  
Author(s):  
Lidong Guo ◽  
Mengyang Xu ◽  
Wenchao Wang ◽  
Shengqiang Gu ◽  
Xia Zhao ◽  
...  
Keyword(s):  
Quality Improvement ◽  
Next Generation Sequencing ◽  
Long Range ◽  
De Novo ◽  
Next Generation ◽  
Negative Effects ◽  
Complementary Information ◽  
Screening Algorithm ◽  
Long Reads ◽  
Generation Sequencing

AbstractSynthetic long reads (SLR) with long-range co-barcoding information have been recently developed and widely applied in genomics researches. We proposed a scaffolding model of the co-barcoding information and developed a scaffolding tool with adopting a top-to-bottom scheme to make full use of the complementary information in SLR datasets and a screening algorithm to reduce negative effects from misassembled contigs in an input assembly. In comparison with other available SLR scaffolding tools, our tool obtained the best quality improvement for different input assemblies, especially for those assembled by the next-generation sequencing reads, where the improvement of contiguity is about several hundred-folds.

scholarly journals DeNovoCNN: A deep learning approach to de novo variant calling in next generation sequencing data

2021 ◽  
Author(s):  
Gelana Khazeeva ◽  
Karolis Sablauskas ◽  
Bart van der Sanden ◽  
Wouter Steyaert ◽  
Michael Kwint ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
De Novo ◽  
Genetic Disorders ◽  
Variant Calling ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Accurate Identification ◽  
Whole Exome ◽  
De Novo Variant ◽  
Generation Sequencing

De novo mutations (DNMs) are an important cause of genetic disorders. The accurate identification of DNMs from sequencing data is therefore fundamental to rare disease research and diagnostics. Unfortunately, identifying reliable DNMs remains a major challenge due to sequence errors, uneven coverage, and mapping artifacts. Here, we developed a deep convolutional neural network (CNN) DNM caller (DeNovoCNN), that encodes alignment of sequence reads for a trio as 160×164 resolution images. DeNovoCNN was trained on DNMs of whole exome sequencing (WES) of 2003 trios achieving on average 99.2% recall and 93.8% precision. We find that DeNovoCNN has increased recall/sensitivity and precision compared to existing de novo calling approaches (GATK, DeNovoGear, Samtools) based on the Genome in a Bottle reference dataset. Sanger validations of DNMs called in both exome and genome datasets confirm that DeNovoCNN outperforms existing methods. Most importantly, we show that DeNovoCNN is robust against different exome sequencing and analyses approaches, thereby allowing it to be applied on other datasets. DeNovoCNN is freely available and can be run on existing alignment (BAM/CRAM) and variant calling (VCF) files from WES and WGS without a need for variant recalling.

scholarly journals Assembly of chromosome-scale contigs by efficiently resolving repetitive sequences with long reads

2018 ◽  
Author(s):  
Huilong Du ◽  
Chengzhi Liang
Keyword(s):  
Single Molecule ◽  
High Efficiency ◽  
Reference Genome ◽  
Repetitive Sequences ◽  
Sequencing Data ◽  
High Quality ◽  
Single Molecule Sequencing ◽  
Genome Maps ◽  
Long Reads ◽  
Novel Method

AbstractDue to the large number of repetitive sequences in complex eukaryotic genomes, fragmented and incompletely assembled genomes lose value as reference sequences, often due to short contigs that cannot be anchored or mispositioned onto chromosomes. Here we report a novel method Highly Efficient Repeat Assembly (HERA), which includes a new concept called a connection graph as well as algorithms for constructing the graph. HERA resolves repeats at high efficiency with single-molecule sequencing data, and enables the assembly of chromosome-scale contigs by further integrating genome maps and Hi-C data. We tested HERA with the genomes of rice R498, maize B73, human HX1 and Tartary buckwheat Pinku1. HERA can correctly assemble most of the tandemly repetitive sequences in rice using single-molecule sequencing data only. Using the same maize and human sequencing data published by Jiao et al. (2017) and Shi et al. (2016), respectively, we dramatically improved on the sequence contiguity compared with the published assemblies, increasing the contig N50 from 1.3 Mb to 61.2 Mb in maize B73 assembly and from 8.3 Mb to 54.4 Mb in human HX1 assembly with HERA. We provided a high-quality maize reference genome with 96.9% of the gaps filled (only 76 gaps left) and several incorrectly positioned sequences fixed compared with the B73 RefGen_v4 assembly. Comparisons between the HERA assembly of HX1 and the human GRCh38 reference genome showed that many gaps in GRCh38 could be filled, and that GRCh38 contained some potential errors that could be fixed. We assembled the Pinku1 genome into 12 scaffolds with a contig N50 size of 27.85 Mb. HERA serves as a new genome assembly/phasing method to generate high quality sequences for complex genomes and as a curation tool to improve the contiguity and completeness of existing reference genomes, including the correction of assembly errors in repetitive regions.

scholarly journals An ensemble strategy that significantly improves de novo assembly of microbial genomes from metagenomic next-generation sequencing data

2015 ◽  
Vol 43 (7) ◽  
pp. e46-e46 ◽  
Author(s):  
Xutao Deng ◽  
Samia N. Naccache ◽  
Terry Ng ◽  
Scot Federman ◽  
Linlin Li ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
De Novo Assembly ◽  
De Novo ◽  
Next Generation Sequencing Data ◽  
De Bruijn Graph ◽  
Next Generation ◽  
Sequencing Data ◽  
Short Reads ◽  
Ensemble Strategy ◽  
Generation Sequencing

Abstract Next-generation sequencing (NGS) approaches rapidly produce millions to billions of short reads, which allow pathogen detection and discovery in human clinical, animal and environmental samples. A major limitation of sequence homology-based identification for highly divergent microorganisms is the short length of reads generated by most highly parallel sequencing technologies. Short reads require a high level of sequence similarities to annotated genes to confidently predict gene function or homology. Such recognition of highly divergent homologues can be improved by reference-free (de novo) assembly of short overlapping sequence reads into larger contigs. We describe an ensemble strategy that integrates the sequential use of various de Bruijn graph and overlap-layout-consensus assemblers with a novel partitioned sub-assembly approach. We also proposed new quality metrics that are suitable for evaluating metagenome de novo assembly. We demonstrate that this new ensemble strategy tested using in silico spike-in, clinical and environmental NGS datasets achieved significantly better contigs than current approaches.

scholarly journals METAMVGL: a multi-view graph-based metagenomic contig binning algorithm by integrating assembly and paired-end graphs

2020 ◽  
Author(s):  
Zhenmiao Zhang ◽  
Lu Zhang
Keyword(s):  
De Novo ◽  
State Of The Art ◽  
Label Propagation ◽  
Next Generation Sequencing Data ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Complete Genomes ◽  
Generation Sequencing ◽  
High Chance ◽  
Mock Communities

AbstractMotivationDue to the complexity of metagenomic community, de novo assembly on next generation sequencing data is commonly unable to produce microbial complete genomes. Metagenomic binning is a crucial task that could group the fragmented contigs into clusters based on their nucleotide compositions and read depths. These features work well on the long contigs, but are not stable for the short ones. Assembly and paired-end graphs can provide the connectedness between contigs, where the linked contigs have high chance to be derived from the same clusters.ResultsWe developed METAMVGL, a multi-view graph-based metagenomic contig binning algorithm by integrating both assembly and paired-end graphs. It could strikingly rescue the short contigs and correct the binning errors from dead ends subgraphs. METAMVGL could learn the graphs’ weights automatically and predict the contig labels in a uniform multi-view label propagation framework. In the experiments, we observed METAMVGL significantly increased the high-confident edges in the combined graph and linked dead ends to the main graph. It also outperformed with many state-of-the-art binning methods, MaxBin2, MetaBAT2, MyCC, CONCOCT, SolidBin and Graphbin on the metagenomic sequencing from simulation, two mock communities and real Sharon data.Availability and implementationThe software is available at https://github.com/ZhangZhenmiao/METAMVGL.

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