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 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.

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 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.

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 Deconvolute individual genomes from metagenome sequences through short read clustering

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8966 ◽  
Author(s):  
Kexue Li ◽  
Yakang Lu ◽  
Li Deng ◽  
Lili Wang ◽  
Lizhen Shi ◽  
...  
Keyword(s):  
Large Scale ◽  
False Negative ◽  
Next Generation Sequencing Data ◽  
Clustering Methods ◽  
Sequencing Data ◽  
Short Reads ◽  
Clustering Problem ◽  
Metagenome Assembly ◽  
Real World Datasets ◽  
Almost All

Metagenome assembly from short next-generation sequencing data is a challenging process due to its large scale and computational complexity. Clustering short reads by species before assembly offers a unique opportunity for parallel downstream assembly of genomes with individualized optimization. However, current read clustering methods suffer either false negative (under-clustering) or false positive (over-clustering) problems. Here we extended our previous read clustering software, SpaRC, by exploiting statistics derived from multiple samples in a dataset to reduce the under-clustering problem. Using synthetic and real-world datasets we demonstrated that this method has the potential to cluster almost all of the short reads from genomes with sufficient sequencing coverage. The improved read clustering in turn leads to improved downstream genome assembly quality.

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 Covariation of the Fecal Microbiome with Diet in Nonpasserine Birds

mSphere ◽  
2021 ◽  
Vol 6 (3) ◽  
Author(s):  
Kangpeng Xiao ◽  
Yutan Fan ◽  
Zhipeng Zhang ◽  
Xuejuan Shen ◽  
Xiaobing Li ◽  
...  
Keyword(s):  
Gut Microbiome ◽  
Dietary Diversity ◽  
Basal Diet ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Future Health ◽  
Amino Acid Synthesis ◽  
Fecal Samples ◽  
Fecal Microbiome ◽  
Microbiome Diversity

ABSTRACT Opportunistic feeding and multiple other environment factors can modulate the gut microbiome, and bias conclusions, when wild animals are used for studying the influence of phylogeny and diet on their gut microbiomes. Here, we controlled for these other confounding factors in our investigation of the magnitude of the effect of diet on the gut microbiome assemblies of nonpasserine birds. We collected fecal samples, at one point in time, from 35 species of birds in a single zoo as well as 6 species of domestic poultry from farms in Guangzhou city to minimize the influences from interfering factors. Specifically, we describe 16S rRNA amplicon data from 129 fecal samples obtained from 41 species of birds, with additional shotgun metagenomic sequencing data generated from 16 of these individuals. Our data show that diets containing native starch increase the abundance of Lactobacillus in the gut microbiome, while those containing plant-derived fiber mainly enrich the level of Clostridium. Greater numbers of Fusobacteria and Proteobacteria are detected in carnivorous birds, while in birds fed a commercial corn-soybean basal diet, a stronger inner-connected microbial community containing Clostridia and Bacteroidia was enriched. Furthermore, the metagenome functions of the microbes (such as lipid metabolism and amino acid synthesis) were adapted to the different food types to achieve a beneficial state for the host. In conclusion, the covariation of diet and gut microbiome identified in our study demonstrates a modulation of the gut microbiome by dietary diversity and helps us better understand how birds live based on diet-microbiome-host interactions. IMPORTANCE Our study identified food source, rather than host phylogeny, as the main factor modulating the gut microbiome diversity of nonpasserine birds, after minimizing the effects of other complex interfering factors such as weather, season, and geography. Adaptive evolution of microbes to food types formed a dietary-microbiome-host interaction reciprocal state. The covariation of diet and gut microbiome, including the response of microbiota assembly to diet in structure and function, is important for health and nutrition in animals. Our findings help resolve the major modulators of gut microbiome diversity in nonpasserine birds, which had not previously been well studied. The diet-microbe interactions and cooccurrence patterns identified in our study may be of special interest for future health assessment and conservation in birds.

scholarly journals Deconvolute individual genomes from metagenome sequences through read clustering

2019 ◽  
Author(s):  
Kexue Li ◽  
Lili Wang ◽  
Lizhen Shi ◽  
Li Deng ◽  
Zhong Wang
Keyword(s):  
Large Scale ◽  
False Negative ◽  
Next Generation Sequencing Data ◽  
Clustering Methods ◽  
Sequencing Data ◽  
Clustering Problem ◽  
Sequencing Coverage ◽  
Metagenome Assembly ◽  
Almost All ◽  
Small Clusters

ABSTRACTMotivationMetagenome assembly from short next-generation sequencing data is a challenging process due to its large scale and computational complexity. Clustering short reads before assembly offers a unique opportunity for parallel downstream assembly of genomes with individualized optimization. However, current read clustering methods suffer either false negative (under-clustering) or false positive (over-clustering) problems.ResultsBased on a previously developed scalable read clustering method on Apache Spark, SpaRC, that has very low false positives, here we extended its capability by adding a new method to further cluster small clusters. This method exploits statistics derived from multiple samples in a dataset to reduce the under-clustering problem. Using a synthetic dataset from mouse gut microbiomes we show that this method has the potential to cluster almost all of the reads from genomes with sufficient sequencing coverage. We also explored several clustering parameters that deferentially affect genomes with various sequencing coverage.Availabilityhttps://bitbucket.org/berkeleylab/jgi-sparc/[email protected]

scholarly journals Comprehensive discovery of CRISPR-targeted terminally redundant sequences in the human gut metagenome: Viruses, plasmids, and more

2021 ◽  
Vol 17 (10) ◽  
pp. e1009428
Author(s):  
Ryota Sugimoto ◽  
Luca Nishimura ◽  
Phuong Thanh Nguyen ◽  
Jumpei Ito ◽  
Nicholas F. Parrish ◽  
...  
Keyword(s):  
De Novo ◽  
Sequence Similarity ◽  
Metagenomic Data ◽  
Marker Genes ◽  
Biological Entity ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Human Gut ◽  
Protein Coding ◽  
Viral Sequences

Viruses are the most numerous biological entity, existing in all environments and infecting all cellular organisms. Compared with cellular life, the evolution and origin of viruses are poorly understood; viruses are enormously diverse, and most lack sequence similarity to cellular genes. To uncover viral sequences without relying on either reference viral sequences from databases or marker genes that characterize specific viral taxa, we developed an analysis pipeline for virus inference based on clustered regularly interspaced short palindromic repeats (CRISPR). CRISPR is a prokaryotic nucleic acid restriction system that stores the memory of previous exposure. Our protocol can infer CRISPR-targeted sequences, including viruses, plasmids, and previously uncharacterized elements, and predict their hosts using unassembled short-read metagenomic sequencing data. By analyzing human gut metagenomic data, we extracted 11,391 terminally redundant CRISPR-targeted sequences, which are likely complete circular genomes. The sequences included 2,154 tailed-phage genomes, together with 257 complete crAssphage genomes, 11 genomes larger than 200 kilobases, 766 genomes of Microviridae species, 56 genomes of Inoviridae species, and 95 previously uncharacterized circular small genomes that have no reliably predicted protein-coding gene. We predicted the host(s) of approximately 70% of the discovered genomes at the taxonomic level of phylum by linking protospacers to taxonomically assigned CRISPR direct repeats. These results demonstrate that our protocol is efficient for de novo inference of CRISPR-targeted sequences and their host prediction.

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