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

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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De novo virus inference and host prediction from metagenome using CRISPR spacers

10.1101/2020.09.04.282665 ◽

2020 ◽

Author(s):

Ryota Sugimoto ◽

Luca Nishimura ◽

Phuong Nguyen Thanh ◽

Jumpei Ito ◽

Nicholas F. Parrish ◽

...

Keyword(s):

De Novo ◽

Sequence Similarity ◽

Metagenomic Data ◽

Marker Genes ◽

Biological Entity ◽

Metagenomic Sequencing ◽

Sequencing Data ◽

Human Gut ◽

Cellular Genes ◽

Viral Sequences

AbstractViruses 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 known to 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 memory of previous exposure. Our protocol can infer viral sequences targeted by CRISPR and predict their hosts using unassembled short-read metagenomic sequencing data. Analysing human gut metagenomic data, we extracted 11,391 terminally redundant CRISPR-targeted sequences which are likely complete circular genomes of viruses or plasmids. The sequences include 257 complete crAssphage family genomes, 11 genomes larger than 200 kilobases, 766 genomes of Microviridae species, 114 genomes of Inoviridae species and many entirely novel genomes of unknown taxa. We predicted the host(s) of approximately 70% of discovered genomes by linking protospacers to taxonomically assigned CRISPR direct repeats. These results support that our protocol is efficient for de novo inference of viral genomes and host prediction. In addition, we investigated the origin of the diversity-generating retroelement (DGR) locus of the crAssphage family. Phylogenetic analysis and gene locus comparisons indicate that DGR is orthologous in human gut crAssphages and shares a common ancestor with baboon-derived crAssphage; however, the locus has likely been lost in multiple lineages recently.

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Biology and taxonomy of crAss-like bacteriophages, the most abundant virus in the human gut

10.1101/295642 ◽

2018 ◽

Cited By ~ 3

Author(s):

Emma Guerin ◽

Andrey Shkoporov ◽

Stephen R. Stockdale ◽

Adam G. Clooney ◽

Feargal J. Ryan ◽

...

Keyword(s):

Classification Scheme ◽

Ex Vivo ◽

Human Microbiome ◽

Biological Entity ◽

Metagenomic Sequencing ◽

Sequencing Data ◽

Human Gut ◽

Taxonomic Analysis ◽

Faecal Samples ◽

Phage Capsid

AbstractCrAssphage is yet to be cultured even though it represents the most abundant virus in the gut microbiota of humans. Recently, sequence based classification was performed on distantly related crAss-like phages from multiple environments, leading to the proposal of a familial level taxonomic group [Yutin N, et al. (2018) Discovery of an expansive bacteriophage family that includes the most abundant viruses from the human gut. Nat Microbiol 3(1):38–46]. Here, we assembled the metagenomic sequencing reads from 702 human faecal virome/phageome samples and obtained 98 complete circular crAss-like phage genomes and 145 contigs ≥70kb. In silico comparative genomics and taxonomic analysis was performed, resulting in a classification scheme of crAss-like phages from human faecal microbiomes into 4 candidate subfamilies composed of 10 candidate genera. Moreover, laboratory analysis was performed on faecal samples from an individual harbouring 7 distinct crAss-like phages. We achieved propagation of crAss-like phages in ex vivo human faecal fermentations and visualised Podoviridae virions by electron microscopy. Furthermore, detection of a crAss-like phage capsid protein could be linked to metagenomic sequencing data confirming crAss-like phage structural annotations.SignificanceCrAssphage is the most abundant biological entity in the human gut, but it remains uncultured in the laboratory and its host(s) is unknown. CrAssphage was not identified in metagenomic studies for many years as its sequence is so different from anything present in databases. To this day, it can only be detected from sequences assembled from metagenomics or viromic datasets (crAss – cross Assembly). In this study, we identified 243 new crAss-like phages from human faecal metagenomic studies. Taxonomic analysis of these crAss-like phages highlighted their extensive diversity within the human microbiome. We also present the first propagation of crAssphage in faecal fermentations and provide the first electron micrographs of this extraordinary bacteriophage.

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Accurate and sensitive detection of microbial eukaryotes from whole metagenome shotgun sequencing

Microbiome ◽

10.1186/s40168-021-01015-y ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Abigail L. Lind ◽

Katherine S. Pollard

Keyword(s):

Gene Families ◽

Shotgun Sequencing ◽

Metagenomic Data ◽

Marker Genes ◽

Metagenomic Sequencing ◽

Sequencing Data ◽

Dna And Rna ◽

Paired Samples ◽

Microbial Eukaryotes ◽

Conserved Gene

Abstract Background Microbial eukaryotes are found alongside bacteria and archaea in natural microbial systems, including host-associated microbiomes. While microbial eukaryotes are critical to these communities, they are challenging to study with shotgun sequencing techniques and are therefore often excluded. Results Here, we present EukDetect, a bioinformatics method to identify eukaryotes in shotgun metagenomic sequencing data. Our approach uses a database of 521,824 universal marker genes from 241 conserved gene families, which we curated from 3713 fungal, protist, non-vertebrate metazoan, and non-streptophyte archaeplastida genomes and transcriptomes. EukDetect has a broad taxonomic coverage of microbial eukaryotes, performs well on low-abundance and closely related species, and is resilient against bacterial contamination in eukaryotic genomes. Using EukDetect, we describe the spatial distribution of eukaryotes along the human gastrointestinal tract, showing that fungi and protists are present in the lumen and mucosa throughout the large intestine. We discover that there is a succession of eukaryotes that colonize the human gut during the first years of life, mirroring patterns of developmental succession observed in gut bacteria. By comparing DNA and RNA sequencing of paired samples from human stool, we find that many eukaryotes continue active transcription after passage through the gut, though some do not, suggesting they are dormant or nonviable. We analyze metagenomic data from the Baltic Sea and find that eukaryotes differ across locations and salinity gradients. Finally, we observe eukaryotes in Arabidopsis leaf samples, many of which are not identifiable from public protein databases. Conclusions EukDetect provides an automated and reliable way to characterize eukaryotes in shotgun sequencing datasets from diverse microbiomes. We demonstrate that it enables discoveries that would be missed or clouded by false positives with standard shotgun sequence analysis. EukDetect will greatly advance our understanding of how microbial eukaryotes contribute to microbiomes.

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

BMC Bioinformatics ◽

10.1186/s12859-021-04284-4 ◽

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.

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SCAPP: an algorithm for improved plasmid assembly in metagenomes

Microbiome ◽

10.1186/s40168-021-01068-z ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

David Pellow ◽

Alvah Zorea ◽

Maraike Probst ◽

Ori Furman ◽

Arik Segal ◽

...

Keyword(s):

Bacterial Species ◽

Bacterial Genome ◽

Biological Knowledge ◽

Assessment Procedure ◽

Metagenomic Sequencing ◽

Sequencing Data ◽

Human Gut ◽

Double Stranded Dna ◽

Wide Range ◽

Python Package

Abstract Background Metagenomic sequencing has led to the identification and assembly of many new bacterial genome sequences. These bacteria often contain plasmids: usually small, circular double-stranded DNA molecules that may transfer across bacterial species and confer antibiotic resistance. These plasmids are generally less studied and understood than their bacterial hosts. Part of the reason for this is insufficient computational tools enabling the analysis of plasmids in metagenomic samples. Results We developed SCAPP (Sequence Contents-Aware Plasmid Peeler)—an algorithm and tool to assemble plasmid sequences from metagenomic sequencing. SCAPP builds on some key ideas from the Recycler algorithm while improving plasmid assemblies by integrating biological knowledge about plasmids. We compared the performance of SCAPP to Recycler and metaplasmidSPAdes on simulated metagenomes, real human gut microbiome samples, and a human gut plasmidome dataset that we generated. We also created plasmidome and metagenome data from the same cow rumen sample and used the parallel sequencing data to create a novel assessment procedure. Overall, SCAPP outperformed Recycler and metaplasmidSPAdes across this wide range of datasets. Conclusions SCAPP is an easy to use Python package that enables the assembly of full plasmid sequences from metagenomic samples. It outperformed existing metagenomic plasmid assemblers in most cases and assembled novel and clinically relevant plasmids in samples we generated such as a human gut plasmidome. SCAPP is open-source software available from: https://github.com/Shamir-Lab/SCAPP.

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Evaluation of the CosmosID Bioinformatics Platform for Prosthetic Joint-Associated Sonicate Fluid Shotgun Metagenomic Data Analysis

Journal of Clinical Microbiology ◽

10.1128/jcm.01182-18 ◽

2018 ◽

Vol 57 (2) ◽

Cited By ~ 8

Author(s):

Qun Yan ◽

Yu Mi Wi ◽

Matthew J. Thoendel ◽

Yash S. Raval ◽

Kerryl E. Greenwood-Quaintance ◽

...

Keyword(s):

Antibiotic Resistance ◽

Metagenomic Data ◽

Metagenomic Sequencing ◽

Antibacterial Resistance ◽

Sequencing Data ◽

Bacterial Detection ◽

Shotgun Metagenomic Sequencing ◽

Prosthetic Joint ◽

Validation Set ◽

Fluid Culture

ABSTRACT We previously demonstrated that shotgun metagenomic sequencing can detect bacteria in sonicate fluid, providing a diagnosis of prosthetic joint infection (PJI). A limitation of the approach that we used is that data analysis was time-consuming and specialized bioinformatics expertise was required, both of which are barriers to routine clinical use. Fortunately, automated commercial analytic platforms that can interpret shotgun metagenomic data are emerging. In this study, we evaluated the CosmosID bioinformatics platform using shotgun metagenomic sequencing data derived from 408 sonicate fluid samples from our prior study with the goal of evaluating the platform vis-à-vis bacterial detection and antibiotic resistance gene detection for predicting staphylococcal antibacterial susceptibility. Samples were divided into a derivation set and a validation set, each consisting of 204 samples; results from the derivation set were used to establish cutoffs, which were then tested in the validation set for identifying pathogens and predicting staphylococcal antibacterial resistance. Metagenomic analysis detected bacteria in 94.8% (109/115) of sonicate fluid culture-positive PJIs and 37.8% (37/98) of sonicate fluid culture-negative PJIs. Metagenomic analysis showed sensitivities ranging from 65.7 to 85.0% for predicting staphylococcal antibacterial resistance. In conclusion, the CosmosID platform has the potential to provide fast, reliable bacterial detection and identification from metagenomic shotgun sequencing data derived from sonicate fluid for the diagnosis of PJI. Strategies for metagenomic detection of antibiotic resistance genes for predicting staphylococcal antibacterial resistance need further development.

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MetaDEGalaxy: Galaxy workflow for differential abundance analysis of 16s metagenomic data

F1000Research ◽

10.12688/f1000research.18866.2 ◽

2019 ◽

Vol 8 ◽

pp. 726

Author(s):

Mike W.C. Thang ◽

Xin-Yi Chua ◽

Gareth Price ◽

Dominique Gorse ◽

Matt A. Field

Keyword(s):

Microbial Communities ◽

Sequence Data ◽

Metagenomic Data ◽

Marker Genes ◽

Metagenomic Sequencing ◽

Differential Analysis ◽

Biomedical Sciences ◽

Metagenomic Sequence ◽

Differential Abundance ◽

Differential Abundance Analysis

Metagenomic sequencing is an increasingly common tool in environmental and biomedical sciences. While software for detailing the composition of microbial communities using 16S rRNA marker genes is relatively mature, increasingly researchers are interested in identifying changes exhibited within microbial communities under differing environmental conditions. In order to gain maximum value from metagenomic sequence data we must improve the existing analysis environment by providing accessible and scalable computational workflows able to generate reproducible results. Here we describe a complete end-to-end open-source metagenomics workflow running within Galaxy for 16S differential abundance analysis. The workflow accepts 454 or Illumina sequence data (either overlapping or non-overlapping paired end reads) and outputs lists of the operational taxonomic unit (OTUs) exhibiting the greatest change under differing conditions. A range of analysis steps and graphing options are available giving users a high-level of control over their data and analyses. Additionally, users are able to input complex sample-specific metadata information which can be incorporated into differential analysis and used for grouping / colouring within graphs. Detailed tutorials containing sample data and existing workflows are available for three different input types: overlapping and non-overlapping read pairs as well as for pre-generated Biological Observation Matrix (BIOM) files. Using the Galaxy platform we developed MetaDEGalaxy, a complete metagenomics differential abundance analysis workflow. MetaDEGalaxy is designed for bench scientists working with 16S data who are interested in comparative metagenomics. MetaDEGalaxy builds on momentum within the wider Galaxy metagenomics community with the hope that more tools will be added as existing methods mature.

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Towards end-to-end disease prediction from raw metagenomic data

10.1101/2020.10.29.360297 ◽

2020 ◽

Author(s):

Maxence Queyrel ◽

Edi Prifti ◽

Jean-Daniel Zucker

Keyword(s):

Dna Sequences ◽

Real Life ◽

Multiple Instance Learning ◽

Disease Classification ◽

Metagenomic Data ◽

Numerical Representation ◽

Metagenomic Sequencing ◽

Sequencing Data ◽

End To End ◽

Bioinformatics Workflows

AbstractAnalysis of the human microbiome using metagenomic sequencing data has demonstrated high ability in discriminating various human diseases. Raw metagenomic sequencing data require multiple complex and computationally heavy bioinformatics steps prior to data analysis. Such data contain millions of short sequences read from the fragmented DNA sequences and are stored as fastq files. Conventional processing pipelines consist multiple steps including quality control, filtering, alignment of sequences against genomic catalogs (genes, species, taxonomic levels, functional pathways, etc.). These pipelines are complex to use, time consuming and rely on a large number of parameters that often provide variability and impact the estimation of the microbiome elements. Recent studies have demonstrated that training Deep Neural Networks directly from raw sequencing data is a promising approach to bypass some of the challenges associated with mainstream bioinformatics pipelines. Most of these methods use the concept of word and sentence embeddings that create a meaningful and numerical representation of DNA sequences, while extracting features and reducing the dimentionality of the data. In this paper we present an end-to-end approach that classifies patients into disease groups directly from raw metagenomic reads: metagenome2vec. This approach is composed of four steps (i) generating a vocabulary of k-mers and learning their numerical embeddings; (ii) learning DNA sequence (read) embeddings; (iii) identifying the genome from which the sequence is most likely to come and (iv) training a multiple instance learning classifier which predicts the phenotype based on the vector representation of the raw data. An attention mechanism is applied in the network so that the model can be interpreted, assigning a weight to the influence of the prediction for each genome. Using two public real-life datasets as well a simulated one, we demonstrated that this original approach reached very high performances, comparable with the state-of-the-art methods applied directly on processed data though mainstream bioinformatics workflows. These results are encouraging for this proof of concept work. We believe that with further dedication, the DNN models have the potential to surpass mainstream bioinformatics workflows in disease classification tasks.

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Viral Metagenomics Revealed Sendai Virus and Coronavirus Infection of Malayan Pangolins (Manis javanica)

Viruses ◽

10.3390/v11110979 ◽

2019 ◽

Vol 11 (11) ◽

pp. 979 ◽

Cited By ~ 127

Author(s):

Ping Liu ◽

Wu Chen ◽

Jin-Ping Chen

Keyword(s):

De Novo ◽

Sendai Virus ◽

Sequence Similarity ◽

Viral Metagenomics ◽

Manis Javanica ◽

Pathogenic Viruses ◽

Viral Communities ◽

High Level ◽

Viral Sequences ◽

Insight Into

Pangolins are endangered animals in urgent need of protection. Identifying and cataloguing the viruses carried by pangolins is a logical approach to evaluate the range of potential pathogens and help with conservation. This study provides insight into viral communities of Malayan Pangolins (Manis javanica) as well as the molecular epidemiology of dominant pathogenic viruses between Malayan Pangolin and other hosts. A total of 62,508 de novo assembled contigs were constructed, and a BLAST search revealed 3600 ones (≥300 nt) were related to viral sequences, of which 68 contigs had a high level of sequence similarity to known viruses, while dominant viruses were the Sendai virus and Coronavirus. This is the first report on the viral diversity of pangolins, expanding our understanding of the virome in endangered species, and providing insight into the overall diversity of viruses that may be capable of directly or indirectly crossing over into other mammals.

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Characterization of the transcriptome and EST-SSR development in Boea clarkeana, a desiccation-tolerant plant endemic to China

10.7287/peerj.preprints.2603v1 ◽

2016 ◽

Author(s):

Ying Wang ◽

Kun Liu ◽

De Bi ◽

Biao Shou Zhou ◽

Wen Jian Shao

Keyword(s):

De Novo ◽

Gene Annotation ◽

Sequence Similarity ◽

Molecular Study ◽

Sequence Information ◽

Sequencing Data ◽

Protein Database ◽

Illumina Hiseq ◽

Significant Similarity ◽

Assembly Technology

Background. Resurrection plants constitute a unique cadre within angiosperms. Boea clarkeana Hemsl. (Boea, Gesneriaceae) is a desiccation-tolerant dicotyledonous herb that is endemic to China. Although research on angiosperms with DT could be instructive for crops, genomic resources for B. clarkeana remain scarce. In addition, transcriptome sequencing could be an effective way to study desiccation-tolerant plants. Methods. In the present study, we used the platform Illumina HiSeqTM 2000 and de novo assembly technology to obtain leaf transcriptomes of B. clarkeana and conducted a BLASTX alignment of the sequencing data and protein databases for sequence classification and annotation. Then, based on the sequence information obtained, we developed EST-SSR markers by means of EST-SSR mining, primer design and polymorphism identification. Results. A total of 91,449 unigenes were generated from the leaf cDNA library of B. clarkeana in this study. Based on a sequence similarity search with a known protein database, 72,087 unigenes were annotated. Among the annotated unigenes, a total of 71,170 unigenes showed significant similarity to known proteins of 463 popular model species in the Nr database, and 59,962 unigenes and 32,336 unigenes were assigned to GO classifications and COG, respectively. In addition, 44,924 unigenes were mapped in 128 KEGG pathways. Furthermore, a total of 7,610 unigenes with 8,563 microsatellites were found. Seventy-four primer pairs were selected from 436 primer pairs designed for polymorphism validation. SSRs with higher polymorphism rates were concentrated on dinucleotides, pentanucleotides and hexanucleotides. Finally, 17 pairs with highly polymorphic and stable loci were selected for polymorphism screening. There were a total of 65 alleles, with 2–6 alleles at each locus. Mainly due to the unique biological characteristics of plants, the HE, HO and PIC per locus were very low, ranging from 0 to 0.196, 0.082 to 0.14 and 0 to 0.155, respectively. Discussion. A substantial fraction transcriptome sequences of B. clarkeana were generated in this study, which is the first molecular-level analysis of this plant. These sequences are valuable resources for gene annotation and discovery and molecular marker development. These sequences could also provide a valuable basis for the future molecular study of B. clarkeana.

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