An information and statistical analysis pipeline for microbial metagenomic sequencing data

The contemporary surge in metagenomic sequencing has transformed knowledge of viral diversity in wildlife. However, evaluating which newly discovered viruses pose sufficient risk of infecting humans to merit detailed laboratory characterization and surveillance remains largely speculative. Machine learning algorithms have been developed to address this imbalance by ranking the relative likelihood of human infection based on viral genome sequences, but are not yet routinely applied to viruses at the time of their discovery. Here, we characterized viral genomes detected through metagenomic sequencing of feces and saliva from common vampire bats (Desmodus rotundus) and used these data as a case study in evaluating zoonotic potential using molecular sequencing data. Of 58 detected viral families, including 17 which infect mammals, the only known zoonosis detected was rabies virus; however, additional genomes were detected from the families Hepeviridae, Coronaviridae, Reoviridae, Astroviridae and Picornaviridae, all of which contain human-infecting species. In phylogenetic analyses, novel vampire bat viruses most frequently grouped with other bat viruses that are not currently known to infect humans. In agreement, machine learning models built from only phylogenetic information ranked all novel viruses similarly, yielding little insight into zoonotic potential. In contrast, genome composition-based machine learning models estimated different levels of zoonotic potential, even for closely related viruses, categorizing one out of four detected hepeviruses and two out of three picornaviruses as having high priority for further research. We highlight the value of evaluating zoonotic potential beyond ad hoc consideration of phylogeny and provide surveillance recommendations for novel viruses in a wildlife host which has frequent contact with humans and domestic animals.

Download Full-text

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.

Download Full-text

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.

Download Full-text

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.

Download Full-text

Accurate Reconstruction of Microbial Strains from Metagenomic Sequencing Using Representative Reference Genomes

10.1101/215707 ◽

2017 ◽

Cited By ~ 2

Author(s):

Zhemin Zhou ◽

Nina Luhmann ◽

Nabil-Fareed Alikhan ◽

Christopher Quince ◽

Mark Achtman

Keyword(s):

Evaluation Studies ◽

Species Level ◽

Metagenomic Sequencing ◽

Sequencing Data ◽

Reference Databases ◽

Microbial Strains ◽

Taxonomic Assignments ◽

Taxonomic Groups ◽

Reference Genomes ◽

Recent Evaluation

AbstractExploring the genetic diversity of microbes within the environment through metagenomic sequencing first requires classifying these reads into taxonomic groups. Current methods compare these sequencing data with existing biased and limited reference databases. Several recent evaluation studies demonstrate that current methods either lack sufficient sensitivity for species-level assignments or suffer from false positives, overestimating the number of species in the metagenome. Both are especially problematic for the identification of low-abundance microbial species, e. g. detecting pathogens in ancient metagenomic samples. We present a new method, SPARSE, which improves taxonomic assignments of metagenomic reads. SPARSE balances existing biased reference databases by grouping reference genomes into similarity-based hierarchical clusters, implemented as an efficient incremental data structure. SPARSE assigns reads to these clusters using a probabilistic model, which specifically penalizes non-specific mappings of reads from unknown sources and hence reduces false-positive assignments. Our evaluation on simulated datasets from two recent evaluation studies demonstrated the improved precision of SPARSE in comparison to other methods for species-level classification. In a third simulation, our method successfully differentiated multiple co-existing Escherichia coli strains from the same sample. In real archaeological datasets, SPARSE identified ancient pathogens with ≤ 0.02% abundance, consistent with published findings that required additional sequencing data. In these datasets, other methods either missed targeted pathogens or reported non-existent ones. SPARSE and all evaluation scripts are available at https://github.com/zheminzhou/SPARSE.

Download Full-text

SHAMAN: a user-friendly website for metataxonomic analysis from raw reads to statistical analysis

10.21203/rs.2.23213/v1 ◽

2020 ◽

Author(s):

Stevenn Volant ◽

Pierre Lechat ◽

Perrine Woringer ◽

Laurence Motreff ◽

Christophe Malabat ◽

...

Keyword(s):

Statistical Analysis ◽

Data Processing ◽

Web Application ◽

Graphical Representation ◽

Statistical Modelling ◽

Metagenomic Data ◽

Sequencing Data ◽

Microbiome Research ◽

Interactive Visualizations ◽

User Friendly

Abstract BackgroundComparing the composition of microbial communities among groups of interest (e.g., patients vs healthy individuals) is a central aspect in microbiome research. It typically involves sequencing, data processing, statistical analysis and graphical representation of the detected signatures. Such an analysis is normally obtained by using a set of different applications that require specific expertise for installation, data processing and in some case, programming skills. ResultsHere, we present SHAMAN, an interactive web application we developed in order to facilitate the use of (i) a bioinformatic workflow for metataxonomic analysis, (ii) a reliable statistical modelling and (iii) to provide among the largest panels of interactive visualizations as compared to the other options that are currently available. SHAMAN is specifically designed for non-expert users who may benefit from using an integrated version of the different analytic steps underlying a proper metagenomic analysis. The application is freely accessible at http://shaman.pasteur.fr/, and may also work as a standalone application with a Docker container (aghozlane/shaman), conda and R. The source code is written in R and is available at https://github.com/aghozlane/shaman. Using two datasets (a mock community sequencing and published 16S rRNA metagenomic data), we illustrate the strengths of SHAMAN in quickly performing a complete metataxonomic analysis. ConclusionsWe aim with SHAMAN to provide the scientific community with a platform that simplifies reproducible quantitative analysis of metagenomic data.

Download Full-text

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.

Download Full-text

AFLAP: Assembly-Free Linkage Analysis Pipeline using k-mers from whole genome sequencing data

10.1101/2020.09.14.296525 ◽

2020 ◽

Author(s):

Kyle Fletcher ◽

Lin Zhang ◽

Juliana Gil ◽

Rongkui Han ◽

Keri Cavanaugh ◽

...

Keyword(s):

Linkage Analysis ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Genetic Map ◽

Genotyping By Sequencing ◽

Genetic Maps ◽

Whole Genome ◽

Sequencing Data ◽

Analysis Pipeline ◽

Genome Assemblies

AbstractBackgroundGenetic maps are an important resource for validation of genome assemblies, trait discovery, and breeding. Next generation sequencing has enabled production of high-density genetic maps constructed with 10,000s of markers. Most current approaches require a genome assembly to identify markers. Our Assembly Free Linkage Analysis Pipeline (AFLAP) removes this requirement by using uniquely segregating k-mers as markers to rapidly construct a genotype table and perform subsequent linkage analysis. This avoids potential biases including preferential read alignment and variant calling.ResultsThe performance of AFLAP was determined in simulations and contrasted to a conventional workflow. We tested AFLAP using 100 F2 individuals of Arabidopsis thaliana, sequenced to low coverage. Genetic maps generated using k-mers contained over 130,000 markers that were concordant with the genomic assembly. The utility of AFLAP was then demonstrated by generating an accurate genetic map using genotyping-by-sequencing data of 235 recombinant inbred lines of Lactuca spp. AFLAP was then applied to 83 F1 individuals of the oomycete Bremia lactucae, sequenced to >5x coverage. The genetic map contained over 90,000 markers ordered in 19 large linkage groups. This genetic map was used to fragment, order, orient, and scaffold the genome, resulting in a much-improved reference assembly.ConclusionsAFLAP can be used to generate high density linkage maps and improve genome assemblies of any organism when a mapping population is available using whole genome sequencing or genotyping-by-sequencing data. Genetic maps produced for B. lactucae were accurately aligned to the genome and guided significant improvements of the reference assembly.

Download Full-text

K-mer counting with low memory consumption enables fast clustering of single-cell sequencing data without read alignment

10.1101/723833 ◽

2019 ◽

Author(s):

Christina Huan Shi ◽

Kevin Y. Yip

Keyword(s):

Single Cell ◽

State Of The Art ◽

Rna Seq ◽

Sequencing Data ◽

Memory Consumption ◽

Analysis Pipeline ◽

Cell Clusters ◽

Single Cell Sequencing ◽

Sequencing Errors ◽

Full Analysis

AbstractK-mer counting has many applications in sequencing data processing and analysis. However, sequencing errors can produce many false k-mers that substantially increase the memory requirement during counting. We propose a fast k-mer counting method, CQF-deNoise, which has a novel component for dynamically identifying and removing false k-mers while preserving counting accuracy. Compared with four state-of-the-art k-mer counting methods, CQF-deNoise consumed 49-76% less memory than the second best method, but still ran competitively fast. The k-mer counts from CQF-deNoise produced cell clusters from single-cell RNA-seq data highly consistent with CellRanger but required only 5% of the running time at the same memory consumption, suggesting that CQF-deNoise can be used for a preview of cell clusters for an early detection of potential data problems, before running a much more time-consuming full analysis pipeline.

Download Full-text