scholarly journals Searching more genomic sequence with less memory for fast and accurate metagenomic profiling

2016 ◽  
Author(s):  
Shea N Gardner ◽  
Sasha K Ames ◽  
Maya B Gokhale ◽  
Tom R Slezak ◽  
Jonathan Allen
Keyword(s):  
Large Scale ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Low Cost ◽  
False Negative ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Metagenomic Data ◽  
Reference Database ◽  
Metagenomic Sequence

Software for rapid, accurate, and comprehensive microbial profiling of metagenomic sequence data on a desktop will play an important role in large scale clinical use of metagenomic data. Here we describe LMAT-ML (Livermore Metagenomics Analysis Toolkit-Marker Library) which can be run with 24 GB of DRAM memory, an amount available on many clusters, or with 16 GB DRAM plus a 24 GB low cost commodity flash drive (NVRAM), a cost effective alternative for desktop or laptop users. We compared results from LMAT with five other rapid, low-memory tools for metagenome analysis for 131 Human Microbiome Project samples, and assessed discordant calls with BLAST. All the tools except LMAT-ML reported overly specific or incorrect species and strain resolution of reads that were in fact much more widely conserved across species, genera, and even families. Several of the tools misclassified reads from synthetic or vector sequence as microbial or human reads as viral. We attribute the high numbers of false positive and false negative calls to a limited reference database with inadequate representation of known diversity. Our comparisons with real world samples show that LMAT-ML is the only tool tested that classifies the majority of reads, and does so with high accuracy.

scholarly journals Comparative genomics identifies thousands of candidate structured RNAs in human microbiomes

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Brayon J. Fremin ◽  
Ami S. Bhatt
Keyword(s):  
Comparative Genomics ◽  
Large Scale ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Human Microbiome ◽  
Automated Analysis ◽  
Metagenomic Data ◽  
Coding Regions ◽  
Intergenic Regions ◽  
Computationally Intensive

Abstract Background Structured RNAs play varied bioregulatory roles within microbes. To date, hundreds of candidate structured RNAs have been predicted using informatic approaches that search for motif structures in genomic sequence data. The human microbiome contains thousands of species and strains of microbes. Yet, much of the metagenomic data from the human microbiome remains unmined for structured RNA motifs primarily due to computational limitations. Results We sought to apply a large-scale, comparative genomics approach to these organisms to identify candidate structured RNAs. With a carefully constructed, though computationally intensive automated analysis, we identify 3161 conserved candidate structured RNAs in intergenic regions, as well as 2022 additional candidate structured RNAs that may overlap coding regions. We validate the RNA expression of 177 of these candidate structures by analyzing small fragment RNA-seq data from four human fecal samples. Conclusions This approach identifies a wide variety of candidate structured RNAs, including tmRNAs, antitoxins, and likely ribosome protein leaders, from a wide variety of taxa. Overall, our pipeline enables conservative predictions of thousands of novel candidate structured RNAs from human microbiomes.

scholarly journals Fast functional annotation of metagenomic shotgun data by DNA alignment to a microbial gene catalog

2017 ◽  
Author(s):  
Stuart M. Brown ◽  
Yuhan Hao ◽  
Hao Chen ◽  
Bobby P. Laungani ◽  
Thahmina A. Ali ◽  
...  
Keyword(s):  
Functional Annotation ◽  
Sequence Data ◽  
Human Microbiome ◽  
Metagenomic Data ◽  
Metagenomic Sequencing ◽  
Alternative Analysis ◽  
Metagenomic Sequence ◽  
Shotgun Metagenomics ◽  
Gene Functions ◽  
Dna Alignment

AbstractBackgroundMetagenomic shotgun sequencing is becoming increasingly popular to study microbes associated with the human body and in environmental samples. A key goal of shotgun metagenomic sequencing is to identify gene functions and metabolic pathways that differ between samples or conditions. However, current methods to identify function in the large number of reads in a high-throughput sequence data file rely on the computationally intensive and low stringency approach of mapping each read to a generic database of proteins or reference microbial genomes.ResultsWe have developed an alternative analysis approach for shotgun metagenomic sequence data utilizing Bowtie2 DNA-DNA alignment of the reads to a database of well annotated genes compiled from human microbiome data. This method is rapid, and provides high stringency matches (>90% DNA sequence identity) of shotgun metagenomics reads to genes with annotated functions. We demonstrate the use of this method with synthetic data, Human Microbiome Project shotgun metagenomic data sets, and data from a study of liver disease. Differentially abundant KEGG gene functions can be detected in these experiments.ConclusionsFunctional annotation of metagenomic shotgun sequence reads can be accomplished by rapid DNA-DNA matching to a custom database of microbial sequences using the Bowtie2 sequence alignment tool. This method can be used for a variety of microbiome studies and allows functional analysis which is otherwise computationally demanding. This rapid annotation method is freely available as a Galaxy workflow within a Docker image.

scholarly journals TaxiBGC: a Taxonomy-guided Approach for the Identification of Experimentally Verified Microbial Biosynthetic Gene Clusters in Shotgun Metagenomic Data

2021 ◽  
Author(s):  
Utpal Bakshi ◽  
Vinod K Gupta ◽  
Aileen R Lee ◽  
John M Davis ◽  
Sriram Chandrasekaran ◽  
...  
Keyword(s):  
Large Scale ◽  
Human Microbiome ◽  
Gene Clusters ◽  
Human Microbiome Project ◽  
Metagenomic Data ◽  
Biosynthetic Gene ◽  
Case Control Studies ◽  
Biosynthetic Gene Clusters ◽  
Host Interactions ◽  
Microbiome Data

Biosynthetic gene clusters (BGCs) in microbial genomes encode for the production of bioactive secondary metabolites (SMs). Given the well-recognized importance of SMs in microbe-microbe and microbe-host interactions, the large-scale identification of BGCs from microbial metagenomes could offer novel functional insights into complex chemical ecology. Despite recent progress, currently available tools for predicting BGCs from shotgun metagenomes have several limitations, including the need for computationally demanding read-assembly and prediction of a narrow breadth of BGC classes. To overcome these limitations, we developed TaxiBGC (Taxonomy-guided Identification of Biosynthetic Gene Clusters), a computational pipeline for identifying experimentally verified BGCs in shotgun metagenomes by first pinpointing the microbial species likely to produce them. We show that our species-centric approach was able to identify BGCs in simulated metagenomes more accurately than by solely detecting BGC genes. By applying TaxiBGC on 5,423 metagenomes from the Human Microbiome Project and various case-control studies, we identified distinct BGC signatures of major human body sites and candidate stool-borne biomarkers for multiple diseases, including inflammatory bowel disease, colorectal cancer, and psychiatric disorders. In all, TaxiBGC demonstrates a significant advantage over existing techniques for systematically characterizing BGCs and inferring their SMs from microbiome data.

scholarly journals The single-species metagenome: subtypingStaphylococcus aureuscore genome sequences from shotgun metagenomic data

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2571 ◽  
Author(s):  
Sandeep J. Joseph ◽  
Ben Li ◽  
Robert A. Petit III ◽  
Zhaohui S. Qin ◽  
Lyndsey Darrow ◽  
...  
Keyword(s):  
New York ◽  
Sequence Data ◽  
Geographic Distance ◽  
Human Microbiome ◽  
Genomic Analysis ◽  
Single Species ◽  
Human Microbiome Project ◽  
Metagenomic Data ◽  
Comparative Genomic ◽  
A Genome

In this study we developed a genome-based method for detectingStaphylococcus aureussubtypes from metagenome shotgun sequence data. We used a binomial mixture model and the coverage counts at >100,000 knownS. aureusSNP (single nucleotide polymorphism) sites derived from prior comparative genomic analysis to estimate the proportion of 40 subtypes in metagenome samples. We were able to obtain >87% sensitivity and >94% specificity at 0.025X coverage forS. aureus. We found that 321 and 149 metagenome samples from the Human Microbiome Project and metaSUB analysis of the New York City subway, respectively, containedS. aureusat genome coverage >0.025. In both projects, CC8 and CC30 were the most commonS. aureusclonal complexes encountered. We found evidence that the subtype composition at different body sites of the same individual were more similar than random sampling and more limited evidence that certain body sites were enriched for particular subtypes. One surprising finding was the apparent high frequency of CC398, a lineage often associated with livestock, in samples from the tongue dorsum. Epidemiologic analysis of the HMP subject population suggested that high BMI (body mass index) and health insurance are possibly associated withS. aureuscarriage but there was limited power to identify factors linked to carriage of even the most common subtype. In the NYC subway data, we found a small signal of geographic distance affecting subtype clustering but other unknown factors influence taxonomic distribution of the species around the city.

scholarly journals A combined RNA-Seq and comparative genomics approach identifies 1,085 candidate structured RNAs expressed in human microbiomes

2020 ◽  
Author(s):  
Brayon J. Fremin ◽  
Ami S. Bhatt
Keyword(s):  
Comparative Genomics ◽  
Experimental Approach ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Computational Approach ◽  
Rna Seq ◽  
Rna Structures ◽  
Experimental Approaches

AbstractStructured RNAs play varied bioregulatory roles within microbes. To date, hundreds of candidate structured RNAs have been predicted using informatic approaches by searching for motif structures in genomic sequence data. However, only a subset of these candidate structured RNAs, those from culturable, well-studied microbes, have been shown to be transcribed. As the human microbiome contains thousands of species and strains of microbes, we sought to apply both informatic and experimental approaches to these organisms to identify novel transcribed structured RNAs. We combine an experimental approach, RNA-Seq, with an informatic approach, comparative genomics across the human microbiome project, to discover 1,085 candidate, conserved structured RNAs that are actively transcribed in human fecal microbiomes. These predictions include novel tracrRNAs that associate with Cas9 and RNA structures encoded in overlapping regions of the genome that are in opposing orientations. In summary, this combined experimental and computational approach enables the discovery of thousands of novel candidate structured RNAs.

scholarly journals Accessible, curated metagenomic data through ExperimentHub

2017 ◽  
Author(s):  
Edoardo Pasolli ◽  
Lucas Schiffer ◽  
Paolo Manghi ◽  
Audrey Renson ◽  
Valerie Obenchain ◽  
...  
Keyword(s):  
Open Source Software ◽  
Large Scale ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Disease Classification ◽  
Metagenomic Data ◽  
Sequencing Data ◽  
Data Types ◽  
Command Line Interface ◽  
Functional Profiles

We present curatedMetagenomicData, a Bioconductor and command-line interface to thousands of metagenomic profiles from the Human Microbiome Project and other publicly available datasets, and ExperimentHub, a platform for convenient cloud-based distribution of data to the R desktop. The resource provides standardized per-participant metadata linked to bacterial, fungal, archaeal, and viral taxonomic abundances, as well as quantitative metabolic functional profiles. The datasets can be immediately analyzed in R or other software with a minimum of bioinformatic expertise and no preprocessing of data. We demonstrate identification of taxonomic/functional correlations, an investigation of gut “enterotypes”, and a comparison of the accuracy of disease classification from different data types. These documented analyses can be reproduced efficiently on a laptop, without the barriers of working with large-scale, raw sequencing data. The building and expansion of curatedMetagenomicData is based entirely on open source software and pipelines, to facilitate the addition of new microbiome datasets and methods.

scholarly journals Sequence Comparison of Vaginolysin from Different Gardnerella Species

Pathogens ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 86
Author(s):  
Erin M. Garcia ◽  
Myrna G. Serrano ◽  
Laahirie Edupuganti ◽  
David J. Edwards ◽  
Gregory A. Buck ◽  
...  
Keyword(s):  
Amino Acid ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Distinct Species ◽  
Vaginal Swab ◽  
Metagenomic Data ◽  
Gardnerella Vaginalis ◽  
Vaginal Epithelial Cells ◽  
Species Specific

Gardnerella vaginalis has recently been split into 13 distinct species. In this study, we tested the hypotheses that species-specific variations in the vaginolysin (VLY) amino acid sequence could influence the interaction between the toxin and vaginal epithelial cells and that VLY variation may be one factor that distinguishes less virulent or commensal strains from more virulent strains. This was assessed by bioinformatic analyses of publicly available Gardnerella spp. sequences and quantification of cytotoxicity and cytokine production from purified, recombinantly produced versions of VLY. After identifying conserved differences that could distinguish distinct VLY types, we analyzed metagenomic data from a cohort of female subjects from the Vaginal Human Microbiome Project to investigate whether these different VLY types exhibited any significant associations with symptoms or Gardnerella spp.-relative abundance in vaginal swab samples. While Type 1 VLY was most prevalent among the subjects and may be associated with increased reports of symptoms, subjects with Type 2 VLY dominant profiles exhibited increased relative Gardnerella spp. abundance. Our findings suggest that amino acid differences alter the interaction of VLY with vaginal keratinocytes, which may potentiate differences in bacterial vaginosis (BV) immunopathology in vivo.

scholarly journals Meta-Apo improves accuracy of 16S-amplicon-based prediction of microbiome function

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Gongchao Jing ◽  
Yufeng Zhang ◽  
Wenzhi Cui ◽  
Lu Liu ◽  
Jian Xu ◽  
...  
Keyword(s):  
16S Rrna ◽  
Large Scale ◽  
Low Cost ◽  
Human Microbiome ◽  
Amplicon Sequencing ◽  
Training Sample ◽  
Rrna Gene ◽  
16S Amplicon Sequencing ◽  
Cross Platform ◽  
Functional Profiles

Abstract Background Due to their much lower costs in experiment and computation than metagenomic whole-genome sequencing (WGS), 16S rRNA gene amplicons have been widely used for predicting the functional profiles of microbiome, via software tools such as PICRUSt 2. However, due to the potential PCR bias and gene profile variation among phylogenetically related genomes, functional profiles predicted from 16S amplicons may deviate from WGS-derived ones, resulting in misleading results. Results Here we present Meta-Apo, which greatly reduces or even eliminates such deviation, thus deduces much more consistent diversity patterns between the two approaches. Tests of Meta-Apo on > 5000 16S-rRNA amplicon human microbiome samples from 4 body sites showed the deviation between the two strategies is significantly reduced by using only 15 WGS-amplicon training sample pairs. Moreover, Meta-Apo enables cross-platform functional comparison between WGS and amplicon samples, thus greatly improve 16S-based microbiome diagnosis, e.g. accuracy of gingivitis diagnosis via 16S-derived functional profiles was elevated from 65 to 95% by WGS-based classification. Therefore, with the low cost of 16S-amplicon sequencing, Meta-Apo can produce a reliable, high-resolution view of microbiome function equivalent to that offered by shotgun WGS. Conclusions This suggests that large-scale, function-oriented microbiome sequencing projects can probably benefit from the lower cost of 16S-amplicon strategy, without sacrificing the precision in functional reconstruction that otherwise requires WGS. An optimized C++ implementation of Meta-Apo is available on GitHub (https://github.com/qibebt-bioinfo/meta-apo) under a GNU GPL license. It takes the functional profiles of a few paired WGS:16S-amplicon samples as training, and outputs the calibrated functional profiles for the much larger number of 16S-amplicon samples.

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

F1000Research ◽  
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.

scholarly journals Misclassification of a whole genome sequence reference defined by the Human Microbiome Project: a detrimental carryover effect to microbiome studies

2019 ◽  
Author(s):  
DJ Darwin R. Bandoy ◽  
B Carol Huang ◽  
Bart C. Weimer
Keyword(s):  
Human Microbiome ◽  
Human Microbiome Project ◽  
Outbreak Detection ◽  
Whole Genome Sequence ◽  
Reference Database ◽  
Whole Genome ◽  
Reference Species ◽  
Genome Sequences ◽  
Genome Homology ◽  
Microbiome Data

AbstractTaxonomic classification is an essential step in the analysis of microbiome data that depends on a reference database of whole genome sequences. Taxonomic classifiers are built on established reference species, such as the Human Microbiome Project database, that is growing rapidly. While constructing a population wide pangenome of the bacterium Hungatella, we discovered that the Human Microbiome Project reference species Hungatella hathewayi (WAL 18680) was significantly different to other members of this genus. Specifically, the reference lacked the core genome as compared to the other members. Further analysis, using average nucleotide identity (ANI) and 16s rRNA comparisons, indicated that WAL18680 was misclassified as Hungatella. The error in classification is being amplified in the taxonomic classifiers and will have a compounding effect as microbiome analyses are done, resulting in inaccurate assignment of community members and will lead to fallacious conclusions and possibly treatment. As automated genome homology assessment expands for microbiome analysis, outbreak detection, and public health reliance on whole genomes increases this issue will likely occur at an increasing rate. These observations highlight the need for developing reference free methods for epidemiological investigation using whole genome sequences and the criticality of accurate reference databases.

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