scholarly journals Squeegee: de-novo identification of reagent and laboratory induced microbial contaminants in low biomass microbiomes

2021 ◽  
Author(s):  
Yunxi Liu ◽  
R. A. Leo Elworth ◽  
Michael D. Jochum ◽  
Kjersti M. Aagaard ◽  
Todd J. Treangen
Keyword(s):  
Computational Analysis ◽  
De Novo ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Negative Control ◽  
Control Data ◽  
Detection Tool ◽  
Health And Disease ◽  
Microbial Contaminants ◽  
Negative Controls

Computational analysis of host-associated microbiomes has opened the door to numerous discoveries relevant to human health and disease. However, contaminant sequences in metagenomic samples can potentially impact the interpretation of findings reported in microbiome studies, especially in low biomass environments. Our hypothesis is that contamination from DNA extraction kits or sampling lab environments will leave taxonomic bread crumbs across multiple distinct sample types, allowing for the detection of microbial contaminants when negative controls are unavailable. To test this hypothesis we implemented Squeegee, a de novo contamination detection tool. We tested Squeegee on simulated and real low biomass metagenomic datasets. On the low biomass samples, we compared Squeegee predictions to experimental negative control data and show that Squeegee accurately recovers known contaminants. We also analyzed 749 metagenomic datasets from the Human Microbiome Project and identified likely previously unreported kit contamination. Collectively, our results highlight that Squeegee can identify microbial contaminants with high precision. Squeegee is open-source and available at: https://gitlab.com/treangenlab/squeegee

scholarly journals MetaCompass: Reference-guided Assembly of Metagenomes

2017 ◽  
Author(s):  
Victoria Cepeda ◽  
Bo Liu ◽  
Mathieu Almeida ◽  
Christopher M. Hill ◽  
Sergey Koren ◽  
...  
Keyword(s):  
De Novo ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Bacterial Genomes ◽  
Guided Assembly ◽  
Metagenomic Assembly

ABSTRACTMetagenomic studies have primarily relied on de novo approaches for reconstructing genes and genomes from microbial mixtures. While database driven approaches have been employed in certain analyses, they have not been used in the assembly of metagenomes. Here we describe the first effective approach for reference-guided metagenomic assembly of low-abundance bacterial genomes that can complement and improve upon de novo metagenomic assembly methods. When combined with de novo assembly approaches, we show that MetaCompass can generate more complete assemblies than can be obtained by de novo assembly alone, and improve on assemblies from the Human Microbiome Project (over 2,000 samples).

Gut Microbiota: In Dynamics of Life and Homoeopathy

2020 ◽  
Author(s):  
Eiphrangdaka L. Suchiang ◽  
Deepak Kumar ◽  
Shabana Yeasmin ◽  
Monisha Singh ◽  
James Michael ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Chronic Diseases ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Present Review ◽  
Human Beings ◽  
Art And Science ◽  
Health And Disease ◽  
Therapeutic Measures ◽  
Micro Organisms

AbstractThe Human Microbiome Project (HMP) launched in 2008 by the National Institute of Health (NIH) fascinated microbiologists with discoveries of micro-organisms inside and outside of human beings. Their correlation with health and disease brings a new insight to preventive and therapeutic measures. At present, focus is more on the micro-organisms residing in the gut and various factors capable of altering their composition. The conclusion made by Dr. Edward Bach regarding the ability of homoeopathic potencies to alter bowel flora and its relation with chronic diseases was investigated and experimented way back. The present review attempts to correlate gut microbiota with the art and science of homoeopathy.

scholarly journals Genome binning of viral entities from bulk metagenomics data

2021 ◽  
Author(s):  
Joachim Johansen ◽  
Damian R Plichta ◽  
Jakob Nybo Nissen ◽  
Marie Louise Jespersen ◽  
Shiraz A Shah ◽  
...  
Keyword(s):  
Deep Learning ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Gastrointestinal Diseases ◽  
Human Gut ◽  
Viral Genomes ◽  
Host Interactions ◽  
Metagenomics Data ◽  
Health And Disease ◽  
Viral Host Interactions

Despite the accelerating number of uncultivated virus sequences discovered in metagenomics and their apparent importance for health and disease, the human gut virome and its interactions with bacteria in the gastrointestinal are not well understood. In addition, a paucity of whole-virome datasets from subjects with gastrointestinal diseases is preventing a deeper understanding of the virome role in disease and in gastrointestinal ecology as a whole. By combining a deep-learning based metagenomics binning algorithm with paired metagenome and metavirome datasets we developed the Phages from Metagenomics Binning (PHAMB) approach for binning thousands of viral genomes directly from bulk metagenomics data. Simultaneously our methodology enables clustering of viral genomes into accurate taxonomic viral populations. We applied this methodology on the Human Microbiome Project 2 (HMP2) cohort and recovered 6,077 HQ genomes from 1,024 viral populations and explored viral-host interactions. We show that binning can be advantageously applied to existing and future metagenomes to illuminate viral ecological dynamics with other microbiome constituents.

scholarly journals Multi-Method Characterisation of the Human Circulating Microbiome

2018 ◽  
Author(s):  
E. Whittle ◽  
M.O. Leonard ◽  
R. Harrison ◽  
T.W. Gant ◽  
D.P Tonge
Keyword(s):  
De Novo ◽  
Genetic Material ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Microbial Populations ◽  
Rrna Gene ◽  
Microbial Culture ◽  
Microbiological Techniques ◽  
Dynamic Communities ◽  
Aerobic And Anaerobic

AbstractThe term microbiome describes the genetic material encoding the various microbial populations that inhabit our body. Whilst colonisation of various body niches (e.g. the gut) by dynamic communities of microorganisms is now universally accepted, the existence of microbial populations in other “classically sterile” locations, including the blood, is a relatively new concept. The presence of bacteria-specific DNA in the blood has been reported in the literature for some time, yet the true origin of this is still the subject of much deliberation. The aim of this study was to investigate the phenomenon of a “blood microbiome” by providing a comprehensive description of bacterially-derived nucleic acids using a range of complementary molecular and classical microbiological techniques. For this purpose we utilised a set of plasma samples from healthy subjects (n = 5) and asthmatic subjects (n = 5). DNA-level analyses involved the amplification and sequencing of the 16S rRNA gene. RNA-level analyses were based upon thede novoassembly of unmapped mRNA reads and subsequent taxonomic identification. Molecular studies were complemented by viability data from classical aerobic and anaerobic microbial culture experiments. At the phylum level, the blood microbiome was predominated by Proteobacteria, Actinobacteria, Firmicutes and Bacteroidetes. The key phyla detected were consistent irrespective of molecular method (DNA vs RNA), and consistent with the results of other published studies.In silicocomparison of our data with that of the Human Microbiome Project revealed that members of the blood microbiome were most likely to have originated from the oral or skin communities. To our surprise, aerobic and anaerobic cultures were positive in eight of out the ten donor samples investigated, and we reflect upon their source. Our data provide further evidence of a core blood microbiome, and provide insight into the potential source of the bacterial DNA / RNA detected in the blood. Further, data reveal the importance of robust experimental procedures, and identify areas for future consideration.

scholarly journals Improving characterization of understudied human microbiomes using targeted phylogenetics

2019 ◽  
Author(s):  
Bruce A Rosa ◽  
Kathie Mihindukulasuriya ◽  
Kymberlie Hallsworth-Pepin ◽  
Aye Wollam ◽  
John Martin ◽  
...  
Keyword(s):  
Human Microbiome ◽  
Human Microbiome Project ◽  
Genomic Sequences ◽  
Bacterial Strains ◽  
Bacterial Genomes ◽  
Genomic Databases ◽  
Disease States ◽  
Health And Disease ◽  
Taxonomic Groups

AbstractWhole genome bacterial sequences are required to better understand microbial functions, niches-pecific bacterial metabolism, and disease states. Although genomic sequences are available for many of the human-associated bacteria from commonly tested body habitats (e.g. stool), as few as 13% of bacterial-derived reads from other sites such as the skin map to known bacterial genomes. To facilitate a better characterization of metagenomic shotgun reads from under-represented body sites, we collected over 10,000 bacterial isolates originating from 14 human body habitats, identified novel taxonomic groups based on full length 16S rRNA sequences, clustered the sequences to ensure that no individual taxonomic group was over-selected for sequencing, prioritized bacteria from under-represented body sites (such as skin, respiratory and urinary tract), and sequenced and assembled genomes for 665 new bacterial strains. Here we show that addition of these genomes improved read mapping rates of HMP metagenomic samples by nearly 30% for the previously under-represented phylum Fusobacteria, and 27.5% of the novel genomes generated here had high representation in at least one of the tested HMP samples, compared to 12.5% of the sequences in the public databases, indicating an enrichment of useful novel genomic sequences resulting from the prioritization procedure. As our understanding of the human microbiome continues to improve and to enter the realm of therapy developments, targeted approaches such as this to improve genomic databases will increase in importance from both an academic and clinical perspective.ImportanceThe human microbiome plays a critically important role in health and disease, but current understanding of the mechanisms underlying the interactions between the varying microbiome and the different host environments is lacking. Having access to a database of fully sequenced bacterial genomes provides invaluable insights into microbial functions, but currently sequenced genomes for the human microbiome have largely come from a limited number of body sites (primarily stool), while other sites such as the skin, respiratory tract and urinary tracts are under-represented, resulting in as little as 13% of bacterial-derived reads mapping to known bacterial genomes. Here, we sequenced and assembled 665 new bacterial genomes, prioritized from a larger database to select under-represented body sites and bacterial taxa in the existing databases. As a result, we substantially improve mapping rates for samples from the Human Microbiome Project and provide an important contribution to human bacterial genomic databases for future studies.

scholarly journals A Profile Hidden Markov Model to investigate the distribution and frequency of LanB-encoding lantibiotic modification genes in the human oral and gut microbiome

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3254 ◽  
Author(s):  
Calum J. Walsh ◽  
Caitriona M. Guinane ◽  
Paul W. O’ Toole ◽  
Paul D. Cotter
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Human Microbiome ◽  
Gene Clusters ◽  
Human Microbiome Project ◽  
Genome Database ◽  
Profile Hidden Markov Model ◽  
Health And Disease ◽  
The Stability

Background The human microbiota plays a key role in health and disease, and bacteriocins, which are small, bacterially produced, antimicrobial peptides, are likely to have an important function in the stability and dynamics of this community. Here we examined the density and distribution of the subclass I lantibiotic modification protein, LanB, in human oral and stool microbiome datasets using a specially constructed profile Hidden Markov Model (HMM). Methods The model was validated by correctly identifying known lanB genes in the genomes of known bacteriocin producers more effectively than other methods, while being sensitive enough to differentiate between different subclasses of lantibiotic modification proteins. This approach was compared with two existing methods to screen both genomic and metagenomic datasets obtained from the Human Microbiome Project (HMP). Results Of the methods evaluated, the new profile HMM identified the greatest number of putative LanB proteins in the stool and oral metagenome data while BlastP identified the fewest. In addition, the model identified more LanB proteins than a pre-existing Pfam lanthionine dehydratase model. Searching the gastrointestinal tract subset of the HMP reference genome database with the new HMM identified seven putative subclass I lantibiotic producers, including two members of the Coprobacillus genus. Conclusions These findings establish custom profile HMMs as a potentially powerful tool in the search for novel bioactive producers with the power to benefit human health, and reinforce the repertoire of apparent bacteriocin-encoding gene clusters that may have been overlooked by culture-dependent mining efforts to date.

scholarly journals The microbiome in rheumatology: Where are we and where should we go?

2020 ◽  
pp. annrheumdis-2019-216631 ◽  
Author(s):  
Julia Manasson ◽  
Rebecca B Blank ◽  
Jose U Scher
Keyword(s):  
Clinical Practice ◽  
Human Health ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Future Directions ◽  
Sequencing Technologies ◽  
Challenges And Opportunities ◽  
Microbiome Research ◽  
Health And Disease ◽  
Micro Organisms

From birth, humans coexist and coevolve with trillions of micro-organisms inhabiting most body surfaces and cavities, referred to as the human microbiome. Advances in sequencing technologies and computational methods have propelled the exploration of the microbiome’s contribution to human health and disease, spearheaded by massive efforts such as the Human Microbiome Project and the Europe-based MetaHit Consortium. Yet, despite the accumulated body of literature and a growing awareness among patients, microbiome research in rheumatology has not had a key impact on clinical practice. Herein, we describe some of the landmark microbiome studies in autoimmunity and rheumatology, the challenges and opportunities of microbiome research and how to navigate them, advances in related fields that have overcome these pitfalls, and future directions of harnessing the microbiome for diagnostic and therapeutic purposes.

scholarly journals Multiple comparative metagenomics using multisetk-mer counting

2016 ◽  
Vol 2 ◽  
pp. e94 ◽  
Author(s):  
Gaëtan Benoit ◽  
Pierre Peterlongo ◽  
Mahendra Mariadassou ◽  
Erwan Drezen ◽  
Sophie Schbath ◽  
...  
Keyword(s):  
Large Scale ◽  
De Novo ◽  
Comparative Method ◽  
Scale Up ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Small Subset ◽  
Taxonomic Profiling ◽  
Biodiversity Knowledge ◽  
Highly Correlated

BackgroundLarge scale metagenomic projects aim to extract biodiversity knowledge between different environmental conditions. Current methods for comparing microbial communities face important limitations. Those based on taxonomical or functional assignation rely on a small subset of the sequences that can be associated to known organisms. On the other hand,de novomethods, that compare the whole sets of sequences, either do not scale up on ambitious metagenomic projects or do not provide precise and exhaustive results.MethodsThese limitations motivated the development of a newde novometagenomic comparative method, called Simka. This method computes a large collection of standard ecological distances by replacing species counts byk-mer counts. Simka scales-up today’s metagenomic projects thanks to a new parallelk-mer counting strategy on multiple datasets.ResultsExperiments on public Human Microbiome Project datasets demonstrate that Simka captures the essential underlying biological structure. Simka was able to compute in a few hours both qualitative and quantitative ecological distances on hundreds of metagenomic samples (690 samples, 32 billions of reads). We also demonstrate that analyzing metagenomes at thek-mer level is highly correlated with extremely precisede novocomparison techniques which rely on all-versus-all sequences alignment strategy or which are based on taxonomic profiling.

scholarly journals Metagenomic analysis through the extended Burrows-Wheeler transform

2020 ◽  
Vol 21 (S8) ◽  
Author(s):  
Veronica Guerrini ◽  
Felipe A. Louza ◽  
Giovanna Rosone
Keyword(s):  
Human Microbiome ◽  
Simulated Data ◽  
High Sensitivity ◽  
Human Microbiome Project ◽  
Positive Control ◽  
Negative Control ◽  
Microbial Composition ◽  
Combinatorial Properties ◽  
Burrows Wheeler Transform ◽  
Ngs Data

Abstract Background The development of Next Generation Sequencing (NGS) has had a major impact on the study of genetic sequences. Among problems that researchers in the field have to face, one of the most challenging is the taxonomic classification of metagenomic reads, i.e., identifying the microorganisms that are present in a sample collected directly from the environment. The analysis of environmental samples (metagenomes) are particularly important to figure out the microbial composition of different ecosystems and it is used in a wide variety of fields: for instance, metagenomic studies in agriculture can help understanding the interactions between plants and microbes, or in ecology, they can provide valuable insights into the functions of environmental communities. Results In this paper, we describe a new lightweight alignment-free and assembly-free framework for metagenomic classification that compares each unknown sequence in the sample to a collection of known genomes. We take advantage of the combinatorial properties of an extension of the Burrows-Wheeler transform, and we sequentially scan the required data structures, so that we can analyze unknown sequences of large collections using little internal memory. The tool LiME (Lightweight Metagenomics via eBWT) is available at https://github.com/veronicaguerrini/LiME. Conclusions In order to assess the reliability of our approach, we run several experiments on NGS data from two simulated metagenomes among those provided in benchmarking analysis and on a real metagenome from the Human Microbiome Project. The experiment results on the simulated data show that LiME is competitive with the widely used taxonomic classifiers. It achieves high levels of precision and specificity – e.g. 99.9% of the positive control reads are correctly assigned and the percentage of classified reads of the negative control is less than 0.01% – while keeping a high sensitivity. On the real metagenome, we show that LiME is able to deliver classification results comparable to that of MagicBlast. Overall, the experiments confirm the effectiveness of our method and its high accuracy even in negative control samples.

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