ORCID Profile
0000-0002-9280-7885
Current Organisation
Queensland University of Technology
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Microbial genetics | Bioinformatics and computational biology | Proteins and peptides | Bioinformatics and computational biology not elsewhere classified |
Publisher: Elsevier BV
Date: 11-2019
Publisher: Oxford University Press (OUP)
Date: 08-04-2016
DOI: 10.1093/BIOINFORMATICS/BTW183
Abstract: Summary: MOCAT2 is a software pipeline for metagenomic sequence assembly and gene prediction with novel features for taxonomic and functional abundance profiling. The automated generation and efficient annotation of non-redundant reference catalogs by propagating pre-computed assignments from 18 databases covering various functional categories allows for fast and comprehensive functional characterization of metagenomes. Availability and Implementation: MOCAT2 is implemented in Perl 5 and Python 2.7, designed for 64-bit UNIX systems and offers support for high-performance computer usage via LSF, PBS or SGE queuing systems source code is freely available under the GPL3 license at mocat.embl.de. Contact: bork@embl.de Supplementary information: Supplementary data are available at Bioinformatics online.
Publisher: Cold Spring Harbor Laboratory
Date: 15-08-2019
DOI: 10.1101/735696
Abstract: Microbial organisms inhabit virtually all environments and encompass a vast biological ersity. The pan-genome concept aims to facilitate an understanding of ersity within defined phylogenetic groups. Hence, pan-genomes are increasingly used to characterize the strain ersity of prokaryotic species. To understand the interdependency of pan-genome features (such as numbers of core and accessory genes) and to study the impact of environmental and phylogenetic constraints on the evolution of conspecific strains, we computed pan-genomes for 155 phylogenetically erse species using 7000 high-quality genomes. We show that many pan-genome features such as functional ersity and core genome nucleotide ersity are correlated to each other. Further, habitat flexibility as approximated by species ubiquity is associated with several pan-genome features, particularly core genome size. In general, environment had a stronger impact on pan-genome features than phylogenetic signal. Similar environmental preferences led to convergent evolution of pan-genomic features in distant phylogenetic clades. For ex le, the soil environment promotes expansion of pan-genome size, while host-associated habitats lead to its reduction.
Publisher: Oxford University Press (OUP)
Date: 08-07-2013
DOI: 10.1093/BIOINFORMATICS/BTT392
Abstract: Motivation: Evaluation of previous systems for automated determination of subcellular location from microscope images has been done using datasets in which each location class consisted of multiple images of the same representative protein. Here, we frame a more challenging and useful problem where previously unseen proteins are to be classified. Results: Using CD-tagging, we generated two new image datasets for evaluation of this problem, which contain several different proteins for each location class. Evaluation of previous methods on these new datasets showed that it is much harder to train a classifier that generalizes across different proteins than one that simply recognizes a protein it was trained on. We therefore developed and evaluated additional approaches, incorporating novel modifications of local features techniques. These extended the notion of local features to exploit both the protein image and any reference markers that were imaged in parallel. With these, we obtained a large accuracy improvement in our new datasets over existing methods. Additionally, these features help achieve classification improvements for other previously studied datasets. Availability: The datasets are available for download at ata/. The software was written in Python and C++ and is available under an open-source license at oftware/. The code is split into a library, which can be easily reused for other data and a small driver script for reproducing all results presented here. A step-by-step tutorial on applying the methods to new datasets is also available at that address. Contact: murphy@cmu.edu Supplementary information: Supplementary data are available at Bioinformatics online.
Publisher: BMJ
Date: 11-01-2022
DOI: 10.1136/GUTJNL-2021-325753
Abstract: Gut microbiota is a key component in obesity and type 2 diabetes, yet mechanisms and metabolites central to this interaction remain unclear. We examined the human gut microbiome’s functional composition in healthy metabolic state and the most severe states of obesity and type 2 diabetes within the MetaCardis cohort. We focused on the role of B vitamins and B7/B8 biotin for regulation of host metabolic state, as these vitamins influence both microbial function and host metabolism and inflammation. We performed metagenomic analyses in 1545 subjects from the MetaCardis cohorts and different murine experiments, including germ-free and antibiotic treated animals, faecal microbiota transfer, bariatric surgery and supplementation with biotin and prebiotics in mice. Severe obesity is associated with an absolute deficiency in bacterial biotin producers and transporters, whose abundances correlate with host metabolic and inflammatory phenotypes. We found suboptimal circulating biotin levels in severe obesity and altered expression of biotin-associated genes in human adipose tissue. In mice, the absence or depletion of gut microbiota by antibiotics confirmed the microbial contribution to host biotin levels. Bariatric surgery, which improves metabolism and inflammation, associates with increased bacterial biotin producers and improved host systemic biotin in humans and mice. Finally, supplementing high-fat diet-fed mice with fructo-oligosaccharides and biotin improves not only the microbiome ersity, but also the potential of bacterial production of biotin and B vitamins, while limiting weight gain and glycaemic deterioration. Strategies combining biotin and prebiotic supplementation could help prevent the deterioration of metabolic states in severe obesity. NCT02059538 .
Publisher: Springer Science and Business Media LLC
Date: 08-12-2021
DOI: 10.1038/S41586-021-04177-9
Abstract: During the transition from a healthy state to cardiometabolic disease, patients become heavily medicated, which leads to an increasingly aberrant gut microbiome and serum metabolome, and complicates biomarker discovery
Publisher: Springer Science and Business Media LLC
Date: 19-04-2018
Publisher: Cold Spring Harbor Laboratory
Date: 16-08-2021
DOI: 10.1101/2021.08.16.456517
Abstract: Metagenomic binning is the step in building metagenome-assembled genomes (MAGs) when sequences predicted to originate from the same genome are automatically grouped together. The most widely-used methods for binning are reference-independent, operating de novo and allow the recovery of genomes from previously uns led clades. However, they do not leverage the knowledge in existing databases. Here, we propose SemiBin, an open source tool that uses neural networks to implement a semi-supervised approach, i.e. SemiBin exploits the information in reference genomes, while retaining the capability of binning genomes that are outside the reference dataset. SemiBin outperforms existing state-of-the-art binning methods in simulated and real microbiome datasets across three different environments (human gut, dog gut, and marine microbiomes). SemiBin returns more high-quality bins with larger taxonomic ersity, including more distinct genera and species. SemiBin is available as open source software at github.com/BigDataBiology/SemiBin/ .
Publisher: Microbiology Society
Date: 08-03-2022
Abstract: Computational algorithms have become an essential component of research, with great efforts by the scientific community to raise standards on development and distribution of code. Despite these efforts, sustainability and reproducibility are major issues since continued validation through software testing is still not a widely adopted practice. Here, we report seven recommendations that help researchers implement software testing in microbial bioinformatics. We have developed these recommendations based on our experience from a collaborative hackathon organised prior to the American Society for Microbiology Next Generation Sequencing (ASM NGS) 2020 conference. We also present a repository hosting ex les and guidelines for testing, available from icrobinfie-hackathon2020/CSIS .
Publisher: Springer Science and Business Media LLC
Date: 03-06-2019
Publisher: Springer Science and Business Media LLC
Date: 04-03-2019
DOI: 10.1038/S41467-019-08844-4
Abstract: Metagenomic sequencing has greatly improved our ability to profile the composition of environmental and host-associated microbial communities. However, the dependency of most methods on reference genomes, which are currently unavailable for a substantial fraction of microbial species, introduces estimation biases. We present an updated and functionally extended tool based on universal (i.e., reference-independent), phylogenetic marker gene (MG)-based operational taxonomic units (mOTUs) enabling the profiling of microbial species. As more than 30% of them could not previously be quantified at this taxonomic resolution, relative abundance estimates based on mOTUs are more accurate compared to other methods. As a new feature, we show that mOTUs, which are based on essential housekeeping genes, are demonstrably well-suited for quantification of basal transcriptional activity of community members. Furthermore, single nucleotide variation profiles estimated using mOTUs reflect those from whole genomes, which allows for comparing microbial strain populations (e.g., across different human body sites).
Publisher: ZappyLab, Inc.
Date: 10-07-2019
DOI: 10.17504/PROTOCOLS.IO.5CZG2X6
Abstract: Trata-se de uma ficha de anamneses direcionada para avaliação audiológica em crianças com a síndrome congênita do vírus da Zica.
Publisher: Wiley
Date: 29-01-2014
Abstract: We show how metagenomic analysis of the human gut antibiotic resistome, compared across large populations and against environmental or agricultural resistomes, suggests a strong anthropogenic cause behind increasing antibiotic resistance in bacteria. This area has been the subject of intense and polarized debate driven by economic and political concerns therefore such recently available insights address an important need. We derive and compare antibiotic resistomes of human gut microbes from 832 in iduals from ten different countries. We observe and describe significant differences between s les from these countries in the gut resistance potential, in line with expectations from antibiotic usage and exposure in medical and food production contexts. Our results imply roles for both of these sources in increased resistance among pathogens in recent history. In contrast, other available metadata such as age, body mass index, sex, or health status have little effect on the antibiotic resistance potential of human gut microbes. Also watch the Video Abstract.
Publisher: Oxford University Press (OUP)
Date: 19-05-2023
DOI: 10.1093/NAR/GKAD384
Abstract: Metagenomics can be used to monitor the spread of antibiotic resistance genes (ARGs). ARGs found in databases such as ResFinder and CARD primarily originate from culturable and pathogenic bacteria, while ARGs from non-culturable and non-pathogenic bacteria remain understudied. Functional metagenomics is based on phenotypic gene selection and can identify ARGs from non-culturable bacteria with a potentially low identity shared with known ARGs. In 2016, the ResFinderFG v1.0 database was created to collect ARGs from functional metagenomics studies. Here, we present the second version of the database, ResFinderFG v2.0, which is available on the Center of Genomic Epidemiology web server (cge.food.dtu.dk/services/ResFinderFG/). It comprises 3913 ARGs identified by functional metagenomics from 50 carefully curated datasets. We assessed its potential to detect ARGs in comparison to other popular databases in gut, soil and water (marine + freshwater) Global Microbial Gene Catalogues (gmgc.embl.de). ResFinderFG v2.0 allowed for the detection of ARGs that were not detected using other databases. These included ARGs conferring resistance to beta-lactams, cycline, phenicol, glycopeptide/cycloserine and trimethoprim/sulfonamide. Thus, ResFinderFG v2.0 can be used to identify ARGs differing from those found in conventional databases and therefore improve the description of resistomes.
Publisher: Oxford University Press (OUP)
Date: 29-04-2017
Publisher: IEEE
Date: 09-2009
Publisher: Springer Science and Business Media LLC
Date: 16-06-2020
Publisher: Oxford University Press (OUP)
Date: 28-10-2023
DOI: 10.1093/NAR/GKAD943
Publisher: Cold Spring Harbor Laboratory
Date: 30-09-2021
DOI: 10.1101/2021.09.30.462010
Abstract: Faecal microbiota transplantation (FMT) is an efficacious therapeutic intervention, but its clinical mode of action and underlying microbiome dynamics remain poorly understood. Here, we analysed the metagenomes associated with 142 FMTs, in a time series-based meta-study across five disease indications. We quantified strain-level dynamics of 1,089 microbial species based on their pangenome, complemented with 47,548 newly constructed metagenome-assembled genomes. Using subsets of procedural-, host- and microbiome-based variables, LASSO-regularised regression models accurately predicted the colonisation and resilience of donor and recipient microbes, as well as turnover of in idual species. Linking this to putative ecological mechanisms, we found these sets of variables to be informative of the underlying processes that shape the post-FMT gut microbiome. Recipient factors and complementarity of donor and recipient microbiomes, encompassing entire communities to in idual strains, were the main determinants of in idual strain population dynamics, and mostly independent of clinical outcomes. Recipient community state and the degree of residual strain depletion provided a neutral baseline for donor strain colonisation success, in addition to inhibitive priority effects between species and conspecific strains, as well as putatively adaptive processes. Our results suggest promising tunable parameters to enhance donor flora colonisation or recipient flora displacement in clinical practice, towards the development of more targeted and personalised therapies.
Publisher: Springer Science and Business Media LLC
Date: 15-12-2021
DOI: 10.1038/S41586-021-04233-4
Abstract: Microbial genes encode the majority of the functional repertoire of life on earth. However, despite increasing efforts in metagenomic sequencing of various habitats
Publisher: Oxford University Press (OUP)
Date: 17-09-2022
DOI: 10.1093/BIB/BBAC396
Abstract: Microbial community classification enables identification of putative type and source of the microbial community, thus facilitating a better understanding of how the taxonomic and functional structure were developed and maintained. However, previous classification models required a trade-off between speed and accuracy, and faced difficulties to be customized for a variety of contexts, especially less studied contexts. Here, we introduced EXPERT based on transfer learning that enabled the classification model to be adaptable in multiple contexts, with both high efficiency and accuracy. More importantly, we demonstrated that transfer learning can facilitate microbial community classification in erse contexts, such as classification of microbial communities for multiple diseases with limited number of s les, as well as prediction of the changes in gut microbiome across successive stages of colorectal cancer. Broadly, EXPERT enables accurate and context-aware customized microbial community classification, and potentiates novel microbial knowledge discovery.
Publisher: Wiley
Date: 14-07-2014
DOI: 10.1111/MMI.12677
Publisher: Springer Science and Business Media LLC
Date: 14-11-2022
DOI: 10.1186/S13059-022-02810-Y
Abstract: Evaluating the quality of metagenomic assemblies is important for constructing reliable metagenome-assembled genomes and downstream analyses. Here, we present metaMIC ( github.com/ZhaoXM-Lab/metaMIC ), a machine learning-based tool for identifying and correcting misassemblies in metagenomic assemblies. Benchmarking results on both simulated and real datasets demonstrate that metaMIC outperforms existing tools when identifying misassembled contigs. Furthermore, metaMIC is able to localize the misassembly breakpoints, and the correction of misassemblies by splitting at misassembly breakpoints can improve downstream scaffolding and binning results.
Publisher: Oxford University Press (OUP)
Date: 06-2023
DOI: 10.1093/BIOINFORMATICS/BTAD209
Abstract: Metagenomic binning methods to reconstruct metagenome-assembled genomes (MAGs) from environmental s les have been widely used in large-scale metagenomic studies. The recently proposed semi-supervised binning method, SemiBin, achieved state-of-the-art binning results in several environments. However, this required annotating contigs, a computationally costly and potentially biased process. We propose SemiBin2, which uses self-supervised learning to learn feature embeddings from the contigs. In simulated and real datasets, we show that self-supervised learning achieves better results than the semi-supervised learning used in SemiBin1 and that SemiBin2 outperforms other state-of-the-art binners. Compared to SemiBin1, SemiBin2 can reconstruct 8.3–21.5% more high-quality bins and requires only 25% of the running time and 11% of peak memory usage in real short-read sequencing s les. To extend SemiBin2 to long-read data, we also propose ensemble-based DBSCAN clustering algorithm, resulting in 13.1–26.3% more high-quality genomes than the second best binner for long-read data. SemiBin2 is available as open source software at github.com/BigDataBiology/SemiBin/ and the analysis scripts used in the study can be found at github.com/BigDataBiology/SemiBin2_benchmark.
Publisher: Springer Science and Business Media LLC
Date: 02-2022
DOI: 10.1038/S41591-022-01688-4
Abstract: Previous microbiome and metabolome analyses exploring non-communicable diseases have paid scant attention to major confounders of study outcomes, such as common, pre-morbid and co-morbid conditions, or polypharmacy. Here, in the context of ischemic heart disease (IHD), we used a study design that recapitulates disease initiation, escalation and response to treatment over time, mirroring a longitudinal study that would otherwise be difficult to perform given the protracted nature of IHD pathogenesis. We recruited 1,241 middle-aged Europeans, including healthy in iduals, in iduals with dysmetabolic morbidities (obesity and type 2 diabetes) but lacking overt IHD diagnosis and in iduals with IHD at three distinct clinical stages—acute coronary syndrome, chronic IHD and IHD with heart failure—and characterized their phenome, gut metagenome and serum and urine metabolome. We found that about 75% of microbiome and metabolome features that distinguish in iduals with IHD from healthy in iduals after adjustment for effects of medication and lifestyle are present in in iduals exhibiting dysmetabolism, suggesting that major alterations of the gut microbiome and metabolome might begin long before clinical onset of IHD. We further categorized microbiome and metabolome signatures related to prodromal dysmetabolism, specific to IHD in general or to each of its three subtypes or related to escalation or de-escalation of IHD. Discriminant analysis based on specific IHD microbiome and metabolome features could better differentiate in iduals with IHD from healthy in iduals or metabolically matched in iduals as compared to the conventional risk markers, pointing to a pathophysiological relevance of these features.
Publisher: Public Library of Science (PLoS)
Date: 28-07-2017
Publisher: Cold Spring Harbor Laboratory
Date: 19-12-2019
DOI: 10.1101/2019.12.17.880385
Abstract: Antimicrobial peptides (AMPs) have the potential to tackle multidrug-resistant pathogens in both clinical and non-clinical contexts. The recent growth in the availability of genomes and metagenomes provides an opportunity for in silico prediction of novel AMP molecules. However, due to the small size of these peptides, standard gene prospection methods cannot be applied in this domain and alternative approaches are necessary. In particular, standard gene prediction methods have low precision for short peptides, and functional classification by homology results in low recall. Here, we present Macrel (for metagenomic AMP classification and retrieval), which is an end-to-end pipeline for the prospection of high-quality AMP candidates from (meta)genomes. For this, we introduce a novel set of 22 peptide features. These were used to build classifiers which perform similarly to the state-of-the-art in the prediction of both antimicrobial and hemolytic activity of peptides, but with enhanced precision (using standard benchmarks as well as a stricter testing regime). We demonstrate that Macrel recovers high-quality AMP candidates using realistic simulations and real data. Macrel is implemented in Python 3. It is available as open source at github.com/BigDataBiology/macrel and through bioconda. Classification of peptides or prediction of AMPs in contigs can also be performed on the webserver: oftware/macrel .
Publisher: Springer Science and Business Media LLC
Date: 23-09-2015
Publisher: No publisher found
Date: 2010
Abstract: The field of high-content screening and analysis consists of a set of methodologies for automated discovery in cell biology and drug development using large amounts of image data. In most cases, imaging is carried out by automated microscopes, often assisted by automated liquid handling and cell culture. Image processing, computer vision, and machine learning are used to automatically process high-dimensional image data into meaningful cell biological results. The key is creating automated analysis pipelines typically consisting of 4 basic steps: (1) image processing (normalization, segmentation, tracing, tracking), (2) spatial transformation to bring images to a common reference frame (registration), (3) computation of image features, and (4) machine learning for modeling and interpretation of data. An overview of these image analysis tools is presented here, along with brief descriptions of a few applications.
Publisher: Springer Science and Business Media LLC
Date: 18-11-2020
DOI: 10.1038/S41467-020-19589-W
Abstract: Microbiota-host-diet interactions contribute to the development of metabolic diseases. Imidazole propionate is a novel microbially produced metabolite from histidine, which impairs glucose metabolism. Here, we show that subjects with prediabetes and diabetes in the MetaCardis cohort from three European countries have elevated serum imidazole propionate levels. Furthermore, imidazole propionate levels were increased in subjects with low bacterial gene richness and Bacteroides 2 enterotype, which have previously been associated with obesity. The Bacteroides 2 enterotype was also associated with increased abundance of the genes involved in imidazole propionate biosynthesis from dietary histidine. Since patients and controls did not differ in their histidine dietary intake, the elevated levels of imidazole propionate in type 2 diabetes likely reflects altered microbial metabolism of histidine, rather than histidine intake per se. Thus the microbiota may contribute to type 2 diabetes by generating imidazole propionate that can modulate host inflammation and metabolism.
Publisher: Ubiquity Press, Ltd.
Date: 27-10-2017
DOI: 10.5334/JORS.161
Publisher: Wiley
Date: 07-2009
Publisher: Elsevier BV
Date: 07-2020
Publisher: Oxford University Press (OUP)
Date: 19-03-2015
DOI: 10.1093/BIOINFORMATICS/BTV156
Abstract: Motivation: Neutrophil extracellular traps (NETs) are believed to be essential in controlling several bacterial pathogens. Quantification of NETs in vitro is an important tool in studies aiming to clarify the biological and chemical factors contributing to NET production, stabilization and degradation. This estimation can be performed on the basis of fluorescent microscopy images using appropriate labelings. In this context, it is desirable to automate the analysis to eliminate both the tedious process of manual annotation and possible operator-specific biases. Results: We propose a framework for the automated determination of NET content, based on visually annotated images which are used to train a supervised machine-learning method. We derive several methods in this framework. The best results are obtained by combining these into a single prediction. The overall Q2 of the combined method is 93%. By having two experts label part of the image set, we were able to compare the performance of the algorithms to the human interoperator variability. We find that the two operators exhibited a very high correlation on their overall assessment of the NET coverage area in the images (R2 is 97%), although there were consistent differences in labeling at pixel level (Q2, which unlike R2 does not correct for additive and multiplicative biases, was only 89%). Availability and implementation: Open source software (under the MIT license) is available at uispedro/Coelho2015_NetsDetermination for both reproducibility and application to new data. Contact: luis@luispedro.org Supplementary information : Supplementary data are available at Bioinformatics online.
Publisher: Oxford University Press (OUP)
Date: 06-2010
DOI: 10.1093/BIOINFORMATICS/BTQ220
Abstract: Motivation: Proteins exhibit complex subcellular distributions, which may include localizing in more than one organelle and varying in location depending on the cell physiology. Estimating the amount of protein distributed in each subcellular location is essential for quantitative understanding and modeling of protein dynamics and how they affect cell behaviors. We have previously described automated methods using fluorescent microscope images to determine the fractions of protein fluorescence in various subcellular locations when the basic locations in which a protein can be present are known. As this set of basic locations may be unknown (especially for studies on a proteome-wide scale), we here describe unsupervised methods to identify the fundamental patterns from images of mixed patterns and estimate the fractional composition of them. Methods: We developed two approaches to the problem, both based on identifying types of objects present in images and representing patterns by frequencies of those object types. One is a basis pursuit method (which is based on a linear mixture model), and the other is based on latent Dirichlet allocation (LDA). For testing both approaches, we used images previously acquired for testing supervised unmixing methods. These images were of cells labeled with various combinations of two organelle-specific probes that had the same fluorescent properties to simulate mixed patterns of subcellular location. Results: We achieved 0.80 and 0.91 correlation between estimated and underlying fractions of the two probes (fundamental patterns) with basis pursuit and LDA approaches, respectively, indicating that our methods can unmix the complex subcellular distribution with reasonably high accuracy. Availability: oftware Contact: murphy@cmu.edu
Publisher: Cold Spring Harbor Laboratory
Date: 27-01-2022
DOI: 10.1101/2022.01.26.477801
Abstract: Most microbes on our planet remain uncultured and poorly studied. Recent efforts to catalog their genetic ersity have revealed that a significant fraction of the observed microbial genes are functional and evolutionary untraceable, lacking homologs in reference databases. Despite their potential biological value, these apparently unrelated orphan genes from uncultivated taxa have been routinely discarded in metagenomics surveys. Here, we analyzed a global multi-habitat dataset covering 151,697 medium and high-quality metagenome assembled genomes (MAGs), 5,969 single- lified genomes (SAGs), and 19,642 reference genomes, and identified 413,335 highly curated novel protein families under strong purifying selection out of previously considered orphan genes. These new protein families, representing a three-fold increase over the total number of prokaryotic orthologous groups described to date, spread out across the prokaryote phylogeny, can span multiple habitats, and are notably overrepresented in recently discovered taxa. By genomic context analysis, we pinpointed thousands of unknown protein families to phylogenetically conserved operons linked to energy production, xenobiotic metabolism and microbial resistance. Most remarkably, we found 980 previously neglected protein families that can accurately distinguish entire uncultivated phyla, classes, and orders, likely representing synapomorphic traits that fostered their ergence. The systematic curation and evolutionary analysis of the unique genetic repertoire of uncultivated taxa opens new avenues for understanding the biology and ecological roles of poorly explored lineages at a global scale.
Publisher: Oxford University Press (OUP)
Date: 24-10-2019
DOI: 10.1093/NAR/GKZ1002
Abstract: Microbiology depends on the availability of annotated microbial genomes for many applications. Comparative genomics approaches have been a major advance, but consistent and accurate annotations of genomes can be hard to obtain. In addition, newer concepts such as the pan-genome concept are still being implemented to help answer biological questions. Hence, we present proGenomes2, which provides 87 920 high-quality genomes in a user-friendly and interactive manner. Genome sequences and annotations can be retrieved in idually or by taxonomic clade. Every genome in the database has been assigned to a species cluster and most genomes could be accurately assigned to one or multiple habitats. In addition, general functional annotations and specific annotations of antibiotic resistance genes and single nucleotide variants are provided. In short, proGenomes2 provides threefold more genomes, enhanced habitat annotations, updated taxonomic and functional annotation and improved linkage to the NCBI BioS le database. The database is available at progenomes.embl.de/.
Publisher: Cold Spring Harbor Laboratory
Date: 08-11-2022
DOI: 10.1101/2022.11.08.22282073
Abstract: The host-microbiota co-metabolite trimethylamine N -oxide (TMAO) is linked to increased thrombotic and cardiovascular risks. Here we, sought to i) characterize which host variables contribute to fasting serum TMAO levels in real-life settings ii) identify potential actionable therapeutic means related to circulating TMAO. We applied “explainable” machine learning, univariate-, multivariate- and mediation analyses of fasting plasma TMAO concentration and a multitude of bioclinical phenotypes in 1,741 adult Europeans of the MetaCardis study. We expanded and validated our epidemiological findings in mechanistic studies in human renal fibroblasts and a murine model of kidney fibrosis following TMAO exposure. Next to age, kidney function was the primary variable predicting circulating TMAO in MetaCardis, with microbiota composition and diet playing minor, albeit significant roles. Mediation analysis revealed a causal relationship between TMAO and kidney function decline that strengthened at more severe stages of cardiometabolic disease. We corroborated our findings in preclinical models where TMAO exposure augmented conversion of human renal fibroblasts into myofibroblasts and increased kidney scarring in vivo . Mechanistically, TMAO aggravated kidney fibrosis due to ERK1/2 hyperactivation synergistically with TGF-β1 signaling. Consistent with our findings, patients receiving next-generation glucose-lowering drugs with reno-protective properties, had significantly lower circulating TMAO when compared to propensity-score matched control in iduals. After age, kidney function is the major determinant of fasting circulating TMAO in adults. Our findings of lower TMAO levels in in iduals medicated with reno-protective anti-diabetic drugs suggests a clinically actionable intervention for decreasing TMAO-associated excess cardiovascular risk that merits urgent investigation in human trials. Raw shotgun sequencing data that support the findings of this study have been deposited in the European Nucleotide Archive with accession codes PRJEB37249, PRJEB38742, PRJEB41311 and PRJEB46098. Serum NMR and urine NMR metabolome data have been uploaded to Metabolights with accession number MTBLS3429 serum GC-MS and isotopically quantified serum metabolites (UPLC–MS/MS) are available from MassIVE with accession numbers MSV000088042 and MSV000088043, respectively.
Publisher: Springer Science and Business Media LLC
Date: 28-04-2022
DOI: 10.1038/S41467-022-29843-Y
Abstract: Metagenomic binning is the step in building metagenome-assembled genomes (MAGs) when sequences predicted to originate from the same genome are automatically grouped together. The most widely-used methods for binning are reference-independent, operating de novo and enable the recovery of genomes from previously uns led clades. However, they do not leverage the knowledge in existing databases. Here, we introduce SemiBin, an open source tool that uses deep siamese neural networks to implement a semi-supervised approach, i.e. SemiBin exploits the information in reference genomes, while retaining the capability of reconstructing high-quality bins that are outside the reference dataset. Using simulated and real microbiome datasets from several different habitats from GMGCv1 (Global Microbial Gene Catalog), including the human gut, non-human guts, and environmental habitats (ocean and soil), we show that SemiBin outperforms existing state-of-the-art binning methods. In particular, compared to other methods, SemiBin returns more high-quality bins with larger taxonomic ersity, including more distinct genera and species.
Publisher: IEEE
Date: 09-2007
Publisher: Springer Science and Business Media LLC
Date: 22-12-2013
DOI: 10.1038/NM.3424
Publisher: Cold Spring Harbor Laboratory
Date: 28-12-2018
DOI: 10.1101/507194
Abstract: The gastrointestinal tract is abundantly colonized by microbes, yet the translocation of oral species to the intestine is considered a rare aberrant event, and a hallmark of disease. By studying salivary and fecal microbial strain populations of 310 species in 470 in iduals from five countries, we found that transmission to, and subsequent colonization of, the large intestine by oral microbes is common and extensive among healthy in iduals. We found evidence for a vast majority of oral species to be transferable, with increased levels of transmission in colorectal cancer and rheumatoid arthritis patients and, more generally, for species described as opportunistic pathogens. This establishes the oral cavity as an endogenous reservoir for gut microbial strains, and oral-fecal transmission as an important process that shapes the gastrointestinal microbiome in health and disease.
Publisher: eLife Sciences Publications, Ltd
Date: 31-10-2017
DOI: 10.7554/ELIFE.26066
Abstract: We present a 3D-fluorescence imaging and classification tool for high throughput analysis of microbial eukaryotes in environmental s les. It entails high-content feature extraction that permits accurate automated taxonomic classification and quantitative data about organism ultrastructures and interactions. Using plankton s les from the Tara Oceans expeditions, we validate its applicability to taxonomic profiling and ecosystem analyses, and discuss its potential for future integration of eukaryotic cell biology into evolutionary and ecological studies.
Publisher: Springer Science and Business Media LLC
Date: 21-12-2020
DOI: 10.1038/S41467-020-20412-9
Abstract: A Correction to this paper has been published: 0.1038/s41467-020-20412-9.
Publisher: Springer Science and Business Media LLC
Date: 08-2018
DOI: 10.1038/S41586-018-0386-6
Abstract: Soils harbour some of the most erse microbiomes on Earth and are essential for both nutrient cycling and carbon storage. To understand soil functioning, it is necessary to model the global distribution patterns and functional gene repertoires of soil microorganisms, as well as the biotic and environmental associations between the ersity and structure of both bacterial and fungal soil communities
Publisher: Cold Spring Harbor Laboratory
Date: 16-12-2020
DOI: 10.1101/2020.12.16.422776
Abstract: Genomes are critical units in microbiology, yet ascertaining quality in prokaryotic genomes remains a formidable challenge. We present GUNC (the Genome UNClutterer), a tool that accurately detects and quantifies genome chimerism based on the lineage homogeneity of in idual contigs using a genome’s full complement of genes. GUNC complements existing approaches by targeting previously underdetected types of contamination: we conservatively estimate that 5.7% of genomes in GenBank, 5.2% in RefSeq, and 15-30% of pre-filtered ‘high quality’ metagenome-assembled genomes in recent studies are undetected chimeras. GUNC provides a fast and robust tool to substantially improve prokaryotic genome quality. Source code (GPLv3+): rp-bork/gunc
Publisher: Cold Spring Harbor Laboratory
Date: 23-06-2021
DOI: 10.1101/2021.06.22.449514
Abstract: Evaluating the quality of metagenomic assemblies is important for constructing reliable metagenome-assembled genomes and downstream analyses. Here, we present metaMIC ( github.com/ZhaoXM-Lab/metaMIC ), a machine-learning based tool for identifying and correcting misassemblies in metagenomic assemblies. Benchmarking results on both simulated and real datasets demonstrate that metaMIC outperforms existing tools when identifying misassembled contigs. Furthermore, metaMIC is able to localize the misassembly breakpoints, and the correction of misassemblies by splitting at misassembly breakpoints can improve downstream scaffolding and binning results.
Publisher: Springer Science and Business Media LLC
Date: 13-06-2021
DOI: 10.1186/S13059-021-02393-0
Abstract: Genomes are critical units in microbiology, yet ascertaining quality in prokaryotic genome assemblies remains a formidable challenge. We present GUNC (the Genome UNClutterer), a tool that accurately detects and quantifies genome chimerism based on the lineage homogeneity of in idual contigs using a genome’s full complement of genes. GUNC complements existing approaches by targeting previously underdetected types of contamination: we conservatively estimate that 5.7% of genomes in GenBank, 5.2% in RefSeq, and 15–30% of pre-filtered “high-quality” metagenome-assembled genomes in recent studies are undetected chimeras. GUNC provides a fast and robust tool to substantially improve prokaryotic genome quality.
Publisher: Springer Science and Business Media LLC
Date: 28-04-2017
Publisher: eLife Sciences Publications, Ltd
Date: 14-09-2017
Publisher: Cold Spring Harbor Laboratory
Date: 13-07-2018
DOI: 10.1101/367755
Abstract: NGLess is a domain specific language for describing next-generation sequence processing pipelines. It was developed with the goal of enabling user-friendly computational reproducibility. Using this framework, we developed NG-meta-profiler , a fast profiler for metagenomes which performs sequence preprocessing, mapping to bundled databases, filtering of the mapping results, and profiling (taxonomic and functional). It is significantly faster than either MOCAT2 or htseq-count and (as it builds on NGLess) its results are perfectly reproducible. These pipelines can easily be customized and extended with other tools. NGLess and NG-meta-profiler are open source software (under the liberal MIT licence) and can be downloaded from ngless.embl.de or installed through bioconda.
Publisher: IEEE
Date: 06-2009
Publisher: Cold Spring Harbor Laboratory
Date: 03-2018
Abstract: Vertical transmission of bacteria from mother to infant at birth is postulated to initiate a life-long host-microbe symbiosis, playing an important role in early infant development. However, only the tracking of strictly defined unique microbial strains can clarify where the intestinal bacteria come from, how long the initial colonizers persist, and whether colonization by other strains from the environment can replace existing ones. Using rare single nucleotide variants in fecal metagenomes of infants and their family members, we show strong evidence of selective and persistent transmission of maternal strain populations to the vaginally born infant and their occasional replacement by strains from the environment, including those from family members, in later childhood. Only strains from the classes Actinobacteria and Bacteroidia, which are essential components of the infant microbiome, are transmitted from the mother and persist for at least 1 yr. In contrast, maternal strains of Clostridia, a dominant class in the mother's gut microbiome, are not observed in the infant. Caesarean-born infants show a striking lack of maternal transmission at birth. After the first year, strain influx from the family environment occurs and continues even in adulthood. Fathers appear to be more frequently donors of novel strains to other family members than receivers. Thus, the infant gut is seeded by selected maternal bacteria, which expand to form a stable community, with a rare but stable continuing strain influx over time.
Publisher: Elsevier BV
Date: 2010
DOI: 10.2139/SSRN.3199484
Publisher: Springer Science and Business Media LLC
Date: 10-02-2016
DOI: 10.1038/NATURE16942
Publisher: eLife Sciences Publications, Ltd
Date: 31-12-2018
Publisher: Springer Science and Business Media LLC
Date: 02-10-2017
DOI: 10.1038/NBT.3960
Abstract: Technical variation in metagenomic analysis must be minimized to confidently assess the contributions of microbiota to human health. Here we tested 21 representative DNA extraction protocols on the same fecal s les and quantified differences in observed microbial community composition. We compared them with differences due to library preparation and s le storage, which we contrasted with observed biological variation within the same specimen or within an in idual over time. We found that DNA extraction had the largest effect on the outcome of metagenomic analysis. To rank DNA extraction protocols, we considered resulting DNA quantity and quality, and we ascertained biases in estimates of community ersity and the ratio between Gram-positive and Gram-negative bacteria. We recommend a standardized DNA extraction method for human fecal s les, for which transferability across labs was established and which was further benchmarked using a mock community of known composition. Its adoption will improve comparability of human gut microbiome studies and facilitate meta-analyses.
Publisher: Springer Science and Business Media LLC
Date: 09-2022
DOI: 10.1038/S41591-022-01913-0
Abstract: Fecal microbiota transplantation (FMT) is a therapeutic intervention for inflammatory diseases of the gastrointestinal tract, but its clinical mode of action and subsequent microbiome dynamics remain poorly understood. Here we analyzed metagenomes from 316 FMTs, s led pre and post intervention, for the treatment of ten different disease indications. We quantified strain-level dynamics of 1,089 microbial species, complemented by 47,548 newly constructed metagenome-assembled genomes. Donor strain colonization and recipient strain resilience were mostly independent of clinical outcomes, but accurately predictable using LASSO-regularized regression models that accounted for host, microbiome and procedural variables. Recipient factors and donor–recipient complementarity, encompassing entire microbial communities to in idual strains, were the main determinants of strain population dynamics, providing insights into the underlying processes that shape the post-FMT gut microbiome. Applying an ecology-based framework to our findings indicated parameters that may inform the development of more effective, targeted microbiome therapies in the future, and suggested how patient stratification can be used to enhance donor microbiota colonization or the displacement of recipient microbes in clinical practice.
Publisher: Wiley
Date: 02-12-2020
Abstract: Bacteria and fungi are of uttermost importance in determining environmental and host functioning. Despite close interactions between animals, plants, their associated microbiomes, and the environment they inhabit, the distribution and role of bacteria and especially fungi across host and environments as well as the cross‐habitat determinants of their community compositions remain little investigated. Using a uniquely broad global dataset of 13 483 metagenomes, we analysed the microbiome structure and function of 25 host‐associated and environmental habitats, focusing on potential interactions between bacteria and fungi. We found that the metagenomic relative abundance ratio of bacteria‐to‐fungi is a distinctive microbial feature of habitats. Compared with fungi, the cross‐habitat distribution pattern of bacteria was more strongly driven by habitat type. Fungal ersity was depleted in host‐associated communities compared with those in the environment, particularly terrestrial habitats, whereas this ersity pattern was less pronounced for bacteria. The relative gene functional potential of bacteria or fungi reflected their ersity patterns and appeared to depend on a balance between substrate availability and biotic interactions. Alongside helping to identify hotspots and sources of microbial ersity, our study provides support for differences in assembly patterns and processes between bacterial and fungal communities across different habitats.
Publisher: Springer Science and Business Media LLC
Date: 06-05-2020
Publisher: Elsevier BV
Date: 11-2019
Publisher: Springer Science and Business Media LLC
Date: 04-2019
Publisher: Cold Spring Harbor Laboratory
Date: 31-01-2021
DOI: 10.1101/2021.01.29.428751
Abstract: Microbial source tracking quantifies the potential origin of microbial communities, facilitates better understanding of how the taxonomic structure and community functions were formed and maintained. However, previous methods involve a tradeoff between speed and accuracy, and have encountered difficulty in source tracking under many context-dependent settings. Here, we present EXPERT for context-aware microbial source tracking, in which we adopted a Transfer Learning approach to profoundly elevate and expand the applicability of source tracking, enabling biologically informed novel microbial knowledge discovery. We demonstrate that EXPERT can predict microbial sources with performance superior to other methods in efficiency and accuracy. More importantly, we demonstrate EXPERT’s context-aware ability on several applications, including tracking the progression of infant gut microbiome development and monitoring the changes of gut microbiome for colorectal cancer patients. Broadly, transfer learning enables accurate and context-aware microbial source tracking and has the potential for novel microbial knowledge discovery.
Publisher: Center for Open Science
Date: 07-12-2021
Abstract: Sharing of research code would greatly benefit neuroscience, but this practice is h ered by a collective action problem. Since the development of the internet, conditional pledge platforms (e.g., Kickstarter) have increasingly been used to solve globally-dispersed collective action problems (Hallam, 2016). However, this strategy has yet to be implemented within academia. In this brief paper, we introduce a general purpose conditional pledge platform for the research community: Project Free Our Knowledge. We highlight a new conditional pledge c aign that was initiated at Brainhack 2021 and aims to motivate a critical mass of neuroscientists to share their research code. Crucially, this commitment activates only when a user-defined threshold of support is reached. We conclude by sharing our vision for how the research community could use collective action c aigns to create a sustained, evidence-based movement for social change in academia.
Publisher: eLife Sciences Publications, Ltd
Date: 12-02-2019
DOI: 10.7554/ELIFE.42693
Abstract: The gastrointestinal tract is abundantly colonized by microbes, yet the translocation of oral species to the intestine is considered a rare aberrant event, and a hallmark of disease. By studying salivary and fecal microbial strain populations of 310 species in 470 in iduals from five countries, we found that transmission to, and subsequent colonization of, the large intestine by oral microbes is common and extensive among healthy in iduals. We found evidence for a vast majority of oral species to be transferable, with increased levels of transmission in colorectal cancer and rheumatoid arthritis patients and, more generally, for species described as opportunistic pathogens. This establishes the oral cavity as an endogenous reservoir for gut microbial strains, and oral-fecal transmission as an important process that shapes the gastrointestinal microbiome in health and disease.
Publisher: American Geophysical Union (AGU)
Date: 03-2019
DOI: 10.1029/2018GB006022
Publisher: PeerJ
Date: 18-12-2020
DOI: 10.7717/PEERJ.10555
Abstract: Antimicrobial peptides (AMPs) have the potential to tackle multidrug-resistant pathogens in both clinical and non-clinical contexts. The recent growth in the availability of genomes and metagenomes provides an opportunity for in silico prediction of novel AMP molecules. However, due to the small size of these peptides, standard gene prospection methods cannot be applied in this domain and alternative approaches are necessary. In particular, standard gene prediction methods have low precision for short peptides, and functional classification by homology results in low recall. Here, we present Macrel (for metagenomic AMP classification and retrieval), which is an end-to-end pipeline for the prospection of high-quality AMP candidates from (meta)genomes. For this, we introduce a novel set of 22 peptide features. These were used to build classifiers which perform similarly to the state-of-the-art in the prediction of both antimicrobial and hemolytic activity of peptides, but with enhanced precision (using standard benchmarks as well as a stricter testing regime). We demonstrate that Macrel recovers high-quality AMP candidates using realistic simulations and real data. Macrel is implemented in Python 3. It is available as open source at github.com/BigDataBiology/macrel and through bioconda. Classification of peptides or prediction of AMPs in contigs can also be performed on the webserver: oftware/macrel .
Publisher: Cold Spring Harbor Laboratory
Date: 19-10-2022
DOI: 10.1101/2022.10.19.512667
Abstract: Metagenomics can be used to monitor the spread of antibiotic resistance genes (ARGs). ARGs found in databases such as ResFinder and CARD primarily originate from culturable and pathogenic bacteria, while ARGs from non-culturable and non-pathogenic bacteria remain understudied. Functional metagenomics is based on phenotypic gene selection and can identify ARGs from non-culturable bacteria with a potentially low identity shared with known ARGs. In 2016, the ResFinderFG v1.0 database was created to collect ARGs from functional metagenomics studies. Here, we present the second version of the database, ResFinderFG v2.0, which is available on the Center of Genomic Epidemiology web server ( cge.food.dtu.dk/services/ResFinderFG/ ). It comprises 3,913 ARGs identified by functional metagenomics from 50 carefully curated datasets. We assessed its potential to detect ARGs in comparison to other popular databases in gut, soil and water (marine + freshwater) Global Microbial Gene Catalogues ( gmgc.embl.de ). ResFinderFG v2.0 allowed for the detection of ARGs that were not detected using other databases. These included ARGs conferring resistance to beta-lactams, cycline, phenicol, glycopeptide/cycloserine and trimethoprim/sulfonamide. Thus, ResFinderFG v2.0 can be used to identify ARGs differing from those found in conventional databases and therefore improve the description of resistomes. Caption: Additional use of ResFinderFG v2.0 database (composed of antibiotic resistance genes obtained with functional metagenomics) on the Center of Genomic Epidemiology webserver ( cge.food.dtu.dk/services/ResFinderFG/ ), allows for more exhaustive resistome descriptions.
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Cold Spring Harbor Laboratory
Date: 09-01-2023
DOI: 10.1101/2023.01.09.523201
Abstract: Metagenomic binning methods to reconstruct metagenome-assembled genomes (MAGs) from environmental s les have been widely used in large-scale metagenomic studies. The recently proposed semi-supervised binning method, SemiBin, achieved state-of-the-art binning results in several environments. However, this required annotating contigs, a computationally costly and potentially biased process. We propose SemiBin2, which uses self-supervised learning to learn feature embeddings from the contigs. In simulated and real datasets, we show that self-supervised learning achieves better results than the semi-supervised learning used in SemiBin1 and that SemiBin2 outperforms other state-of-the-art binners. Compared to SemiBin1, SemiBin2 can reconstruct 8.3%–21.5% more high-quality bins and requires only 25% of the running time and 11% of peak memory usage in real short-read sequencing s les. To extend SemiBin2 to long-read data, we also propose ensemble-based DBSCAN clustering algorithm, resulting in 13.1%–26.3% more high-quality genomes than the second best binner for long-read data. SemiBin2 is available as open source software at github.com/BigDataBiology/SemiBin/ and the analysis scripts used in the study can be found at github.com/BigDataBiology/SemiBin2_benchmark .
Publisher: EMBO
Date: 12-2017
Abstract: Population genomics of prokaryotes has been studied in depth in only a small number of primarily pathogenic bacteria, as genome sequences of isolates of erse origin are lacking for most species. Here, we conducted a large-scale survey of population structure in prevalent human gut microbial species, s led from their natural environment, with a culture-independent metagenomic approach. We examined the variation landscape of 71 species in 2,144 human fecal metagenomes and found that in 44 of these, accounting for 72% of the total assigned microbial abundance, single-nucleotide variation clearly indicates the existence of sub-populations (here termed subspecies). A single subspecies (per species) usually dominates within each host, as expected from ecological theory. At the global scale, geographic distributions of subspecies differ between phyla, with Firmicutes subspecies being significantly more geographically restricted. To investigate the functional significance of the delineated subspecies, we identified genes that consistently distinguish them in a manner that is independent of reference genomes. We further associated these subspecies-specific genes with properties of the microbial community and the host. For ex le, two of the three
Publisher: American Association for the Advancement of Science (AAAS)
Date: 22-05-2015
Abstract: Microbes are dominant drivers of biogeochemical processes, yet drawing a global picture of functional ersity, microbial community structure, and their ecological determinants remains a grand challenge. We analyzed 7.2 terabases of metagenomic data from 243 Tara Oceans s les from 68 locations in epipelagic and mesopelagic waters across the globe to generate an ocean microbial reference gene catalog with million nonredundant, mostly novel sequences from viruses, prokaryotes, and picoeukaryotes. Using 139 prokaryote-enriched s les, containing ,000 species, we show vertical stratification with epipelagic community composition mostly driven by temperature rather than other environmental factors or geography. We identify ocean microbial core functionality and reveal that % of its abundance is shared with the human gut microbiome despite the physicochemical differences between these two ecosystems.
Publisher: Cold Spring Harbor Laboratory
Date: 31-08-2023
Publisher: Springer Science and Business Media LLC
Date: 20-10-2013
DOI: 10.1038/NMETH.2693
Abstract: To quantify known and unknown microorganisms at species-level resolution using shotgun sequencing data, we developed a method that establishes metagenomic operational taxonomic units (mOTUs) based on single-copy phylogenetic marker genes. Applied to 252 human fecal s les, the method revealed that on average 43% of the species abundance and 58% of the richness cannot be captured by current reference genome-based methods. An implementation of the method is available at www.bork.embl.de/software/mOTU/.
Publisher: Springer Science and Business Media LLC
Date: 22-10-2018
DOI: 10.1038/S41564-018-0257-9
Abstract: To minimize the impact of antibiotics, gut microorganisms harbour and exchange antibiotics resistance genes, collectively called their resistome. Using shotgun sequencing-based metagenomics, we analysed the partial eradication and subsequent regrowth of the gut microbiota in 12 healthy men over a 6-month period following a 4-day intervention with a cocktail of 3 last-resort antibiotics: meropenem, gentamicin and vancomycin. Initial changes included blooms of enterobacteria and other pathobionts, such as Enterococcus faecalis and Fusobacterium nucleatum, and the depletion of Bifidobacterium species and butyrate producers. The gut microbiota of the subjects recovered to near-baseline composition within 1.5 months, although 9 common species, which were present in all subjects before the treatment, remained undetectable in most of the subjects after 180 days. Species that harbour β-lactam resistance genes were positively selected for during and after the intervention. Harbouring glycopeptide or aminoglycoside resistance genes increased the odds of de novo colonization, however, the former also decreased the odds of survival. Compositional changes under antibiotic intervention in vivo matched results from in vitro susceptibility tests. Despite a mild yet long-lasting imprint following antibiotics exposure, the gut microbiota of healthy young adults are resilient to a short-term broad-spectrum antibiotics intervention and their antibiotics resistance gene carriage modulates their recovery processes.
Publisher: Elsevier BV
Date: 07-2010
Start Date: 11-2023
End Date: 10-2027
Amount: $979,506.00
Funder: Australian Research Council
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