CoMeta: classification of metagenomes using k-mers.
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ABSTRACT:
SUBMITTER: Kawulok J
PROVIDER: S-EPMC4401624 | biostudies-literature | 2015
REPOSITORIES: biostudies-literature
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Project description:BackgroundCurrent taxonomic classification tools use exact string matching algorithms that are effective to tackle the data from the next generation sequencing technology. However, the unique error patterns in the third generation sequencing (TGS) technologies could reduce the accuracy of these programs.ResultsWe developed a Classification tool using Discriminative K-mers and Approximate Matching algorithm (CDKAM). This approximate matching method was used for searching k-mers, which included two phases, a quick mapping phase and a dynamic programming phase. Simulated datasets as well as real TGS datasets have been tested to compare the performance of CDKAM with existing methods. We showed that CDKAM performed better in many aspects, especially when classifying TGS data with average length 1000-1500 bases.ConclusionsCDKAM is an effective program with higher accuracy and lower memory requirement for TGS metagenome sequence classification. It produces a high species-level accuracy.
| S-EPMC7576720 | biostudies-literature
Project description:BackgroundThe problem of supervised DNA sequence classification arises in several fields of computational molecular biology. Although this problem has been extensively studied, it is still computationally challenging due to size of the datasets that modern sequencing technologies can produce.ResultsWe introduce CLARK a novel approach to classify metagenomic reads at the species or genus level with high accuracy and high speed. Extensive experimental results on various metagenomic samples show that the classification accuracy of CLARK is better or comparable to the best state-of-the-art tools and it is significantly faster than any of its competitors. In its fastest single-threaded mode CLARK classifies, with high accuracy, about 32 million metagenomic short reads per minute. CLARK can also classify BAC clones or transcripts to chromosome arms and centromeric regions.ConclusionsCLARK is a versatile, fast and accurate sequence classification method, especially useful for metagenomics and genomics applications. It is freely available at http://clark.cs.ucr.edu/ .
| S-EPMC4428112 | biostudies-literature
Project description:BACKGROUND: Comparison and classification of metagenome samples is one of the major tasks in the study of microbial communities of natural environments or niches on human bodies. Bioinformatics methods play important roles on this task, including 16S rRNA gene analysis and some alignment-based or alignment-free methods on metagenomic data. Alignment-free methods have the advantage of not depending on known genome annotations and therefore have high potential in studying complicated microbiomes. However, the existing alignment-free methods are all based on unsupervised learning strategy (e.g., PCA or hierarchical clustering). These types of methods are powerful in revealing major similarities and grouping relations between microbiome samples, but cannot be applied for discriminating predefined classes of interest which might not be the dominating assortment in the data. Supervised classification is needed in the latter scenario, with the goal of classifying samples into predefined classes and finding the features that can discriminate the classes. The effectiveness of supervised classification with alignment-based features on metagenomic data have been shown in some recent studies. The application of alignment-free supervised classification methods on metagenome data has not been well explored yet. RESULTS: We developed a method for this task using k-tuple frequencies as features counted directly from metagenome short reads and the R-SVM (Recursive SVM) for feature selection and classification. We tested our method on a simulation dataset, a real dataset composed of several known genomes, and a real metagenome NGS short reads dataset. Experiments on simulated data showed that the method can classify the classes almost perfectly and can recover major sequence signatures that distinguish the two classes. On the real human gut metagenome data, the method can discriminate samples of inflammatory bowel disease (IBD) patients from control samples with high accuracy, which cannot be separated when comparing the samples with unsupervised clustering approaches. CONCLUSIONS: The proposed alignment-free supervised classification method can perform well in discriminating of metagenomic samples of predefined classes and in selecting characteristic sequence features for the discrimination. This study shows as an example on the feasibility of using metagenome sequence features of microbiomes on human bodies to study specific human health conditions using supervised machine learning methods.
| S-EPMC3849074 | biostudies-literature
Project description:Bacteriophages have key roles in microbial communities, to a large extent shaping the taxonomic and functional composition of the microbiome, but data on the connections between phage diversity and the composition of communities are scarce. Using taxon-specific marker genes, we identified and monitored 20 viral taxa in 252 human gut metagenomic samples, mostly at the level of genera. On average, five phage taxa were identified in each sample, with up to three of these being highly abundant. The abundances of most phage taxa vary by up to four orders of magnitude between the samples, and several taxa that are highly abundant in some samples are absent in others. Significant correlations exist between the abundances of some phage taxa and human host metadata: for example, 'Group 936 lactococcal phages' are more prevalent and abundant in Danish samples than in samples from Spain or the United States of America. Quantification of phages that exist as integrated prophages revealed that the abundance profiles of prophages are highly individual-specific and remain unique to an individual over a 1-year time period, and prediction of prophage lysis across the samples identified hundreds of prophages that are apparently active in the gut and vary across the samples, in terms of presence and lytic state. Finally, a prophage-host network of the human gut was established and includes numerous novel host-phage associations.
| S-EPMC4069399 | biostudies-literature
Project description:Microbial communities play key roles in ocean ecosystems through regulation of biogeochemical processes such as carbon and nutrient cycling, food web dynamics, and gut microbiomes of invertebrates, fish, reptiles, and mammals. Assessments of marine microbial diversity are therefore critical to understanding spatiotemporal variations in microbial community structure and function in ocean ecosystems. With recent advances in DNA shotgun sequencing for metagenome samples and computational analysis, it is now possible to access the taxonomic and genomic content of ocean microbial communities to study their structural patterns, diversity, and functional potential. However, existing taxonomic classification tools depend upon manually curated phylogenetic trees, which can create inaccuracies in metagenomes from less well-characterized communities, such as from ocean water. Herein, we explore the utility of deep learning tools-DeepMicrobes and a novel Residual Network architecture-that leverage natural language processing and convolutional neural network architectures to map input sequence data (k-mers) to output labels (taxonomic groups) without reliance on a curated taxonomic tree. We trained both models using metagenomic reads simulated from marine microbial genomes in the MarRef database. The performance of both models (accuracy, precision, and percent microbe predicted) was compared with the standard taxonomic classification tool Kraken2 using 10 complex metagenomic data sets simulated from MarRef. Our results demonstrate that time, compute power, and microbial genomic diversity still pose challenges for machine learning (ML). Moreover, our results suggest that high genome coverage and rectification of class imbalance are prerequisites for a well-trained model, and therefore should be a major consideration in future ML work. IMPORTANCE Taxonomic profiling of microbial communities is essential to model microbial interactions and inform habitat conservation. This work develops approaches in constructing training/testing data sets from publicly available marine metagenomes and evaluates the performance of machine learning (ML) approaches in read-based taxonomic classification of marine metagenomes. Predictions from two models are used to test accuracy in metagenomic classification and to guide improvements in ML approaches. Our study provides insights on the methods, results, and challenges of deep learning on marine microbial metagenomic data sets. Future machine learning approaches can be improved by rectifying genome coverage and class imbalance in the training data sets, developing alternative models, and increasing the accessibility of computational resources for model training and refinement.
| S-EPMC10580933 | biostudies-literature
Project description:Large-scale metagenomic datasets enable the recovery of hundreds of population genomes from environmental samples. However, these genomes do not typically represent the full diversity of complex microbial communities. Gene-centric approaches can be used to gain a comprehensive view of diversity by examining each read independently, but traditional pairwise comparison approaches typically over-classify taxonomy and scale poorly with increasing metagenome and database sizes. Here we introduce GraftM, a tool that uses gene specific packages to rapidly identify gene families in metagenomic data using hidden Markov models (HMMs) or DIAMOND databases, and classifies these sequences using placement into pre-constructed gene trees. The speed and accuracy of GraftM was benchmarked with in silico and in vitro mock communities using taxonomic markers, and was found to have higher accuracy at the family level with a processing time 2.0-3.7× faster than currently available software. Exploration of a wetland metagenome using 16S rRNA- and methyl-coenzyme M reductase (McrA)-specific gpkgs revealed taxonomic and functional shifts across a depth gradient. Analysis of the NCBI nr database using the McrA gpkg allowed the detection of novel sequences belonging to phylum-level lineages. A growing collection of gpkgs is available online (https://github.com/geronimp/graftM_gpkgs), where curated packages can be uploaded and exchanged.
| S-EPMC6007438 | biostudies-literature
Project description:SMERS - sediment microbial electrochemial remediation systems
| PRJEB18734 | ENA
Project description:Shotgun metagenome sequencing has become a fast, cheap and high-throughput technology for characterizing microbial communities in complex environments and human body sites. However, accurate identification of microorganisms at the strain/species level remains extremely challenging. We present a novel k-mer-based approach, termed GSMer, that identifies genome-specific markers (GSMs) from currently sequenced microbial genomes, which were then used for strain/species-level identification in metagenomes. Using 5390 sequenced microbial genomes, 8 770 321 50-mer strain-specific and 11 736 360 species-specific GSMs were identified for 4088 strains and 2005 species (4933 strains), respectively. The GSMs were first evaluated against mock community metagenomes, recently sequenced genomes and real metagenomes from different body sites, suggesting that the identified GSMs were specific to their targeting genomes. Sensitivity evaluation against synthetic metagenomes with different coverage suggested that 50 GSMs per strain were sufficient to identify most microbial strains with ≥0.25× coverage, and 10% of selected GSMs in a database should be detected for confident positive callings. Application of GSMs identified 45 and 74 microbial strains/species significantly associated with type 2 diabetes patients and obese/lean individuals from corresponding gastrointestinal tract metagenomes, respectively. Our result agreed with previous studies but provided strain-level information. The approach can be directly applied to identify microbial strains/species from raw metagenomes, without the effort of complex data pre-processing.
| S-EPMC4005670 | biostudies-literature
Project description:The use of k-mers has been a successful strategy for improving metagenomics studies, including taxonomic classifications, or de novo assemblies, and can be used to obtain sequences of interest from the available databases. The aim of this manuscript was to propose a simple but efficient strategy to generate k-mers and to use them to obtain and analyse in silico 16S rRNA sequence fragments. A total of 513,309 bacterial sequences contained in the SILVA database were considered for the study, and homemade PHP scripts were used to search for specific nucleotide chains, recover fragments of bacterial sequences, make calculations and organize information. Consensus sequences matching conserved regions were constructed by aligning most of the primers used in the literature. Sequences of k nucleotides (9- to 15-mers) were extracted from the generated primer contigs. Frequency analysis revealed that k-mer size was inversely proportional to the occurrence of k-mers in the different conserved regions, suggesting a stringency relationship; high numbers of duplicate reactions were observed with short k-mers, and a lower proportion of sequences were obtained with large ones, with the best results obtained using 12-mers. Using 12-mers with the proposed method to obtain and study sequences was found to be a reliable approach for the analysis of 16S rRNA sequences and this strategy may probably be extended to other biomarkers. Furthermore, additional applications such as evaluating the degree of conservation and designing primers and other calculations are proposed as examples.
| S-EPMC5537200 | biostudies-other
Project description:BackgroundFor a sustainable production of food, research on agricultural soil microbial communities is inevitable. Due to its immense complexity, soil is still some kind of black box. Soil study designs for identifying microbiome members of relevance have various scopes and focus on particular environmental factors. To identify common features of soil microbiomes, data from multiple studies should be compiled and processed. Taxonomic compositions and functional capabilities of microbial communities associated with soils and plants have been identified and characterized in the past few decades. From a fertile Loess-Chernozem-type soil located in Germany, metagenomically assembled genomes (MAGs) classified as members of the phylum Thaumarchaeota/Thermoproteota were obtained. These possibly represent keystone agricultural soil community members encoding functions of relevance for soil fertility and plant health. Their importance for the analyzed microbiomes is corroborated by the fact that they were predicted to contribute to the cycling of nitrogen, feature the genetic potential to fix carbon dioxide and possess genes with predicted functions in plant-growth-promotion (PGP). To expand the knowledge on soil community members belonging to the phylum Thaumarchaeota, we conducted a meta-analysis integrating primary studies on European agricultural soil microbiomes.ResultsTaxonomic classification of the selected soil metagenomes revealed the shared agricultural soil core microbiome of European soils from 19 locations. Metadata reporting was heterogeneous between the different studies. According to the available metadata, we separated the data into 68 treatments. The phylum Thaumarchaeota is part of the core microbiome and represents a major constituent of the archaeal subcommunities in all European agricultural soils. At a higher taxonomic resolution, 2074 genera constituted the core microbiome. We observed that viral genera strongly contribute to variation in taxonomic profiles. By binning of metagenomically assembled contigs, Thaumarchaeota MAGs could be recovered from several European soil metagenomes. Notably, many of them were classified as members of the family Nitrososphaeraceae, highlighting the importance of this family for agricultural soils. The specific Loess-Chernozem Thaumarchaeota MAGs were most abundant in their original soil, but also seem to be of importance in other agricultural soil microbial communities. Metabolic reconstruction of Switzerland_1_MAG_2 revealed its genetic potential i.a. regarding carbon dioxide (CO[Formula: see text]) fixation, ammonia oxidation, exopolysaccharide production and a beneficial effect on plant growth. Similar genetic features were also present in other reconstructed MAGs. Three Nitrososphaeraceae MAGs are all most likely members of a so far unknown genus.ConclusionsOn a broad view, European agricultural soil microbiomes are similarly structured. Differences in community structure were observable, although analysis was complicated by heterogeneity in metadata recording. Our study highlights the need for standardized metadata reporting and the benefits of networking open data. Future soil sequencing studies should also consider high sequencing depths in order to enable reconstruction of genome bins. Intriguingly, the family Nitrososphaeraceae commonly seems to be of importance in agricultural microbiomes.
| S-EPMC10064710 | biostudies-literature