Browse
Submit Data
Databases
API
Help

Dataset Information

37 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

HmmUFOtu: An HMM and phylogenetic placement based ultra-fast taxonomic assignment and OTU picking tool for microbiome amplicon sequencing studies.

ABSTRACT: Culture-independent analysis of microbial communities frequently relies on amplification and sequencing of the prokaryotic 16S ribosomal RNA gene. Typical analysis pipelines group sequences into operational taxonomic units (OTUs) to infer taxonomic and phylogenetic relationships. Here, we present HmmUFOtu, a novel tool for processing microbiome amplicon sequencing data, which performs rapid per-read phylogenetic placement, followed by phylogenetically informed clustering into OTUs and taxonomy assignment. Compared to standard pipelines, HmmUFOtu more accurately and reliably recapitulates microbial community diversity and composition in simulated and real datasets without relying on heuristics or sacrificing speed or accuracy.

SUBMITTER: Zheng Q

PROVIDER: S-EPMC6020470 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

HmmUFOtu: An HMM and phylogenetic placement based ultra-fast taxonomic assignment and OTU picking tool for microbiome amplicon sequencing studies.

Zheng Qi Q Bartow-McKenney Casey C Meisel Jacquelyn S JS Grice Elizabeth A EA

Genome biology 20180627 1

Culture-independent analysis of microbial communities frequently relies on amplification and sequencing of the prokaryotic 16S ribosomal RNA gene. Typical analysis pipelines group sequences into operational taxonomic units (OTUs) to infer taxonomic and phylogenetic relationships. Here, we present HmmUFOtu, a novel tool for processing microbiome amplicon sequencing data, which performs rapid per-read phylogenetic placement, followed by phylogenetically informed clustering into OTUs and taxonomy a ...[more]

PMID: 29950165

Similar Datasets

Improved OTU-picking using long-read 16S rRNA gene amplicon sequencing and generic hierarchical clustering.

Project description:High-throughput bacterial 16S rRNA gene sequencing followed by clustering of short sequences into operational taxonomic units (OTUs) is widely used for microbiome profiling. However, clustering of short 16S rRNA gene reads into biologically meaningful OTUs is challenging, in part because nucleotide variation along the 16S rRNA gene is only partially captured by short reads. The recent emergence of long-read platforms, such as single-molecule real-time (SMRT) sequencing from Pacific Biosciences, offers the potential for improved taxonomic and phylogenetic profiling. Here, we evaluate the performance of long- and short-read 16S rRNA gene sequencing using simulated and experimental data, followed by OTU inference using computational pipelines based on heuristic and complete-linkage hierarchical clustering.In simulated data, long-read sequencing was shown to improve OTU quality and decrease variance. We then profiled 40 human gut microbiome samples using a combination of Illumina MiSeq and Blautia-specific SMRT sequencing, further supporting the notion that long reads can identify additional OTUs. We implemented a complete-linkage hierarchical clustering strategy using a flexible computational pipeline, tailored specifically for PacBio circular consensus sequencing (CCS) data that outperforms heuristic methods in most settings: https://github.com/oscar-franzen/oclust/ .Our data demonstrate that long reads can improve OTU inference; however, the choice of clustering algorithm and associated clustering thresholds has significant impact on performance.

| S-EPMC4593230 | biostudies-literature

OTUX: V-region specific OTU database for improved 16S rRNA OTU picking and efficient cross-study taxonomic comparison of microbiomes.

Project description:Many microbiome studies employ reference-based operational taxonomic unit (OTU)-picking methods, which in general, rely on databases cataloguing reference OTUs identified through clustering full-length 16S rRNA genes. Given that the rate of accumulation of mutations are not uniform throughout the length of a 16S rRNA gene across different taxonomic clades, results of OTU identification or taxonomic classification obtained using 'short-read' sequence queries (as generated by next-generation sequencing platforms) can be inconsistent and of suboptimal accuracy. De novo OTU clustering results too can significantly vary depending upon the hypervariable region (V-region) targeted for sequencing. As a consequence, comparison of microbiomes profiled in different scientific studies becomes difficult and often poses a challenge in analysing new findings in context of prior knowledge. The OTUX approach of reference-based OTU-picking proposes to overcome these limitations by using 'customized' OTU reference databases, which can cater to different sets of short-read sequences corresponding to different 16S V-regions. The results obtained with OTUX-approach (which are in terms of OTUX-OTU identifiers) can also be 'mapped back' or represented in terms of other OTU database identifiers/taxonomy, e.g. Greengenes, thus allowing for easy cross-study comparisons. Validation with simulated datasets indicates more efficient, accurate, and consistent taxonomic classifications obtained using OTUX-approach, as compared with conventional methods.

| S-EPMC6476724 | biostudies-literature

Comparison of Methods for Picking the Operational Taxonomic Units From Amplicon Sequences.

Project description:With the advent of next-generation sequencing technology, it has become convenient and cost efficient to thoroughly characterize the microbial diversity and taxonomic composition in various environmental samples. Millions of sequencing data can be generated, and how to utilize this enormous sequence resource has become a critical concern for microbial ecologists. One particular challenge is the OTUs (operational taxonomic units) picking in 16S rRNA sequence analysis. Lucky, this challenge can be directly addressed by sequence clustering that attempts to group similar sequences. Therefore, numerous clustering methods have been proposed to help to cluster 16S rRNA sequences into OTUs. However, each method has its clustering mechanism, and different methods produce diverse outputs. Even a slight parameter change for the same method can also generate distinct results, and how to choose an appropriate method has become a challenge for inexperienced users. A lot of time and resources can be wasted in selecting clustering tools and analyzing the clustering results. In this study, we introduced the recent advance of clustering methods for OTUs picking, which mainly focus on three aspects: (i) the principles of existing clustering algorithms, (ii) benchmark dataset construction for OTU picking and evaluation metrics, and (iii) the performance of different methods with various distance thresholds on benchmark datasets. This paper aims to assist biological researchers to select the reasonable clustering methods for analyzing their collected sequences and help algorithm developers to design more efficient sequences clustering methods.

| S-EPMC8024490 | biostudies-literature

Fast and sensitive taxonomic assignment to metagenomic contigs.

Project description:SummaryMMseqs2 taxonomy is a new tool to assign taxonomic labels to metagenomic contigs. It extracts all possible protein fragments from each contig, quickly retains those that can contribute to taxonomic annotation, assigns them with robust labels and determines the contig's taxonomic identity by weighted voting. Its fragment extraction step is suitable for the analysis of all domains of life. MMseqs2 taxonomy is 2-18× faster than state-of-the-art tools and also contains new modules for creating and manipulating taxonomic reference databases as well as reporting and visualizing taxonomic assignments.Availability and implementationMMseqs2 taxonomy is part of the MMseqs2 free open-source software package available for Linux, macOS and Windows at https://mmseqs.com.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC8479651 | biostudies-literature

Taxator-tk: precise taxonomic assignment of metagenomes by fast approximation of evolutionary neighborhoods.

Project description:MOTIVATION:Metagenomics characterizes microbial communities by random shotgun sequencing of DNA isolated directly from an environment of interest. An essential step in computational metagenome analysis is taxonomic sequence assignment, which allows identifying the sequenced community members and reconstructing taxonomic bins with sequence data for the individual taxa. For the massive datasets generated by next-generation sequencing technologies, this cannot be performed with de-novo phylogenetic inference methods. We describe an algorithm and the accompanying software, taxator-tk, which performs taxonomic sequence assignment by fast approximate determination of evolutionary neighbors from sequence similarities. RESULTS:Taxator-tk was precise in its taxonomic assignment across all ranks and taxa for a range of evolutionary distances and for short as well as for long sequences. In addition to the taxonomic binning of metagenomes, it is well suited for profiling microbial communities from metagenome samples because it identifies bacterial, archaeal and eukaryotic community members without being affected by varying primer binding strengths, as in marker gene amplification, or copy number variations of marker genes across different taxa. Taxator-tk has an efficient, parallelized implementation that allows the assignment of 6?Gb of sequence data per day on a standard multiprocessor system with 10?CPU cores and microbial RefSeq as the genomic reference data.

| S-EPMC4380030 | biostudies-literature

M-pick, a modularity-based method for OTU picking of 16S rRNA sequences.

Project description:BackgroundBinning 16S rRNA sequences into operational taxonomic units (OTUs) is an initial crucial step in analyzing large sequence datasets generated to determine microbial community compositions in various environments including that of the human gut. Various methods have been developed, but most suffer from either inaccuracies or from being unable to handle millions of sequences generated in current studies. Furthermore, existing binning methods usually require a priori decisions regarding binning parameters such as a distance level for defining an OTU.ResultsWe present a novel modularity-based approach (M-pick) to address the aforementioned problems. The new method utilizes ideas from community detection in graphs, where sequences are viewed as vertices on a weighted graph, each pair of sequences is connected by an imaginary edge, and the similarity of a pair of sequences represents the weight of the edge. M-pick first generates a graph based on pairwise sequence distances and then applies a modularity-based community detection technique on the graph to generate OTUs to capture the community structures in sequence data. To compare the performance of M-pick with that of existing methods, specifically CROP and ESPRIT-Tree, sequence data from different hypervariable regions of 16S rRNA were used and binning results were compared.ConclusionsA new modularity-based clustering method for OTU picking of 16S rRNA sequences is developed in this study. The algorithm does not require a predetermined cut-off level, and our simulation studies suggest that it is superior to existing methods that require specified distance levels to define OTUs. The source code is available at http://plaza.ufl.edu/xywang/Mpick.htm.

| S-EPMC3599145 | biostudies-literature

PhyloPhlAn is a new method for improved phylogenetic and taxonomic placement of microbes.

Project description:New microbial genomes are constantly being sequenced, and it is crucial to accurately determine their taxonomic identities and evolutionary relationships. Here we report PhyloPhlAn, a new method to assign microbial phylogeny and putative taxonomy using >400 proteins optimized from among 3,737 genomes. This method measures the sequence diversity of all clades, classifies genomes from deep-branching candidate divisions through closely related subspecies and improves consistency between phylogenetic and taxonomic groupings. PhyloPhlAn improved taxonomic accuracy for existing and newly sequenced genomes, detecting 157 erroneous labels, correcting 46 and placing or refining 130 new genomes. We provide examples of accurate classifications from subspecies (Sulfolobus spp.) to phyla, and of preliminary rooting of deep-branching candidate divisions, including consistent statistical support for Caldiserica (formerly candidate division OP5). PhyloPhlAn will thus be useful for both phylogenetic assessment and taxonomic quality control of newly sequenced genomes. The final phylogenies, conserved protein sequences and open-source implementation are available online.

| S-EPMC3760377 | biostudies-literature

MetAmp: combining amplicon data from multiple markers for OTU analysis.

Project description:MotivationWe present a novel method and corresponding application, MetAmp, to combine amplicon data from multiple genomic markers into Operational Taxonomic Units (OTUs) for microbial community analysis, calibrating the markers using data from known microbial genomes. When amplicons for multiple markers such as the 16S rRNA gene hypervariable regions are available, MetAmp improves the accuracy of OTU-based methods for characterizing bacterial composition and community structure. MetAmp works best with at least three markers, and is applicable to non-bacterial analyses and to non 16S markers. Our application and testing have been limited to 16S analysis of microbial communities.ResultsWe clustered standard test sequences derived from the Human Microbiome Mock Community test sets and compared MetAmp and other tools with respect to their ability to recover OTUs for these benchmark bacterial communities. MetAmp compared favorably to QIIME, UPARSE and Mothur using amplicons from one, two, and three markers.Availability and implementationMetAmp is available at http://izhbannikov.github.io/MetAmp/.

| S-EPMC4443678 | biostudies-literature

KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold.

Project description:SummaryKofamKOALA is a web server to assign KEGG Orthologs (KOs) to protein sequences by homology search against a database of profile hidden Markov models (KOfam) with pre-computed adaptive score thresholds. KofamKOALA is faster than existing KO assignment tools with its accuracy being comparable to the best performing tools. Function annotation by KofamKOALA helps linking genes to KEGG resources such as the KEGG pathway maps and facilitates molecular network reconstruction.Availability and implementationKofamKOALA, KofamScan and KOfam are freely available from GenomeNet (https://www.genome.jp/tools/kofamkoala/).Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC7141845 | biostudies-literature

Taxonomic Sampling and Rare Genomic Changes Overcome Long-Branch Attraction in the Phylogenetic Placement of Pseudoscorpions.

Project description:Long-branch attraction is a systematic artifact that results in erroneous groupings of fast-evolving taxa. The combination of short, deep internodes in tandem with long-branch attraction artifacts has produced empirically intractable parts of the Tree of Life. One such group is the arthropod subphylum Chelicerata, whose backbone phylogeny has remained unstable despite improvements in phylogenetic methods and genome-scale data sets. Pseudoscorpion placement is particularly variable across data sets and analytical frameworks, with this group either clustering with other long-branch orders or with Arachnopulmonata (scorpions and tetrapulmonates). To surmount long-branch attraction, we investigated the effect of taxonomic sampling via sequential deletion of basally branching pseudoscorpion superfamilies, as well as varying gene occupancy thresholds in supermatrices. We show that concatenated supermatrices and coalescent-based summary species tree approaches support a sister group relationship of pseudoscorpions and scorpions, when more of the basally branching taxa are sampled. Matrix completeness had demonstrably less influence on tree topology. As an external arbiter of phylogenetic placement, we leveraged the recent discovery of an ancient genome duplication in the common ancestor of Arachnopulmonata as a litmus test for competing hypotheses of pseudoscorpion relationships. We generated a high-quality developmental transcriptome and the first genome for pseudoscorpions to assess the incidence of arachnopulmonate-specific duplications (e.g., homeobox genes and miRNAs). Our results support the inclusion of pseudoscorpions in Arachnopulmonata (new definition), as the sister group of scorpions. Panscorpiones (new name) is proposed for the clade uniting Scorpiones and Pseudoscorpiones.

| S-EPMC8136511 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data