Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

MetaGen: reference-free learning with multiple metagenomic samples.

ABSTRACT: A major goal of metagenomics is to identify and study the entire collection of microbial species in a set of targeted samples. We describe a statistical metagenomic algorithm that simultaneously identifies microbial species and estimates their abundances without using reference genomes. As a trade-off, we require multiple metagenomic samples, usually ≥10 samples, to get highly accurate binning results. Compared to reference-free methods based primarily on k-mer distributions or coverage information, the proposed approach achieves a higher species binning accuracy and is particularly powerful when sequencing coverage is low. We demonstrated the performance of this new method through both simulation and real metagenomic studies. The MetaGen software is available at https://github.com/BioAlgs/MetaGen .

SUBMITTER: Xing X

PROVIDER: S-EPMC5627425 | biostudies-other | 2017 Oct

REPOSITORIES: biostudies-other

ACCESS DATA

Json Xml

Publications

MetaGen: reference-free learning with multiple metagenomic samples.

Xing Xin X Liu Jun S JS Zhong Wenxuan W

Genome biology 20171003 1

A major goal of metagenomics is to identify and study the entire collection of microbial species in a set of targeted samples. We describe a statistical metagenomic algorithm that simultaneously identifies microbial species and estimates their abundances without using reference genomes. As a trade-off, we require multiple metagenomic samples, usually ≥10 samples, to get highly accurate binning results. Compared to reference-free methods based primarily on k-mer distributions or coverage informat ...[more]

PMID: 28974263

Similar Datasets

Joint analysis of multiple metagenomic samples.

Project description:The availability of metagenomic sequencing data, generated by sequencing DNA pooled from multiple microbes living jointly, has increased sharply in the last few years with developments in sequencing technology. Characterizing the contents of metagenomic samples is a challenging task, which has been extensively attempted by both supervised and unsupervised techniques, each with its own limitations. Common to practically all the methods is the processing of single samples only; when multiple samples are sequenced, each is analyzed separately and the results are combined. In this paper we propose to perform a combined analysis of a set of samples in order to obtain a better characterization of each of the samples, and provide two applications of this principle. First, we use an unsupervised probabilistic mixture model to infer hidden components shared across metagenomic samples. We incorporate the model in a novel framework for studying association of microbial sequence elements with phenotypes, analogous to the genome-wide association studies performed on human genomes: We demonstrate that stratification may result in false discoveries of such associations, and that the components inferred by the model can be used to correct for this stratification. Second, we propose a novel read clustering (also termed "binning") algorithm which operates on multiple samples simultaneously, leveraging on the assumption that the different samples contain the same microbial species, possibly in different proportions. We show that integrating information across multiple samples yields more precise binning on each of the samples. Moreover, for both applications we demonstrate that given a fixed depth of coverage, the average per-sample performance generally increases with the number of sequenced samples as long as the per-sample coverage is high enough.

| S-EPMC3280959 | biostudies-literature

The Nubeam reference-free approach to analyze metagenomic sequencing reads.

Project description:We present Nubeam (nucleotide be a matrix) as a novel reference-free approach to analyze short sequencing reads. Nubeam represents nucleotides by matrices, transforms a read into a product of matrices, and assigns numbers to reads based on the product matrix. Nubeam capitalizes on the noncommutative property of matrix multiplication, such that different reads are assigned different numbers and similar reads similar numbers. A sample, which is a collection of reads, becomes a collection of numbers that form an empirical distribution. We demonstrate that the genetic difference between samples can be quantified by the distance between empirical distributions. Nubeam includes the k-mer method as a special case, but unlike the k-mer method, it is convenient for Nubeam to account for GC bias and nucleotide quality. As a reference-free approach, Nubeam avoids reference bias and mapping bias, and can work with organisms without reference genomes. Thus, Nubeam is ideal to analyze data sets from metagenomics whole genome shotgun (WGS) sequencing, where the amount of unmapped reads is substantial. When applied to a WGS sequencing data set to quantify distances between metagenomics samples from various human body habitats, Nubeam recapitulates findings made by mapping-based methods and sheds light on contributions of unmapped reads. Nubeam is also useful in analyzing 16S rRNA sequencing data, which is a more prevalent type of data set in metagenomics studies. In our analysis, Nubeam recapitulated the findings that natural microbiota in mouse gut are resilient under challenges, and Nubeam detected differences in vaginal microbiota between cases of polycystic ovary syndrome and healthy controls.

| S-EPMC7545149 | biostudies-literature

Quantifying and comparing bacterial growth dynamics in multiple metagenomic samples.

Project description:The accurate quantification of microbial growth dynamics for species without complete genome sequences is biologically important, but computationally challenging in metagenomics. Here we present dynamic estimator of microbial communities (DEMIC; https://sourceforge.net/projects/demic/ ), a multi-sample algorithm based on contigs and coverage values, to infer the relative distances of contigs from the replication origin and to accurately compare bacterial growth rates between samples. We demonstrate robust performances of DEMIC for various sample sizes and assembly qualities using multiple synthetic and real datasets.

| S-EPMC6289653 | biostudies-literature

metaVaR: Introducing metavariant species models for reference-free metagenomic-based population genomics.

Project description:The availability of large metagenomic data offers great opportunities for the population genomic analysis of uncultured organisms, which represent a large part of the unexplored biosphere and play a key ecological role. However, the majority of these organisms lack a reference genome or transcriptome, which constitutes a technical obstacle for classical population genomic analyses. We introduce the metavariant species (MVS) model, in which a species is represented only by intra-species nucleotide polymorphism. We designed a method combining reference-free variant calling, multiple density-based clustering and maximum-weighted independent set algorithms to cluster intra-species variants into MVSs directly from multisample metagenomic raw reads without a reference genome or read assembly. The frequencies of the MVS variants are then used to compute population genomic statistics such as FST, in order to estimate genomic differentiation between populations and to identify loci under natural selection. The MVS construction was tested on simulated and real metagenomic data. MVSs showed the required quality for robust population genomics and allowed an accurate estimation of genomic differentiation (ΔFST < 0.0001 and <0.03 on simulated and real data respectively). Loci predicted under natural selection on real data were all detected by MVSs. MVSs represent a new paradigm that may simplify and enhance holistic approaches for population genomics and the evolution of microorganisms.

| S-EPMC7773188 | biostudies-literature

Establishment of CYP2D6 reference samples by multiple validated genotyping platforms.

Project description:Cytochrome P450 2D6 (cytochrome P450, family 2, subfamily D, polypeptide 6 (CYP2D6)), a highly polymorphic drug-metabolizing enzyme, is involved in the metabolism of one-quarter of the most commonly prescribed medications. Here we have applied multiple genotyping methods and Sanger sequencing to assign precise and reproducible CYP2D6 genotypes, including copy numbers, for 48 HapMap samples. Furthermore, by analyzing a set of 50 human liver microsomes using endoxifen formation from N-desmethyl-tamoxifen as the phenotype of interest, we observed a significant positive correlation between CYP2D6 genotype-assigned activity score and endoxifen formation rate (rs = 0.68 by rank correlation test, P = 5.3 × 10(-8)), which corroborated the genotype-phenotype prediction derived from our genotyping methodologies. In the future, these 48 publicly available HapMap samples characterized by multiple substantiated CYP2D6 genotyping platforms could serve as a reference resource for assay development, validation, quality control and proficiency testing for other CYP2D6 genotyping projects and for programs pursuing clinical pharmacogenomic testing implementation.

| S-EPMC4237721 | biostudies-literature

Evaluation of multiple displacement amplification for metagenomic analysis of low biomass samples.

Project description:Combining multiple displacement amplification (MDA) with metagenomics enables the analysis of samples with extremely low DNA concentrations, making them suitable for high-throughput sequencing. Although amplification bias and nonspecific amplification have been reported from MDA-amplified samples, the impact of MDA on metagenomic datasets is not well understood. We compared three MDA methods (i.e. bulk MDA, emulsion MDA, and primase MDA) for metagenomic analysis of two DNA template concentrations (approx. 1 and 100 pg) derived from a microbial community standard "mock community" and two low biomass environmental samples (i.e. borehole fluid and groundwater). We assessed the impact of MDA on metagenome-based community composition, assembly quality, functional profiles, and binning. We found amplification bias against high GC content genomes but relatively low nonspecific amplification such as chimeras, artifacts, or contamination for all MDA methods. We observed MDA-associated representational bias for microbial community profiles, especially for low-input DNA and with the primase MDA method. Nevertheless, similar taxa were represented in MDA-amplified libraries to those of unamplified samples. The MDA libraries were highly fragmented, but similar functional profiles to the unamplified libraries were obtained for bulk MDA and emulsion MDA at higher DNA input and across these MDA libraries for the groundwater sample. Medium to low-quality bins were possible for the high input bulk MDA metagenomes for the most simple microbial communities, borehole fluid, and mock community. Although MDA-based amplification should be avoided, it can still reveal meaningful taxonomic and functional information from samples with extremely low DNA concentration where direct metagenomics is otherwise impossible.

| S-EPMC10945365 | biostudies-literature

Contact-free determination of viscosity in multiple parallel samples.

Project description:Viscosity is an inherent characteristic of fluids and is therefore an important parameter in many different processes. Current methods to measure viscosity involve direct contact with the liquid sample, which is often undesirable. Here we present a simple, precise and robust contact-free method to determine viscosity, using a single drive motor, inexpensive components and disposable sample vessels. The measurement principle involves the detection of viscosity-dependent angular positions in a rotating liquid relative to the direction of centrifugal acceleration in an orbitally shaken vessel. The signal can be detected using different optical methods, as shown here using fluorescence and transmitted light. The sensitivity of the system can be adjusted over a wide range by varying the sample volume or the shaking diameter, and multiple samples can be analysed in parallel. This novel viscometer is also applicable to characterize non-Newtonian shear rate-dependent fluids.

| S-EPMC6554296 | biostudies-literature

Definition of the Metagenomic Profile of Ocean Water Samples From the Gulf of Mexico Based on Comparison With Reference Samples From Sites Worldwide.

Project description:Computational and statistical analysis of shotgun metagenomes can predict gene abundance and is helpful for elucidating the functional and taxonomic compositions of environmental samples. Gene products are compared against physicochemical conditions or perturbations to shed light on the functions performed by the microbial community of an environmental sample; however, this information is not always available. The present study proposes a method for inferring the metabolic potential of metagenome samples by constructing a reference based on determining the probability distribution of the counts of each enzyme annotated. To test the methodology, we used marine water samples distributed worldwide as references. Then, the references were utilized to compare the annotated enzymes of two different water samples extracted from the Gulf of Mexico (GoM) to distinguish those enzymes with atypical behavior. The enzymes whose annotation counts presented frequencies significantly different from those of the reference were used to perform metabolic reconstruction, which naturally identified pathways. We found that several of the enzymes were involved in the biodegradation of petroleum, which is consistent with the impact of human hydrocarbon extraction activity and its ubiquitous presence in the GoM. The examination of other reconstructed pathways revealed significant enzymes indicating the presence of microbial communities characterizing each ocean depth and ocean cycle, providing a fingerprint of each sampled site.

| S-EPMC8846951 | biostudies-literature

ReorientExpress: reference-free orientation of nanopore cDNA reads with deep learning.

Project description:We describe ReorientExpress, a method to perform reference-free orientation of transcriptomic long sequencing reads. ReorientExpress uses deep learning to correctly predict the orientation of the majority of reads, and in particular when trained on a closely related species or in combination with read clustering. ReorientExpress enables long-read transcriptomics in non-model organisms and samples without a genome reference without using additional technologies and is available at https://github.com/comprna/reorientexpress.

| S-EPMC6883653 | biostudies-literature

Deep learning enables reference-free isotropic super-resolution for volumetric fluorescence microscopy.

Project description:Volumetric imaging by fluorescence microscopy is often limited by anisotropic spatial resolution, in which the axial resolution is inferior to the lateral resolution. To address this problem, we present a deep-learning-enabled unsupervised super-resolution technique that enhances anisotropic images in volumetric fluorescence microscopy. In contrast to the existing deep learning approaches that require matched high-resolution target images, our method greatly reduces the effort to be put into practice as the training of a network requires only a single 3D image stack, without a priori knowledge of the image formation process, registration of training data, or separate acquisition of target data. This is achieved based on the optimal transport-driven cycle-consistent generative adversarial network that learns from an unpaired matching between high-resolution 2D images in the lateral image plane and low-resolution 2D images in other planes. Using fluorescence confocal microscopy and light-sheet microscopy, we demonstrate that the trained network not only enhances axial resolution but also restores suppressed visual details between the imaging planes and removes imaging artifacts.

| S-EPMC9178036 | 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