A Statistical Framework for Accurate Taxonomic Assignment of Metagenomic Sequencing Reads

The advent of next-generation sequencing technologies has greatly promoted the field of metagenomics which studies genetic material recovered directly from an environment. Characterization of genomic composition of a metagenomic sample is essential for understanding the structure of the microbial community. Multiple genomes contained in a metagenomic sample can be identified and quantitated through homology searches of sequence reads with known sequences catalogued in reference databases. Traditionally, reads with multiple genomic hits are assigned to non-specific or high ranks of the taxonomy tree, thereby impacting on accurate estimates of relative abundance of multiple genomes present in a sample. Instead of assigning reads one by one to the taxonomy tree as many existing methods do, we propose a statistical framework to model the identified candidate genomes to which sequence reads have hits. After obtaining the estimated proportion of reads generated by each genome, sequence reads are assigned to the candidate genomes and the taxonomy tree based on the estimated probability by taking into account both sequence alignment scores and estimated genome abundance. The proposed method is comprehensively tested on both simulated datasets and two real datasets. It assigns reads to the low taxonomic ranks very accurately. Our statistical approach of taxonomic assignment of metagenomic reads, TAMER, is implemented in R and available at http://faculty.wcas.northwestern.edu/hji403/MetaR.htm.     

Eu-Detect: An algorithm for detecting eukaryotic sequences in metagenomic data sets

Physical partitioning techniques are routinely employed (during sample preparation stage) for segregating the prokaryotic and eukaryotic fractions of metagenomic samples. In spite of these efforts, several metagenomic studies focusing on bacterial and archaeal populations have reported the presence of contaminating eukaryotic sequences in metagenomic data sets. Contaminating sequences originate not only from genomes of micro-eukaryotic species but also from genomes of (higher) eukaryotic host cells. The latter scenario usually occurs in the case of host-associated metagenomes. Identification and removal of contaminating sequences is important, since these sequences not only impact estimates of microbial diversity but also affect the accuracy of several downstream analyses. Currently, the computational techniques used for identifying contaminating eukaryotic sequences, being alignment based, are slow, inefficient, and require huge computing resources. In this article, we present Eu-Detect, an alignment-free algorithm that can rapidly identify eukaryotic sequences contaminating metagenomic data sets. Validation results indicate that on a desktop with modest hardware specifications, the Eu-Detect algorithm is able to rapidly segregate DNA sequence fragments of prokaryotic and eukaryotic origin, with high sensitivity. A Web server for the Eu-Detect algorithm is available at http://metagenomics.atc.tcs.com/Eu-Detect/.     

Genometa - a fast and accurate classifier for short metagenomic shotgun reads

SUMMARY: Metagenomic studies use high-throughput sequence data to investigate microbial communities in situ. However, considerable challenges remain in the analysis of these data, particularly with regard to speed and reliable analysis of microbial species as opposed to higher level taxa such as phyla. We here present Genometa, a computationally undemanding graphical user interface program that enables identification of bacterial species and gene content from datasets generated by inexpensive high-throughput short read sequencing technologies. Our approach was first verified on two simulated metagenomic short read datasets, detecting 100% and 94% of the bacterial species included with few false positives or false negatives. Subsequent comparative benchmarking analysis against three popular metagenomic algorithms on an Illumina human gut dataset revealed Genometa to attribute the most reads to bacteria at species level (i.e. including all strains of that species) and demonstrate similar or better accuracy than the other programs. Lastly, speed was demonstrated to be many times that of BLAST due to the use of modern short read aligners. Our method is highly accurate if bacteria in the sample are represented by genomes in the reference sequence but cannot find species absent from the reference. This method is one of the most user-friendly and resource efficient approaches and is thus feasible for rapidly analysing millions of short reads on a personal computer. AVAILABILITY: The Genometa program, a step by step tutorial and Java source code are freely available from http://genomics1.mh-hannover.de/genometa/ and on http://code.google.com/p/genometa/. This program has been tested on Ubuntu Linux and Windows XP/7.     

A platform-independent method for detecting errors in metagenomic sequencing data: DRISEE

We provide a novel method, DRISEE (duplicate read inferred sequencing error estimation), to assess sequencing quality (alternatively referred to as "noise" or "error") within and/or between sequencing samples. DRISEE provides positional error estimates that can be used to inform read trimming within a sample. It also provides global (whole sample) error estimates that can be used to identify samples with high or varying levels of sequencing error that may confound downstream analyses, particularly in the case of studies that utilize data from multiple sequencing samples. For shotgun metagenomic data, we believe that DRISEE provides estimates of sequencing error that are more accurate and less constrained by technical limitations than existing methods that rely on reference genomes or the use of scores (e.g. Phred). Here, DRISEE is applied to (non amplicon) data sets from both the 454 and Illumina platforms. The DRISEE error estimate is obtained by analyzing sets of artifactual duplicate reads (ADRs), a known by-product of both sequencing platforms. We present DRISEE as an open-source, platform-independent method to assess sequencing error in shotgun metagenomic data, and utilize it to discover previously uncharacterized error in de novo sequence data from the 454 and Illumina sequencing platforms.     

A Model-Based Approach for Species Abundance Quantification Based on Shotgun Metagenomic Data

The human microbiome, which includes the collective microbes residing in or on the human body, has a profound influence on the human health. DNA sequencing technology has made the large-scale human microbiome studies possible by using shotgun metagenomic sequencing. One important aspect of data analysis of such metagenomic data is to quantify the bacterial abundances based on the metagenomic sequencing data. Existing methods almost always quantify such abundances one sample at a time, which ignore certain systematic differences in read coverage along the genomes due to GC contents, copy number variation and the bacterial origin of replication. In order to account for such differences in read counts, we propose a multi-sample Poisson model to quantify microbial abundances based on read counts that are assigned to species-specific taxonomic markers. Our model takes into account the marker-specific effects when normalizing the sequencing count data in order to obtain more accurate quantification of the species abundances. Compared to currently available methods on simulated data and real data sets, our method has demonstrated an improved accuracy in bacterial abundance quantification, which leads to more biologically interesting results from downstream data analysis.     

Signal Processing for Metagenomics: Extracting Information from the Soup

Traditionally, studies in microbial genomics have focused on single-genomes from cultured species, thereby limiting their focus to the small percentage of species that can be cultured outside their natural environment. Fortunately, recent advances in high-throughput sequencing and computational analyses have ushered in the new field of metagenomics, which aims to decode the genomes of microbes from natural communities without the need for cultivation. Although metagenomic studies have shed a great deal of insight into bacterial diversity and coding capacity, several computational challenges remain due to the massive size and complexity of metagenomic sequence data. Current tools and techniques are reviewed in this paper which address challenges in 1) genomic fragment annotation, 2) phylogenetic reconstruction, 3) functional classification of samples, and 4) interpreting complementary metaproteomics and metametabolomics data. Also surveyed are important applications of metagenomic studies, including microbial forensics and the roles of microbial communities in shaping human health and soil ecology.     

Metagenomic abundance estimation and diagnostic testing on species level

One goal of sequencing-based metagenomic community analysis is the quantitative taxonomic assessment of microbial community compositions. In particular, relative quantification of taxons is of high relevance for metagenomic diagnostics or microbial community comparison. However, the majority of existing approaches quantify at low resolution (e.g. at phylum level), rely on the existence of special genes (e.g. 16S), or have severe problems discerning species with highly similar genome sequences. Yet, problems as metagenomic diagnostics require accurate quantification on species level. We developed Genome Abundance Similarity Correction (GASiC), a method to estimate true genome abundances via read alignment by considering reference genome similarities in a non-negative LASSO approach. We demonstrate GASiC’s superior performance over existing methods on simulated benchmark data as well as on real data. In addition, we present applications to datasets of both bacterial DNA and viral RNA source. We further discuss our approach as an alternative to PCR-based DNA quantification.     

MetaVelvet-SL: an extension of the Velvet assembler to a de novo metagenomic assembler utilizing supervised learning

The assembly of multiple genomes from mixed sequence reads is a bottleneck in metagenomic analysis. A single-genome assembly program (assembler) is not capable of resolving metagenome sequences, so assemblers designed specifically for metagenomics have been developed. MetaVelvet is an extension of the single-genome assembler Velvet. It has been proved to generate assemblies with higher N50 scores and higher quality than single-genome assemblers such as Velvet and SOAPdenovo when applied to metagenomic sequence reads and is frequently used in this research community. One important open problem for MetaVelvet is its low accuracy and sensitivity in detecting chimeric nodes in the assembly (de Bruijn) graph, which prevents the generation of longer contigs and scaffolds. We have tackled this problem of classifying chimeric nodes using supervised machine learning to significantly improve the performance of MetaVelvet and developed a new tool, called MetaVelvet-SL. A Support Vector Machine is used for learning the classification model based on 94 features extracted from candidate nodes. In extensive experiments, MetaVelvet-SL outperformed the original MetaVelvet and other state-of-the-art metagenomic assemblers, IDBA-UD, Ray Meta and Omega, to reconstruct accurate longer assemblies with higher N50 scores for both simulated data sets and real data sets of human gut microbial sequences.     

宏基因组重叠群分箱方法研究综述

宏基因组学技术可以直接从环境中提取微生物的全部遗传物质,而不需要像传统方法一样在培养基上纯培养。这种技术的出现为科学家对微生物群落的结构和功能的认识提供了重要的方法,同时对疾病的诊治、环境的治理以及生命的认识具有重大的意义。从环境中提取出微生物全部遗传物质,对其进行测序从而得到它们的reads片段,通过reads组装工具可以进一步组装成重叠群片段。对重叠群片段进行分箱,可以从宏基因组样本中重建出更多完整的基因。分箱效果的好坏直接影响到后续的生物分析,因此如何将这些含有不同微生物基因混合的重叠群序列进行有效的分箱成为了宏基因组学研究的热点和难点。机器学习方法被广泛应用于宏基因组重叠群分箱,通常分为有监督重叠群分类方法和无监督重叠群聚类方法。该综述针对宏基因组重叠群分箱方法进行了较为全面的阐述,深入剖析了重叠群分类方法与聚类方法,发现其存在分类准确率较低、分箱时间较长、难以从复杂数据集中重建更多微生物基因等问题,并对未来重叠群分箱方法的研究和发展进行了展望。作者建议可以使用半监督学习、集成学习以及深度学习方法,并采用更有效的数据特征表示等途径来提高分箱效果。     

Accurate genome relative abundance estimation for closely related species in a metagenomic sample

Background

Metagenomics has a great potential to discover previously unattainable information about microbial communities. An important prerequisite for such discoveries is to accurately estimate the composition of microbial communities. Most of prevalent homology-based approaches utilize solely the results of an alignment tool such as BLAST, limiting their estimation accuracy to high ranks of the taxonomy tree.

Results

We developed a new homology-based approach called Taxonomic Analysis by Elimination and Correction (TAEC), which utilizes the similarity in the genomic sequence in addition to the result of an alignment tool. The proposed method is comprehensively tested on various simulated benchmark datasets of diverse complexity of microbial structure. Compared with other available methods designed for estimating taxonomic composition at a relatively low taxonomic rank, TAEC demonstrates greater accuracy in quantification of genomes in a given microbial sample. We also applied TAEC on two real metagenomic datasets, oral cavity dataset and Crohn’s disease dataset. Our results, while agreeing with previous findings at higher ranks of the taxonomy tree, provide accurate estimation of taxonomic compositions at the species/strain level, narrowing down which species/strains need more attention in the study of oral cavity and the Crohn’s disease.

Conclusions

By taking account of the similarity in the genomic sequence TAEC outperforms other available tools in estimating taxonomic composition at a very low rank, especially when closely related species/strains exist in a metagenomic sample.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-242) contains supplementary material, which is available to authorized users.     

MetaSim: a sequencing simulator for genomics and metagenomics

Background

The new research field of metagenomics is providing exciting insights into various, previously unclassified ecological systems. Next-generation sequencing technologies are producing a rapid increase of environmental data in public databases. There is great need for specialized software solutions and statistical methods for dealing with complex metagenome data sets.

Methodology/Principal Findings

To facilitate the development and improvement of metagenomic tools and the planning of metagenomic projects, we introduce a sequencing simulator called MetaSim. Our software can be used to generate collections of synthetic reads that reflect the diverse taxonomical composition of typical metagenome data sets. Based on a database of given genomes, the program allows the user to design a metagenome by specifying the number of genomes present at different levels of the NCBI taxonomy, and then to collect reads from the metagenome using a simulation of a number of different sequencing technologies. A population sampler optionally produces evolved sequences based on source genomes and a given evolutionary tree.

Conclusions/Significance

MetaSim allows the user to simulate individual read datasets that can be used as standardized test scenarios for planning sequencing projects or for benchmarking metagenomic software.     

Individual genome assembly from complex community short-read metagenomic datasets

Assembling individual genomes from complex community metagenomic data remains a challenging issue for environmental studies. We evaluated the quality of genome assemblies from community short read data (Illumina 100 bp pair-ended sequences) using datasets recovered from freshwater and soil microbial communities as well as in silico simulations. Our analyses revealed that the genome of a single genotype (or species) can be accurately assembled from a complex metagenome when it shows at least about 20 × coverage. At lower coverage, however, the derived assemblies contained a substantial fraction of non-target sequences (chimeras), which explains, at least in part, the higher number of hypothetical genes recovered in metagenomic relative to genomic projects. We also provide examples of how to detect intrapopulation structure in metagenomic datasets and estimate the type and frequency of errors in assembled genes and contigs from datasets of varied species complexity.     

Resolving Prokaryotic Taxonomy without rRNA: Longer Oligonucleotide Word Lengths Improve Genome and Metagenome Taxonomic Classification

Oligonucleotide signatures, especially tetranucleotide signatures, have been used as method for homology binning by exploiting an organism’s inherent biases towards the use of specific oligonucleotide words. Tetranucleotide signatures have been especially useful in environmental metagenomics samples as many of these samples contain organisms from poorly classified phyla which cannot be easily identified using traditional homology methods, including NCBI BLAST. This study examines oligonucleotide signatures across 1,424 completed genomes from across the tree of life, substantially expanding upon previous work. A comprehensive analysis of mononucleotide through nonanucleotide word lengths suggests that longer word lengths substantially improve the classification of DNA fragments across a range of sizes of relevance to high throughput sequencing. We find that, at present, heptanucleotide signatures represent an optimal balance between prediction accuracy and computational time for resolving taxonomy using both genomic and metagenomic fragments. We directly compare the ability of tetranucleotide and heptanucleotide world lengths (tetranucleotide signatures are the current standard for oligonucleotide word usage analyses) for taxonomic binning of metagenome reads. We present evidence that heptanucleotide word lengths consistently provide more taxonomic resolving power, particularly in distinguishing between closely related organisms that are often present in metagenomic samples. This implies that longer oligonucleotide word lengths should replace tetranucleotide signatures for most analyses. Finally, we show that the application of longer word lengths to metagenomic datasets leads to more accurate taxonomic binning of DNA scaffolds and have the potential to substantially improve taxonomic assignment and assembly of metagenomic data.     

Sequencing our way to more accurate community abundance

Over the last two decades, there has been a huge increase in our understanding of microbial diversity, structure and composition enabled by high-throughput sequencing technologies. Yet, it is unclear how the number of sequences translates to the number of cells or species within the community. In some cases, additional observational data may be required to ensure relative abundance patterns from sequence reads are biologically meaningful. The goal of DNA-based methods for biodiversity assessments is to obtain robust community abundance data, simultaneously, from environmental samples. In this issue of Molecular Ecology Resources, Pierella Karlusich et al. (2022) describe a new method for quantifying phytoplankton cell abundance. Using Tara Oceans data sets, the authors propose the photosynthetic gene psbO for reporting accurate relative abundance of the entire phytoplankton community from metagenomic data. The authors demonstrate higher correlations with traditional optical methods (including microscopy and flow cytometry), using their new method, improving upon molecular abundance assessments using multicopy marker genes. Furthermore, to facilitate application of their approach, the authors curated a psbO gene database for accessible taxonomic queries. This is an important step towards improving species abundance estimates from molecular data and eventually reporting of absolute species abundance, enhancing our understanding of community dynamics.     

Metagenomic taxonomic classification using extreme learning machines

Next-generation sequencing technologies have allowed researchers to determine the collective genomes of microbial communities co-existing within diverse ecological environments. Varying species abundance, length and complexities within different communities, coupled with discovery of new species makes the problem of taxonomic assignment to short DNA sequence reads extremely challenging. We have developed a new sequence composition-based taxonomic classifier using extreme learning machines referred to as TAC-ELM for metagenomic analysis. TAC-ELM uses the framework of extreme learning machines to quickly and accurately learn the weights for a neural network model. The input features consist of GC content and oligonucleotides. TAC-ELM is evaluated on two metagenomic benchmarks with sequence read lengths reflecting the traditional and current sequencing technologies. Our empirical results indicate the strength of the developed approach, which outperforms state-of-the-art taxonomic classifiers in terms of accuracy and implementation complexity. We also perform experiments that evaluate the pervasive case within metagenome analysis, where a species may not have been previously sequenced or discovered and will not exist in the reference genome databases. TAC-ELM was also combined with BLAST to show improved classification results. Code and Supplementary Results: http://www.cs.gmu.edu/~mlbio/TAC-ELM (BSD License).     

An Improved Methodology to Overcome Key Issues in Human Fecal Metagenomic DNA Extraction

Microbes are ubiquitously distributed in nature, and recent culture-independent studies have highlighted the significance of gut microbiota in human health and disease. Fecal DNA is the primary source for the majority of human gut microbiome studies. However, further improvement is needed to obtain fecal metagenomic DNA with sufficient amount and good quality but low host genomic DNA contamination. In the current study, we demonstrate a quick, robust, unbiased,and cost-effective method for the isolation of high molecular weight(23 kb) metagenomic DNA(260/280 ratio 1.8) with a good yield(55.8 ± 3.8 ng/mg of feces). We also confirm that there is very low human genomic DNA contamination(eubacterial: human genomic DNA marker genes = 2~(27.9):1) in the human feces. The newly-developed method robustly performs for fresh as well as stored fecal samples as demonstrated by 16 S r RNA gene sequencing using 454 FLX+.Moreover, 16 S r RNA gene analysis indicated that compared to other DNA extraction methods tested, the fecal metagenomic DNA isolated with current methodology retains species richnessand does not show microbial diversity biases, which is further confirmed by q PCR with a known quantity of spike-in genomes. Overall, our data highlight a protocol with a balance between quality,amount, user-friendliness, and cost effectiveness for its suitability toward usage for cultureindependent analysis of the human gut microbiome, which provides a robust solution to overcome key issues associated with fecal metagenomic DNA isolation in human gut microbiome studies.