Metagenomics: Read Length Matters

Obtaining an unbiased view of the phylogenetic composition and functional diversity within a microbial community is one central objective of metagenomic analysis. New technologies, such as 454 pyrosequencing, have dramatically reduced sequencing costs, to a level where metagenomic analysis may become a viable alternative to more-focused assessments of the phylogenetic (e.g., 16S rRNA genes) and functional diversity of microbial communities. To determine whether the short (~100 to 200 bp) sequence reads obtained from pyrosequencing are appropriate for the phylogenetic and functional characterization of microbial communities, the results of BLAST and COG analyses were compared for long (~750 bp) and randomly derived short reads from each of two microbial and one virioplankton metagenome libraries. Overall, BLASTX searches against the GenBank nr database found far fewer homologs within the short-sequence libraries. This was especially pronounced for a Chesapeake Bay virioplankton metagenome library. Increasing the short-read sampling depth or the length of derived short reads (up to 400 bp) did not completely resolve the discrepancy in BLASTX homolog detection. Only in cases where the long-read sequence had a close homolog (low BLAST E-score) did the derived short-read sequence also find a significant homolog. Thus, more-distant homologs of microbial and viral genes are not detected by short-read sequences. Among COG hits, derived short reads sampled at a depth of two short reads per long read missed up to 72% of the COG hits found using long reads. Noting the current limitation in computational approaches for the analysis of short sequences, the use of short-read-length libraries does not appear to be an appropriate tool for the metagenomic characterization of microbial communities.     

Annotation of metagenome short reads using proxygenes

MOTIVATION: A typical metagenome dataset generated using a 454 pyrosequencing platform consists of short reads sampled from the collective genome of a microbial community. The amount of sequence in such datasets is usually insufficient for assembly, and traditional gene prediction cannot be applied to unassembled short reads. As a result, analysis of such datasets usually involves comparisons in terms of relative abundances of various protein families. The latter requires assignment of individual reads to protein families, which is hindered by the fact that short reads contain only a fragment, usually small, of a protein. RESULTS: We have considered the assignment of pyrosequencing reads to protein families directly using RPS-BLAST against COG and Pfam databases and indirectly via proxygenes that are identified using BLASTx searches against protein sequence databases. Using simulated metagenome datasets as benchmarks, we show that the proxygene method is more accurate than the direct assignment. We introduce a clustering method which significantly reduces the size of a metagenome dataset while maintaining a faithful representation of its functional and taxonomic content.     

A metagenetic approach for revealing community structure of marine planktonic copepods

Marine planktonic copepods are an ecologically important group with high species richness and abundance. Here, we propose a new metagenetic approach for revealing the community structure of marine planktonic copepods using 454 pyrosequencing of nuclear large subunit ribosomal DNA. We determined an appropriate similarity threshold for clustering pyrosequencing data into molecular operational taxonomic units (MOTUs) using an artificial community containing 33 morphologically identified species. The 99% similarity threshold had high species‐level resolution for MOTU clustering but overestimated species richness. The artificial community was appropriately clustered into MOTUs at 97% similarity, with little inflation in MOTU numbers and with relatively high species‐level resolution. The number of sequence reads of each MOTU was correlated with dry weight of that taxon, suggesting that sequence reads could be used as a proxy for biomass. Next, we applied the method to field‐collected samples, and the results corresponded reasonably well with morphological analysis of these communities. Numbers of MOTUs were well correlated with species richness at 97% similarity, and large numbers of sequence reads were generally observed in MOTUs derived from species with large biomass. Further, MOTUs were successfully classified into taxonomic groups at the family level at 97% similarity; similar patterns of species richness and biomass were revealed within families with metagenetic and morphological analyses. At the 99% similarity threshold, MOTUs with high proportions of sequence reads were identified as biomass‐dominant species in each field‐collected sample. The metagenetic approach reported here can be an effective tool for rapid and comprehensive assessment of copepod community structure.     

Metabolic reconstruction for metagenomic data and its application to the human microbiome

Microbial communities carry out the majority of the biochemical activity on the planet, and they play integral roles in processes including metabolism and immune homeostasis in the human microbiome. Shotgun sequencing of such communities' metagenomes provides information complementary to organismal abundances from taxonomic markers, but the resulting data typically comprise short reads from hundreds of different organisms and are at best challenging to assemble comparably to single-organism genomes. Here, we describe an alternative approach to infer the functional and metabolic potential of a microbial community metagenome. We determined the gene families and pathways present or absent within a community, as well as their relative abundances, directly from short sequence reads. We validated this methodology using a collection of synthetic metagenomes, recovering the presence and abundance both of large pathways and of small functional modules with high accuracy. We subsequently applied this method, HUMAnN, to the microbial communities of 649 metagenomes drawn from seven primary body sites on 102 individuals as part of the Human Microbiome Project (HMP). This provided a means to compare functional diversity and organismal ecology in the human microbiome, and we determined a core of 24 ubiquitously present modules. Core pathways were often implemented by different enzyme families within different body sites, and 168 functional modules and 196 metabolic pathways varied in metagenomic abundance specifically to one or more niches within the microbiome. These included glycosaminoglycan degradation in the gut, as well as phosphate and amino acid transport linked to host phenotype (vaginal pH) in the posterior fornix. An implementation of our methodology is available at http://huttenhower.sph.harvard.edu/humann. This provides a means to accurately and efficiently characterize microbial metabolic pathways and functional modules directly from high-throughput sequencing reads, enabling the determination of community roles in the HMP cohort and in future metagenomic studies.     

High-Specificity Targeted Functional Profiling in Microbial Communities with ShortBRED

Profiling microbial community function from metagenomic sequencing data remains a computationally challenging problem. Mapping millions of DNA reads from such samples to reference protein databases requires long run-times, and short read lengths can result in spurious hits to unrelated proteins (loss of specificity). We developed ShortBRED (Short, Better Representative Extract Dataset) to address these challenges, facilitating fast, accurate functional profiling of metagenomic samples. ShortBRED consists of two components: (i) a method that reduces reference proteins of interest to short, highly representative amino acid sequences (“markers”) and (ii) a search step that maps reads to these markers to quantify the relative abundance of their associated proteins. After evaluating ShortBRED on synthetic data, we applied it to profile antibiotic resistance protein families in the gut microbiomes of individuals from the United States, China, Malawi, and Venezuela. Our results support antibiotic resistance as a core function in the human gut microbiome, with tetracycline-resistant ribosomal protection proteins and Class A beta-lactamases being the most widely distributed resistance mechanisms worldwide. ShortBRED markers are applicable to other homology-based search tasks, which we demonstrate here by identifying phylogenetic signatures of antibiotic resistance across more than 3,000 microbial isolate genomes. ShortBRED can be applied to profile a wide variety of protein families of interest; the software, source code, and documentation are available for download at http://huttenhower.sph.harvard.edu/shortbred     

Rapid phylogenetic and functional classification of short genomic fragments with signature peptides

ABSTRACT: BACKGROUND: Classification is difficult for shotgun metagenomics data from environments such as soils, where the diversity of sequences is high and where reference sequences from close relatives may not exist. Approaches based on sequence-similarity scores must deal with the confounding effects that inheritance and functional pressures exert on the relation between scores and phylogenetic distance, while approaches based on sequence alignment and tree-building are typically limited to a small fraction of gene families. We describe an approach based on finding one or more exact matches between a read and a precomputed set of peptide 10-mers. RESULTS: At even the largest phylogenetic distances, thousands of 10-mer peptide exact matches can be found between pairs of bacterial genomes. Genes that share one or more peptide 10-mers typically have high reciprocal BLAST scores. Among a set of 403 representative bacterial genomes, some 20 million 10-mer peptides were found to be shared. We assign each of these peptides as a signature of a particular node in a phylogenetic reference tree based on the RNA polymerase genes. We classify the phylogeny of a genomic fragment (e.g., read) at the most specific node on the reference tree that is consistent with the phylogeny of observed signature peptides it contains. Using both synthetic data from four newly-sequenced soil-bacterium genomes and ten real soil metagenomics data sets, we demonstrate a sensitivity and specificity comparable to that of the MEGAN metagenomics analysis package using BLASTX against the NR database. Phylogenetic and functional similarity metrics applied to real metagenomics data indicates a signal-to-noise ratio of approximately 400 for distinguishing among environments. Our method assigns ~6.6 Gbp/hr on a single CPU, compared with 25 kbp/hr for methods based on BLASTX against the NR database. CONCLUSIONS: Classification by exact matching against a precomputed list of signature peptides provides comparable results to existing techniques for reads longer than about 300 bp and does not degrade severely with shorter reads. Orders of magnitude faster than existing methods, the approach is suitable now for inclusion in analysis pipelines and appears to be extensible in several different directions.     

Quantifying microbial communities with 454 pyrosequencing: does read abundance count?

Pyrosequencing technologies have revolutionized how we describe and compare complex microbial communities. In 454 pyrosequencing data sets, the abundance of reads pertaining to taxa or phylotypes is commonly interpreted as a measure of genic or taxon abundance, useful for quantitative comparisons of community similarity. Potentially systematic biases inherent in sample processing, amplification and sequencing, however, may alter read abundance and reduce the utility of quantitative metrics. Here, we examine the relationship between read abundance and biological abundance in a sample of house dust spiked with known quantities and identities of fungi along a dilution gradient. Our results show one order of magnitude differences in read abundance among species. Precision of quantification within species along the dilution gradient varied from R(2) of 0.96-0.54. Read-quality based processing stringency profoundly affected the abundance of one species containing long homopolymers in a read orientation-biased manner. Order-level composition of background environmental fungal communities determined from pyrosequencing data was comparable with that derived from cloning and Sanger sequencing and was not biased by read orientation. We conclude that read abundance is approximately quantitative within species, but between-species comparisons can be biased by innate sequence structure. Our results showed a trade off between sequence quality stringency and quantification. Careful consideration of sequence processing methods and community analyses are warranted when testing hypotheses using read abundance data.     

MetaVelvet: an extension of Velvet assembler to de novo metagenome assembly from short sequence reads

An important step in ‘metagenomics’ analysis is the assembly of multiple genomes from mixed sequence reads of multiple species in a microbial community. Most conventional pipelines use a single-genome assembler with carefully optimized parameters. A limitation of a single-genome assembler for de novo metagenome assembly is that sequences of highly abundant species are likely misidentified as repeats in a single genome, resulting in a number of small fragmented scaffolds. We extended a single-genome assembler for short reads, known as ‘Velvet’, to metagenome assembly, which we called ‘MetaVelvet’, for mixed short reads of multiple species. Our fundamental concept was to first decompose a de Bruijn graph constructed from mixed short reads into individual sub-graphs, and second, to build scaffolds based on each decomposed de Bruijn sub-graph as an isolate species genome. We made use of two features, the coverage (abundance) difference and graph connectivity, for the decomposition of the de Bruijn graph. For simulated datasets, MetaVelvet succeeded in generating significantly higher N50 scores than any single-genome assemblers. MetaVelvet also reconstructed relatively low-coverage genome sequences as scaffolds. On real datasets of human gut microbial read data, MetaVelvet produced longer scaffolds and increased the number of predicted genes.     

Factors that affect large subunit ribosomal DNA amplicon sequencing studies of fungal communities: classification method, primer choice, and error

Nuclear large subunit ribosomal DNA is widely used in fungal phylogenetics and to an increasing extent also amplicon-based environmental sequencing. The relatively short reads produced by next-generation sequencing, however, makes primer choice and sequence error important variables for obtaining accurate taxonomic classifications. In this simulation study we tested the performance of three classification methods: 1) a similarity-based method (BLAST + Metagenomic Analyzer, MEGAN); 2) a composition-based method (Ribosomal Database Project na?ve bayesian classifier, NBC); and, 3) a phylogeny-based method (Statistical Assignment Package, SAP). We also tested the effects of sequence length, primer choice, and sequence error on classification accuracy and perceived community composition. Using a leave-one-out cross validation approach, results for classifications to the genus rank were as follows: BLAST + MEGAN had the lowest error rate and was particularly robust to sequence error; SAP accuracy was highest when long LSU query sequences were classified; and, NBC runs significantly faster than the other tested methods. All methods performed poorly with the shortest 50-100 bp sequences. Increasing simulated sequence error reduced classification accuracy. Community shifts were detected due to sequence error and primer selection even though there was no change in the underlying community composition. Short read datasets from individual primers, as well as pooled datasets, appear to only approximate the true community composition. We hope this work informs investigators of some of the factors that affect the quality and interpretation of their environmental gene surveys.     

Haplotype Estimation Using Sequencing Reads

High-throughput sequencing technologies produce short sequence reads that can contain phase information if they span two or more heterozygote genotypes. This information is not routinely used by current methods that infer haplotypes from genotype data. We have extended the SHAPEIT2 method to use phase-informative sequencing reads to improve phasing accuracy. Our model incorporates the read information in a probabilistic model through base quality scores within each read. The method is primarily designed for high-coverage sequence data or data sets that already have genotypes called. One important application is phasing of single samples sequenced at high coverage for use in medical sequencing and studies of rare diseases. Our method can also use existing panels of reference haplotypes. We tested the method by using a mother-father-child trio sequenced at high-coverage by Illumina together with the low-coverage sequence data from the 1000 Genomes Project (1000GP). We found that use of phase-informative reads increases the mean distance between switch errors by 22% from 274.4 kb to 328.6 kb. We also used male chromosome X haplotypes from the 1000GP samples to simulate sequencing reads with varying insert size, read length, and base error rate. When using short 100 bp paired-end reads, we found that using mixtures of insert sizes produced the best results. When using longer reads with high error rates (5–20 kb read with 4%–15% error per base), phasing performance was substantially improved.     

Effects of short read quality and quantity on a de novo vertebrate transcriptome assembly

For many researchers, next generation sequencing data holds the key to answering a category of questions previously unassailable. One of the important and challenging steps in achieving these goals is accurately assembling the massive quantity of short sequencing reads into full nucleic acid sequences. For research groups working with non-model or wild systems, short read assembly can pose a significant challenge due to the lack of pre-existing EST or genome reference libraries. While many publications describe the overall process of sequencing and assembly, few address the topic of how many and what types of reads are best for assembly. The goal of this project was use real world data to explore the effects of read quantity and short read quality scores on the resulting de novo assemblies. Using several samples of short reads of various sizes and qualities we produced many assemblies in an automated manner. We observe how the properties of read length, read quality, and read quantity affect the resulting assemblies and provide some general recommendations based on our real-world data set.     

Communities of Arbuscular Mycorrhizal Fungi Detected in Forest Soil Are Spatially Heterogeneous but Do Not Vary throughout the Growing Season

Despite the important ecosystem role played by arbuscular mycorrhizal fungi (AMF), little is known about spatial and temporal variation in soil AMF communities. We used pyrosequencing to characterise AMF communities in soil samples (n = 44) from a natural forest ecosystem. Fungal taxa were identified by BLAST matching of reads against the MaarjAM database of AMF SSU rRNA gene diversity. Sub-sampling within our dataset and experimental shortening of a set of long reads indicated that our approaches to taxonomic identification and diversity analysis were robust to variations in pyrosequencing read length and numbers of reads per sample. Different forest plots (each 10×10 m and separated from one another by 30 m) contained significantly different soil AMF communities, and the pairwise similarity of communities decreased with distance up to 50 m. However, there were no significant changes in community composition between different time points in the growing season (May-September). Spatial structure in soil AMF communities may be related to the heterogeneous vegetation of the natural forest study system, while the temporal stability of communities suggests that AMF in soil represent a fairly constant local species pool from which mycorrhizae form and disband during the season.     

Performance, accuracy, and Web server for evolutionary placement of short sequence reads under maximum likelihood

We present an evolutionary placement algorithm (EPA) and a Web server for the rapid assignment of sequence fragments (short reads) to edges of a given phylogenetic tree under the maximum-likelihood model. The accuracy of the algorithm is evaluated on several real-world data sets and compared with placement by pair-wise sequence comparison, using edit distances and BLAST. We introduce a slow and accurate as well as a fast and less accurate placement algorithm. For the slow algorithm, we develop additional heuristic techniques that yield almost the same run times as the fast version with only a small loss of accuracy. When those additional heuristics are employed, the run time of the more accurate algorithm is comparable with that of a simple BLAST search for data sets with a high number of short query sequences. Moreover, the accuracy of the EPA is significantly higher, in particular when the sample of taxa in the reference topology is sparse or inadequate. Our algorithm, which has been integrated into RAxML, therefore provides an equally fast but more accurate alternative to BLAST for tree-based inference of the evolutionary origin and composition of short sequence reads. We are also actively developing a Web server that offers a freely available service for computing read placements on trees using the EPA.     

Comparative Analysis of Functional Metagenomic Annotation and the Mappability of Short Reads

To assess the functional capacities of microbial communities, including those inhabiting the human body, shotgun metagenomic reads are often aligned to a database of known genes. Such homology-based annotation practices critically rely on the assumption that short reads can map to orthologous genes of similar function. This assumption, however, and the various factors that impact short read annotation, have not been systematically evaluated. To address this challenge, we generated an extremely large database of simulated reads (totaling 15.9 Gb), spanning over 500,000 microbial genes and 170 curated genomes and including, for many genomes, every possible read of a given length. We annotated each read using common metagenomic protocols, fully characterizing the effect of read length, sequencing error, phylogeny, database coverage, and mapping parameters. We additionally rigorously quantified gene-, genome-, and protocol-specific annotation biases. Overall, our findings provide a first comprehensive evaluation of the capabilities and limitations of functional metagenomic annotation, providing crucial goal-specific best-practice guidelines to inform future metagenomic research.     

Composition-based classification of short metagenomic sequences elucidates the landscapes of taxonomic and functional enrichment of microorganisms

Compared with traditional algorithms for long metagenomic sequence classification, characterizing microorganisms’ taxonomic and functional abundance based on tens of millions of very short reads are much more challenging. We describe an efficient composition and phylogeny-based algorithm [Metagenome Composition Vector (MetaCV)] to classify very short metagenomic reads (75–100 bp) into specific taxonomic and functional groups. We applied MetaCV to the Meta-HIT data (371-Gb 75-bp reads of 109 human gut metagenomes), and this single-read-based, instead of assembly-based, classification has a high resolution to characterize the composition and structure of human gut microbiota, especially for low abundance species. Most strikingly, it only took MetaCV 10 days to do all the computation work on a server with five 24-core nodes. To our knowledge, MetaCV, benefited from the strategy of composition comparison, is the first algorithm that can classify millions of very short reads within affordable time.     

Assessment of metagenomic assembly using simulated next generation sequencing data

Due to the complexity of the protocols and a limited knowledge of the nature of microbial communities, simulating metagenomic sequences plays an important role in testing the performance of existing tools and data analysis methods with metagenomic data. We developed metagenomic read simulators with platform-specific (Sanger, pyrosequencing, Illumina) base-error models, and simulated metagenomes of differing community complexities. We first evaluated the effect of rigorous quality control on Illumina data. Although quality filtering removed a large proportion of the data, it greatly improved the accuracy and contig lengths of resulting assemblies. We then compared the quality-trimmed Illumina assemblies to those from Sanger and pyrosequencing. For the simple community (10 genomes) all sequencing technologies assembled a similar amount and accurately represented the expected functional composition. For the more complex community (100 genomes) Illumina produced the best assemblies and more correctly resembled the expected functional composition. For the most complex community (400 genomes) there was very little assembly of reads from any sequencing technology. However, due to the longer read length the Sanger reads still represented the overall functional composition reasonably well. We further examined the effect of scaffolding of contigs using paired-end Illumina reads. It dramatically increased contig lengths of the simple community and yielded minor improvements to the more complex communities. Although the increase in contig length was accompanied by increased chimericity, it resulted in more complete genes and a better characterization of the functional repertoire. The metagenomic simulators developed for this research are freely available.     

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.     

The metagenome of a biogas-producing microbial community of a production-scale biogas plant fermenter analysed by the 454-pyrosequencing technology

Composition and gene content of a biogas-producing microbial community from a production-scale biogas plant fed with renewable primary products was analysed by means of a metagenomic approach applying the ultrafast 454-pyrosequencing technology. Sequencing of isolated total community DNA on a Genome Sequencer FLX System resulted in 616,072 reads with an average read length of 230 bases accounting for 141,664,289 bases sequence information. Assignment of obtained single reads to COG (Clusters of Orthologous Groups of proteins) categories revealed a genetic profile characteristic for an anaerobic microbial consortium conducting fermentative metabolic pathways. Assembly of single reads resulted in the formation of 8752 contigs larger than 500 bases in size. Contigs longer than 10kb mainly encode house-keeping proteins, e.g. DNA polymerase, recombinase, DNA ligase, sigma factor RpoD and genes involved in sugar and amino acid metabolism. A significant portion of contigs was allocated to the genome sequence of the archaeal methanogen Methanoculleus marisnigri JR1. Mapping of single reads to the M. marisnigri JR1 genome revealed that approximately 64% of the reference genome including methanogenesis gene regions are deeply covered. These results suggest that species related to those of the genus Methanoculleus play a dominant role in methanogenesis in the analysed fermentation sample. Moreover, assignment of numerous contig sequences to clostridial genomes including gene regions for cellulolytic functions indicates that clostridia are important for hydrolysis of cellulosic plant biomass in the biogas fermenter under study. Metagenome sequence data from a biogas-producing microbial community residing in a fermenter of a biogas plant provide the basis for a rational approach to improve the biotechnological process of biogas production.