Quality control of microbiota metagenomics by k-mer analysis
© Plaza Onate et al.; licensee BioMed Central. 2015
Received: 20 June 2014
Accepted: 26 February 2015
Published: 14 March 2015
The biological and clinical consequences of the tight interactions between host and microbiota are rapidly being unraveled by next generation sequencing technologies and sophisticated bioinformatics, also referred to as microbiota metagenomics. The recent success of metagenomics has created a demand to rapidly apply the technology to large case–control cohort studies and to studies of microbiota from various habitats, including habitats relatively poor in microbes. It is therefore of foremost importance to enable a robust and rapid quality assessment of metagenomic data from samples that challenge present technological limits (sample numbers and size). Here we demonstrate that the distribution of overlapping k-mers of metagenome sequence data predicts sequence quality as defined by gene distribution and efficiency of sequence mapping to a reference gene catalogue.
We used serial dilutions of gut microbiota metagenomic datasets to generate well-defined high to low quality metagenomes. We also analyzed a collection of 52 microbiota-derived metagenomes. We demonstrate that k-mer distributions of metagenomic sequence data identify sequence contaminations, such as sequences derived from “empty” ligation products. Of note, k-mer distributions were also able to predict the frequency of sequences mapping to a reference gene catalogue not only for the well-defined serial dilution datasets, but also for 52 human gut microbiota derived metagenomic datasets.
We propose that k-mer analysis of raw metagenome sequence reads should be implemented as a first quality assessment prior to more extensive bioinformatics analysis, such as sequence filtering and gene mapping. With the rising demand for metagenomic analysis of microbiota it is crucial to provide tools for rapid and efficient decision making. This will eventually lead to a faster turn-around time, improved analytical quality including sample quality metrics and a significant cost reduction. Finally, improved quality assessment will have a major impact on the robustness of biological and clinical conclusions drawn from metagenomic studies.
KeywordsMetagenomics Next generation sequencing Quality control Sampling bias Sample size limits
Analysis of human microbiota has in recent years unraveled a universe of intricate interactions between man and microorganisms with direct implications for health and disease [1-5]. A large proportion of commensal bacterial species are presently either highly fastidious or cannot be cultured in vitro. This has been a major obstacle to accurately describe the microbiota composition. Metagenomic analysis based on state-of-the-art next generation sequencing (NGS) along with sophisticated bioinformatics overcomes these barriers by analyzing complex samples ex vivo.
Quantitative metagenomic analysis creates a gene and species profile, which allows the identification and phylogenetic classification of known as well as novel genes and species. Arumugam and co-workers discovered 3 functionally distinct gut microbiota compositions designated “enterotypes” . Indeed, highly diverse consortia of commensals may functionally synergize to derive energy from nutrients in a highly coordinated and efficient manner. An imbalance of gut microbiota composition has been associated with a large range of pathologies, such as obesity , allergy and autoimmunity .
Although most studies make use of bacteria rich stool samples, a range of other body habitats with a much lower bacterial load is steadily gaining interest, such as vaginal, skin, oral and nasal body habitats . A recent study demonstrates that it is technically feasible to analyze microbiota composition in samples of poor genomic DNA quantity and quality, such as dental plaques of pre-historic skeletons . However, it is also increasingly clear that this type of analysis is often associated with strong biases, which are difficult to discern and complicated to correct . The increasing number of samples and the use of samples from sites of low microbial density augment the importance of speed and quality control of sample processing, sequencing and data analysis. A number of studies have addressed this need by developing bioinformatics tools to monitor and correct NGS errors. Errors in this context refers to direct sequence errors at the individual base level [10,11], but also the distribution and abundance of individual sequences including sequences derived from sample or technological contaminants [12-14]. We developed a novel method, which rapidly determines and quantifies the quality of metagenomic sequence distribution at the sample level. Metagenomic analysis of complex microbiota communities is particularly sensitive to errors in sequence distribution, because abundance measures of individual bacterial genes and strains are based on sequence distribution within a given sample.
The information density of bacterial genomes is higher than complex eukaryotic organisms, because they harbor much less non-coding nucleotides . Moreover, bacterial genome size is tightly linked with host symbiosis. Indeed, commensals with a long history of host symbiosis generally have small genome sizes as compared to more recent bacterial symbionts . The metagenome of human gut microbiota consists of approximately 1000 different bacterial genomes and therefore has a size of approximately 1 Gbp. Of note, no single bacterial strain surpasses an abundance of 0.5% of the total gut microbiota , emphasizing its highly diverse nature. We therefore hypothesize that contrary to genomes of individual bacterial strains  a metagenome of high diversity fragmented into short sequences of length k (k-mers), would be distributed uniformly if k is sufficiently small.
K-mers are regarded as strings of length k restricted to the 4-letter alphabet (A, G, C, T). They have been used to solve various problems, such as rapid comparison of DNA sequences , estimation of bacterial genome size  and phylogeny of double-stranded DNA viruses . We propose to introduce an automated k-mer distribution analysis of raw DNA sequences directly downstream of the deep-sequencing analysis. Practically, we count the occurrence of all k4 possible k-mers in the raw metagenomics sequence dataset (palindromic k-mers are aggregated when sequencing direction is arbitrary) and evaluate their distribution using a metric based on the information theory of Shannon .
Here we show that k-mer distributions of good quality metagenomic sequence data of complex gut microbiota samples are equally distributed unlike genomic sequences of individual bacterial species. We furthermore demonstrate that k-mer distribution is associated with the quality of the metagenomic data. Moreover, the Shannon Entropy of the k-mer distribution predicts the rate of sequence mapping to a predefined reference gene catalogue. Our approach analysis unprocessed raw sequences and may significantly facilitate the decision making of whether to 1) recollect, 2) reprocess a sample or 3) increase number of sequence reads before continuing with more extensive analysis. Moreover, it introduces a quality metric that may help validate conclusions made from metagenomic data.
Faecal sample collection and processing
Faecal samples from 30 human donors were collected in dedicated hermetically closed plastic containers kept anaerobically (oxygen poor and CO2 rich) with activated Anaerocult® A strips (Merck Millipore, Molsheim, France). Samples were aliquoted anaerobically and cryopreserved (−80°C) within 24 hours. Microbiota from 2.5g of stool were separated from the fecal matrix on an inverse Nycodenz® gradient under anaerobic conditions as previously described . The separation yielded an average of 1.59x1011 (95% confidence interval = [7.8x1010:3.2x1011]) purified microbial cells per sample. Undiluted as well as four 10xfold serial dilutions of microbiota were pelleted by centrifugation (3000xg for 10 minutes) and cryo-preserved as dry-pellets for subsequent DNA extraction.
Genomic DNA was extracted using two distinct but overlapping protocols for whole stool and gradient purified commensals, respectively. Whole stool samples were treated as previously described . Briefly, 200 mg of faecal sample was lysed chemically (guanidine thiocyanate and N-lauroyl sarcosine) and mechanically (glass beads) followed by elimination of cell debris by centrifugation and precipitation of genomic DNA. Finally, genomic DNA was RNase treated. DNA concentration and molecular size were estimated by Nanodrop (Thermo Scientific) and agarose gel electrophoresis. Gradient purified commensal samples were treated similar to whole stool samples with the exception that DNA precipitation was performed in smaller volumes and with extra-long incubation times.
Metagenomic library construction
DNA quantity used for serial dilution library constructions
Sample Size (10 x bacteria)
Purified dsDNA (ng/ml) 1
DNA for ligation (μg) 2
Purified dsDNA (ng/ml) 1
DNA for ligation (μg) 2
Metagenomic sequencing and data analysis
Microbiota gene content was determined by high-throughput SOLiD sequencing of total faecal DNA . An average of 34.3 million ± 36 million (mean ± s.d.) and 52.6 million ± 56.8 million 35-base-long single reads were determined for each sample from 10 dilution series samples and 52 whole stool samples, respectively (a total of 3.1 Gb of sequence). Raw sequences for all dilution series samples have been deposited in the European Bioinformatics Institute (EBI) European Nucleotide Archive (ENA) under the accession number PRJEB7925. By using Bowtie (version 1.0.0)  an average of 4.6 million ± 3.5 million and 13.8 million ± 15.4 million reads per individual from the two groups of samples, respectively, were mapped on the reference catalogue of 3.3 million genes  with a maximum of 3 mismatches. Reads mapping at multiple positions were discarded and an average of 3.6 million ± 2.7 million and 13.0 million ± 14.7 million uniquely mapped reads per individual from the two sample groups, respectively, were retained for estimating the abundance of each reference gene by using METEOR software . Abundance of each gene in an individual was normalized with the method coined Reads Per Kilobase per Million (RPKM) as previously described . Briefly, gene abundance was determined as the number of reads that uniquely mapped to a defined gene. Subsequently, normalized gene abundances were transformed in frequencies by dividing them by the total number of uniquely mapped reads for a given sample. The resulting microbial gene profile was used for further analyses.
Bacterial genome sequences
28 bacterial genomes from a range of species covering common human commensals were extracted from the collection of available reference genomes from NCBI (cf. Additional file 1: Table S1).
The abundances of all overlapping k-mer sequences present in a set of whole-genome shotgun short-read sequences were counted with in-house developed C++ software (www.mgps.eu/people/fplaza/) optimized for small k, which supports colour space reads and the CSFasta file format as input. Sequence reads with missing colour cells were discarded and remaining reads were trimmed to 35 bases. K-mer analysis of bacterial genomes was conducted with Jellyfish version 1.1 (http://www.cbcb.umd.edu/software/jellyfish/). The frequencies of different k-mers at each abundance value contained in a set of sequences are plotted as a k-mer abundance histogram. A repeated sequence in a sampled genome affects the shape of these k-mer abundance spectra depending on its length and copy number. A DNA sequence of length l will contain (l – k +1) different k-mers if it does not contain repeats of length greater than k–1.
Each k-mer has a reverse complement. E.g. the complement of 4mer ATTC is GAAT. Note that some k-mers are their own reverse complement (e.g. AGCT) if and only if k is even. Since the shot-gun short-read sequencing technology applied does not differentiate according to sequence orientation, we apply a “canonical representation”, which consider k-mers and their reverse complement equivalent (e.g. the 4-mers ATTC and GAAT are grouped together).
If the same sequence occurs n times in a genome, shotgun sequencing would sample k-mers from this sequence n times more often than those that occur in a single-copy (also referred to as average read depth). Therefore, repeated sequences in the genome result in higher abundances of associated k-mers. These collections of k-mers at higher-than-normal abundances appear as multiple peaks at different positions along the x-axis of the k-mer abundance histogram.
Hierarchical cluster analysis
Agglomerative hierarchical cluster analysis of k-mer distributions of individual bacterial genomes performed according to Ward’s minimum variance method  was accomplished using JMP7 software (SAS Software, NC, USA). The optimal number of clusters was identified according to the largest distance change between successive junctions of the associated dendrogram plot. Validity and reproducibility of the classification obtained with hierarchical cluster analysis was assessed using non-hierarchical k-means cluster analysis, in which the optimal number of clusters identified through hierarchical cluster analysis was pre-specified. Reproducibility of the classifications obtained with both hierarchical and non-hierarchical clustering was assessed by determination of the kappa value.
The study was conducted in accordance with the Declaration of Helsinki. Human stool samples were obtained following acquisition of the study participants’ written informed consent and the study protocol was reviewed and approved by local ethics committee of Pitié-Salpêtrière Hospital, Paris (“Les Comités de protection des personnes”).
Spearman’s rank correlation was calculated using the R project (http://www.R-project.org, Vienna, Austria). P-values < 0.05 were considered statistically significant.
Results and discussion
K-mer distribution of complex microbiota is homogenous irrespective of bacterial composition
Moreover, individual bacterial genomes aggregated into 6 clusters defined by their k-mer distribution using agglomerative hierarchical cluster analysis (Figure 1D). The clusters were validated with a non-hierarchical K-means cluster analysis. The agreement between the two clustering techniques was good as defined by Cohen’s kappa agreement value (κ = 0.48). Interestingly, the identified clusters are associated with the phylogeny of the bacteria and can be used to evaluate taxonomic relations, as previously suggested . Deductions from this result suggest that 4-mer analysis of metagenomes of complex bacterial mixtures can be decomposed into a linear regression of k-mer distribution vectors of individual bacteria genomes and a residual, which would represent the component unexplained by known bacterial genomes. In other words, this type of analysis could identify novel bacterial species and potentially elucidate their phylogenetic descent. This approach is beyond the scope of the present study.
Quantitative metagenomic analysis of serially diluted gut microbiota identifies lowest analyzable sample size limit
K-mer distribution analysis of metagenomic sequences identifies the same lower sample size limit as quantitative metagenomic analysis
These observations demonstrate that metagenomic quality, as defined by the capacity to precisely and robustly define gene distributions of microbiota, can be predicted by a k-mer distribution analysis of metagenomic raw sequences. It is however not clear if the skewed k-mer distribution observed for the highest sample dilutions (corresponding to low quality metagenomes) is due to aberrant bacterial gene sequences, as observed by correlative analysis of mapped reads (Figure 2), or due to concomitant non-mappable sequences similar to but distinct from the barcode-cassette sequences discussed above. We therefore filtered raw metagenome sequences to only contain mappable sequences. 4-mer analysis revealed an almost equal distribution of 4-mers for all dilution series metagenomes (Additional file 1: Figure S3A) resulting in very similar Shannon-Entropy for 4-mer distributions of all samples (Additional file 1: Figure S3B). Equally, k-mer frequencies correlated perfectly between dilution series samples from the same donor (Additional file 1: Figure S3C). The predictive features of the k-mer analysis are therefore relying on a secondary but concomitant degradation of sequence quality and distribution.
K-mer distribution predicts metagenomic sequence mapping to a reference gene catalogue
The metagenomic protocol employed in the present study enabled analysis of samples containing more than 108 bacteria (1000-fold dilution). This lower limit fits most live habitat derived microbiota, whereas e.g. analysis of dental plaques from skeletons  or other low density microbiota habitats, may be inherently biased in gene and/or species distribution due to limiting sample size. Our study suggests that for these studies it is important to validate the employed metagenomic protocol (e.g. by analyzing a serial dilution of a known quantity of commensals) as described here. Of note, the present study monitors the gene distribution of microbiota. It is likely that reducing the zoom from gene to a given phylogenetic level would equilibrate a large amount of the variance observed at the gene distribution level of low quality metagenomic datasets.
Our study demonstrates that a k-mer distribution analysis of metagenomic raw sequence reads identifies metagenomes of low quality and predicts low gene mapping efficiency. Low quality metagenomes were defined as metagenomes for which the gene distribution was considerably different from a reference sample. In the present study this was modelled by concentrated versus dilute samples of two stool samples. Metagenome quality was lowered by a significant reduction of sample size. It remains to be validated if the technology would also apply to metagenomes suffering from e.g. technical biases or contaminations.
We propose that k-mer analysis of raw metagenome sequence reads should be implemented as a first quality assessment of raw NGS data prior to filtering and gene mapping analysis. It would allow a qualified decision as to whether 1) obtained metagenomic dataset should be further analyzed (filtering, gene mapping etc.), 2) if more sequence reads should be acquired to surpass a predetermined threshold of mapped reads or 3) sample should be discarded or reprocessed to improve metagenomic quality. With the rising demand for metagenomic analysis of microbiota it is crucial to provide tools for rapid and efficient decision making. This will eventually lead to a faster turn-around time, higher quality analysis including measurable quality metrics and a significant cost reduction. Finally, increased quality would have a major impact on the robustness of biological and clinical conclusions drawn from metagenomic studies.
The authors acknowledge the funding agencies and the volunteers providing samples for the study. The study was funded by INSERM, the University Pierre et Marie Curie ËMERGENCE” program, Fondation pour l’Aide a la Recherche sur la Sclerose En Plaques (ARSEP), ARTHRITIS Fondation COURTIN and Agence nationale de la recherché (ANR). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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