Title Unsupervised binning of environmental genomic fragmentsbased on an error robust selection of l-mers

Background: With the rapid development of genome sequencing techniques, traditional research methods based on the isolation and cultivation of microorganisms are being gradually replaced by metagenomics, which is also known as environmental genomics. The first step, which is still a major bottleneck, of metagenomics is the taxonomic characterization of DNA fragments (reads) resulting from sequencing a sample of mixed species. This step is usually referred as “binning”. Existing binning methods are based on supervised or semi-supervised approaches which rely heavily on reference genomes of known microorganisms and phylogenetic marker genes. Due to the limited availability of reference genomes and the bias and instability of marker genes, existing binning methods may not be applicable in many cases. Results: In this paper, we present an unsupervised binning method based on the distribution of a carefully selected set of l-mers (substrings of length l in DNA fragments). From our experiments, we show that our method can accurately bin DNA fragments with various lengths and relative species abundance ratios without using any reference and training datasets. Another feature of our method is its error robustness. The binning accuracy decreases by less than 1% when the sequencing error rate increases from 0% to 5%. Note that the typical sequencing error rate of existing commercial sequencing platforms is less than 2%. Conclusions: We provide a new and effective tool to solve the metagenome binning problem without using any reference datasets or markers information of any known reference genomes (species). The source code of our software tool, the reference genomes of the species for generating the test datasets and the corresponding test datasets are available at http://i.cs.hku.hk/~alse/MetaCluster/. Background Microbes are essential for almost every process in the biosphere and for every part of human life in both the positive and negative sense, for example, the production of yoghurt with Lactobacillus and alcohol brewing with yeast as well as the fatal pathogen of pulmonary tuberculosis and cholera. The impact of microbes on humans is not limited to several kinds of species. The complex environment of human life is maintained by microbial communities which are composed of dozens to thousands kinds of individual microbes. The unbalance or abnormal diversity of these microbial communities is proved to be associated with common diseases like pericementitis [1] and gastrointestinal [2] disturbance. Understanding how microbial community diversity affects health and disease may contribute to better diagnosis, prevention, and treatment of diseases. During the last centuries, researches on microbes have been based on the isolation, cultivation and purification of * Correspondence: byang@cs.hku.hk; chin@cs.hku.hk State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, Jiangsu, 210096 PR China Department of Computer Science, The University of Hong Kong, Pok Fu Lam Road, Hong Kong Yang et al. BMC Bioinformatics 2010, 11(Suppl 2):S5 http://www.biomedcentral. com/1471-2105/11/S2/S5 © 2010 Yang and Chin; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. individual microorganism from complex communities. But 99% of all microbial diversity in the biosphere, as yet, is uncultivable [3], because of the highly artificial and limited number of conditions used currently for cultivation. For the uncultivable majority of the microbial communities, rapidly developing genome sequencing techniques can help. Metagenomics, which is also known as the environmental genomics, applies the shotgun sequencing technique to mixed genome samples, obtained directly from an environmental sample or series of related samples, producing high-throughput randomly sampled DNA fragments of these genome samples [4]. Since 2004, several metagenomics sequencing projects have been successfully implemented, such as Acid Mine Drainage Biofilm (AMD) for dozens of species [5] and the recent Human Gut Microbiome (HGM) for more than thousands of species [6]. Different from traditional single genome sequencing researches where all DNA fragments are coming from one single species, the metagenomics sequencing dataset contains DNA fragments from different species where most of their genomes are unknown. The data analysis process of metagenomic sequencing (MS) dataset requires an additional analyzing step, called “binning” [7]. The binning step assigns the DNA fragments to the taxonomy tree which provides a general map of the microbe distribution of the mixed sample, basically answering the essential question of metagenomic research: what’s in the mix? Various phylogenetic resolution or taxonomical rank of binning from high level such as kingdom to low level such as genus depends on the research requirements and the quality of the MS dataset. A number of currently available binning methods fall into two broad categories: sequence similarity-based and sequence composition-based. The first, for example based on BLAST [8], classifies fragments based on the distribution of BLAST hits of phylogenetic specific marker genes to taxonomic classes [9]. The application of this kind of method is limited due to the limited availability of reference genomes of known microorganisms. As mentioned above, less than 1% of all microorganisms can be cultured and sequenced today. So more generic features such as structure and composition form the basis of methods developed to distinguish components from mixed sequencing dataset in a supervised or semi-supervised manner. In general, these methods extract the composition features of reference genomes or marker regions (e.g. the widely applied fingerprint gene 16S rRNA [10], recA and rpoB). Then, a classifier is generated based on different machine learning methods like SVM or SOM with the selected training dataset. For semi-supervised methods, the marker information of the testing datasets is translated to additional constraints during the clustering or classification process. Compared to the sequence similarity-based methods, sequence composition-based methods achieve better performance. But there are still limitations on these approaches; For example, not all species inside the sample carry the known phylogenetic markers. According to several metagenomic projects, such as the enhanced biological phosphorus removing (EBPR) sludge [11], Sargasso Sea [4] and the Minnesota soil samples [12], only 0.17%, 0.06% and 0.017% of the contigs (fragments) respectively are known to carry 16S rRNA markers. Even if we consider other markers such as recA and rpoB, less than 1% of the fragments could be identified. Also, some species may share multiple markers with other species, which leads to incorrect classifications. For example, according to recent reports, multiple kinds of 16S rRNA molecules can exist in a single bacterium [13]. Moreover, the marker gene information is provided by the existing cultivation and isolation techniques with limitation and bias in the selection of specific microorganisms. The training datasets with the bias caused by technical limitation will also introduce bias into the classification and clustering results. These two categories of methods suffer from the limitation or instability of different kinds of reference information and not much research has been done for the clustering DNA fragments from species without genomes information [14]. To address this issue, we propose an unsupervised method for clustering DNA fragments based on l-mer (short DNA substrings of length l in the fragments) distribution [15-19]. Previous research of this compositional signature has shown that species belonging to different categories, even down to genus, tend to be quite distinct in terms of the l-mer frequency of their whole genomes or genome fragments [15,20]. So by comparing the l-mer distribution of fragments, we may be able to bin the DNA fragments into the correct taxonomical groups. Figure 1(A) shows the l-mer distributions of two species in the same genus while Figure 1(C) shows the l-mer distributions of two species in different genuses. The distributions are similar in 1(A) and are quite different in 1(C). However, the difficulty of applying l-mer distribution lies in the resulting high-dimensional data. The l-mer frequency of each input DNA fragment is usually represented by a feature vector with 4 components. Consider the palindrome reverse and complement DNA string in the sequencing datasets, the dimension of the l-mer vector could be decreased to about 4/2. Details of the generation of the l-mer vectors will be described late in the Methods Section. When l ≥ 4, the dimension of the feature vector is very large (i.e. when l = 4, the dimension of the feature vector is 136) and the clustering problem Yang et al. BMC Bioinformatics 2010, 11(Suppl 2):S5 http://www.biomedcentral. com/1471-2105/11/S2/S5 Page 2 of 11