An overview of our methodology is shown in Figure 1. Community genomic sequence was obtained from two previously described biofilm samples from the UBA location of the Richmond Mine at Iron Mountain: a pink subaerial biofilm collected in June 2005 ('UBA') 55 and a thicker floating biofilm collected in November 2005 ('UBA BS') 12. These two biofilms contained overlapping subsets of organisms in different proportions. The UBA biofilm was dominated by bacterial Leptospirillum spp. group II and group III (Nitrospirae) populations, for which near-complete genomes have been reconstructed 5556. The most abundant microorganisms represented in the UBA BS genomic data were from archaeal populations, including an uncultivated representative of a novel euryarchaeal lineage, ARMAN-2 20, and A-plasma, E-plasma, and I-plasma, members of the order Thermoplasmatales. To facilitate reconstruction of genomes from these and other lower-abundance organisms, a combined assembly included unassigned sequences from UBA and all sequences from UBA BS. Random shotgun sequences derived from both ends of approximately 3-kb DNA fragments, and each fragment was likely sampled from a different individual cell with a potentially distinct genome sequence. Therefore, genome reconstructions represent composite sequences. However, single nucleotide polymorphism density was typically very low (< 0.3%). For a small subset of the many cases where there were subpopulations with different gene content, alternative genome paths were also reconstructed 955.

From the combined dataset, near-complete genomes were reconstructed for ARMAN-2, I-plasma, E-plasma, G-plasma, and A-plasma (Table 1). In addition to sequences that were assigned to these deeply sampled genomes, 14,700 sequences remained unassigned to any organism, including 7,030 contigs longer than 1.4 kb and 3,631 contigs longer than 2.0 kb. A number of shallowly sampled 16S rRNA gene-containing sequence fragments were recovered, indicating substantial sampling of diverse lower-abundance community members (Figure 2).

We constructed a dataset that contained all sequences from the combined assembly (assigned and unassigned), previously assembled composite genome sequences, and the genome sequence from Ferroplasma acidarmanus fer1, which was cultivated from AMD solutions in the Richmond Mine 857 (Figure 1, Table 1). To analyze the distribution of genome signatures among and between populations, all contigs and assembled genomes were fragmented into 5-kb pieces, then pooled and clustered by self-organizing map (SOM) 58 based on tetranucleotide frequency distributions (Figure 1; see Materials and methods for details). The SOM is an unsupervised neural network algorithm that clusters multidimensional data and represents it on a two-dimensional map. SOMs of tetranucleotide frequencies have been used previously to successfully bin sequence fragments from isolate genomes 3359 and some environmental samples 464852. We utilized an implementation of the SOM, emergent SOM (ESOM), which is distinguished by its use of large borderless maps (for example, thousands of neurons) and visualization of underlying distance structure with background topography 60. This visualization, where map 'elevation' represents the distance in tetranucleotide frequency between data points, is referred to as the U-Matrix 60. Thus, genomic clusters were visualized not only by the cohesive clustering of fragments from each genome, but also by distance structure whereby barriers between clusters represent the large differences in genome signatures between genomes relative to those within genomes (Figure 3). This visualization of genomic clustering was used to evaluate the accuracy of the binning based on assembled genomes and to identify novel regions of sequence signature space.

Inspection of the clustering results in light of assembly information provided a broad measure of the ability of tetranucleotide frequency-based ESOM (tetra-ESOM) to resolve sequences from coexisting populations of the community. To quantify the degree of segregation of fragments from genomes at various evolutionary distances, we adapted a method using fixed point kernel densities (Figure 4; Additional data file 1). We found that sequence fragments from closely related strains or species could not be distinguished. For example, two strains of F. acidarmanus sharing 97% average nucleotide identity (fer1 and fer1(env) 8) mapped directly on top of each other, as did two types of Leptospirillum group II, which share 95% average nucleotide identity 55 (only one type of Leptospirillum group II is shown in Figure 3 for this reason; Figures 3 and 4). Sequences from Ferroplasma types I and II, which share 83% average nucleotide identity and are known to participate in homologous recombination 10, were segregated to some extent by tetra-ESOM, but type II was split and there was no well-defined boundary between the two types. Good separation of Leptospirillum groups II and III was achieved, except for certain genomic regions containing mobile elements, as described further below. Among members of the Thermoplasmatales, populations were distinguished by genome signatures but borders were variably well-defined (Figure 3). In particular, G- and E-plasma were not well resolved. I-plasma, which is quite divergent from the other Thermoplasmatales (Figure 2), was the only member of the Thermoplasmatales for which a distance-based border was clearly delineated. Although genomes with similar %GC were generally more difficult to separate, several genomes with near-identical %GC were easily separated (for example, G-plasma versus Ferroplasma) (Figures 3 and 4).

To quantitatively evaluate binning performance on sequence fragments of different lengths, tetra-SOMs were run on the same dataset (including unassigned sequences and reconstructed composite genomes) but with sequences broken into various fragment sizes. Binning accuracy was calculated for a subset of genomes for which deeply sampled and manually curated assemblies are available (Additional data file 2). For sequence fragments 5 kb or larger, sensitivity (percentage of fragments from each genome correctly identified) and precision (percentage of fragments in each bin belonging to the correct genome) rates of > 90% were achieved (Additional data file 2). Sensitivity was somewhat lower for Leptospirillum groups II and III due to poor resolution of certain genomic regions between these two populations. When Leptospirillum was considered as a single group, binning sensitivity was comparable to the other reference genomes. Sensitivity decreased notably only when shorter (< 5 kb) sequence fragments were analyzed, but precision remained remarkably high even for 1,400-bp fragments (Additional data file 2). Lower sensitivity is due to sequence fragments that fall between clusters, beyond the borders of any bin. Notably, the tetra-ESOM correctly assigned sequence fragments as short as 500 bp, provided that some larger fragments were included in the analysis (Additional data file 2b). To address the question of how genome completeness influences performance, genomes randomly subsampled at different levels were analyzed by tetra-ESOM. Binning accuracy was maintained even at 20% genome sequence; only at 10% subsampling was a notable decline observed, and even then only for certain genomes (Additional data file 3).

Incorrectly assigned fragments often contained mobile elements or other features expected to have atypical nucleotide composition. The majority (54 of 94) of incorrectly binned fragments from all five reference genomes show evidence of transposons, prophage, or integrated plasmids. Other frequently unresolved genomic regions contain CRISPR elements 61 and rRNA genes, both of which have constrained sequences and thus atypical tetranucleotide patterns 62. The region of the ESOM map containing a mixture of Leptospirillum groups II and III (Figure 3) was dominated by fragments (80 of 92) encoding mobile elements that may be exchangeable between the two Leptospirillum groups (for example, integrated plasmid-like sequence 56) and strain/group-unique regions believed to have been recently acquired (for example, prophage).

Interestingly, many strain-unique regions were correctly binned with their host genomes. There are 197 strain-unique genes between the fer1 and fer1(env) genomes, the majority of which occur in distinct genomic blocks of up to 24 genes with atypical %GC content inferred to be the result of prophage insertion 8. Ninety-six percent (22 of 23) of sequence fragments containing these genomic islands were accurately assigned as Ferroplasma in our binning analysis.

The tetra-ESOM revealed large regions of the map that were devoid of sequence fragments of known organism affiliation (Figure 3, regions 11 to 17). We used mate pair linkage with rRNA gene-containing contigs, phylogenetic analysis, and/or close relatedness (synteny and identity) to other community members to identify these bins as follows: a new type of Leptospirillum most closely related to Leptospirillum ferrodiazotrophum (group III); several members of the Thermoplasmatales for which genomic sequence had not been previously obtained (C-plasma, D-plasma, and a divergent type of A-plasma); several Actinobacteria; and multiple more shallowly sampled populations, including a gammaproteobacterium and several Sulfobacillus-like organisms (Figures 2 and 3). A small, prominent region of the map adjacent to the Leptospirillum groups contained approximately 250 kb of composite sequence (Figure 3, region 11) inferred to be a Leptospirillum plasmid 56. Tetranucleotide usage patterns of this putative plasmid are quite distinct from those of either Leptospirillum groups (Additional data file 4).

We calculated tetranucleotide frequencies for viral genomes that were recently reconstructed from the same genomic datasets and linked to their hosts via CRISPR viral resistance system sequences (Additional data file 4) 12. Three of the viruses closely resemble their hosts' tetranucleotide usage (AMDV1, Leptospirillum groups II and III; AMDV4, E-plasma; AMDV3, A-/E-/G-plasma), a trend that has been observed previously for cultivated viruses and hosts 4163. Interestingly, two viruses have very different tetranucleotide frequency patterns (AMDV2, E-plasma; AMDV5, I-plasma; Additional data file 4).

As expected, the frequency at which each tetranucleotide occurs is related to overall %GC: GC-rich tetranucleotides are abundant in high-GC genomes and uncommon in low-GC genomes. However, patterns of tetranucleotide usage extend beyond trends in %GC (Additional data file 4) and genomes with near-identical %GC were effectively segregated by tetra-SOM. Because tetranucleotide frequencies are calculated with a 1-bp sliding window and reverse complementary pairs of tetranucleotides are summed together, all possible reading frames on both strands are sampled. In addition to spanning complete single codons, adjacent pairs of partial codons are also sampled (Figure 5). Therefore, tetranucleotide frequency captures amino acid composition and synonymous codon usage, as well as information regarding avoidance of certain adjacent codons ('codon pair bias' 64).

To assess the contributions of these potential sources of genome signature signal, we compared SOMs based on amino acid composition, codon composition, and tetranucleotide frequency. Amino acid composition alone distinguished certain genomes (Additional data file 5). This was especially true for phylogenetically distant organisms (for example, archaea versus bacteria), but some separation was also apparent among groups within some lineages such as Ferroplasma versus other Thermoplasmatales. SOMs based on codon composition were notably more accurate than amino acid composition and comparable to those based on tetranucleotide frequency (Additional data file 5).

Additional features of the relationship between codon composition and tetranucleotide frequency were revealed by comparing the observed frequency of tetranucleotides to the frequency predicted from genome-wide codon usage (see Materials and methods). Observed and predicted tetranucleotide frequency correlated strongly (Figure 6), and differences in the frequencies of individual tetranucleotides between genomes are correlated with differences in corresponding codon usage between genomes (Additional data file 6). Exceptions to this trend are primarily palindromic tetranucleotides that occur less frequently than predicted (Figure 6b). Five of the 16 possible palindromic tetranucleotides are most strongly and consistently underrepresented: AATT, ATAT, TATA, GATC, and GGCC. The extent to which palindromic tetranucleotides are avoided in both viral and microbial genomes varies significantly and thus could be a factor in defining genome signatures (Additional data file 4). To test this possibility, we visualized the SOM distance structure for only one tetranucleotide at a time and found that certain palindromic tetranucleotides (GATC, TATA, ATAT) are particularly informative in distinguishing members of the Thermoplasmatales that share near-identical %GC (Ferroplasma types I and II, G-plasma, E-plasma). However, SOMs run excluding all 16 palindromic tetranucleotides distinguished populations with accuracy comparable to that achieved using all tetranucleotides, indicating that palindrome avoidance is not a primary component of the genome signature.

The correlation of genome signatures with codon usage raises the question of whether they persist in intergenic regions. Thus, we extracted intergenic regions from assembled and annotated genomes and analyzed them with coding regions by tetra-ESOM (intergenic regions were concatenated to tally tetranucleotide frequencies but care was taken to avoid artifacts; see Materials and methods). Intergenic regions from each genome formed discrete, cohesive clusters that mapped adjacent to coding regions from the same genome but were separated by U-Matrix boundaries (Additional data file 7). Intergenic sequences from each genome were grouped based on length, concatenated, and analyzed by ESOM; all size classes of intergenic regions from the same genome clustered together regardless of length, from the shortest (4 to 20 bp) to longest (> 1,000 bp) (data not shown). The noncoding complement of each Thermoplasmatales genome formed a distinct cluster adjacent to noncoding regions of the other Thermoplasmatales. The only outlier to this trend was A-plasma, which has the highest %GC among these organisms. Based on U-Matrix background, the distance between noncoding sequences of different genomes is comparable to the distance between noncoding and coding sequences of the same genome. To determine if the presence of noncoding sequence influences binning accuracy in the initial experiments, we calculated the percentage of coding sequence on incorrectly binned fragments from the five reference genomes (5 kb and 1 kb window sizes). For many genomes, the incorrectly binned fragments do indeed have a smaller average percentage of coding sequence. However, this percentage varied widely on incorrectly binned fragments. Only a small fraction of such fragments had a percentage of coding sequence smaller than one standard deviation below the genome-wide average (Additional data file 8).

For sequence signatures to differentiate populations in a genome-wide manner, it is necessary that within-genome differences resulting from atypical regions of amino acid and/or synonymous codon usage are smaller than between-genome differences. This issue is especially relevant in AMD, where proteins are under diverse constraints depending on whether they function in the extracellular (around pH 1) or intracellular (around pH 5) environment 65. Indeed, proteins from the AMD populations in these two fractions have disparate isoelectric points owing to the unique amino acid composition of acid-stable proteins 66. We identified 106 Leptospirillum group II-UBA proteins that are consistently enriched in the extracellular fraction according to environmental shotgun proteomics data 5566 and compared sequence signatures of their genes with the other 2,522 Leptospirillum group II genes. No systematic differences were detected via tetra-ESOM, suggesting that genome signatures persist even when gene sequences are influenced by considerable protein-coding constraints (Additional data file 9).

Selection for codons that optimize translation rate may also influence codon usage. We analyzed genome signatures for the 50 Leptospirillum group II proteins most abundantly detected via environmental shotgun proteomics 5566. With the exception of one subset of genes encoding mainly ribosomal proteins (which mapped into the mixed region between Leptospirillum groups II and III), highly expressed genes clustered with the rest of the genome (Additional data file 9).

It has been suggested that environmental constraints strongly shape nucleotide composition 26495051. If this were the case, two effects should be apparent in genome signatures of AMD populations. First, shared pressures deriving from the extreme AMD environment would drive genome signatures together, potentially obscuring differences between populations. Second, since each genome encodes proteins destined for diverse environments (that is, intracellular and extracellular), there should be prominent intra-genome variation of genome signature and scattering of fragments from the same genome into disparate regions of the SOM. Neither of these expectations is met in the AMD dataset. There are vast differences in nucleotide composition between populations, with genomic %GC ranging from 35% (ARMAN-4 and ARMAN-5) to 69% (low-abundance Actinobacteria) and genome signatures forming discrete clusters. Amino acid compositional constraints required for stability of proteins exposed to acidic solutions do not result in sequence signatures that are markedly distinct from the rest of the genome. In other words, within-population differences in genome signature are small relative to differences between populations. Although we do not rule out some environmental influence on genome signatures, we conclude that, in AMD, this influence is not strong enough to obscure differences between populations. Similar community-wide analyses need to be conducted in other systems to determine whether our findings extend to other extremophilic microbial communities.

Our results show that genome signatures are related to several traits, including %GC, amino acid composition, synonymous codon usage, and palindrome avoidance. These characteristics are interrelated and further connected to a host of biochemical, ecological, and evolutionary processes (Additional data file 10). Large differences in %GC and/or amino acid composition guarantee distinctive genome signatures but are not required to differentiate genomes. At finer evolutionary scales, where %GC and amino acid composition are not informative, populations can be readily distinguished through subtle differences in tetranucleotide frequency, which correlate with genome-specific synonymous codon usage. Tetra-ESOM analyses based on codon usage and tetranucleotide frequency displayed similar clustering resolution, indicating that little signal derives from longer-range characteristics such as codon pair bias. It should be noted, however, that using tetranucleotide frequency rather than codon composition has practical advantages for binning because it is independent of coding strand and reading frame and thus insensitive to errors in gene-calling or frame shifts due to poor quality sequence. These issues are particularly important for short, low-coverage sequence fragments.

Although genome signatures are largely manifested through codon composition, the observation that population-specific signatures also occur in non-coding regions (Additional data file 7) suggests a mechanism of generation that is independent of protein coding. We hypothesize this underlying process is mutational bias associated with DNA replication and repair, which exerts directional pressure on nucleotide composition 24. In fact, between-genome codon biases can be predicted solely by %GC and context-dependent nucleotide biases (that is, mutation rates at each site are dependent on the identity of neighboring nucleotides) calculated from non-coding regions 6768. It is interesting to note that non-coding regions mapped into discrete clusters, distinct from coding regions of the same genome or non-coding regions of different genomes, including those with identical %GC. Differences in genome signature of coding and non-coding sequences from the same genome are to be expected based on differing functional constraints on these regions (for example, coding amino acids versus small RNAs or regulatory elements such as promoters). The distinction of non-coding regions from different genomes is consistent with genome-specific mutational biases.

An alternative to the mutation bias hypothesis, at least for coding sequences, is that genome signatures are shaped by factors related to translation. Changes in codon usage can be driven by changes in the tRNA gene complement 6970 that may occur, for example, through interaction with plasmids and viruses 71. However, we found AMD genomes with distinct genome signatures, such as G-plasma, E-plasma, and Ferroplasma, that have only minor differences in tRNA gene content, and these differences do not correspond to observed differences in codon usage. In addition to tRNA gene complement, there may be changes in tRNA gene regulation, which can significantly impact cellular tRNA concentrations and have been correlated with changes in codon usage 72. Thus, although we cannot rule out a tRNA regulatory influence on genome signatures, our findings suggest that coevolution of tRNA gene content and codon usage is not a primary mechanism underlying the divergence of genome signatures in related AMD populations.

Codon bias can also arise as the result of selection for certain codons that are optimal for fast and/or accurate translation 73. This form of codon bias primarily influences the subset of genes encoding highly expressed proteins, is prevalent for fast-growing organisms 6974, and correlates with ecological strategy 75. In fact, a Leptospirillum group II genome fragment encoding nine ribosomal proteins and two translation elongation factors had distinctive tetranucleotide composition, indicating that this mode of codon bias occurs in AMD organisms. However, as commonly construed, translational selection would influence within-genome codon bias, not the genome-wide codon biases that differentiate populations as observed in our study. It is tempting to speculate that differences in ecological strategy (for example, response rate to resource availability 76) could have genome-wide influence on codon usage, but there is currently no evidence in our dataset to suggest that this is the case.

Finally, restriction avoidance places another selective genome-wide constraint on DNA composition that may contribute to genome signatures. Under-representation of palindromic tetranucleotides (Figure 6) has been attributed to avoidance of enzymes designed to recognize and degrade foreign DNA 223246. Our data show that palindrome avoidance contributes to the genome signature but is not the sole or even primary determinant. Most archaeal viruses and bacteriophage have sequence signatures that resemble their hosts, including avoidance of specific subsets of palindromes. However, mismatches between the tetranucleotide signatures of AMDV2 and AMDV5 and their respective hosts point to the lesser importance of palindrome avoidance in these organisms. In the case of AMDV5, other evidence suggests a recent alteration in host range 12. It is interesting to note that the genomes of archaeal AMD viruses encode several restriction modification (RM) system genes. These may have significance for virus host-interactions 77 and for influencing genome signatures. Broad host range viruses or viruses that jump to new hosts can potentially drive changes in the host sequence signatures if they replace or supplement the restriction systems of the host. Alternatively, the degree of similarity in tetranucleotide signatures of viruses and their hosts may be a function of the extent to which the virus relies upon its host's replication and translation machinery (for example, associated with a lysogenic versus lytic lifestyle) 414263.

Due to the high levels of diversity in most natural systems, random sequencing approaches yield fragmentary data, often comprising genomic sequences no more than a few kilobases in length. While more comprehensive coverage of individual organisms can be achieved by single cell genomics 787980 or targeted, large-insert approaches 8182, random shotgun approaches retain two important advantages: the random nature provides insights that are unbiased by preconceived notions of community composition; and population-level variation is captured because each sequencing read derives from a different individual cell.

A key challenge for virtually all shotgun metagenomics investigations is the assignment of genome fragments to the organism they derive from. This step links organism to metabolism and function and is essential if we are to understand microbial community dynamics and predict ecosystem level impacts of changes in community membership and structure. Binning is particularly challenging for lower-abundance organisms, which may play keystone roles that are critical to ecosystem function. Thus, our finding that tetra-ESOM can resolve the phylogenetic affiliation of genome fragments on the scale of two mate-paired reads is of great significance. This approach has clear applicability to low-complexity datasets such as those derived from our AMD biofilms, bioreactors 83, and enrichment cultures 84. In fact, even for the relatively extensively analyzed AMD dataset, it revealed multiple new genomic clusters, including a near complete genome of a novel actinobacterium (GJ Dick et al., in preparation), a putative plasmid, and many discrete but less well-sampled populations.

Tetra-ESOM may also provide a powerful method for analysis of unassembled data from complex samples such as soil, seawater, and the human microbiome if representative isolate genomes are available. The feasibility of binning metagenomic sequences from complex samples using reference genomes will increase with current initiatives to fill in the phylogenetic tree with genome sequences from cultivated microorganisms.

An important advantage of unsupervised, compositional-based approaches such as tetra-ESOM is that gene sequences need not be represented in databases to be identified; only representation of the genome signature is required. This is in contrast to fragment recruitment 13 and BLAST-based binning approaches that only work for homologous sequences. We found that clusters of a few hundred kilobases of sequence (as little as 20% of the genome) were resolved, suggesting that a few fosmids or bacterial artificial chromosomes linked to 16S rRNA genes can be sufficient to serve as a reference to define a bin. Thus, recent progress in using large-insert metagenomic libraries to link 16S rRNA genes to genomic sequence from diverse uncultivated microorganisms is very valuable in this regard 85.

Because the reach of composition-based approaches to binning extends beyond gene content of reference genomes, they hold great promise for identifying and classifying genes from the variable fraction of the pan-genome (present in only a subset of strains or species), an important determinant of pathogenicity and niche differentiation 868788. In AMD populations, genome reconstruction has shown that this strain-variable fraction often involves inserted plasmid and virus sequences 89. In the current study, these integrated elements clustered either with the host genome or in regions shared between different species or genera. Since horizontally transferred DNA is rapidly converted to the genome signature of its new host 222889, the extent to which such genomic regions reflect the genome-wide signature of nucleotide composition is likely a function of the donor of the genetic material and how recently they were acquired. Recently acquired sequences with distinctive tetranucleotide patterns may bin incorrectly, and unexpected binning outcomes can be used to identify laterally transferred regions 6290.

Although the tetra-ESOM method works well to separate sequence fragments from organisms distinct at the genus or higher level, it has some limitations. Tetra-ESOM is generally unable to distinguish closely related species or strains. An important question, especially for more diverse samples, is whether limitations in genome sequence signature space will impose an inherent constraint on the number of populations that can be resolved. There are a staggering 6 × 10²²² ways to code for a typical protein in our samples (based on an average protein size of 467 amino acids and assuming an average of 3 possible ways to code for any amino acid). This richness of protein coding space suggests ample capacity for numerous genome signatures. To date, SOMs have shown promising results in resolving up to 81 complete genomes, in successfully classifying fragments of 1,502 genomes into phylogenetic groups, and in visualizing phylogenetic clustering of sequences in complex environmental samples 46. However, it remains difficult to assess the accuracy and phylogenetic resolution of oligonucleotide-based SOMs on metagenomic datasets from diverse natural microbial communities. Another concern is computational demand. Continued increases in processor speeds will likely need to be supplemented with more efficient and/or accurate algorithms such as the recently introduced hyperbolic SOM 91 and growing SOM 59.

An overview of the samples and methodology used in this study is provided in Figure 1. Sample collection, DNA extraction, random fragmentation and cloning of approximately 3-kb fragments, Sanger sequencing, assembly, and curation of community genomics data were performed using phred/phrap/consed package as detailed previously 1255. The combined UBAs nonLeptos dataset was constructed by assembling sequencing reads derived from both the UBA BS and UBA biofilm samples (with UBA reads previously assigned to Leptospirillum spp. removed). This included 229,082 reads and approximately 210 Mb of total sequence, which assembled into 15,929 contigs and 36.6 Mb of composite sequence.

The phylogenetic tree of 16S rRNA genes was constructed by neighbor joining (default parameters) with the ARB software package 92 and 'SILVA SSU ref' database 93.

Tetranucleotide frequencies were determined for each assembled contig using a custom Perl script. Frequencies were calculated with a 1-bp sliding window and pairs of reverse complementary tetranucleotides were summed in order to avoid strand bias. Longer contigs and assembled genomes were split into 5-kb windows and only contigs longer than 2 kb were considered unless noted otherwise. To assess binning accuracy, data points (representing contigs/windows) are colored according to their genome of origin (when known), but this information is not available to the clustering process.

Contigs were clustered by tetranucleotide frequency utilizing Databionics ESOM Tools 94. The input for tetra-ESOM was a 136-dimensional vector (representing the frequencies of the 136 unique reverse complement tetranucleotide pairs, normalized for contig length) for each contig/window. These raw frequencies were transformed with the 'Robust ZT' option built into Databionics ESOM Tools, which normalizes the data using robust estimates of mean and variance. Data were permuted before each run to avoid errors due to sampling order. Maps were toroidal (borderless) with Euclidean grid distance and dimensions scaled from the default map size (50 × 82) as a function of the number of data points, to a ratio of approximately 5.5 map nodes per data point. For example, a typical clustering with approximately 7,500 data points was run on map with dimensions 155 × 255. Training was conducted with the K-Batch algorithm (k = 0.15%) for 20 training epochs. The standard best match search method was used with local best match search radius of 8. Other training parameters were as follows: Gaussian weight initialization method; Euclidean data space function; starting value for training radius of 50 with linear cooling to 1; starting value for learning rate of 0.5 with linear cooling to 0.1; Gaussian kernel function.

To quantify the degree of clustering between closely related genomes, we analyzed SOM maps using fixed point kernel densities 95. Spatial data from the SOM was imported into ArcGIS (ESRI Software) and clusters were defined using Hawth's Analysis Tools for ArcGIS 96. Cluster boundaries were determined using density estimators that captured 90% of data points from each genome (Additional data file 1). We then calculated separation between genomes as a percentage (Non-overlapping points/Total number of points) for two bins being compared. Average amino acid identity was calculated as described previously 1.

The predicted frequency of each unique pair of reverse complementary tetranucleotides was calculated based on genome-wide frequencies of potentially contributing codons. As shown in Figure 5, for any given tetranucleotide there are 12 potentially associated codons depending on coding strand and reading frame. Four codons (numbers 3, 4, 9, and 10 in Figure 5) are fully captured by the tetranucleotide, four are partially captured at two of three positions (numbers 2, 5, 8, and 11), and four are partially captured at one of three positions (codons 1, 6, 7, and 12). Each of these three classes is weighted according to their contribution: 1, 2/3, and 1/3 respectively. For partially captured codons, contributions of all possibilities were taken into account; for example, in Figure 5, codon number 5 (TGX) there are four possible codons - TGA, TGT, TGC, and TGG.

Sensitivity (percentage of fragments from each genome correctly identified) and precision (percentage of fragments in each bin belonging to the correct genome) of binning were calculated for a subset of assembled genomes that are deeply sampled and manually curated (Table 1; Additional data file 2). Fragment size was varied in two ways: all contigs were broken into a given size (2, 4, 6, or 10 kb); or 10% of each genome was randomly selected and fragmented (0.5, 1.0, 1.5, or 2.0 kb) while the remaining fraction of the genome was fragmented into 5-kb windows (Additional data file 2). Bin territories were defined manually, using boundaries apparent via distance-based background topology (U-Matrix) as guidelines. It is important to note this method allows data points between bins or near borders to remain unclassified. Analysis of subsampled genomes was conducted with assembled genomes only - unassigned fragments were excluded to prevent them from contributing to definition of bins. Genomes were fragmented into 5-kb sequences, which were then randomly selected to obtain the indicated percentage of the genome.

Intergenic regions were extracted and concatenated, with 'N's inserted between regions to avoid generation of erroneous tetranucleotides. Intergenic regions were grouped by size (in 20-bp bins) to monitor variance in sequence signatures from intergenic regions of differing lengths. All coding sequences were similarly concatenated with interleaving 'N's. Concatenated coding and non-coding regions were then broken into 5-kb windows and run against the same background dataset of assembled genomes and unassigned sequences as usual.

Shotgun proteomics data were obtained for Leptospirillum group II extracellular and whole cell fractions from the ABend, ABfront, and UBA locations of the Richmond mine 5566. Proteins were defined as enriched in the extracellular fraction if, in at least two of the three samples, they were only detected in the extracellular fraction, or the ratio of spectral counts from extracellular to intracellular fraction was > 2. The 50 most abundantly expressed proteins were identified on the basis of tandem mass spectrometry (MS/MS) spectral counts. ESOM analysis of genes encoding extracellular and highly expressed proteins were both conducted as described above; open reading frames were concatenated, interleaved with 'N's, then split into 5-kb windows and analyzed along with the full dataset.

This Whole Genome Shotgun project has been deposited at DDBJ/EMBL/GenBank under the project accessions ACXJ00000000 (unassigned contigs), ACXK00000000 (A-plasma), ACXL00000000 (E-plasma), ACXM00000000, (I-plasma), and ACVJ00000000 (ARMAN-2, described in detail in BJ Baker et al., in preparation). The versions described in this paper are the first versions, ACXJ01000000, ACXK01000000, ACXL01000000, ACXM01000000, and ACVJ01000000.