Oligonucleotide Frequencies Research Articles

GB5mCPred: Cross-species 5mc Site Predictor Based on Bootstrap-based Stochastic Gradient Boosting Method for Poaceae

Background: One of the most prevalent epigenetic alterations in all three kingdoms of life is 5mC, which plays a part in a wide range of biological functions. Although in-vitro techniques are more effective in detecting epigenetic alterations, they are time and money-intensive. Artificial intelligence-based in silico approaches have been used to overcome these obstacles. background: One of the most prevalent epigenetic alterations in all three kingdoms of life is 5mC, which plays a part in a wide range of biological functions. Although in-vitro techniques are more effective in detecting epigenetic alterations, they are time and money intensive. Artificial intelligence-based in silico approaches have been used to overcome these obstacles. Aim: This study aimed to develop an ML-based predictor for the detection of 5mC sites in Poaceae. Objective: The objective of this study was the evaluation of machine learning and deep learning models for the prediction of 5mC sites in rice. Method: In this study, the vectorization of DNA sequences has been performed using three distinct feature sets- Oligo Nucleotide Frequencies (k = 2), Mono-nucleotide Binary Encoding, and Chemical Properties of Nucleotides. Two deep learning models, long short-term memory (LSTM) and Bidirectional LSTM (Bi-LSTM), as well as nine machine learning models, including random forest, gradient boosting, naïve bayes, regression tree, k-Nearest neighbour, support vector machine, adaboost, multiple logistic regression, and artificial neural network, were investigated. Also, bootstrap resampling was used to build more efficient models along with a hybrid feature selection module for dimensional reduction and removal of irrelevant features of the vector space. Result: Random Forest gains the maximum accuracy, specificity and MCC, i.e., 92.6%, 86.41% and 0.84. Gradient Boosting obtained the maximum sensitivity, i.e., 96.85%. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) technique showed that the best three models were Random Forest, Gradient Boosting, and Support Vector Machine in terms of accurate prediction of 5mC sites in rice. We developed an R-package, ‘GB5mCPred,’ and it is available in CRAN (https://cran.r-project.org/web/packages/GB5mcPred/index.html). Also, a user-friendly prediction server was made based on this algorithm (http://cabgrid.res.in:5474/). Conclusion: With nearly equal TOPSIS scores, Random Forest, Gradient Boosting, and Support Vector Machine ended up being the best three models. The major rationale may be found in their architectural design since they are gradual learning models that can capture the 5mC sites more correctly than other learning models.

Oligonucleotide usage in coronavirus genomes mimics that in exon regions in host genomes

BackgroundViruses use various host factors for their growth, and efficient growth requires efficient use of these factors. Our previous study revealed that the occurrence frequency of oligonucleotides in the influenza virus genome is distinctly different among derived hosts, and the frequency tends to adapt to the host cells in which they grow. We aimed to study the adaptation mechanisms of a zoonotic virus to host cells.MethodsHerein, we compared the frequency of oligonucleotides in the genome of alpha- and betacoronavirus with those in the genomes of humans and bats, which are typical hosts of the viruses.ResultsBy comparing the oligonucleotide frequency in coronaviruses and their host genomes, we found a statistically tested positive correlation between the frequency of coronaviruses and that of the exon regions of the host from which the virus is derived. To examine the characteristics of early-stage changes in the viral genome, which are assumed to accompany the host change from non-humans to humans, we compared the oligonucleotide frequency between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at the beginning of the pandemic and the prevalent variants thereafter, and found changes towards the frequency of the host exon regions.ConclusionsIn alpha- and betacoronaviruses, the genome oligonucleotide frequency is thought to change in response to the cellular environment in which the virus is replicating, and actually the frequency has approached the frequency in exon regions in the host.

AI-based search for convergently expanding, advantageous mutations in SARS-CoV-2 by focusing on oligonucleotide frequencies.

Among mutations that occur in SARS-CoV-2, efficient identification of mutations advantageous for viral replication and transmission is important to characterize and defeat this rampant virus. Mutations rapidly expanding frequency in a viral population are candidates for advantageous mutations, but neutral mutations hitchhiking with advantageous mutations are also likely to be included. To distinguish these, we focus on mutations that appear to occur independently in different lineages and expand in frequency in a convergent evolutionary manner. Batch-learning SOM (BLSOM) can separate SARS-CoV-2 genome sequences according by lineage from only providing the oligonucleotide composition. Focusing on remarkably expanding 20-mers, each of which is only represented by one copy in the viral genome, allows us to correlate the expanding 20-mers to mutations. Using visualization functions in BLSOM, we can efficiently identify mutations that have expanded remarkably both in the Omicron lineage, which is phylogenetically distinct from other lineages, and in other lineages. Most of these mutations involved changes in amino acids, but there were a few that did not, such as an intergenic mutation.

Generating human papillomavirus (HPV) reference databases to maximize genomic mapping.

Genomic experiments analyzing human papillomaviruses (HPVs) require a carefully selected list of sequences as a reference database to map millions of reads. The available sources, such as the Papillomavirus Episteme (PaVE), are organized based on variations in the L1 gene rather than the whole HPV sequence. Moreover, the PaVE process uses complex multiple sequence alignments containing hundreds or thousands of sequences. These issues complicate the generation of a reference database for genomics, leading to the generation of per-analysis-defined databases. Here, we propose a de novo strategy considering all HPV sequences reported in the NCBI database to define a subset of highly representative HPV sequences. The strategy is based on oligonucleotide frequency profiling of the whole sequence followed by hierarchical clustering. Using data from HPV capture experiments, we demonstrate that this strategy selects suitable sequences as a reference database to map most mappable reads unambiguously. We provide some recommendations to improve HPV mapping. The generated .fasta files can be accessed at https://github.com/vtrevino/HPV-Ref-Genomes .

Neutralism versus selectionism: Chargaff's second parity rule, revisited.

Of Chargaff's four "rules" on DNA base frequencies, the functional interpretation of his second parity rule (PR2) is the most contentious. Thermophile base compositions (GC%) were taken by Galtier and Lobry (1997) as favoring Sueoka's neutral PR2 hypothesis over Forsdyke's selective PR2 hypothesis, namely that mutations improving local within-species recombination efficiency had generated a genome-wide potential for the strands of duplex DNA to separate and initiate recombination through the "kissing" of the tips of stem-loops. However, following Chargaff's GC rule, base composition mainly reflects a species-specific, genome-wide, evolutionary pressure. GC% could not have consistently followed the dictates of temperature, since it plays fundamental roles in both sustaining species integrity and, through primarily neutral genome-wide mutation, fostering speciation. Evidence for a local within-species recombination-initiating role of base order was obtained with a novel technology that masked the contribution of base composition to nucleic acid folding energy. Forsdyke's results were consistent with his PR2 hypothesis, appeared to resolve some root problems in biology and provided a theoretical underpinning for alignment-free taxonomic analyses using relative oligonucleotide frequencies (k-mer analysis). Moreover, consistent with Chargaff's cluster rule, discovery of the thermoadaptive role of the "purine-loading" of open reading frames made less tenable the Galtier-Lobry anti-selectionist arguments.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10709-021-00119-5.

Cluster Identification in Metagenomics – A Novel Technique of Dimensionality Reduction through Autoencoders

Analysis of metagenomic data is not only challenging because they are acquired from a sample in their natural habitats but also because of the high volume and high dimensionality. The fact that no prior lab based cultivation is carried out in metagenomics makes the inference on the presence of numerous microorganisms all the more challenging, accentuating the need for an informative visualization of this data. In a successful visualization, the congruent reads of the sequences should appear in clusters depending on the diversity and taxonomy of the microorganisms in the sequenced sample. The metagenomic data represented by their oligonucleotide frequency vectors is inherently high dimensional and therefore impossible to visualize as is. This raises the need for a dimensionality reduction technique to convert these higher dimensional sequence data into lower dimensional data for visualization purposes. In this process, preservation of the genomic characteristics must be given highest priority. Currently, for dimensionality reduction purposes in metagenomics, Principal Component Analysis (PCA) which is a linear technique and t-distributed Stochastic Neighbor Embedding (t-SNE), a non-linear technique, are widely used. Albeit their wide use, these techniques are not exceptionally suited to the domain of metagenomics with certain shortcomings and weaknesses. Our research explores the possibility of using autoencoders, a deep learning technique, that has the potential to overcome the prevailing impediments of the existing dimensionality reduction techniques eventually leading to richer visualizations.

Neutralism Versus Selectionism: Chargaff's Second Parity Rule, Revisited

Of Chargaff's four rules on DNA base frequencies, the functional interpretation of his second parity rule (PR2) is the most contentious. Thermophile base compositions (GC%) were taken by Galtier and Lobry (1997) as favoring Sueoka's neutral PR2 hypothesis over Forsdyke's selective PR2 hypothesis, namely that mutations improving local within-species recombination efficiency had generated a genome-wide potential for the strands of duplex DNA to separate and initiate recombination through the kissing of the tips of stem-loops. However, following Chargaff's GC rule, base composition mainly reflects a species-specific, genome-wide, evolutionary pressure. GC% could not have consistently followed the dictates of temperature, since it plays fundamental roles in both sustaining species integrity and, through primarily neutral genome-wide mutation, fostering speciation. Evidence for a local within-species recombination-initiating role of base order was obtained with a novel technology that masked the contribution of base composition to nucleic acid folding energy. Forsdyke's results were consistent with his PR2 hypothesis, appeared to resolve some root problems in biology and provided a theoretical underpinning for alignment-free taxonomic analyses using relative oligonucleotide frequencies (k-mer analysis). Moreover, consistent with Chargaff's cluster rule, discovery of the thermoadaptive role of the purine-loading of open reading frames made less tenable the Galtier-Lobry anti-selectionist arguments.

Binnacle: Using Scaffolds to Improve the Contiguity and Quality of Metagenomic Bins.

High-throughput sequencing has revolutionized the field of microbiology, however, reconstructing complete genomes of organisms from whole metagenomic shotgun sequencing data remains a challenge. Recovered genomes are often highly fragmented, due to uneven abundances of organisms, repeats within and across genomes, sequencing errors, and strain-level variation. To address the fragmented nature of metagenomic assemblies, scientists rely on a process called binning, which clusters together contigs inferred to originate from the same organism. Existing binning algorithms use oligonucleotide frequencies and contig abundance (coverage) within and across samples to group together contigs from the same organism. However, these algorithms often miss short contigs and contigs from regions with unusual coverage or DNA composition characteristics, such as mobile elements. Here, we propose that information from assembly graphs can assist current strategies for metagenomic binning. We use MetaCarvel, a metagenomic scaffolding tool, to construct assembly graphs where contigs are nodes and edges are inferred based on paired-end reads. We developed a tool, Binnacle, that extracts information from the assembly graphs and clusters scaffolds into comprehensive bins. Binnacle also provides wrapper scripts to integrate with existing binning methods. The Binnacle pipeline can be found on GitHub (https://github.com/marbl/binnacle). We show that binning graph-based scaffolds, rather than contigs, improves the contiguity and quality of the resulting bins, and captures a broader set of the genes of the organisms being reconstructed.

CLUSTERING FRAGMEN METAGENOM MENGGUNAKAN METODE GROWING SELF ORGANIZING MAP (GSOM) ( Studi Kasus Dinas Lingkungan Hidup Kota Jayapura)

Metagenome is a microorganism that is taken directly from nature. The sequencen process of metagenome results in the mixing of various organisms. The data collection method used was Observation, using a direct plunge technique to the Jayapura City Environmental Department. This study uses data from metagenome fragments of 300 microbes. The technique of collecting metagenome fragment data used is cluster sampling. The research location is at the Jayapura City Environmental Agency. The purpose of this study is to analyze the effectiveness and efficiency of the Growing Self Organizing Map method in large-scale microbial grouping with short fragment lengths based on oligonucleotide frequencies. For feature extraction, k-mer frequency and spaced are used. Short fragments are used because in previous studies, the length of the fragment used was a long fragment (? 8 kbp), so in this study we want to overcome the drawbacks of using short fragments in the grouping of metagenome fragments. The results of the grouping of metagenome fragments will be tested for effectiveness and efficiency.

Machine learning analyses of bacterial oligonucleotide frequencies to assess the benthic impact of aquaculture

Aquaculture is a rapidly expanding industry and is now one of the primary sources of all consumed seafood. Intensive aquaculture production is associated with organic enrichment, which occurs as organic material settles onto the seafloor, creating anoxic conditions which disrupt ecological processes. Bacteria are sensitive bioindicators of organic enrichment, and supervised classifiers using features derived from 16s rRNA gene sequences have shown potential to become useful in aquaculture environmental monitoring. Current taxonomy-based approaches, however, are time intensive and built upon emergent features which cannot easily be condensed into a monitoring pipeline. Here, we used a taxonomy-free approach to examine 16s rRNA gene sequences derived from flocculent matter underneath and in proximity to hard-bottom salmon aquaculture sites in Newfoundland, Canada. Tetranucleotide frequencies (k = 4) were tabulated from sample sequences and included as features in a machine learning pipeline using the random forest algorithm to predict 4 levels of benthic disturbance; resulting classifications were compared to those obtained using a published taxonomy-based approach. Our results show that k-mer count features can effectively be used to create highly accurate predictions of benthic disturbance and can resolve intermediate changes in seafloor condition. In addition, we present a robust assessment of model performance which accounts for the effect of randomness in model creation. This work outlines a flexible framework for environmental assessments at aquaculture sites that is highly reproducible and free of taxonomy-assignment bias.

Predicting virus-host association by Kernelized logistic matrix factorization and similarity network fusion

BackgroundViruses are closely related to bacteria and human diseases. It is of great significance to predict associations between viruses and hosts for understanding the dynamics and complex functional networks in microbial community. With the rapid development of the metagenomics sequencing, some methods based on sequence similarity and genomic homology have been used to predict associations between viruses and hosts. However, the known virus-host association network was ignored in these methods.ResultsWe proposed a kernelized logistic matrix factorization with integrating different information to predict potential virus-host associations on the heterogeneous network (ILMF-VH) which is constructed by connecting a virus network with a host network based on known virus-host associations. The virus network is constructed based on oligonucleotide frequency measurement, and the host network is constructed by integrating oligonucleotide frequency similarity and Gaussian interaction profile kernel similarity through similarity network fusion. The host prediction accuracy of our method is better than other methods. In addition, case studies show that the host of crAssphage predicted by ILMF-VH is consistent with presumed host in previous studies, and another potential host Escherichia coli is also predicted.ConclusionsThe proposed model is an effective computational tool for predicting interactions between viruses and hosts effectively, and it has great potential for discovering novel hosts of viruses.

Viral population analysis of the taiga tick, Ixodes persulcatus, by using Batch Learning Self-Organizing Maps and BLAST search.

Ticks transmit a wide range of viral, bacterial, and protozoal pathogens, which are often zoonotic. Several novel tick-borne viral pathogens have been reported during the past few years. The aim of this study was to investigate a diversity of tick viral populations, which may contain as-yet unidentified viruses, using a combination of high throughput pyrosequencing and Batch Learning Self-Organizing Map (BLSOM) program, which enables phylogenetic estimation based on the similarity of oligonucleotide frequencies. DNA/cDNA prepared from virus-enriched fractions obtained from Ixodes persulcatus ticks was pyrosequenced. After de novo assembly, contigs were cataloged by the BLSOM program. In total 41 different viral families and order including those previously associated with human and animal diseases such as Bunyavirales, Flaviviridae, and Reoviridae, were detected. Therefore, our strategy is applicable for viral population analysis of other arthropods of medical and veterinary importance, such as mosquitos and lice. The results lead to the contribution to the prediction of emerging tick-borne viral diseases. A sufficient understanding of tick viral populations will also empower to analyze and understand tick biology including vector competency and interactions with other pathogens.

Base Composition, Speciation, and Why the Mitochondrial Barcode Precisely Classifies

While its mechanism and biological significance are unknown, the utility of a short mitochondrial DNA sequence as a “barcode” providing accurate species identification has revolutionized the classification of organisms. Since highest accuracy was achieved with recently diverged species, hopes were raised that barcodes would throw light on the speciation process. Indeed, a failure of a maternally donated, rapidly mutating, mitochondrial genome to coadapt its gene products with those of a paternally donated nuclear genome could result in developmental failure, thus creating a post-zygotic barrier leading to reproductive isolation and sympatric branching into independent species. However, the barcode itself encodes a highly conserved, species-invariant, protein, and the discriminatory power resides in the non-amino acid specific bases of synonymous codons. It is here shown how the latter could register changes in the oligonucleotide frequencies of nuclear DNA that, when they fail to match in pairing meiotic chromosomes, could reproductively isolate the parents (whose hybrid is sterile) so launching a primary divergence into two species. It is proposed that, while not itself contributing to speciation, the barcode sequence provides an index of the nuclear DNA oligonucleotide frequencies that drive speciation.

Alignment-free $d_2^*$ oligonucleotide frequency dissimilarity measure improves prediction of hosts from metagenomically-derived viral sequences.

Viruses and their host genomes often share similar oligonucleotide frequency (ONF) patterns, which can be used to predict the host of a given virus by finding the host with the greatest ONF similarity. We comprehensively compared 11 ONF metrics using several k-mer lengths for predicting host taxonomy from among ∼32 000 prokaryotic genomes for 1427 virus isolate genomes whose true hosts are known. The background-subtracting measure n}{}d_2^* at k = 6 gave the highest host prediction accuracy (33%, genus level) with reasonable computational times. Requiring a maximum dissimilarity score for making predictions (thresholding) and taking the consensus of the 30 most similar hosts further improved accuracy. Using a previous dataset of 820 bacteriophage and 2699 bacterial genomes, n}{}d_2^* host prediction accuracies with thresholding and consensus methods (genus-level: 64%) exceeded previous Euclidian distance ONF (32%) or homology-based (22-62%) methods. When applied to metagenomically-assembled marine SUP05 viruses and the human gut virus crAssphage, n}{}d_2^*-based predictions overlapped (i.e. some same, some different) with the previously inferred hosts of these viruses. The extent of overlap improved when only using host genomes or metagenomic contigs from the same habitat or samples as the query viruses. The n}{}d_2^* ONF method will greatly improve the characterization of novel, metagenomic viruses.

The exceptional genomic word symmetry along DNA sequences.

BackgroundThe second Chargaff’s parity rule and its extensions are recognized as universal phenomena in DNA sequences. However, parity of the frequencies of reverse complementary oligonucleotides could be a mere consequence of the single nucleotide parity rule, if nucleotide independence is assumed. Exceptional symmetry (symmetry beyond that expected under an independent nucleotide assumption) was proposed previously as a meaningful measure of the extension of the second parity rule to oligonucleotides. The global exceptional symmetry was detected in long and short genomes.ResultsTo explore the exceptional genomic word symmetry along the genome sequences, we propose a sliding window method to extract the values of exceptional symmetry (for all words or by word groups). We compare the exceptional symmetry effect size distribution in all human chromosomes against control scenarios (positive and negative controls), testing the differences and performing a residual analysis. We explore local exceptional symmetry in equivalent composition word groups, and find that the behaviour of the local exceptional symmetry depends on the word group.ConclusionsWe conclude that the exceptional symmetry is a local phenomenon in genome sequences, with distinct characteristics along the sequence of each chromosome. The local exceptional symmetry along the genomic sequences shows outlying segments, and those segments have high biological annotation density.

Horizontally Transferred Genetic Elements in the Tsetse Fly Genome: An Alignment-Free Clustering Approach Using Batch Learning Self-Organising Map (BLSOM).

Tsetse flies (Glossina spp.) are the primary vectors of trypanosomes, which can cause human and animal African trypanosomiasis in Sub-Saharan African countries. The objective of this study was to explore the genome of Glossina morsitans morsitans for evidence of horizontal gene transfer (HGT) from microorganisms. We employed an alignment-free clustering method, that is, batch learning self-organising map (BLSOM), in which sequence fragments are clustered based on the similarity of oligonucleotide frequencies independently of sequence homology. After an initial scan of HGT events using BLSOM, we identified 3.8% of the tsetse fly genome as HGT candidates. The predicted donors of these HGT candidates included known symbionts, such as Wolbachia, as well as bacteria that have not previously been associated with the tsetse fly. We detected HGT candidates from diverse bacteria such as Bacillus and Flavobacteria, suggesting a past association between these taxa. Functional annotation revealed that the HGT candidates encoded loci in various functional pathways, such as metabolic and antibiotic biosynthesis pathways. These findings provide a basis for understanding the coevolutionary history of the tsetse fly and its microbes and establish the effectiveness of BLSOM for the detection of HGT events.

Genomes of two new ammonia-oxidizing archaea enriched from deep marine sediments.

Ammonia-oxidizing archaea (AOA) are ubiquitous and abundant and contribute significantly to the carbon and nitrogen cycles in the ocean. In this study, we assembled AOA draft genomes from two deep marine sediments from Donghae, South Korea, and Svalbard, Arctic region, by sequencing the enriched metagenomes. Three major microorganism clusters belonging to Thaumarchaeota, Epsilonproteobacteria, and Gammaproteobacteria were deduced from their 16S rRNA genes, GC contents, and oligonucleotide frequencies. Three archaeal genomes were identified, two of which were distinct and were designated Ca. “Nitrosopumilus koreensis” AR1 and “Nitrosopumilus sediminis” AR2. AR1 and AR2 exhibited average nucleotide identities of 85.2% and 79.5% to N. maritimus, respectively. The AR1 and AR2 genomes contained genes pertaining to energy metabolism and carbon fixation as conserved in other AOA, but, conversely, had fewer heme-containing proteins and more copper-containing proteins than other AOA. Most of the distinctive AR1 and AR2 genes were located in genomic islands (GIs) that were not present in other AOA genomes or in a reference water-column metagenome from the Sargasso Sea. A putative gene cluster involved in urea utilization was found in the AR2 genome, but not the AR1 genome, suggesting niche specialization in marine AOA. Co-cultured bacterial genome analysis suggested that bacterial sulfur and nitrogen metabolism could be involved in interactions with AOA. Our results provide fundamental information concerning the metabolic potential of deep marine sedimentary AOA.

A novel bioinformatics method for efficient knowledge discovery by BLSOM from big genomic sequence data.

With remarkable increase of genomic sequence data of a wide range of species, novel tools are needed for comprehensive analyses of the big sequence data. Self-Organizing Map (SOM) is an effective tool for clustering and visualizing high-dimensional data such as oligonucleotide composition on one map. By modifying the conventional SOM, we have previously developed Batch-Learning SOM (BLSOM), which allows classification of sequence fragments according to species, solely depending on the oligonucleotide composition. In the present study, we introduce the oligonucleotide BLSOM used for characterization of vertebrate genome sequences. We first analyzed pentanucleotide compositions in 100 kb sequences derived from a wide range of vertebrate genomes and then the compositions in the human and mouse genomes in order to investigate an efficient method for detecting differences between the closely related genomes. BLSOM can recognize the species-specific key combination of oligonucleotide frequencies in each genome, which is called a “genome signature,” and the specific regions specifically enriched in transcription-factor-binding sequences. Because the classification and visualization power is very high, BLSOM is an efficient powerful tool for extracting a wide range of information from massive amounts of genomic sequences (i.e., big sequence data).

Visualization of genome signatures of eukaryote genomes by batch-learning self-organizing map with a special emphasis on Drosophila genomes.

A strategy of evolutionary studies that can compare vast numbers of genome sequences is becoming increasingly important with the remarkable progress of high-throughput DNA sequencing methods. We previously established a sequence alignment-free clustering method “BLSOM” for di-, tri-, and tetranucleotide compositions in genome sequences, which can characterize sequence characteristics (genome signatures) of a wide range of species. In the present study, we generated BLSOMs for tetra- and pentanucleotide compositions in approximately one million sequence fragments derived from 101 eukaryotes, for which almost complete genome sequences were available. BLSOM recognized phylotype-specific characteristics (e.g., key combinations of oligonucleotide frequencies) in the genome sequences, permitting phylotype-specific clustering of the sequences without any information regarding the species. In our detailed examination of 12 Drosophila species, the correlation between their phylogenetic classification and the classification on the BLSOMs was observed to visualize oligonucleotides diagnostic for species-specific clustering.

A novel approach, based on BLSOMs (Batch Learning Self-Organizing Maps), to the microbiome analysis of ticks

Ticks transmit a variety of viral, bacterial and protozoal pathogens, which are often zoonotic. The aim of this study was to identify diverse tick microbiomes, which may contain as-yet unidentified pathogens, using a metagenomic approach. DNA prepared from bacteria/archaea-enriched fractions obtained from seven tick species, namely Amblyomma testudinarium, Amblyomma variegatum, Haemaphysalis formosensis, Haemaphysalis longicornis, Ixodes ovatus, Ixodes persulcatus and Ixodes ricinus, was subjected to pyrosequencing after whole-genome amplification. The resulting sequence reads were phylotyped using a Batch Learning Self-Organizing Map (BLSOM) program, which allowed phylogenetic estimation based on similarity of oligonucleotide frequencies, and functional annotation by BLASTX similarity searches. In addition to bacteria previously associated with human/animal diseases, such as Anaplasma, Bartonella, Borrelia, Ehrlichia, Francisella and Rickettsia, BLSOM analysis detected microorganisms belonging to the phylum Chlamydiae in some tick species. This was confirmed by pan-Chlamydia PCR and sequencing analysis. Gene sequences associated with bacterial pathogenesis were also identified, some of which were suspected to originate from horizontal gene transfer. These efforts to construct a database of tick microbes may lead to the ability to predict emerging tick-borne diseases. Furthermore, a comprehensive understanding of tick microbiomes will be useful for understanding tick biology, including vector competency and interactions with pathogens and symbionts.