Phylogenomic study and classification of mitochondrial DNA through virtual genomic fingerprints.

In the present study, we evaluated the ability of the Virtual Analysis Method for Phylogenomic fingerprint Estimation (VAMPhyRE) toolkit to classify human mitochondrial DNA (mtDNA) haplogroups. In total, 357 random mtDNA sequences were obtained from different haplogroups, based on the classification of PhyloTree. Additionally, we included a control group of five sequences (Pan paniscus, Pan troglodytes, Homo sapiens neanderthalensis, Yoruba15, and the revised Cambridge reference sequence). VAMPhyRE employs a virtual hybridization technique, using probes that specifically bind to their complementary sequences in the genome. We used 65,536 probes of 8 nucleotides to identify potential sites where hybridization occurs between the mtDNA and the specific probe, forming different heteroduplexes and thus, creating a unique and specific genomic fingerprint for each sequence. Genomic fingerprints were compared, and a table of distances was calculated to obtain a mitochondrial phylogenomic tree with the macrohaplogroups, L, N, M, and R, and their corresponding haplogroups, according to universal nomenclature. The results obtained suggest an accuracy of 97.25% for the distribution of the 357 mtDNA sequences in the four macrohaplogroups and their corresponding haplogroups when compared with other mtDNA classification tools that require reference sequences and do not offer an analysis based on an evolutionary approach. These data are available online at http://biomedbiotec.encb.ipn.mx/VAMPhyRE/.

In Silico Genomic Fingerprints of the Bacillus anthracis Group Obtained by Virtual Hybridization.

In this study we evaluate the capacity of Virtual Hybridization to identify between highly related bacterial strains. Eight genomic fingerprints were obtained by virtual hybridization for the Bacillus anthracis genome set, and a set of 15,264 13-nucleotide short probes designed to produce genomic fingerprints unique for each organism. The data obtained from each genomic fingerprint were used to obtain hybridization patterns simulating a DNA microarray. Two virtual hybridization methods were used: the Direct and the Extended method to identify the number of potential hybridization sites and thus determine the minimum sensitivity value to discriminate between genomes with 99.9% similarity. Genomic fingerprints were compared using both methods and phylogenomic trees were constructed to verify that the minimum detection value is 0.000017. Results obtained from the genomic fingerprints suggest that the distribution in the trees is correct, as compared to other taxonomic methods. Specific virtual hybridization sites for each of the genomes studied were also identified.

HOMO: A novel script for data mining of microarrays.

UNLABELLED: HOMO.pl is a perl script that allows extracting important registers from an extensive data table or microarrays results. It is very useful in data mining for microarrays analysis. HOMO works as a homogenizer that converts the initial data table into more specific and manageable data according to a list of important genes or terms. This is very useful when a pathway, a condition, or a GO-Term is studied. The HOMO script has two inputs and one principal output. A table with the microarray data results is used as an input and a list of genes or important terms is used as a second input. The output is an adjusted table from the microarray results that contains only the genes included in the input list. HOMO's principal goal is to simplify the subsequent analyses to the microarray data. AVAILABILITY: HOMO.pl and a suite of example files are available by electronic mail request.

Design of a set of probes with high potential for influenza virus epidemiological surveillance.

An Influenza Probe Set (IPS) consisting in 1,249 9-mer probes for genomic fingerprinting of closely and distantly related Influenza Virus strains was designed and tested in silico. The IPS was derived from alignments of Influenza genomes. The RNA segments of 5,133 influenza strains having diverse degree of relatedness were concatenated and aligned. After alignment, 9-mer sites having high Shannon entropy were searched. Additional criteria such as: G+C content between 35 to 65%, absence of dimer or trimer consecutive repeats, a minimum of 2 differences between 9mers and selecting only sequences with Tm values between 34.5 and 36.5oC were applied for selecting probes with high sequential entropy. Virtual Hybridization was used to predict Genomic Fingerprints to assess the capability of the IPS to discriminate between influenza and related strains. Distance scores between pairs of Influenza Genomic Fingerprints were calculated, and used for estimating Taxonomic Trees. Visual examination of both Genomic Fingerprints and Taxonomic Trees suggest that the IPS is able to discriminate between distant and closely related Influenza strains. It is proposed that the IPS can be used to investigate, by virtual or experimental hybridization, any new, and potentially virulent, strain.

LifePrint: a novel k-tuple distance method for construction of phylogenetic trees.

PURPOSE: Here we describe LifePrint, a sequence alignment-independent k-tuple distance method to estimate relatedness between complete genomes. METHODS: We designed a representative sample of all possible DNA tuples of length 9 (9-tuples). The final sample comprises 1878 tuples (called the LifePrint set of 9-tuples; LPS9) that are distinct from each other by at least two internal and noncontiguous nucleotide differences. For validation of our k-tuple distance method, we analyzed several real and simulated viroid genomes. Using different distance metrics, we scrutinized diverse viroid genomes to estimate the k-tuple distances between these genomic sequences. Then we used the estimated genomic k-tuple distances to construct phylogenetic trees using the neighbor-joining algorithm. A comparison of the accuracy of LPS9 and the previously reported 5-tuple method was made using symmetric differences between the trees estimated from each method and a simulated "true" phylogenetic tree. RESULTS: The identified optimal search scheme for LPS9 allows only up to two nucleotide differences between each 9-tuple and the scrutinized genome. Similarity search results of simulated viroid genomes indicate that, in most cases, LPS9 is able to detect single-base substitutions between genomes efficiently. Analysis of simulated genomic variants with a high proportion of base substitutions indicates that LPS9 is able to discern relationships between genomic variants with up to 40% of nucleotide substitution. CONCLUSION: Our LPS9 method generates more accurate phylogenetic reconstructions than the previously proposed 5-tuples strategy. LPS9-reconstructed trees show higher bootstrap proportion values than distance trees derived from the 5-tuple method.

Bacterial classification using genomic fingerprints obtained by virtual hybridization.

In silico genomic fingerprints were produced by virtual hybridization of 191 fully sequenced bacterial genomes using a set of 15,264 13-mer probes specially designed to produce universal whole genome fingerprints. A novel approach for constructing phylogenetic trees, based on comparative analysis of genomic fingerprints, was developed. The resultant bacterial phylogenetic tree had strong similarities to those produced from the alignment of conserved sequences. Notably, the trees derived from the alignment of other conserved COG genes divided the Bacillus and Corynebacterium genera into the same subgroups produced by the novel bacterial tree. A number of discrepancies between both techniques were observed for the grouping of some Lactobacillus species. However, a detailed analysis of the alignment of these genomes using other bioinformatics tools revealed that the grouping of these organisms in the novel tree was more satisfactory than the groupings from previous classifications, which used only a few conserved genes. All these data suggest that the bacterial taxonomy produced by genomic fingerprints is satisfactory, but sometimes different from classical taxonomies. Discrepancies probably arise because the fingerprinting technique analyzes genomic sequences and reveals more information than previously used approaches.

In silico evaluation of a novel DNA chip based fingerprinting technology for viral identification.

The identification of microorganisms by whole genome DNA fingerprinting was tested "in silico". 94 HPV genome sequences were submitted to virtual hybridization analysis on a DNA chip with 342 probes. This Universal Fingerprinting Chip (UFC) constitutes a representative set of probes of all the possible 8-mer sequences having at least two internal and non contiguous sequence differences between all them. A virtual hybridization analysis was performed in order to find the fingerprinting pattern that represents the signals produced for the hybridization of the probes allowing at most a single mismatch. All the fingerprints for each virus were compared against each other in order to obtain all the pairwise distances measures. A match-extension strategy was applied to identify only the shared signals corresponding to the hybridization of the probes with homologous sequences between two HPV genomes. A phylogenetic tree was constructed from the fingerprint distances using the Neighbor-Joining algorithm implemented in the program Phylip 3.61. This tree was compared with that produced from the alignment of whole genome HPV sequences calculated with the program Clustal_X 1.83. The similarities between both trees are suggesting that the UFC-8 is able to discriminate accurately between viral genomes. A fingerprint comparative analysis suggests that the UFC-8 can differentiate between HPV types and sub-types.