BOOL-AN: A method for comparative sequence analysis and phylogenetic reconstruction

A novel discrete mathematical approach is proposed as an additional tool for molecular systematics which does not require prior statistical assumptions concerning the evolutionary process. The method is based on algorithms generating mathematical representations directly from DNA/RNA or protein sequences, followed by the output of numerical (scalar or vector) and visual characteristics (graphs). The binary encoded sequence information is transformed into a compact analytical form, called the Iterative Canonical Form (or ICF) of Boolean functions, which can then be used as a generalized molecular descriptor. The method provides raw vector data for calculating different distance matrices, which in turn can be analyzed by neighbor-joining or UPGMA to derive a phylogenetic tree, or by principal coordinates analysis to get an ordination scattergram. The new method and the associated software for inferring phylogenetic trees are called the Boolean analysis or BOOL-AN

On Map Representations of DNA

We have constructed graphical (qualitative and visual) representations of DNA sequences as 2D maps and their numerical (quantitative and computational) analysis. The maps are obtained by transforming the four-letter sequences (where letters represent the four nucleic bases) via a spiral representation over triangular and square cells grids into a four-color map. The so constructed maps are then represented by distance matrices. We consider the use of several matrix invariants as DNA descriptors for determining the degree of similarity of a selection of DNA sequences. (doi: 10.5562/cca2338

Estimation of Similarity between DNA Sequences and Its Graphical Representation

Bioinformatics, which is now a well known field of study, originated in the context of biological sequence analysis. Recently graphical representation takes place for the research on DNA sequence. Research in biological sequence is mainly based on the function and its structure. Bioinformatics finds wide range of applications specifically in the domain of molecular biology which focuses on the analysis of molecules viz. DNA, RNA, Protein etc. In this review, we mainly deal with the similarity analysis between sequences and graphical representation of DNA sequence.Comment: 8 pages, 13 Figures, 4 Table

Molecular Distance Maps: An alignment-free computational tool for analyzing and visualizing DNA sequences\u27 interrelationships

In an attempt to identify and classify species based on genetic evidence, we propose a novel combination of methods to quantify and visualize the interrelationships between thousand of species. This is possible by using Chaos Game Representation (CGR) of DNA sequences to compute genomic signatures which we then compare by computing pairwise distances. In the last step, the original DNA sequences are embedded in a high dimensional space using Multi-Dimensional Scaling (MDS) before everything is projected on a Euclidean 3D space. To start with, we apply this method to a mitochondrial DNA dataset from NCBI containing over 3,000 species. The analysis shows that the oligomer composition of full mtDNA sequences can be a source of taxonomic information, suggesting that this method could be used for unclassified species and taxonomic controversies. Next, we test the hypothesis that CGR-based genomic signature is preserved along a species\u27 genome by comparing inter- and intra-genomic signatures of nuclear DNA sequences from six different organisms, one from each kingdom of life. We also compare six different distances and we assess their performance using statistical measures. Our results support the existence of a genomic signature for a species\u27 genome at the kingdom level. In addition, we test whether CGR-based genomic signatures originating only from nuclear DNA can be used to distinguish between closely-related species and we answer in the negative. To overcome this limitation, we propose the concept of ``composite signatures\u27\u27 which combine information from different types of DNA and we show that they can effectively distinguish all closely-related species under consideration. We also propose the concept of ``assembled signatures\u27\u27 which, among other advantages, do not require a long contiguous DNA sequence but can be built from smaller ones consisting of ~100-300 base pairs. Finally, we design an interactive webtool MoDMaps3D for building three-dimensional Molecular Distance Maps. The user can explore an already existing map or build his/her own using NCBI\u27s accession numbers as input. MoDMaps3D is platform independent, written in Javascript and can run in all major modern browsers

Predicting Proteome-Early Drug Induced Cardiac Toxicity Relationships (Pro-EDICToRs) with Node Overlapping Parameters (NOPs) of a new class of Blood Mass-Spectra graphs

The 11th International Electronic Conference on Synthetic Organic Chemistry session Computational ChemistryBlood Serum Proteome-Mass Spectra (SP-MS) may allow detecting Proteome-Early Drug Induced Cardiac Toxicity Relationships (called here Pro-EDICToRs). However, due to the thousands of proteins in the SP identifying general Pro-EDICToRs patterns instead of a single protein marker may represents a more realistic alternative. In this sense, first we introduced a novel Cartesian 2D spectrum graph for SP-MS. Next, we introduced the graph node-overlapping parameters (nopk) to numerically characterize SP-MS using them as inputs to seek a Quantitative Proteome-Toxicity Relationship (QPTR) classifier for Pro-EDICToRs with accuracy higher than 80%. Principal Component Analysis (PCA) on the nopk values present in the QPTR model explains with one factor (F1) the 82.7% of variance. Next, these nopk values were used to construct by the first time a Pro-EDICToRs Complex Network having nodes (samples) linked by edges (similarity between two samples). We compared the topology of two sub-networks (cardiac toxicity and control samples); finding extreme relative differences for the re-linking (P) and Zagreb (M2) indices (9.5 and 54.2 % respectively) out of 11 parameters. We also compared subnetworks with well known ideal random networks including Barabasi-Albert, Kleinberg Small World, Erdos-Renyi, and Epsstein Power Law models. Finally, we proposed Partial Order (PO) schemes of the 115 samples based on LDA-probabilities, F1-scores and/or network node degrees. PCA-CN and LDA-PCA based POs with Tanimoto’s coefficients equal or higher than 0.75 are promising for the study of Pro-EDICToRs. These results shows that simple QPTRs models based on MS graph numerical parameters are an interesting tool for proteome researchThe authors thank projects funded by the Xunta de Galicia (PXIB20304PR and BTF20302PR) and the Ministerio de Sanidad y Consumo (PI061457). González-Díaz H. acknowledges tenure track research position funded by the Program Isidro Parga Pondal, Xunta de Galici

DV-Curve Representation of Protein Sequences and Its Application

Based on the detailed hydrophobic-hydrophilic(HP) model of amino acids, we propose dual-vector curve (DV-curve) representation of protein sequences, which uses two vectors to represent one alphabet of protein sequences. This graphical representation not only avoids degeneracy, but also has good visualization no matter how long these sequences are, and can reflect the length of protein sequence. Then we transform the 2D-graphical representation into a numerical characterization that can facilitate quantitative comparison of protein sequences. The utility of this approach is illustrated by two examples: one is similarity/dissimilarity comparison among different ND6 protein sequences based on their DV-curve figures the other is the phylogenetic analysis among coronaviruses based on their spike proteins