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Introduction: Coronaviruses comprise a group of enveloped, positive-sense single-stranded RNA viruses that infect humans as well as a wide range of animals. The study was performed on a set of 573 sequences belonging to SARS, MERS and SARS-CoV-2 (CoVID-19) viruses. The sequences were represented with alignment-free sequence descriptors and analyzed with different chemometric methods: Euclidean/Mahalanobis distances, principal component analysis and self-organizing maps (Kohonen networks). We report the cluster structures of the data. The sequences are well-clustered regarding the type of virus; however, some of them show the tendency to belong to more than one virus type. Background: This is a study of 573 genome sequences belonging to SARS, MERS and SARS-CoV-2 (CoVID-19) coronaviruses. Objective: The aim was to compare the virus sequences, which originate from different places around the world. Methods: The study used alignment free sequence descriptors for the representation of sequences and chemometric methods for analyzing clusters. Results: Majority of genome sequences are clustered with respect to the virus type, but some of them are outliers. Conclusion: We indicate 71 sequences, which tend to belong to more than one cluster.

Background: In this report, we consider a data set, which consists of 310 Zika virus genome sequences taken from different continents, Africa, Asia and South America. The sequences, which were compiled from GenBank, were derived from the host cells of different mammalian species (Simiiformes, Aedes opok, Aedes africanus, Aedes luteocephalus, Aedes dalzieli, Aedes aegypti, and Homo sapiens). Method: For chemometrical treatment, the sequences have been represented by sequence descriptors derived from their graphs or neighborhood matrices. The set was analyzed with three chemometrical methods: Mahalanobis distances, principal component analysis (PCA) and self organizing maps (SOM). A good separation of samples with respect to the region of origin was observed using these three methods. Results: Study of 310 Zika virus genome sequences from different continents. To characterize and compare Zika virus sequences from around the world using alignment-free sequence comparison and chemometrical methods. Conclusion: Mahalanobis distance analysis, self organizing maps, principal components were used to carry out the chemometrical analyses of the Zika sequence data. Genome sequences are clustered with respect to the region of origin (continent, country). Africa samples are well separated from Asian and South American ones.

Applications of nanomaterials in biomedical, industrial, and consumer goods areas are expanding rapidly because of their unique physicochemical properties. Hazard assessment of nanosubstances is necessary for the protection of human and ecological health. We studied the proteomics patterns of three cell lines: co-culture of Caco-2 and HT29-MTX cells, primary small airway epithelial cells, and THP-1macrophage-like cells. The cells were exposed at 10 μg and 100 μg concentrations for 3 and 24 hours to multi-walled carbon nanotubes and TiO2 nanobelts (TiO2-NB). The data were analysed with the hierarchical clustering method and principal components analysis. In all cases, time of exposure is the most important factor in separation and clustering of proteomics patterns. Furthermore, the sets of proteins, which are specific for long (24 hours) exposure, are identified.

Today we witness an explosion of data emerging from proteomic research, i.e., information about the entire protein status of the cells. This chapter is focused on chemometrical analysis of the proteomics data as an example of the handling of large amounts of data points and extracting information and building knowledge, such as the identification of protein changes associated with specific toxic endpoints and conclusion on potential biomarkers. Data compression using graphical and numerical representation of proteomics data as well as chemometrical analysis tools such as clustering, classification and genetic algorithm will be briefly described. Bio-descriptors will be discussed and the added value of using them in combination with chemical descriptors for modelling. Furthermore, application examples of the methods and tools will be shown, demonstrating how knowledge can be extracted from proteomics data to support the understanding of toxicity mechanisms and help build predictive models.

Exposure to endocrine disrupting chemicals is an important public health concern although only a few endocrine disruption chemicals have been identified so far. To speed up their identification, in silico toxicological models appear to be the most appropriate, since the potential endocrine disruption of a large number of compounds can be estimated in a short time. In this study three in silico models (Endocrine disruptome software, VirtualToxLab and COSMOS KNIME) have been used. In silico predictions of the endocrine disruption potential of biocidal active substances have been made and predictions then compared with the available in vitro experimental binding affinities to androgen, estrogen, glucocorticoid and thyroid receptors. The chosen models had similar accuracies (around 60%), while differences were shown between the models in specificity and sensitivity. VirtualToxLab was the most balanced model. Additionally, three combined models were prepared and evaluated. As expected, the majority rule approach model was more accurate and balanced. However, the positive consensus rule model, that improved the specificity of predictions (≥80% for all studied nuclear receptors) was more applicable. This reduction of false positive predictions is especially useful in the search for positive (active) compounds. On the other hand, the novel negative consensus rule model improved the specificity of prediction (≥80% for all studied nuclear receptors), giving good predictions of negative (inactive) compounds that can be excluded from further testing. The results obtained by these combined models have great added value, since they can significantly reduce further experimental testing.

Knowing the mutagenic and carcinogenic properties of chemicals is very important for their hazard (and risk) assessment. One of the crucial events that trigger genotoxic and sometimes carcinogenic effects is the forming of adducts between chemical compounds and nucleic acids and histones. This review takes a look at the mechanisms related to specific functional groups (structural alerts or toxicophores) that may trigger genotoxic or epigenetic effects in the cells. We present up-to-date information about defined structural alerts with their mechanisms and the software based on this knowledge (QSAR models and classification schemes).

Large worldwide use of chemicals has caused great concern about their possible adverse effects on human health, flora and fauna. Increased production of new chemicals has also increased demand for their risk assessment. Traditionally, results from animal tests have been used to assess toxicity of chemicals. However, such methods are ethically questionable since they involve killing and causing suffering of the test animals. Therefore, new in silico methods are being sought to replace the traditional in vivo and in vitro testing methods. In this article we report on one method that can be used to build robust models for the prediction of compounds' properties from their chemical structure. The method has been developed by combining a genetic algorithm, a counter-propagation artificial neural network and cross-validation. It has been tested using existing data on toxicity to fathead minnow (Pimephales promelas). The results show that the method may give reliable results for chemicals belonging to the applicability domain of the developed models. Therefore, it can aid the risk assessment of chemicals and consequently reduce demand for animal tests.

In the paper first the two main learning strategies of the artificial neural networks (ANNs), the error-back propagation (EBP) and Kohonen self-organizing maps (SOM) are briefly described. Next, two nonstandard network layouts of the ANNs (bottle-neck and pyramidal decision tree) one for each of both learning strategies are suggested. In the last part, the use of counter-propagation (CP) ANN for handling chemical structures in QSAR modeling, classification and clustering is discussed. These concepts are of particular interest for the computer-aided drug research and in computer-aided toxicology. In the case study the results of fish toxicity research are described.

In silico modelling is an important alternative method for the evaluation of properties of chemical compounds. Basically, two concepts are used in its applications: QSAR modelling for endpoint predictions, and grouping (categorization) of large groups of chemicals. In the presented report we address both of these concepts. As a case study we present the results on a set of polychlorinated biphenyls (PCBs) and some of their metabolites. Their mutagenicity and carcinogenic potency were evaluated with CAESAR and T.E.S.T. models, which are freely available over the internet. We discuss the value and reliability of the predictions, the applicability domain of models and the ability to create prioritized groupings of PCBs as a category of chemicals.

We studied the ingredients of cosmetic products as potential endocrine disruptors (ED) by in silico methods (docking). The structures of 14 human nuclear receptors have been retrieved from the protein data bank (PDB). We only considered the mechanism linked with direct binding to nuclear receptors with well-defined crystal structures. Predictions were performed using the Endocrine Disruptome docking program http://endocrinedisruptome.ki.si/ (Kolšek et al., 2013). 122 compounds were estimated to be possible endocrine disruptors bind to at least one of the receptors, 21 of them which are predicted to be probable toxicants for endocrine disruption as they bind to more than five receptors simultaneously. According to the literature survey and lack of experimental data it remains a challenge to prove or disprove the in silico results experimentally also for other potential endocrine disruptors.

The randomly selected set of 558 chemicals from Cosmetic inventory was studied with internet accessible program package CAESAR. Four toxic endpoints were considered: mutagenicity, carcinogenicity, developmental toxicity and skin sensitization. The CAESAR program provides beside the predictions comprehensive information on applicability domain and the similarity between the considered compound and the compounds from model’s training set. This information was used to implement for clustering and classification of chemicals. As the technique the Self Organizing Maps was applied. This technique also enables us to define to each cluster the cluster indicator, i.e., the characteristic compound, which is considered as a representative for a cluster.

Human life has been at the edge of catastrophe for millennia due diseases which emerge and reemerge at random. The recent outbreak of the Zika virus (ZIKV) is one such menace that shook the global public health community abruptly. Modern technologies, including computational tools as well as experimental approaches, need to be harnessed fast and effectively in a coordinated manner in order to properly address such challenges. In this paper, based on our earlier research, we have proposed a four-pronged approach to tackle the emerging pathogens like ZIKV: (a) Epidemiological modelling of spread mechanisms of ZIKV; (b) assessment of the public health risk of newly emerging strains of the pathogens by comparing them with existing strains/pathogens using fast computational sequence comparison methods; (c) implementation of vaccine design methods in order to produce a set of probable peptide vaccine candidates for quick synthesis/production and testing in the laboratory; and (d) designing of novel therapeutic molecules and their laboratory testing as well as validation of new drugs or repurposing of drugs for use against ZIKV. For each of these stages, we provide an extensive review of the technical challenges and current state-of-the-art. Further, we outline the future areas of research and discuss how they can work together to proactively combat ZIKV or future emerging pathogens.

The set of 16 polycyclic aromatic hydrocarbon compounds was examined with the Internet available quantitative structure–activity relationship (QSAR) CAESAR models. For mutagenicity, carcinogenicity, developmental toxicity, and skin sensitization, the report includes the predicted classifications, the analysis of applicability domains, and the similarity sets, which consist of the similar compounds from the training sets. These results were further analyzed with chemometrical methods, that is, hierarchical clustering, principal component analysis, and self-organizing maps, which were used for clustering and to define the cluster indicators. Such analysis assists the users in planning the application of QSAR models for hazard communication in regulatory compliance and in research of new active compounds. Copyright © 2013 John Wiley & Sons, Ltd.

Background CPANNatNIC is software for development of counter-propagation artificial neural network models. Besides the interface for training of a new neural network it also provides an interface for visualisation of the results which was developed to aid in interpretation of the results and to use the program as a tool for read-across. Results The work presents the details of the program’s interface. Parts of the interface are presented and how they can be used. The examples provided show how the user can build a new model and view the results of predictions using the interface. Examples are given to show how the software may be used in read-across. Conclusions CPANNatNIC provides a simple user interface for model development and visualisation. The interface implements options which may simplify read-across procedure. Statistical results show better prediction accuracy of read-across predictions than model predictions where similar compounds could be identified, which indicates the importance of using read-across and usefulness of the program. Electronic supplementary material The online version of this article (doi:10.1186/s13321-017-0218-y) contains supplementary material, which is available to authorized users.

Extensive use of pharmaceuticals as human and veterinary medication raises concerns for their adverse effects on non-target organisms. The purpose of this study was to employ multiple linear regression (MLR) to predict the toxicities of a diverse set of pharmaceuticals to fish. The descriptor pool consisted of about 1500 descriptors calculated using Dragon 5.4, Spartan 06 and Codessa 2.2 software. Descriptor selection was made by the heuristic method available in Codessa 2.2. The data set was divided into training and test sets using Kohonen networks. The training set contained approximately 65% of the compounds of the full data set (99 compounds). The training set model contained eight descriptors from all dimensions, all of which were obtained from Dragon 5.4. The statistical parameters of the model for the training set are R(2 )= 0.664, F = 13.588, and R(cv)(2) (LOO) = 0.542 while it achieves R(2 )= 0.605 for the test set. The training, test and external sets have no response outliers considering the standardized residual greater than three. The external validation of the model was made with a set of pharmaceuticals obtained from several databases. The R(pred)(2) is 0.777, reflecting a relatively good predictive power for the external set.

A quantitative structure-activity relationship (QSAR) study on a set of 66 structurally-similar 6-fluoroquinolones was performed using a large pool of theoretical molecular descriptors. Ab initio geometry optimizations were carried out to reproduce the geometrical and electronic structure parameters. The resulting molecular structures were confirmed to be minima via harmonic frequency calculations. Obtained atomic charges, HOMO and LUMO energies, orbital electron densities, dipole moment, energy and many other properties served as quantum-chemical descriptors. A multiple linear regression (MLR) technique was applied to generate a linear model for predicting the biological activity, Minimal Inhibitory Concentration (MIC), treated as negative decade logarithm, (pMIC). The heuristic method was used to optimize the model parameters and select the most significant descriptors. The model was tested internally using the CV LOO procedure on the training set and validated against the external validation set. The result (Q 2 ext = 0.7393), which was obtained on an external, previously excluded validation data set, shows the predictive performances of this model (R 2tr = 0.7416, Q 2 tr = 0.6613) in establishing (Q)SAR of 6-fluoroquinolones. This validated model could be proficiently used to design new 6-fluoroquinolones with possible higher activity.

A chemometrical study regarding a 10-years water quality monitoring plan at 15 sampling points along a section of the Reconquista River and its stream channels, which embraces 21 campaigns, is presented. The original data were pre-treated in order to eliminate missing data and outliers, obtaining a final data matrix of 270 samples by 26 physical-chemistry variables. Multivariate statistical methods like multi curve resolution, canonical correlation analysis and factor analysis methods, as well as current univariate statistics were applied. The interpretation was simplified when variables were separated in groups containing environmentally and chemically related variables instead of analyzing them all together. These methods have shown that the presence of metals likely come from at least 3 different type of sources. Although the stream channels arriving to the main river course are highly polluted, their flow rates are so low that do not significantly decrease its water quality. They mainly contribute to the high levels of biochemical-oxygen demand and chemical-oxygen demand as well as nitrogen-content species. Furthermore, regarding metals, the pollution of the river coming from the upstream is higher than those introduced by all channels. Fil: Garcia Reiriz, Alejandro Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; Argentina Fil: Magallanes, Jorge Federico. Comisión Nacional de Energía Atómica; Argentina Fil: Vracko, Marjan. National Institute of Chemistry; Eslovenia Fil: Zupan, Jure. National Institute of Chemistry; Eslovenia Fil: Reich, Silvia Leonor. Universidad Nacional de San Martín. Instituto de Investigación en Ingeniería Ambiental; Argentina Fil: Cicerone, Daniel. Comisión Nacional de Energía Atómica; Argentina. Universidad Nacional de San Martín. Instituto de Investigación en Ingeniería Ambiental; Argentina

This paper describes numerical characterization of proteome maps based on partial ordering of protein spots with respect to the mass and the charge. The partial ordering diagram is embedded directly over the 2D map and the corresponding adjacency matrix is constructed. The adjacency matrix is augmented by including the information on the abundance of proteins in a gel as suitably scaled diagonal entries of the matrix. The approach is illustrated on proteome maps of Anderson et al. (1996) based on experimental results from liver cells of rats exposed to four peroxisome proliferators. We used the leading eigenvectors of the adjacency matrices as maps descriptors in order to determine the degree of similarity between proteome maps.

Quantitative structure-activity relationship study on three diverse sets of structurally similar fluoroquinolones was performed using a comprehensive set of molecular descriptors. Multiple linear regression technique was applied as a preprocessing tool to find the set of relevant descriptors (10) which are subsequently used in the artificial neural networks approach (non-linear procedure). The biological activity in the series (minimal inhibitory concentration (μg/mL) was treated as negative decade logarithm, pMIC). Using the non-linear technique counter propagation artificial neural networks, we obtained good predictive models. All models were validated using cross validation leave-one-out procedure. The results (the best models: Assay1, R = 0.8108; Assay2, R = 0.8454, and Assay3, R = 0.9212) obtained on external, previously excluded test datasets show the ability of these models in providing structure-activity relationship of fluoroquinolones. Thus, we demonstrated the advantage of non-linear approach in prediction of biological activity in these series. Furthermore, these validated models could be proficiently used for the design of novel structurally similar fluoroquinolone analogues with potentially higher activity.

One of the main goals of the new chemical regulation REACH (Registration, Evaluation and Authorization of Chemicals) is to fill the gaps on the toxicological properties of chemicals that affect human health. Carcinogenicity is one of the endpoints under consideration. The information obtained from (quantitative) structure-activity relationship ((Q)SAR) models is accepted as an alternative solution to avoid expensive and time-consuming animal tests. The reported results were obtained within the framework of the European project 'Computer Assisted Evaluation of industrial chemical Substances According to Regulations (CAESAR)'. In this article, we demonstrate intermediate results for counter propagation artificial neural network (CP ANN) models for the prediction category of the carcinogenic potency using two-dimensional (2D) descriptors from different software programs. A total of 805 non-congeneric chemicals were extracted from the Carcinogenic Potency Database (CPDBAS). The resulting models had prediction accuracies for internal (training) and external (test) sets as high as 91-93% and 68-70%, respectively. The sensitivity and specificity of the test set were 69-73 and 63-72% correspondingly. High specificity is critical in models for regulatory use that are aimed at ensuring public safety. Thus, the errors that give rise to false negatives are much more relevant. We discuss how we can increase the number of correctly predicted carcinogens using the correlation between the threshold and the values of the sensitivity and specificity.

The new European chemicals regulation Regis- tration, Evaluation, Authorization and Restriction of Chem- icals entered into force in June 2007 and accelerated the development of quantitative structure-activity relationship (QSAR) models for a variety of endpoints, including carcino- genicity. Here, we would like to present quantitative (continu- ous) and qualitative (categorical) models for non-congeneric chemicals for prediction of carcinogenic potency. A dataset of 805 substances was obtained after a preliminary screen- ing of findings of rodent carcinogenicity for 1,481 chemi- cals accessible via Distributed Structure-Searchable Toxic- ity (DSSTox) Public Database Network originated from the Lois Gold Carcinogenic Potency Database (CPDB). Twenty seven two-dimensional MDL descriptors were selected us- ing Kohonen mapping and principal component analysis. The counter propagation artificial neural network (CP ANN) technique was applied. Quantitative models were developed exploring the relationship between the experimental and pre- dicted carcinogenic potency expressed as a tumorgenic dose TD50 for rats. The obtained models showed low prediction power with correlation coefficient less than 0.5 for the test set. In the next step, qualitative models were developed. We found that the qualitative models exhibit good accuracy for the training set (92%). The model demonstrated good pre- dicted performance for the test set. It was obtained accuracy (68%), sensitivity (73%), and specificity (63%). We believe that CP ANN method is a good in silico approach for model- ing and predicting rodent carcinogenicity for non-congeneric chemicals and may find application for other toxicological endpoints.

We consider graphical representation for RNA secondary structure by assigning four horizontal lines to the unpaired bases A, C, G, U and four horizontal lines to paired bases A ,C ,G ,U. The RNA graphical representation based on eight equidistant horizontal lines can be viewed as natural extension of the earlier four-line graphical representation of DNA (Randicet al., Chem Phys Lett, 2003, 371, 202) to RNA. Numerical characterization of RNA is based on 8 8 matrices recording the adjacency of the nucleotide bases in the primary sequence of RNA when RNA is read from 5 to 3. The approach is illustrated on a set of nine viral RNAs, reported in the literature. © 2009 Wiley Periodicals, Inc. Int J Quantum Chem 109: 2982-2995, 2009

Knowing the mutagenic and carcinogenic properties of chemicals is very important for their hazard (and risk) assessment. One of the crucial events that trigger genotoxic and sometimes carcinogenic effects is the forming of adducts between chemical compounds and nucleic acids and histones. This review takes a look at the mechanisms related to specific functional groups (structural alerts or toxicophores) that may trigger genotoxic or epigenetic effects in the cells. We present up-to-date information about defined structural alerts with their mechanisms and the software based on this knowledge (QSAR models and classification schemes)., Poznavanje mutagenih in rakotvornih lastnosti kemikalij je zelo pomembno za njihovo oceno tveganja in nevarnosti. Eden od ključnih dogodkov, ki sprožijo genotoksični in včasih kancerogeni učinkek je tvorba aduktov med kemikalijami in nukleinskimi kislinami ter histoni. Ta članek povzema pregled mehanizmov povezanih s specifičnimi funkcionalnimi skupinami (strukturnimi alerti ali tveganimi kemijskimi strukturami), ki lahko sprožijo genotoksične ali epigenetske učinke v celicah. Predstavlja aktualne informacije o poznanih strukturnih alertih, njihove mehanizme interakcij z genetskim materialom in programsko opremo, ki na osnovi poznavanja teh mehanizmov z uporabo QSAR modelov in klasifikacijskih shem omogočajo napovedovanje genostoksičnosti še nepoznanih kemikalij.

In this work we analyzed proteomic maps obtained from hepatocytes, which were treated with 14 halocarbons. A similarity index was introduced as a robust measure of similarity between two maps or between two selections of spots within the maps. A searching algorithm was used to identify the spots that may play an important role in toxicity mechanism. The highest correlation coefficients obtained between the similarity index and biological parameter were larger than 0.9.

We consider a characterization of proteomics maps based on an alternative kind of neighborhood graphs for the protein spots on 2-D gel. The novel approach considers for every protein spot only the nearest neighborhood consisting of protein spots of higher abundance. The approach has the simplicity and advantages of the recently introduced characterization of proteome maps based on considering the nearest neighborhoods of protein spots, but it also has important additional desirable computational features. The characterization of the nearest neighborhood graphs of 2-D gel proteomics maps is sensitive to the number of spots considered and may lead to changes in the degree of similarity of different maps when the number of points has been changed, thus imposing restrictions on the protocol used for comparison of maps. The novel approach presented in this work is less sensitive to the number of points used in the analysis because graphs are constructed in a stepwise process in which the role of more distant neighbors has been diminished by linking a new spot to the nearest spot that has been already part of the neighborhood graph. In this way a graph with N + 1 spots is obtained from the graph on N spots by adding a single new link, while in the case of the nearest neighborhood graphs adding a new spot introduces novel neighborhoods and generally results in a graph that may differ significantly from the neighborhood graph on N points.

The set of 95 aromatic amines and their mutagenic potency was treated with counter propagation neural network, which enables analysis of self-organising maps (SOMs) and also the prediction of mutagenicity. Compounds were described with four classes of descriptors: topostructural (TS), topochemical (TC), geometrical, and quantum chemical (QC). The models were tested on their prediction ability with leave-one-out (LOO) cross-validation method. The squares of correlation coefficient lie between 0.65 and 0.75 and are comparable with models obtained by linear methods. In addition, we analysed self-organising maps and found clusters of structurally similar compounds.

The counter-propagation artificial neural network (CP ANN) technique was applied for the independent variable selection and for structure-mutagenic potency modeling on a set of 95 aromatic and heteroaromatic amines with biological activity investigated experimentally by an in vitro assay. The molecular structures were represented by 275 independent variables classified as topostructural, topochemical, geometrical and quantum-chemical descriptors. As a result of the neural network modeling, the following descriptors were found to be the most important for structure-activity relationship: 5 chi -path connectivity index of order h = 5, 3chibC-bond cluster connectivity index of order h = 3, J(B)-Balaban's J index based on bond types, SHSNH2-electrotopological state index values for atoms, phia-flexibility index (kappa p1 x kappa p2/nvx), IC0-mean information content or complexity of a graph based on the 0 order neighborhood of vertices in a hydrogen-filled graph and ELUMO. The leave one out (LOO) method was used in order to test and select the models for mutagenicity prediction. The statistical parameters for the 7-descriptors model are R(Model) = 0.96 and Rcv = 0.85, respectively. In the next step, the number of variables was reduced and the 4-descriptors model was found (R(Model) = 0.95 and Rcv = 0.85) and classified as the best one.

In the presented work, we propose two approaches to study the similarity between proteomic maps. First, we introduced similarity index as a robust parameter to express the degree of similarity among proteomic maps. Second, we treated the proteomic maps with self-organizing map (SOM) technique taking the abundances of spots as input variables. The study was performed on a set of reported proteomic maps, which were experimentally derived from mouse liver after the animals were treated with peroxisome proliferators. (Entire study included five peroxisome proliferators, control, and a non-peroxisome proliferating compound.) In original paper, the authors analyzed the data with the principal component method. Later other authors reported the study on the same data set using different numerical representations of proteomic maps. The presented results are in agreement with reported conclusions.

We present a novel 2-D graphical representation for DNA sequences which has an important advantage over the existing graphical representations of DNA in being very compact. It is based on: (1) use of binary labels for the four nucleic acid bases, and (2) use of the ‘worm’ curve as template on which binary codes are placed. The approach is illustrated on DNA sequences of the first exon of human β-globin and gorilla β-globin.

The recently proposed 2-D graphical representation of DNA based on four horizontal lines involves an arbitrary assignment of the four types of bases to the lines. While each such assignment is legitimate, it is desirable to have a scheme free of such arbitrary choices among non-equivalent geometrical representations. We outline one such approach, which is based on the construction of a 12-component vector whose components are the leading eigenvalues of the L/L matrices associated with DNA. The examination of similarities/dissimilarities among the coding sequences of the first exon of b-globin gene of different species illustrates the utility of the approach. 2003 Elsevier Science B.V. All rights reserved.