A new multiplatform computer program for numerical identification of microorganisms.

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1 JCM Accepts, published online ahead of print on 14 October 2009 J. Clin. Microbiol. doi: /jcm Copyright 2009, American Society for Microbiology and/or the Listed Authors/Institutions. All Rights Reserved A new multiplatform computer program for numerical identification of microorganisms. Oscar Flores 1, Lluís A. Belanche 2 and Anicet R. Blanch 3* Molecular Modelling and Bioinformatics Group, Institute for Research in Biomedicine, Parc Científic de Barcelona. Baldiri Reixac 10, Barcelona 2 Department of Software. Technical University of Catalonia. Jordi Girona 1-3, Barcelona. 3 Department of Microbiology, University of Barcelona, Avda. Diagonal 645, Barcelona. Running head: Computer-assisted numerical identification of bacteria Key words: Vibrio, software, taxonomical identification, bacteria classification *To whom correspondence should be addressed. Tel.: Fax: ablanch@ub.edu 1

2 Abstract The classification of bacteria using genomic methods or expensive biochemical-based commercial kits is sometimes beyond the reach of many laboratories who need to perform many classifications of unknown bacterial strains in a fast, cheap and reliable way. A new computer program, Identax, for the computerassisted identification of microorganisms using only results obtained by conventional biochemical tests is presented. Identax improves current microbial identification software and provides a multiplatform and user-friendly program. It can be executed from any operating system and can be downloaded without any cost from the website Note Traditional identification and classification of microorganisms is usually based on numerical taxonomy that was introduced at the end of the 1950s (15, 16, 17). Numerical taxonomic methods were applied extensively for classification and identification in subsequent years (6, 18), and they were strengthened by the extensive use of computers in various research fields (1, 8, 9, 14, 20). Later, computer-assisted identification programs were developed for the identification of bacterial groups, based mostly on phenotype data obtained by traditional methods or raw data from commercial kits (2, 4, 5, 7, 11, 13). Recently, genomic analyses have proven successful in defining taxa within different microbial groups. The use of 16S rrna has been proposed as the most reliable tool for allocating bacterial strains to families and genera. Moreover, other molecular methods, such as multilocus sequencing typing, have also been needed for the assignation of strains to species (19). However, the use of these molecular methods for routine analyses and rapid diagnosis, which are usually required for clinical and environmental analyses involving high numbers of samples or strains, remains impractical. Identification procedures for routine practices need to be simple, low-cost and rapid in order to be effective and successful. In this way, conventional biochemical identification provides the optimal approach for microbial identification in lieu of complete genomic characterization. Evidently, genomic analyses should be 2

3 carried out for studies of systematic bacteriology or biodiversity when molecular taxonomical criteria are needed. Numerical approaches are currently applied to identify microorganisms, but there is no wide-ranging software that could be applied to any microbial group and run on any computer system. Identax has been developed as a platform-independent computer program for numerical identification of microorganisms. It is user-friendly and runs on almost all existing operating systems and computer platforms, as it relies on the flexibility and power of the Java platform. It could be applied for any microbial group by creating, updating or merging microbial databases on unlimited phenotypic characters. The software allows the identification of any microorganism that is listed on an available database. It can also generate interactive dichotomous trees for rapid offline analysis. In this study, the Identax software was developed and evaluated by using recently updated biochemical databases on the genus Vibrio (12). Later, it has been also evaluated with other databases of different bacterial groups that have been defined by other authors in previous taxonomical studies (3, 10). Features. Identax has two main features. The first is the fast identification of unknown bacterial strains from phenotype data, represented as the dichotomous result (positive or negative) of a set of biochemical tests. The second main feature is the generation of dichotomous trees that will allow the isolation of one taxon from the others with the lowest possible number of tests. The first feature is the most adequate from the point of view of decision support systems, as the software recommends, in real time, the test with the most discriminative potential. It can also detect and handle false positives and show the current candidates. The second feature consists in the generation of a dichotomous tree. Each node represents a test and its two branches correspond to a negative or to a positive result on the test. This tree offers an overview of the search space and allows rapid identification without the need of using a computer. Programming fundamentals. The core of the software consists of a simple and 3

4 efficient model of conditional probabilities. This model is based on a Bayes' theorem approach (4) but a few optimizations have been applied to ensure the scalability and efficiency of the algorithm as well as avoid some inherent restrictions. To calculate the probability that an unknown isolate belongs to a given species or taxon, the following formula is used: ( R) P t i = P j taxon ( R t ) P( t ) i i ( R t ) P( t ) where P(x y) is the conditional probability of the event x assuming the occurrence of the event y, P(x) is the unconditional probability of the event x and j runs on the taxa. In this case, R represents a specific combination of results for all the experiments on the taxon in the dataset used, and t i is a specific taxon. To score the discriminative power of each test, all possible pairs of taxa are evaluated to see if they are discriminated by this test (i.e., one is expected to return a positive and the other a negative result). The pseudo code for this algorithm is the following: for each test t score[t] = 0 for each non-discarded taxon pair <x, y> if t(x) differs from t(y) then score[t] score[t] + 1 end if end for end for Apart from this simple count, the absolute value of the difference between the prior probability that taxon x and taxon y will give a positive result on the test t is used as a secondary sorting factor, as the software understands that, for example, discrimination between a probability of 0 and a probability of 1 is better than another one between 0.15 and The application of industry-standard software engineering procedures as three layer architectural design and iterative development ensures the robustness of the software. Bacteria identification reliability is directly 4 P j j

5 115 linked to the reliability of the matrices which are published in other articles (3, 10, 12) Identification parameters. The basis of this program is the used matrix (database), which can be retrieved from the literature or can be created by the users themselves. This matrix contains, at position XY, the probability of retrieving a positive result for test X on taxon Y. The variability for a test is included on the probability of retrieving a positive result. Consequently, databases should be defined in consistence with taxonomical studies of the bacterial group of interest by user. Identax permits the user to tune its operation by setting the confidence thresholds desired. Once a matrix has been imported into the software system, the next step needed to start the identification is to input the set of results of the biochemical tests, in order to achieve the confidence threshold established. If the identification threshold is not achieved with the results introduced, Identax suggests additional tests to be performed to reach the threshold. System requirements. Due to its architecture, Identax works well on most operating systems. The wide range of supported operating systems and minimum hardware requirements are those specified by the Java Virtual Machine version 6 or higher ( The software has been tested without any problem in Microsoft Windows XP/Vista, Apple Mac OS X 10.5 and Ubuntu Linux Apart from the platform-independent executable JAVA, two system-customized installation packages for Windows XP/Vista and Mac OS X are provided. Customization. Identax allows the import of reference matrices in XLS and CSV formats. So, any user can create their own datasets or use the ones published in the literature. The import of matrices from previous identification software files (4) is also supported. The generation of work summaries can be customized by a system of templates and the trees generated can be exported in XML, TreeML or DOT formats. Although there are many programs that allow a custom representation of the trees generated in the enumerated formats, GraphViz ( is recommended for its capacity and ease of use. Identax also includes an interactive tree viewer that allows export to graphical formats (JPG, BMP and PNG), although it may be difficult to customize for large-tree representations. Finally, as an opensource project, Identax code can be adapted by any user to their necessities, and for 5

6 147 this reason complete javadoc documentation is included with the source code For a detailed description of the program features, a users manual is available at the project homepage at Identax can be downloaded at no cost from this website. Users can also find a set of matrices to identify different bacterial groups, that are based on previous published taxonomical studies (3, 10, 12) Conclusions. Identax is open platform software that could be used by any laboratory for microbial identification and classification purposes. Users could easily adapt Identax to the analysis of the results of routine tests to identify microorganisms. There is no need to modify the traditional methods or commercial kits used in routine analyses. Historical data could be used to develop the most appropriate database to be used as reference by Identax. Previously existing databases could be imported for their application in Identax. Databases could also be shared by Identax users if permitted by the creators. Consequently, Identax is a valuable tool to support and facilitate microbial identification for several purposes (medical, ecological, environmental, etc.), at any microbiology laboratory, using conventional biochemical methods. References 1. Bascomb, S., Lapage, S.P., Curtis, M. A. and Willcox, W. R Identification of bacteria by computer: identification of reference strains. J. Gen. Microbiol. 77: Boeufgras, J. M., J. L. Blazer, F. Allards, and I. Diaz A new computer program for routine interpretation of API identification systems. In 2nd Conference on Taxonomy and Automatic Identification of Bacteria. Scientific Computer Department, API Systems, La Balme-Les Grottes, France. 3. Bryant,T.N PIBWin -- software for probabilistic identification. J Appl Microbiol 97: Bryant,T.N., Lee,J.V. and West,P.A Numerical classification of species of 6

7 Vibrio and related genera. Journal of Applied Bacteriology 61: Cox, R. P., and J. K. Thomsen Computer-aided identification of lactic acid bacteria using the API CHL system. Lett. Appl. Microbiol. 10: Dybowski, W., and D. A. Franklin Conditional probability and the identification of bacteria: a pilot study. J. Gen. Microbiol. 54: Freney, J., M. T. Duperron, C. Courtier, W. Hansen, F. Allard, J. M. Boeufgras, D. Monget, and J. Fleurette Evaluation of API Coryne in comparison with conventional methods for identifying coryneform bacteria. J. Clin. Microbiol. 29: Gyllenberg, H. G A model for computer identification of microorganisms. J. Gen. Microbiol. 39: Lapage, S. P., S. Bascomb, W. R. Willcox, and M. A. Curtis Identification of bacteria by computer: general aspects and perspectives. J. Gen. Microbiol. 77: Manero, A. and A. R. Blanch Identification of Enterococcus spp. with a biochemical key. Appl. Environ. Microbiol. 65: Miller, J.M.; Alachi, P Evaluation of new computer-enhanced identification program for microorganism: adaptation of BioBase for identification of members of the family Enterobacteriaceae. J. Clin. Microbiol. 34: Noguerola, I. and Blanch, A.R Identification of Vibrio spp. with a set of dichotomous keys. J. Appl. Microbiol. 105: Rhoden, D. L., G. A. Hancock, and J. M. Miller Numerical approach to reference identification of Staphylococcus, Stomatococcus, and Micrococcus spp. J. Clin. Microbiol. 31: Schnider, J Computer-aided numerical identification of gram negative fermentative rods on a desk-top computer. J. Appl. Bacteriol. 47: Sneath, P. H. A. 1957a. Some thoughts on bacterial classification. J. Gen. Microbiol. 17: Sneath, P. H. A. 1957b. The application of computers to taxonomy. J. Gen. Microbiol. 17:

8 Sneath, P. H. A., and R. R. Sokal Numerical taxonomy. Nature (London) 193: Sneath, P. H. A., and R. R. Sokal Numerical taxonomy. The principles and practice of numerical classification. W. H. Freeman & Co., San Francisco. 19. Stackebrandt, E., W. Frederiksen, G. M. Garrity, P. A. D. Grimont, P. Kampfer, M. C. J. Maiden, X. Nesme, R. Rossello-Mora, J. Swings, H. G. Truper, L. Vauterin, A. C. Ward, and W. B. Whitman Report of the ad hoc committee for the re-evaluation of the species definition in bacteriology. Int. J. Syst. Evol. Microbiol. 52: Willcox, W. R., S. P. Lapage, S. Bascomb, and M. A. Curtis Identification of bacteria by computer: theory and programming. J. Gen. Microbiol. 77: Downloaded from on April 22, 2018 by guest 8

Introduction to OTU Clustering. Susan Huse August 4, 2016

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