Evaluation of the Biolog automated microbial identification system

Biolog's identification system was used to identify 39 American Type Culture Collection reference taxa and 45 gram-negative isolates from water samples. Of the reference strains, 98% were identified to genus level and 76% to species level within 4 to 24 h. Identification of some authentic strains of Enterobacter, Klebsiella, and Serratia was unreliable. A total of 93% of the water isolates were identified.     

Evaluation of the Biolog system for the identification of Bacillus anthracis

The potential of the Biolog system for the identification of Bacillus anthracis was evaluated. In-house generated databases allowed the correct identification of 19 of 20 isolates of B. anthracis within 24 h. Five strains of the closely related B. cereus/thuringiensis group were misidentified as B. anthracis. For this reason the test could only serve as a primary screen with further testing being required to confirm identity. In addition 20% of all the strains of bacilli examined during the study gave unreadable reaction profiles due to false-positive reactions.     

Evaluation of Biolog system for identification of strains of Paenibacillus azotofixans

Biolog system was evaluated for the identification of strains of Paenibacillus azotofixans as no data concerning this species were in the list of Bacillus currently identified using Biolog data base. The P. azotofixans type strain P3L5 was first tested with the results recorded manually or using the automatic plate reader. In both cases, P3L5 utilized 22 carbon sources and when the results obtained were compared to data of Biolog software (Release 3.50), P3L5 was identified as B. azotoformans with a similarity coefficient of 0.913 (data recorded manually) and of 0.791 (data recorded automatically). Metabolic profiles of P3L5 were also compared after readings of 4 and 24 h using the computer-driven automatic plate reader. No significant difference was observed in both cases and P3L5 was identified again as B. azotoformans with indices of similarity considered only for excellent identification. Besides P3L5, other 15 P. azotofixans strains were tested with the Biolog system and all were identified as B. azotoformans with similarity coefficients varying from 0.511 to 0.927. Phenotypic and genetic characteristics of B. azotoformans were compared to those described for P. azotofixans to explain the misidentification of the latter species. We could conclude that these two species are quite different and that data of Biolog software are from P. azotofixans and not from B. azotoformans.     

Evaluation of the Biolog system for the identification of food and beverage yeasts

The inconvenience of conventional yeast identification methods has resulted in the development of rapid, commercial systems, mainly for clinical yeast species. The Biolog system (Biolog Inc., Hayward, CA, USA) is a new semi-automated, computer-linked technology for rapid identification of clinical and non-clinical yeasts. The system is based around a microtitre tray and includes assimilation and oxidation tests. This paper evaluates the Biolog system for the identification of 21 species (72 strains) of yeasts of food and wine origin. Species correctly identified included Saccharomyces cerevisiae , Debaryomyces hansenii , Yarrowia lipolytica , Kluyveromyces marxianus , Kloeckera apiculata , Dekkera bruxellensis and Schizosaccharomyces pombe. Zygosaccharomyces bailii and Zygosaccharomyces rouxii were identified correctly 50% of the time and Pichia membranaefaciens 20% of the time.     

Identification of Acinetobacter isolates using the Biolog identification system

The Biolog system was assessed for its ability to identify genospecies of Acinetobacter from a wastewater treatment plant. A success rate of 83% was achieved in identifying to genus level, but different genospecies identifications were obtained, with the possible exception of genospecies 7, when the results were compared with other published phenotypic identification schemes.     

Discrepancies in identification of Vibrio cholerae strains as members of Aeromonadaceae and Enterobacteriaceae by automated microbial identification system

Aims: Incidental observation of a discrepancy in identification of Vibrio cholerae prompted a study to understand the ability of an automated microbial identification system to identify this important pathogen. Methods and Results: Twenty clinical isolates of V. cholerae showing difference in genetic profiles by random amplified polymorphic DNA (RAPD) fingerprinting, serologically confirmed as O1, and showing presence of ctxA and tcpA genes in PCR were subjected to analysis by Vitek 2 Compact automated identification system for identification. Vitek 2 Compact detected 10 of 20 isolates correctly, whereas the remaining 10 were identified as various members of Aeromonadaceae and Enterobacteriaceae. Conclusions: Our results indicate that Vitek 2 Compact automated microbial system does not always identify V. cholerae strains correctly. Significance and Impact of Study: These observations should create awareness among end users about possible misidentifications by automated systems and encourage simultaneous use of serology and/or PCR for correct identification at least for V. cholerae, which is one of the most important enteric pathogens.     

Development of an automated microbial sensor system

An automated whole cell biosensor system was developed by integration of immobilized microbial cells in a flow-through system with screen-printed flow-through electrodes as detectors. The detectors used were thick-film Pt-electrodes in a 3-electrode configuration constructed as sandwich flow-through cells with a volume of about 36 microliters polarized at -900 mV. The measuring principle was the determination of oxygen consumption due to the microbial metabolism. Fructose was used as model analyte. The microorganisms were immobilized on cellulose-acetate membranes and integrated into a newly created reaction chamber (membrane reactor). The microbial cells used were Rhodococcus erythropolis and Issatchenkia orientalis known to be suitable for the determination of biological oxygen demand.     

MICRID: a computer-assisted microbial identification system.

An extensive computer-assisted identification system for bacteria and yeasts (117 genera and 1,430 species) was developed, and applications proved very useful in teaching situations.     

MICRID: a computer-assisted microbial identification system.

An extensive computer-assisted identification system for bacteria and yeasts (117 genera and 1,430 species) was developed, and applications proved very useful in teaching situations.     

Evaluation of a commercial microbial identification system based on fatty acid profiles for rapid, accurate identification of plant pathogenic bacteria

D.E. STEAD, J.E. SELLWOOD, J. WILSON AND I. VINEY, 1992. Fatty acid profiles of 773 strains representing 25 taxa of plant pathogenic and related saprophytic bacteria were compared with two commercially available broad-spectrum libraries and one self-generated library based primarily on cultures from the National Collection of Plant Pathogenic Bacteria. The accuracy of identification at specific level was often 100%, although for some closely related species and infraspecific taxa accuracy was sometimes significantly less than this. The accuracy of identification of Xanthomonas campestris pathovars was much better than for Pseudomonas syringae pathovars. Almost all identifications were made within24–48 h. Standardization of cultural conditions was essential. Hydroxy fatty acids were of great taxonomic value in classification of Gram-negative bacteria. Improved library development and standardization of cultural and analytical techniques will further increase the accuracy of identification. Fatty acid profiling offers a valuable rapid, accurate method for identification of many bacteria.     

Towards an automated system for the identification of notifiable pathogens: using as an example.

Simple and rapid identification of pathogen species is crucial to the control of many diseases. Here, James Kay, Andrew Shinn and Christina Sommerville demonstrate that statistical classifiers discriminate a notifiable pathogen Gyrodactylus salaris Malmberg, 1957, a lethal ectoparasite of Atlantic salmon, Salmo salar L., from its benign close relatives.     

Evaluation of biodegradability by the reduction of Tetrazolium Violet in Biolog microplates

A novel biodegradation test using Biolog MT microplates was developed. The method was based on the reduction of Tetrazolium Violet during mineralization of organic substrates. Both a microbial mixed culture (activated sludge) and pure culture of a bacterium (Pseudomonas aeruginosa) were used as inocula to evaluate its applicability. The procedure was successfully demonstrated with the ozonated samples of p-nitrophenol. Compared with previous methods, the proposed method is fast and convenient to use in practice.     

An automated system for genome analysis to support microbial whole-genome shotgun sequencing

We developed a semi-automated genome analysis system called GAMBLER in order to support the current whole-genome sequencing project focusing on alkaliphilic Bacillus halodurans C-125. GAMBLER was designed to reduce the human intervention required and to reduce the complications in annotating thousands of ORFs in the microbial genome. GAMBLER automates three major routines: analyzing assembly results provided by genome assembler software, assigning ORFs, and homology searching. GAMBLER is equipped with an interface for convenience of annotation. All processes and options are manipulatable through a WWW browser that enables scientists to share their genome analysis results without choosing computer platforms.     

Improved microbial gene identification with GLIMMER.

The GLIMMER system for microbial gene identification finds approximately 97-98% of all genes in a genome when compared with published annotation. This paper reports on two new results: (i) significant technical improvements to GLIMMER that improve its accuracy still further, and (ii) a comprehensive evaluation that demonstrates that the accuracy of the system is likely to be higher than previously recognized. A significant proportion of the genes missed by the system appear to be hypothetical proteins whose existence is only supported by the predictions of other programs. When the analysis is restricted to genes that have significant homology to genes in other organisms, GLIMMER misses <1% of known genes.     

Comparison of API 20NE and Biolog GN identification systems assessed by techniques of multivariate analyses.

The increasing use of commercial multitest systems for identification of environmental bacteria creates the problem of how to compare the identification results obtained from different systems. The limited use of species designations in such comparisons is caused by low usage of environmental bacteria in the development of commercial identification schemes. Two multivariate statistical methods, the Mantel's test and the co-inertia analysis, were applied to analyze data derived from the Biolog GN and the API 20NE systems of identification for 50 environmental bacterial strains. We found these two methods to be useful for revealing the relationship between the two sets of numerical taxonomic traits. Both of these methods showed that the distances according to the Biolog GN results between the studied strains were related to those derived from the API 20NE results, despite the differences in the test sets of the two systems. In addition, the co-inertia analysis allowed us to visualise the relationships between classifications of strains derived from the two identification systems and, simultaneously, to estimate the contribution of particular tests to the differentiation of bacterial strains.     

Evaluation of an automated DNA sequencing system developed in RIKEN

For the advancement of Human Genome Project, we have developed an automated DNA sequencing system HUGA-I. It is composed of several automated instruments and transfer robots connecting them. In this paper we describe the results of the performance evaluation test of HUGA-I. Although some of the system units showed good performances, the total performance of the HUGA-I was about 1/6 of the designed value. By revealing principal reasons of this poor performance, we would like to contribute to the automation in genome analysis, particularly in human genome analysis.Since the sequence technology advanced remarkably in these years, the system units of HUGA-I become older than those which are now commercially available and the throughput of it is out of our expectations. Nevertheless, we believe that it is meaningful to introduce the exact performance of HUGA-I and present the bottle neck points in the automating sequencing processes. Because, automation in the gene analysis is ultimately important, in particular for the analysis of large genomes such as the human genome. The aims of this paper are to introduce the results in performance evaluation of HUGA-I and to elucidate the bottle neck points in the automation of sequencing processes.The authors express their sincere thanks to Mr. Morisada Hayakawa and Mrs. Nobuko Kato for their technical asistance.     

昆虫图像自动鉴别技术

昆虫是地球上物种多样性最为丰富的生物类群,其物种鉴定任务复杂而艰巨,可靠的物种鉴定是开展昆虫学工作的重要基础之一。当前,国内外的人工昆虫物种鉴定能力均不能满足实际需求,因而人们开始不断探索利用计算机自动鉴定昆虫的原理和方法。目前,模式识别技术的迅猛发展已为昆虫图像的自动鉴定提供可能。文章概述昆虫图像自动鉴定技术研究的历史与现状,总结主要原理和方法,介绍工作流程,并展望发展前景。     

Systematic and fully automated identification of protein sequence patterns.

We present an efficient algorithm to systematically and automatically identify patterns in protein sequence families. The procedure is based on the Splash deterministic pattern discovery algorithm and on a framework to assess the statistical significance of patterns. We demonstrate its application to the fully automated discovery of patterns in 974 PROSITE families (the complete subset of PROSITE families which are defined by patterns and contain DR records). Splash generates patterns with better specificity and undiminished sensitivity, or vice versa, in 28% of the families; identical statistics were obtained in 48% of the families, worse statistics in 15%, and mixed behavior in the remaining 9%. In about 75% of the cases, Splash patterns identify sequence sites that overlap more than 50% with the corresponding PROSITE pattern. The procedure is sufficiently rapid to enable its use for daily curation of existing motif and profile databases. Third, our results show that the statistical significance of discovered patterns correlates well with their biological significance. The trypsin subfamily of serine proteases is used to illustrate this method's ability to exhaustively discover all motifs in a family that are statistically and biologically significant. Finally, we discuss applications of sequence patterns to multiple sequence alignment and the training of more sensitive score-based motif models, akin to the procedure used by PSI-BLAST. All results are available at httpl//www.research.ibm.com/spat/.