MASCOT HTML and XML parser: an implementation of a novel object model for protein identification data

Protein identification using MS is an important technique in proteomics as well as a major generator of proteomics data. We have designed the protein identification data object model (PDOM) and developed a parser based on this model to facilitate the analysis and storage of these data. The parser works with HTML or XML files saved or exported from MASCOT MS/MS ions search in peptide summary report or MASCOT PMF search in protein summary report. The program creates PDOM objects, eliminates redundancy in the input file, and has the capability to output any PDOM object to a relational database. This program facilitates additional analysis of MASCOT search results and aids the storage of protein identification information. The implementation is extensible and can serve as a template to develop parsers for other search engines. The parser can be used as a stand-alone application or can be driven by other Java programs. It is currently being used as the front end for a system that loads HTML and XML result files of MASCOT searches into a relational database. The source code is freely available at http://www.ccbm.jhu.edu and the program uses only free and open-source Java libraries.     

Computer-assisted identification of anaerobic bacteria.

A computer program was developed to identify anaerobic bacteria by using simultaneous pattern recognition via a Bayesian probabilistic model. The system is intended for use as a rapid, precise, and reproducible aid in the identification of unknown isolates. The program operates on a data base of 28 genera comprising 238 species of anaerobic bacteria that can be separated by the program. Input to the program consists of biochemical and gas chromatographic test results in binary format. The system is flexible and yields outputs of: (i) most probable species, (ii) significant test results conflicting with established data, and (iii) differential tests of significance for missing test results.     

Computer-assisted identification of anaerobic bacteria.

A computer program was developed to identify anaerobic bacteria by using simultaneous pattern recognition via a Bayesian probabilistic model. The system is intended for use as a rapid, precise, and reproducible aid in the identification of unknown isolates. The program operates on a data base of 28 genera comprising 238 species of anaerobic bacteria that can be separated by the program. Input to the program consists of biochemical and gas chromatographic test results in binary format. The system is flexible and yields outputs of: (i) most probable species, (ii) significant test results conflicting with established data, and (iii) differential tests of significance for missing test results.     

Computer Identification of Bacteria on the Basis of Their Antibiotic Susceptibility Patterns

A computer program utilizing a Baysean mathematical model was developed to identify bacteria solely on the basis of their antibiotic sensitivities. The model contains probability data on the antibiotic sensitivity patterns for 31 species of bacteria, which account for over 99% of all isolates submitted to our laboratory for testing. During a 4-month test period, antibiotic sensitivity data on 1,000 clinical isolates were processed by the program. The identification achieved by using the model was the same as that of the laboratory for over 86% of the isolates.     

北方果树食心虫远程便捷识别系统

针对目前果树食心虫识别方法实用性差、效率低、准确性不高的问题,开发了北方果树食心虫的远程便捷识别系统。本系统以科学性和实用性相结合为准则,创新性的以"果树种类"→"果树生育期"→"取食部位"→"受害状"→"虫体粗略特征"→"虫体细微特征"为逻辑,模拟果农识别食心虫时由表及里,由内到外,由粗及细的过程,并由此建立了远程便捷识别诊断的模型。总结苹果、梨、枣、桃、杏5种果树在萌芽、开花、抽枝、展叶和座果5个生育阶段时,桃小食心虫、梨小食心虫、桃蛀螟等14种常见食心虫在果树的芽、花、枝、叶和果实上取食的危害状,食心虫的粗略和细微识别特征及食心虫的发生规律、预测预报和防治方法等详细信息。按识别诊断模型的逻辑编写识别检索表,并建立果树发育阶段、果树受害部位及各种食心虫的信息数据库,及匹配的图片信息数据库。利用VBScript程序表达模型的逻辑关系,通过搭建web平台,基于Windows Server2003的IIS6.0架构本系统,实现了远程数据共享,即北方果树食心虫的远程便捷识别系统。本系统包含了北方果树上主要食心虫各方面信息,且操作快捷简单,较传统的食心虫分类大大提高了效率,为果园的食心虫识别和防治提供了极大的便利。     

A teaching exercise for the identification of bacteria using an interactive computer program

An interactive computer program written in Fortran is described which provides an exercise in the identification of bacteria. The program, IDEN, provides a novel way of enhancing a student's approach to systematic bacteriology and numerical identification procedures. An unknown bacterium is assigned to a student who is provided with a list of possible species and a list of tests which may be carried out. The student's objective is the correct identification of the unknown in the most economical manner. An indication is given concerning the way in which this program can be used to enhance theoretical instruction and practical investigation.     

Nonlinear system modelling via optimal design of neural trees

This paper introduces a flexible neural tree model. The model is computed as a flexible multi-layer feed-forward neural network. A hybrid learning/evolutionary approach to automatically optimize the neural tree model is also proposed. The approach includes a modified probabilistic incremental program evolution algorithm (MPIPE) to evolve and determine a optimal structure of the neural tree and a parameter learning algorithm to optimize the free parameters embedded in the neural tree. The performance and effectiveness of the proposed method are evaluated using function approximation, time series prediction and system identification problems and compared with the related methods.     

The Education and Training of Biology Teachers

A technique for the numerical identification of bacteria using normalized likelihoods calculated from a probabilistic database is described, and the principles of the technique are explained. A simple computer program, which can be used to perform the calculations and identifications on a microcomputer in undergraduate classes, is presented. The program is annotated so that the steps in the calculation can be linked to the equivalent steps in the program to assist in teaching the principles of programming. Specimen results from the program, and examples of how they should be interpreted and explained, are given. It is expected that the program will be of use to teachers of undergraduates to teach the principles of numerical identification and an important use of computers in biology.     

Incorporating sequence information into the scoring function: a hidden Markov model for improved peptide identification

MOTIVATION: The identification of peptides by tandem mass spectrometry (MS/MS) is a central method of proteomics research, but due to the complexity of MS/MS data and the large databases searched, the accuracy of peptide identification algorithms remains limited. To improve the accuracy of identification we applied a machine-learning approach using a hidden Markov model (HMM) to capture the complex and often subtle links between a peptide sequence and its MS/MS spectrum. Model: Our model, HMM_Score, represents ion types as HMM states and calculates the maximum joint probability for a peptide/spectrum pair using emission probabilities from three factors: the amino acids adjacent to each fragmentation site, the mass dependence of ion types and the intensity dependence of ion types. The Viterbi algorithm is used to calculate the most probable assignment between ion types in a spectrum and a peptide sequence, then a correction factor is added to account for the propensity of the model to favor longer peptides. An expectation value is calculated based on the model score to assess the significance of each peptide/spectrum match. RESULTS: We trained and tested HMM_Score on three data sets generated by two different mass spectrometer types. For a reference data set recently reported in the literature and validated using seven identification algorithms, HMM_Score produced 43% more positive identification results at a 1% false positive rate than the best of two other commonly used algorithms, Mascot and X!Tandem. HMM_Score is a highly accurate platform for peptide identification that works well for a variety of mass spectrometer and biological sample types. AVAILABILITY: The program is freely available on ProteomeCommons via an OpenSource license. See http://bioinfo.unc.edu/downloads/ for the download link.     

CLASSIFY: A group teaching exercise in microbial identification and numerical taxonomy using a Commodore 64 microcomputer

An exercise designed to teach the basic principles of numerical taxonomy is described. The exercise utilizes a computer program, CLASSIFY, written in BASIC for the Commodore 64 microcomputer, which will accept student data obtained from the identification of bacteria, calculate the similarity coefficients, and perform Single Linkage Cluster Analysis. Bacterial identification can be achieved using live cultures and rapid identification kits, or, alternatively, via the bacterial culture simulation program, BUG SIMULATOR, which would be useful for data generation by students inexperienced in handling microbes (e.g. secondary school students).     

Modelling of biofilms

A mixed-culture biofilm (MCB) model is available which describes the progression of biofilm thickness and the spatial distribution and development in time of dissolved and paniculate components in the biofilm. The MCB model is able to predict the physico-chemical conditions at the interface between the biofilm and the solid surface, on which the biofilm grows, as a function of the conditions in the bulk fluid, the microbial composition of the biofilm, and the transport and transformation processes which take place in the biofilm. The mass balance equations of the MCB model are generally valid and can be applied to almost any microbial system if its kinetics and stoichiometry can be provided. AQUASIM is a new computer program for the identification and simulation of aquatic systems. The program solves the equations of the MCB model. It has a window-type user interface and includes routines for simulation, sensitivity analysis, automatic parameter estimation and data fitting. The MCB model has been developed and is primarily used in the field of waste water treatment. However, under certain conditions and with some additional simplifications this model can also be used for the investigation of biofouling and biocorrosion problems. The possibilities and limitations of the application of the MCB model and of AQUASIM to this type of problem are briefly discussed.     

Automatic identification of structural gene fragments using a set of peptides

A computer program is designed to facilitate the identification of coding gene's fragments using a set of peptides. The program is written on Basic programming language for personal computer "Iskra-226" (USSR). To accelerate some operations, computer code commands are used. Treatment of 50 DNA fragments by means of 10 peptides takes ca. 1 h of computer time. The program outputs list coding gene's fragments and corresponding peptides. The suggested algorithm is based on our finding that the number of false identifications of a coding gene fragments may be predicted by Poisson distribution and minimized using correct criteria. The suggested method enables one to evaluate the reliability of the true identification of DNA fragments in case of mistakes in primary structure of the gene fragments or peptides.     

A new identification aid combining features of a polyclave and an analytical key

A new identification aid combining features of a polyclave and an analytical key is described. It is based on presence-or-absence characters and is presented in the form of a matrix, with the characters in rows and the taxa in columns. The PHYTOTAB program package is used to order the matrix, in order to facilitate identification. The method was used to construct an identification aid for vegetative material and has wider taxonomic and teaching implications.     

CARBANA: Carbon analysis program forprotein sequences

There are lots of works gone into proteins to understand the nature of proteins. Hydrophobic interaction is the dominant force that drives the proteins to carry out the biochemical reactions in all living system. Carbon is the only element that contributes towards this hydrophobic interaction. Studies find that globular proteins prefer to have 31.45% of carbon for its stability. Taking this as standard, a carbon analysis program has been developed to study the carbon distribution profile of protein sequences. This carbon analysis program has been made available online. This can be accessed at www.rajasekaran.net.in/tools/carbana.html. This new program is hoped to help in identification and development of active sites, study of protein stability, evolutionary understating of proteins, gene identification, ligand binding site identification, and to solve the long-standing problem of protein-protein and protein-DNA interactions.     

Use of a computer for the purpose of identifying bacteria of the family Enterobacteriaceae and the determination of the minimal set of differential tests]

Algorhythm and a program for identification of bacteria of the Enterobacteriaceae family, based on Edwards and Ewing's diagnostic scheme, were worked out. Use of this program permitted to analyze different sets of abbreviated biochemical tests. To determine the genera and species of enterobacteria a minimal set of 11 tests is suggested, including indol formation, Voges-Proskauer's reaction, the presence of urease enzymes, gelatinase, lysine decraboxylase, phenylalanine deaminase, glucose fermentation (gas), or lactose, inosite, sorbit, arabinose, rhamnose. The program admits increase of both the biochemical tests, and toxonomic groups of bacteria, this permitting to consider several families. The presence of strains deviating by properties from this scheme points to the necessity of further improvement of diagnostic schemes for the enterobacteria identification.