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PubMed2006

Sub-speciating Campylobacter jejuni by proteomic analysis of its protein biomarkers and their post-translational modifications.

We have identified several protein biomarkers of three Campylobacter jejuni strains (RM1221, RM1859, and RM3782) by proteomic techniques. The protein biomarkers identified are prominently observed in the time-of-flight mass spectra (TOF MS) of bacterial cell lysate supernatants ionized by matrix-assisted laser desorption/ionization (MALDI). The protein biomarkers identified were: DNA-binding protein HU, translation initiation factor IF-1, cytochrome c553, a transthyretin-like periplasmic protein, chaperonin GroES, thioredoxin Trx, and ribosomal proteins: L7/L12 (50S), L24 (50S), S16 (30S), L29 (50S), and S15 (30S), and conserved proteins similar to strain NCTC 11168 proteins Cj1164 and Cj1225. The protein biomarkers identified appear to represent high copy, intact proteins. The significant findings are as follows: (1) Biomarker mass shifts between these strains were due to amino acid substitutions of the primary polypeptide sequence and not due to changes in post-translational modifications (PTMs). (2) If present, a PTM of a protein biomarker appeared consistently for all three strains, which supported that the biomarker mass shifts observed between strains were not due to PTM variability. (3) The PTMs observed included N-terminal methionine (N-Met) cleavage as well as a number of other PTMs. (4) It was discovered that protein biomarkers of C. jejuni (as well as other thermophilic Campylobacters) appear to violate the N-Met cleavage rule of bacterial proteins, which predicts N-Met cleavage if the penultimate residue is threonine. Two protein biomarkers (HU and 30S ribosomal protein S16) that have a penultimate threonine residue do not show N-Met cleavage. In all other cases, the rule correctly predicted N-Met cleavage among the biomarkers analyzed. This exception to the N-Met cleavage rule has implications for the development of bioinformatics algorithms for protein/pathogen identification. (5) There were fewer biomarker mass shifts between strains RM1221 and RM1859 compared to strain RM3782. As the mass shifts were due to the frequency of amino acid substitutions (and thus underlying genetic variations), this suggested that strains RM1221 and RM1859 were phylogenetically closer to one another than to strain RM3782 (in addition, a protein biomarker prominent in the spectra of RM1221 and RM1859 was absent from the RM3782 spectrum due to a nonsense mutation in the gene of the biomarker). These observations were confirmed by a nitrate reduction test, which showed that RM1221 and RM1859 were C. jejuni subsp. jejuni whereas RM3782 was C. jejuni subsp. doylei. This result suggests that detection/identification of protein biomarkers by pattern recognition and/or bioinformatics algorithms may easily subspeciate bacterial microorganisms. (6) Finally, the number and variation of PTMs detected in this relatively small number of protein biomarkers suggest that bioinformatics algorithms for pathogen identification may need to incorporate many more possible PTMs than suggested previously in the literature.

Amino Acid Sequence↗

PubMed2002

Large-scale computational identification of HIV T-cell epitopes.

Bioinformatics-driven T-cell epitope-identification methods can enhance vaccine target selection significantly. We evaluated three unrelated computational methods to screen Pol, Gag and Env sequences extracted from the Los Alamos HIV database for HLA-A*0201 and HLA-B*3501 T-cell epitope candidates. The hidden Markov model predicted 389 HLA-B*3501-restricted candidates from 374 HIV-1 and 97 HIV-2 sequences. The artificial neural network (ANN) model, and Bioinformatics and Molecular Analysis Section (BIMAS) quantitative matrix predictions for A*0201 yielded 1122 HIV-1 and 548 HIV-2 candidates. The overall sequence coverage of the predicted A*0201 T-cell epitopes was 2.7% (HIV-1)and 3.0% (HIV-2). HLA-B*3501-predicted epitopes covered 0.9% (HIV-1) and 1.4% (HIV-2) of the total sequence. Comparison of 890 ANN- and 397 BIMAS-derived HIV-1 A*0201- restricted epitope candidates showed that only 13-19% of the predicted and 26% of the experimentally confirmed T-cell epitopes were captured by both methods. Extrapolating these results, we estimated that at least 247 predicted HIV-1 epitopes are yet to be discovered as active A*0201-restricted T-cell epitopes. Adequate comparison and combined usage of various predictive bioinformatics methods, rather than uncritical use of any single prediction method, will enable cost-effective and efficient T-cell epitope screening.

Computational Biology↗

PubMed2001

Individual metabolism should guide agriculture toward foods for improved health and nutrition.

Genomics and bioinformatics have the vast potential to identify genes that cause disease by investigating whole-genome databases. Comparison of an individual's geno-type with a genomic database will allow the prescription of drugs to be tailored to an individual's genotype. This same bioinformatic approach, applied to the study of human metabolites, has the potential to identify and validate targets to improve personalized nutritional health and thus serve to define the added value for the next generation of foods and crops. Advances in high-throughput analytic chemistry and computing technologies make the creation of a vast database of metabolites possible for several subsets of metabolites, including lipids and organic acids. In creating integrative databases of metabolites for bioinformatic investigation, the current concept of measuring single biomarkers must be expanded to 3 dimensions to 1) include a highly comprehensive set of metabolite measurements (a profile) by multiparallel analyses, 2) measure the metabolic profile of individuals over time rather than simply in the fasted state, and 3) integrate these metabolic profiles with genomic, expression, and proteomic databases. Application of the knowledge of individual metabolism will revolutionize the ability of nutrition to deliver health benefits through food in the same way that knowledge of genomics will revolutionize individual treatment of dis-ease with pharmaceuticals.

Biomarkers↗

PubMed2005

Software agents in molecular computational biology.

Progress made in applying agent systems to molecular computational biology is reviewed and strategies by which to exploit agent technology to greater advantage are investigated. Communities of software agents could play an important role in helping genome scientists design reagents for future research. The advent of genome sequencing in cattle and swine increases the complexity of data analysis required to conduct research in livestock genomics. Databases are always expanding and semantic differences among data are common. Agent platforms have been developed to deal with generic issues such as agent communication, life cycle management and advertisement of services (white and yellow pages). This frees computational biologists from the drudgery of having to re-invent the wheel on these common chores, giving them more time to focus on biology and bioinformatics. Agent platforms that comply with the Foundation for Intelligent Physical Agents (FIPA) standards are able to interoperate. In other words, agents developed on different platforms can communicate and cooperate with one another if domain-specific higher-level communication protocol details are agreed upon between different agent developers. Many software agent platforms are peer-to-peer, which means that even if some of the agents and data repositories are temporarily unavailable, a subset of the goals of the system can still be met. Past use of software agents in bioinformatics indicates that an agent approach should prove fruitful. Examination of current problems in bioinformatics indicates that existing agent platforms should be adaptable to novel situations.

Algorithms↗

PubMed2003

caCORE: a common infrastructure for cancer informatics.

MOTIVATION: Sites with substantive bioinformatics operations are challenged to build data processing and delivery infrastructure that provides reliable access and enables data integration. Locally generated data must be processed and stored such that relationships to external data sources can be presented. Consistency and comparability across data sets requires annotation with controlled vocabularies and, further, metadata standards for data representation. Programmatic access to the processed data should be supported to ensure the maximum possible value is extracted. Confronted with these challenges at the National Cancer Institute Center for Bioinformatics, we decided to develop a robust infrastructure for data management and integration that supports advanced biomedical applications. RESULTS: We have developed an interconnected set of software and services called caCORE. Enterprise Vocabulary Services (EVS) provide controlled vocabulary, dictionary and thesaurus services. The Cancer Data Standards Repository (caDSR) provides a metadata registry for common data elements. Cancer Bioinformatics Infrastructure Objects (caBIO) implements an object-oriented model of the biomedical domain and provides Java, Simple Object Access Protocol and HTTP-XML application programming interfaces. caCORE has been used to develop scientific applications that bring together data from distinct genomic and clinical science sources. AVAILABILITY: caCORE downloads and web interfaces can be accessed from links on the caCORE web site (http://ncicb.nci.nih.gov/core). caBIO software is distributed under an open source license that permits unrestricted academic and commercial use. Vocabulary and metadata content in the EVS and caDSR, respectively, is similarly unrestricted, and is available through web applications and FTP downloads. SUPPLEMENTARY INFORMATION: http://ncicb.nci.nih.gov/core/publications contains links to the caBIO 1.0 class diagram and the caCORE 1.0 Technical Guide, which provide detailed information on the present caCORE architecture, data sources and APIs. Updated information appears on a regular basis on the caCORE web site (http://ncicb.nci.nih.gov/core).

Animals↗

PubMed2005

A novel approach for clustering proteomics data using Bayesian fast Fourier transform.

MOTIVATION: Bioinformatics clustering tools are useful at all levels of proteomic data analysis. Proteomics studies can provide a wealth of information and rapidly generate large quantities of data from the analysis of biological specimens. The high dimensionality of data generated from these studies requires the development of improved bioinformatics tools for efficient and accurate data analyses. For proteome profiling of a particular system or organism, a number of specialized software tools are needed. Indeed, significant advances in the informatics and software tools necessary to support the analysis and management of these massive amounts of data are needed. Clustering algorithms based on probabilistic and Bayesian models provide an alternative to heuristic algorithms. The number of clusters (diseased and non-diseased groups) is reduced to the choice of the number of components of a mixture of underlying probability. The Bayesian approach is a tool for including information from the data to the analysis. It offers an estimation of the uncertainties of the data and the parameters involved. RESULTS: We present novel algorithms that can organize, cluster and derive meaningful patterns of expression from large-scaled proteomics experiments. We processed raw data using a graphical-based algorithm by transforming it from a real space data-expression to a complex space data-expression using discrete Fourier transformation; then we used a thresholding approach to denoise and reduce the length of each spectrum. Bayesian clustering was applied to the reconstructed data. In comparison with several other algorithms used in this study including K-means, (Kohonen self-organizing map (SOM), and linear discriminant analysis, the Bayesian-Fourier model-based approach displayed superior performances consistently, in selecting the correct model and the number of clusters, thus providing a novel approach for accurate diagnosis of the disease. Using this approach, we were able to successfully denoise proteomic spectra and reach up to a 99% total reduction of the number of peaks compared to the original data. In addition, the Bayesian-based approach generated a better classification rate in comparison with other classification algorithms. This new finding will allow us to apply the Fourier transformation for the selection of the protein profile for each sample, and to develop a novel bioinformatic strategy based on Bayesian clustering for biomarker discovery and optimal diagnosis.

Algorithms↗

PubMed2006

XQTav: an XQuery processor for Taverna environment.

UNLABELLED: Taverna workbench is an environment for construction, visualization and execution of bioinformatic workflows that integrate specialized tools available through the internet. It is gaining popularity fast, because of supporting the most important bioinformatic services and its simple, yet robust graphical notation. Here we present XQTav-an extension of Taverna that provides full integration with XQuery (the query language for XML) engine. XQTav allows execution of XQuery scripts in Taverna workflow diagrams. All existing Taverna processors can be accessed in the XQuery scripts. This provides an alternative way of specifying subworkflows in Taverna and is useful when one deals with query-like algorithms (e.g. filters and inner joins). Moreover, XQtav may be used to automatically generate an XQuery script that is equivalent to Taverna's workflow. This constitutes another way of creating and enacting bioinformatic workflows: overall structure of a diagram is drawn in Taverna environment, XQuery code is generated and possibly adjusted by hand. It can be executed by XQuery engines or incorporated into other software environments. AVAILABILITY: XQtav is an open source software. It may be downloaded from http://xqtav.sourceforge.net/. The page also contains various tutorials and examples, including the one described in this report.

Algorithms↗

PubMed2005

KEGG as a glycome informatics resource.

Bioinformatics approaches to carbohydrate research have recently begun using large amounts of protein and carbohydrate data. In this field called glycome informatics, the foremost necessity is a comprehensive resource for genome-scale bioinformatics analysis of glycan data. Although the accumulation of experimental data may be useful as a reference of biological and biochemical information on carbohydrates, this is insufficient for bioinformatics analysis. Thus, we have developed a glycome informatics resource (http://www.genome.jp/kegg/glycan/) in KEGG (Kyoto Encyclopedia of Genes and Genomes), an integrated knowledge base of protein networks, genomic information, and chemical information. This review describes three noteworthy features: (1) GLYCAN, a database of carbohydrate structures; (2) glycan-related pathways; and (3) Composite Structure Map (CSM), a map illustrating all possible variations of carbohydrate structures within organisms. GLYCAN includes two useful tools: an intuitive drawing tool called KegDraw, and an efficient glycan search and alignment tool called KEGG Carbohydrate Matcher (KCaM). KEGG's glycan biosynthesis and metabolism pathways, integrating carbohydrate structures, proteins, and reactions, are also a pivotal resource. CSM is constructed as a bridge between carbohydrate functions and structures. CSM is able to display, for example, expression data of glycosyltransferases in a compact manner. In all the KEGG resources, various objects including KEGG pathways, chemical compounds, as well as carbohydrate structures are commonly represented as graphs, which are widely studied and utilized in the computer science field.

Carbohydrates↗

PubMed1998

BioViews: Java-based tools for genomic data visualization.

Visualization tools for bioinformatics ideally should provide universal access to the most current data in an interactive and intuitive graphical user interface. Since the introduction of Java, a language designed for distributed programming over the Web, the technology now exists to build a genomic data visualization tool that meets these requirements. Using Java we have developed a prototype genome browser applet (BioViews) that incorporates a three-level graphical view of genomic data: a physical map, an annotated sequence map, and a DNA sequence display. Annotated biological features are displayed on the physical and sequence-based maps, and the different views are interconnected. The applet is linked to several databases and can retrieve features and display hyperlinked textual data on selected features. In addition to browsing genomic data, different types of analyses can be performed interactively and the results of these analyses visualized alongside prior annotations. Our genome browser is built on top of extensible, reusable graphic components specifically designed for bioinformatics. Other groups can (and do) reuse this work in various ways. Genome centers can reuse large parts of the genome browser with minor modifications, bioinformatics groups working on sequence analysis can reuse components to build front ends for analysis programs, and biology laboratories can reuse components to publish results as dynamic Web documents.

Animals↗

PubMed2005

The SOL Genomics Network: a comparative resource for Solanaceae biology and beyond.

The SOL Genomics Network (SGN; http://sgn.cornell.edu) is a rapidly evolving comparative resource for the plants of the Solanaceae family, which includes important crop and model plants such as potato (Solanum tuberosum), eggplant (Solanum melongena), pepper (Capsicum annuum), and tomato (Solanum lycopersicum). The aim of SGN is to relate these species to one another using a comparative genomics approach and to tie them to the other dicots through the fully sequenced genome of Arabidopsis (Arabidopsis thaliana). SGN currently houses map and marker data for Solanaceae species, a large expressed sequence tag collection with computationally derived unigene sets, an extensive database of phenotypic information for a mutagenized tomato population, and associated tools such as real-time quantitative trait loci. Recently, the International Solanaceae Project (SOL) was formed as an umbrella organization for Solanaceae research in over 30 countries to address important questions in plant biology. The first cornerstone of the SOL project is the sequencing of the entire euchromatic portion of the tomato genome. SGN is collaborating with other bioinformatics centers in building the bioinformatics infrastructure for the tomato sequencing project and implementing the bioinformatics strategy of the larger SOL project. The overarching goal of SGN is to make information available in an intuitive comparative format, thereby facilitating a systems approach to investigations into the basis of adaptation and phenotypic diversity in the Solanaceae family, other species in the Asterid clade such as coffee (Coffea arabica), Rubiaciae, and beyond.

Arabidopsis↗

PubMed2002

Mobile access to biological databases on the Internet.

We have developed a new way of accessing biological databases and bioinformatics applications on the Internet. This new service, bioinformatics wireless application protocol (BioWAP) service, which is accessible by mobile devices makes it possible to access bioinformatics services, where normal PC or personal digital assistant (PDA) connections are not feasible. The BioWAP service includes major biological databases and applications demonstrating a simple method of implementing WAP interfaces to uncompliant applications, i.e. the applications that are not WAP or Internet based. The BioWAP service can be browsed with any WAP terminal.

Computational Biology↗

PubMed2006

A gzip-based algorithm to identify bacterial families by 16S rRNA.

AIMS: Microbial family identification of 16S rDNA sequences by applying a strategy based on algorithms for data compression. METHODS AND RESULTS: Perl scripts were developed to analyse similarities in microbial sequences, based on a gzip data compression technique. For each bacterial family (n=196) a 16S rRNA reference file was constructed to compare new queries looking at compression performance. An online user-friendly bioinformatics tool was built up to attribute a bacterial family to a 16S rRNA sequence. It was successfully applied to recognize different bacterial families, including Legionellaceae, Bacillaceae, Enterobacteriaceae, Acetobacteriaceae and Rhizobiaceae. The percentage of positive identifications is higher than 95% for fragments over 450 bp. CONCLUSIONS: A new bioinformatics approach has been developed to assign a taxonomic classification to a 16SrDNA sequence. An online tool provides quick and easy sequence attribution. The general principle can be applied to other genes of taxonomic interest. SIGNIFICANCE AND IMPACT OF THE STUDY: Availability of simple bioinformatics tools can support the development of molecular-based analysis and classification of bacteria, especially for environmental or uncultured strains.

Algorithms↗

PubMed2026

Validation of an integrated metagenomic pipeline combining optimized wet-lab processing and tiered reporting for CSF pathogen detection.

UNLABELLED: Metagenomic next-generation sequencing (mNGS) in the infectious disease diagnostic space has been gaining traction and is popular for aiding in the diagnosis of central nervous system infections. However, many challenges and obstacles remain in making this technology a gold standard for infectious disease diagnostic testing. One major challenge is being able to distinguish between the clinically relevant organisms from background contamination. We performed a validation study for mNGS on cerebrospinal fluid (CSF) that utilized positive clinical samples and contrived samples that incorporated a bioinformatics pipeline that can better distinguish between background contamination and clinically relevant organisms and used a three-tiered reporting algorithm meant to decrease the inherent subjectivity that comes with interpreting and reporting data from clinical metagenomic sequencing. The validation of this assay and category-based reporting pipeline revealed an overall concordance of 91.8%, with a sensitivity of 100% and a specificity of 72.4%. In addition, we improved the detection of clinically relevant RNA viruses to almost 100% in the CSF by modifying the wet lab processing of the sample. This bioinformatics pipeline with a category-based reporting algorithm will provide more confidence in reporting microorganisms detected with this technology, mNGS, and improving patient care. IMPORTANCE: Metagenomic next-generation sequencing (mNGS) can offer a broad, unbiased approach for the detection of infectious pathogens and has shown promise in diagnosing central nervous system infections. Despite its potential, clinical implementation remains limited by challenges in distinguishing clinically relevant organisms from background contamination. This study validated an mNGS assay for cerebrospinal fluid that incorporates an optimized bioinformatics pipeline with a three-tiered reporting algorithm designed to reduce subjectivity and enhance diagnostic confidence. The assay also has improved detection of clinically relevant RNA viruses through modified wet-lab processing. These findings support the clinical utility of a structured, category-based reporting approach for mNGS, advancing its reliability as a diagnostic tool in infectious disease testing.

Metagenomics↗

PubMed2006

Identification of transcriptionally active open reading frames within the RD1 genomic segment of Mycobacterium tuberculosis.

OBJECTIVE: To identify transcriptionally active open reading frames (ORFs), predicted by bioinformatics, within RD1 genomic segment of Mycobacterium tuberculosis using reverse transcription-polymerase chain reaction (RT-PCR). MATERIALS AND METHODS: M. tuberculosis H37Rv was grown in Middlebrook 7H9 medium for 8 weeks and total RNA was isolated using standard procedures. The cDNA was synthesized using first-strand cDNA synthesis kit and general primers provided in the kit [pd (N)6, and/or Not I-d(T)18] as well as forward primers specific for each predicted RD1 ORF. Specific forward and reverse primers in PCR were used to amplify ORF-specific cDNA. The amplified products were identified on the basis of size using agarose gel electrophoresis, and their identity was confirmed by DNA sequencing. RESULTS: RT-PCR demonstrated expression of 13 of the 14 bioinformatics-predicted ORFs within RD1 genomic segment of M. tuberculosis. However, cDNA synthesis and PCR amplifications of specific products varied with respect to primer requirement and reaction conditions, respectively. All ORFs of <1.5 kb were amplified in standard RT-PCR, whereas several large-size ORFs (>1.5 kb) required internal primers for amplification in semi-nested RT-PCR. The sequencing of RT-PCR-amplified products of ORFs confirmed their identity. CONCLUSION: Bioinformatics analysis of DNA can accurately predict ORFs within M. tuberculosis-specific genomic regions, and RT-PCR is a suitable technique to confirm their expression in bacteria.

Bacterial Proteins↗

PubMed2005

Wildfire: distributed, Grid-enabled workflow construction and execution.

BACKGROUND: We observe two trends in bioinformatics: (i) analyses are increasing in complexity, often requiring several applications to be run as a workflow; and (ii) multiple CPU clusters and Grids are available to more scientists. The traditional solution to the problem of running workflows across multiple CPUs required programming, often in a scripting language such as perl. Programming places such solutions beyond the reach of many bioinformatics consumers. RESULTS: We present Wildfire, a graphical user interface for constructing and running workflows. Wildfire borrows user interface features from Jemboss and adds a drag-and-drop interface allowing the user to compose EMBOSS (and other) programs into workflows. For execution, Wildfire uses GEL, the underlying workflow execution engine, which can exploit available parallelism on multiple CPU machines including Beowulf-class clusters and Grids. CONCLUSION: Wildfire simplifies the tasks of constructing and executing bioinformatics workflows.

Algorithms↗

PubMed2005

High performance workflow implementation for protein surface characterization using grid technology.

BACKGROUND: This study concerns the development of a high performance workflow that, using grid technology, correlates different kinds of Bioinformatics data, starting from the base pairs of the nucleotide sequence to the exposed residues of the protein surface. The implementation of this workflow is based on the Italian Grid.it project infrastructure, that is a network of several computational resources and storage facilities distributed at different grid sites. METHODS: Workflows are very common in Bioinformatics because they allow to process large quantities of data by delegating the management of resources to the information streaming. Grid technology optimizes the computational load during the different workflow steps, dividing the more expensive tasks into a set of small jobs. RESULTS: Grid technology allows efficient database management, a crucial problem for obtaining good results in Bioinformatics applications. The proposed workflow is implemented to integrate huge amounts of data and the results themselves must be stored into a relational database, which results as the added value to the global knowledge. CONCLUSION: A web interface has been developed to make this technology accessible to grid users. Once the workflow has started, by means of the simplified interface, it is possible to follow all the different steps throughout the data processing. Eventually, when the workflow has been terminated, the different features of the protein, like the amino acids exposed on the protein surface, can be compared with the data present in the output database.

Automation↗

PubMed2006

TF Target Mapper: a BLAST search tool for the identification of Transcription Factor target genes.

BACKGROUND: In the current era of high throughput genomics a major challenge is the genome-wide identification of target genes for specific transcription factors. Chromatin immunoprecipitation (ChIP) allows the isolation of in vivo binding sites of transcription factors and provides a powerful tool for examining gene regulation. Crosslinked chromatin is immunoprecipitated with antibodies against specific transcription factors, thus enriching for sequences bound in vivo by these factors in the immunoprecipitated DNA. Cloning and sequencing the immunoprecipitated sequences allows identification of transcription factor target genes. Routinely, thousands of such sequenced clones are used in BLAST searches to map their exact location in the genome and the genes located in the vicinity. These genes represent potential targets of the transcription factor of interest. Such bioinformatics analysis is very laborious if performed manually and for this reason there is a need for developing bioinformatic tools to automate and facilitate it. RESULTS: In order to facilitate this analysis we generated TF Target Mapper (Transcription Factor Target Mapper). TF Target Mapper is a BLAST search tool allowing rapid extraction of annotated information on genes around each hit. It combines sequence cleaning/filtering, pattern searching and BLAST searches with extraction of information on genes located around each BLAST hit and comparisons of the output list of genes or gene ontology IDs with user-implemented lists. We successfully applied and tested TF Target Mapper to analyse sequences bound in vivo by the transcription factor GATA-1. We show that TF Target Mapper efficiently extracted information on genes around ChIPed sequences, thus identifying known (e.g. alpha-globin and zeta-globin) and potentially novel GATA-1 gene targets. CONCLUSION: TF Target Mapper is a very efficient BLAST search tool that allows the rapid extraction of annotated information on the genes around each hit. It can contribute to the comprehensive bioinformatic transcriptome/regulome analysis, by providing insight into the mechanisms of action of specific transcription factors, thus helping to elucidate the pathways these factors regulate.

Amino Acid Sequence↗

PubMed2006

An interactive visualization tool to explore the biophysical properties of amino acids and their contribution to substitution matrices.

BACKGROUND: Quantitative descriptions of amino acid similarity, expressed as probabilistic models of evolutionary interchangeability, are central to many mainstream bioinformatic procedures such as sequence alignment, homology searching, and protein structural prediction. Here we present a web-based, user-friendly analysis tool that allows any researcher to quickly and easily visualize relationships between these bioinformatic metrics and to explore their relationships to underlying indices of amino acid molecular descriptors. RESULTS: We demonstrate the three fundamental types of question that our software can address by taking as a specific example the connections between 49 measures of amino acid biophysical properties (e.g., size, charge and hydrophobicity), a generalized model of amino acid substitution (as represented by the PAM74-100 matrix), and the mutational distance that separates amino acids within the standard genetic code (i.e., the number of point mutations required for interconversion during protein evolution). We show that our software allows a user to recapture the insights from several key publications on these topics in just a few minutes. CONCLUSION: Our software facilitates rapid, interactive exploration of three interconnected topics: (i) the multidimensional molecular descriptors of the twenty proteinaceous amino acids, (ii) the correlation of these biophysical measurements with observed patterns of amino acid substitution, and (iii) the causal basis for differences between any two observed patterns of amino acid substitution. This software acts as an intuitive bioinformatic exploration tool that can guide more comprehensive statistical analyses relating to a diverse array of specific research questions.

Amino Acid Sequence↗