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PubMed2006

EGenBio: a data management system for evolutionary genomics and biodiversity.

BACKGROUND: Evolutionary genomics requires management and filtering of large numbers of diverse genomic sequences for accurate analysis and inference on evolutionary processes of genomic and functional change. We developed Evolutionary Genomics and Biodiversity (EGenBio; http://egenbio.lsu.edu) to begin to address this. DESCRIPTION: EGenBio is a system for manipulation and filtering of large numbers of sequences, integrating curated sequence alignments and phylogenetic trees, managing evolutionary analyses, and visualizing their output. EGenBio is organized into three conceptual divisions, Evolution, Genomics, and Biodiversity. The Genomics division includes tools for selecting pre-aligned sequences from different genes and species, and for modifying and filtering these alignments for further analysis. Species searches are handled through queries that can be modified based on a tree-based navigation system and saved. The Biodiversity division contains tools for analyzing individual sequences or sequence alignments, whereas the Evolution division contains tools involving phylogenetic trees. Alignments are annotated with analytical results and modification history using our PRAED format. A miscellaneous Tools section and Help framework are also available. EGenBio was developed around our comparative genomic research and a prototype database of mtDNA genomes. It utilizes MySQL-relational databases and dynamic page generation, and calls numerous custom programs. CONCLUSION: EGenBio was designed to serve as a platform for tools and resources to ease combined analysis in evolution, genomics, and biodiversity.

Animals↗

PubMed2003

Prediction of protein function using protein-protein interaction data.

Assigning functions to novel proteins is one of the most important problems in the postgenomic era. Several approaches have been applied to this problem, including the analysis of gene expression patterns, phylogenetic profiles, protein fusions, and protein-protein interactions. In this paper, we develop a novel approach that employs the theory of Markov random fields to infer a protein's functions using protein-protein interaction data and the functional annotations of protein's interaction partners. For each function of interest and protein, we predict the probability that the protein has such function using Bayesian approaches. Unlike other available approaches for protein annotation in which a protein has or does not have a function of interest, we give a probability for having the function. This probability indicates how confident we are about the prediction. We employ our method to predict protein functions based on "biochemical function," "subcellular location," and "cellular role" for yeast proteins defined in the Yeast Proteome Database (YPD, www.incyte.com), using the protein-protein interaction data from the Munich Information Center for Protein Sequences (MIPS, mips.gsf.de). We show that our approach outperforms other available methods for function prediction based on protein interaction data. The supplementary data is available at www-hto.usc.edu/~msms/ProteinFunction.

Bayes Theorem↗

PubMed2004

A classification of disulfide patterns and its relationship to protein structure and function.

We report a detailed classification of disulfide patterns to further understand the role of disulfides in protein structure and function. The classification is applied to a unique searchable database of disulfide patterns derived from the SwissProt and Pfam databases. The disulfide database contains seven times the number of publicly available disulfide annotations. Each disulfide pattern in the database captures the topology and cysteine spacing of a protein domain. We have clustered the domains by their disulfide patterns and visualized the results using a novel representation termed the "classification wheel." The classification is applied to 40,620 protein domains with 2-10 disulfides. The effectiveness of the classification is evaluated by determining the extent to which proteins of similar structure and function are grouped together through comparison with the SCOP and Pfam databases, respectively. In general, proteins with similar disulfide patterns have similar structure and function, even in cases of low sequence similarity, and we illustrate this with specific examples. Using a measure of disulfide topology complexity, we find that there is a predominance of less complex topologies. We also explored the importance of loss or addition of disulfides to protein structure and function by linking classification wheels through disulfide subpattern comparisons. This classification, when coupled with our disulfide database, will serve as a useful resource for searching and comparing disulfide patterns, and understanding their role in protein structure, folding, and stability. Proteins in the disulfide clusters that do not contain structural information are prime candidates for structural genomics initiatives, because they may correspond to novel structures.

Animals↗

PubMed2004

Predicting co-complexed protein pairs using genomic and proteomic data integration.

BACKGROUND: Identifying all protein-protein interactions in an organism is a major objective of proteomics. A related goal is to know which protein pairs are present in the same protein complex. High-throughput methods such as yeast two-hybrid (Y2H) and affinity purification coupled with mass spectrometry (APMS) have been used to detect interacting proteins on a genomic scale. However, both Y2H and APMS methods have substantial false-positive rates. Aside from high-throughput interaction screens, other gene- or protein-pair characteristics may also be informative of physical interaction. Therefore it is desirable to integrate multiple datasets and utilize their different predictive value for more accurate prediction of co-complexed relationship. RESULTS: Using a supervised machine learning approach--probabilistic decision tree, we integrated high-throughput protein interaction datasets and other gene- and protein-pair characteristics to predict co-complexed pairs (CCP) of proteins. Our predictions proved more sensitive and specific than predictions based on Y2H or APMS methods alone or in combination. Among the top predictions not annotated as CCPs in our reference set (obtained from the MIPS complex catalogue), a significant fraction was found to physically interact according to a separate database (YPD, Yeast Proteome Database), and the remaining predictions may potentially represent unknown CCPs. CONCLUSIONS: We demonstrated that the probabilistic decision tree approach can be successfully used to predict co-complexed protein (CCP) pairs from other characteristics. Our top-scoring CCP predictions provide testable hypotheses for experimental validation.

Computational Biology↗

PubMed2002

Predicting functional linkages from gene fusions with confidence.

Pairs of genes that function together in a pathway or cellular system can sometimes be found fused together in another organism as a Rosetta Stone protein--a fusion protein whose separate domains are homologous to the two functionally-related proteins. The finding of such a Rosetta Stone protein allows the prediction of a functional linkage between the component proteins. The significance of these deduced functional linkages, however, varies depending on the prevalence of each of the two domains. Here, we develop a statistical measure for the significance of predicted functional linkages, and test this measure for proteins of E. coli on a functional benchmark based on the KEGG database. By applying this statistical measure, proteins can be linked with over 70% accuracy. Using the Rosetta Stone method and this scoring scheme, we find all significant functional linkages for proteins of E. coli, P. horikshii and S. cerevisiae, and measure the extent of the resulting protein networks.

Artificial Gene Fusion↗

PubMed2003

Viewing and annotating sequence data with Artemis.

Artemis is a widely used software tool for annotating and viewing sequence data. No database is required to use Artemis. Instead, individual sequence data files can be analysed with little or no formatting, making it particularly suited to the study of small genomes and chromosomes, and straightforward for a novice user to get started. Since its release in 1999, Artemis has been used to annotate a diverse collection of prokaryotic and eukaryotic genomes, ranging from Streptomyces coelicolor to, more recently, a large proportion of the Plasmodium falciparum genome. Artemis allows annotated genomes to be easily browsed and makes it simple to add useful biological information to raw sequence data. This paper gives an overview of some of the features of Artemis and includes how it facilitates manual gene prediction and can provide an overview of entire chromosomes or small compact genomes--useful for uncovering unusual features such as pathogenicity islands.

Animals↗

PubMed2003

ESTWeb: bioinformatics services for EST sequencing projects.

ESTWeb is an internet based software package designed for uniform data processing and storage for large-scale EST sequencing projects. The package provides for: (a) reception of sequencing chromatograms; (b) sequence processing such as base-calling, vector screening, comparison with public databases; (c) storage of data and analysis in a relational database, (d) generation of a graphical report of individual sequence quality; and (e) issuing of reports with statistics of productivity and redundancy. The software facilitates real-time monitoring and evaluation of EST sequence acquisition progress along an EST sequencing project.

Computational Biology↗

PubMed2005

Gene expression signature of estrogen receptor alpha status in breast cancer.

BACKGROUND: Estrogens are known to regulate the proliferation of breast cancer cells and to modify their phenotypic properties. Identification of estrogen-regulated genes in human breast tumors is an essential step toward understanding the molecular mechanisms of estrogen action in cancer. To this end we generated and compared the Serial Analysis of Gene Expression (SAGE) profiles of 26 human breast carcinomas based on their estrogen receptor alpha (ER) status. Thus, producing a breast cancer SAGE database of almost 2.5 million tags, representing over 50,000 transcripts. RESULTS: We identified 520 transcripts differentially expressed between ERalpha-positive (+) and ERalpha-negative (-) primary breast tumors (Fold change >or= 2; p < 0.05). Furthermore, we identified 220 high-affinity Estrogen Responsive Elements (EREs) distributed on the promoter regions of 163 out of the 473 up-modulated genes in ERalpha (+) breast tumors. In brief, we observed predominantly up-regulation of cell growth related genes, DNA binding and transcription factor activity related genes based on Gene Ontology (GO) biological functional annotation. GO terms over-representation analysis showed a statistically significant enrichment of various transcript families including: metal ion binding related transcripts (p = 0.011), calcium ion binding related transcripts (p = 0.033) and steroid hormone receptor activity related transcripts (p = 0.031). SAGE data associated with ERalpha status was compared with reported information from breast cancer DNA microarrays studies. A significant proportion of ERalpha associated gene expression changes was validated by this cross-platform comparison. However, our SAGE study also identified novel sets of genes as highly expressed in ERalpha (+) invasive breast tumors not previously reported. These observations were further validated in an independent set of human breast tumors by means of real time RT-PCR. CONCLUSION: The integration of the breast cancer comparative transcriptome analysis based on ERalpha status coupled to the genome-wide identification of high-affinity EREs and GO over-representation analysis, provide useful information for validation and discovery of signaling networks related to estrogen response in this malignancy.

Biomarkers, Tumor↗

PubMed2004

The UCSC Table Browser data retrieval tool.

The University of California Santa Cruz (UCSC) Table Browser (http://genome.ucsc.edu/cgi-bin/hgText) provides text-based access to a large collection of genome assemblies and annotation data stored in the Genome Browser Database. A flexible alternative to the graphical-based Genome Browser, this tool offers an enhanced level of query support that includes restrictions based on field values, free-form SQL queries and combined queries on multiple tables. Output can be filtered to restrict the fields and lines returned, and may be organized into one of several formats, including a simple tab- delimited file that can be loaded into a spreadsheet or database as well as advanced formats that may be uploaded into the Genome Browser as custom annotation tracks. The Table Browser User's Guide located on the UCSC website provides instructions and detailed examples for constructing queries and configuring output.

Animals↗

PubMed2005

Exploring relationships and mining data with the UCSC Gene Sorter.

In parallel with the human genome sequencing and assembly effort, many tools have been developed to examine the structure and function of the human gene set. The University of California Santa Cruz (UCSC) Gene Sorter has been created as a gene-based counterpart to the chromosome-oriented UCSC Genome Browser to facilitate the study of gene function and evolution. This simple, but powerful tool provides a graphical display of related genes that can be sorted and filtered based on a variety of criteria. Genes may be ordered based on such characteristics as expression profiles, proximity in genome, shared Gene Ontology (GO) terms, and protein similarity. The display can be restricted to a gene set meeting a specific set of constraints by filtering on expression levels, gene name or ID, chromosomal position, and so on. The default set of information for each gene entry-gene name, selected expression data, a BLASTP E-value, genomic position, and a description-can be configured to include many other types of data, including expanded expression data, related accession numbers and IDs, orthologs in other species, GO terms, and much more. The Gene Sorter, a CGI-based Web application written in C with a MySQL database, is tightly integrated with the other applications in the UCSC Genome Browser suite. Available on a selected subset of the genome assemblies found in the Genome Browser, it further enhances the usefulness of the UCSC tool set in interactive genomic exploration and analysis.

Computational Biology↗

PubMed2001

Cell signalling - the proteomics of it all.

A challenge for biomedical scientists today is to arrive at an understanding of cellular behavior on a global scale. The advent of DNA microarrays has greatly facilitated discovery of gene expression profiles associated with different cellular states. The problem of understanding cellular signaling at the level of the interacting proteins is in some ways more challenging. Ashman et al. discuss the current methods available for studying protein interactions on a global scale, as well as directions for the future. Technical hurdles exist at many stages, from the isolation of protein complexes, to the determination of their composition, to the software and databases needed to analyze the results of large-scale, high-throughput datasets. Ashman et al. suggest that, with advances in technology and cooperation among academia and industry, a global protein interaction map that underlies cellular behavior will emerge as an essential resource for basic and applied research.

Computational Biology↗

PubMed2005

COPASAAR--a database for proteomic analysis of single amino acid repeats.

BACKGROUND: Single amino acid repeats make up a significant proportion in all of the proteomes that have currently been determined. They have been shown to be functionally and medically significant, and are associated with cancers and neuro-degenerative diseases such as Huntington's Chorea, where a poly-glutamine repeat is responsible for causing the disease. The COPASAAR database is a new tool to facilitate the rapid analysis of single amino acid repeats at a proteome level. The database aims to simplify the comparison of repeat distributions between proteomes in order to provide a better understanding of their function and evolution. RESULTS: A comparative analysis of all proteomes in the database (currently 244) shows that single amino acid repeats account for about 12-14% of the proteome of any given species. They are more common in eukaryotes (14%) than in either archaea or bacteria (both 13%). Individual analyses of proteomes show that long single amino acid repeats (6+ residues) are much more common in the Eukaryotes and that longer repeats are usually made up of hydrophilic amino acids such as glutamine, glutamic acid, asparagine, aspartic acid and serine. CONCLUSION: COPASAAR is a useful tool for comparative proteomics that provides rapid access to amino acid repeat data that can be readily data-mined. The COPASAAR database can be queried at the kingdom, proteome or individual protein level. As the amount of available proteome data increases this will be increasingly important in order to automate proteome comparison. The insights gained from these studies will give a better insight into the evolution of protein sequence and function.

Algorithms↗

PubMed2004

Strategic proteome analysis of Candida magnoliae with an unsequenced genome.

Erythritol is a noncariogenic, low calorie sweetener. It is safe for people with diabetes and obese people. Candida magnoliae is an industrially important organism because of its ability to produce erythritol as a major product. The genome of C. magnoliae has not been sequenced yet, limiting the available proteome database. Therefore, systematic approaches were employed to construct the proteome map of C. magnoliae. Proteomic analysis with systematic approaches is based on two-dimensional electrophoresis, matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS), tandem mass spectrometry (MS/MS) and database interrogation. First, 24 spots were analyzed using peptide mass fingerprinting along with MALDI-TOF MS with high mass accuracy. Only four spots were reliably identified as carbonyl reductase and its isoforms. The reason for low sequence coverage seemed to be that these identification strategies were based on the presence of the protein database obtained from the publicly accessible genome database and the availability of cross-species protein identification. MS/MS (MS/MS ion search and de novo sequencing) in combination with similarity searches allowed successful identification of 39 spots. Several proteins including transaldolase identified by MS/MS ion searches were further confirmed by partial sequences from the expressed sequence tag database. In this study, 51 protein spots were analyzed and then potentially identified. The identified proteins were involved in glycolysis, stress response, other essential metabolisms and cell structures.

Algorithms↗

PubMed2005

Comparative analysis of human intronless proteins.

The availability of the complete genome sequences of Homo sapiens together with those of taxonomically diverse organisms provides an opportunity to carry out cross-species comparison. Comparisons of protein sequences from different organisms are significant source of information as these could help in answering questions regarding the fraction of proteins that are shared by humans and organisms representing the three domains of life, viz., archaea, bacteria, and eukaryota. In the present study, a comparative analysis of the proteins encoded by intronless genes in humans was undertaken. We identified 1125 human intronless proteins that are solely present in eukaryotic lineage. More than two-thirds of these eukaryotic specific proteins appear to be mammalia specific while a small fraction of proteins are conserved in bilateria and coelomata, indicating that diversification of these proteins occurred after the divergence of the major lineages of the eukaryotic crown group. A large fraction of mammalia specific proteins are enriched in proteins responsible for transport and binding, cell envelope, and housekeeping function particularly translation. Another 228 intronless proteins are observed that do not exhibit homology to any of the proteins in the database. The distribution of human intronless proteins suggests that lineage specific expansion is one of the most important sources of organizational diversity in crown-group eukaryotes. The presence of these eukaryotic as well as human specific intronless proteins provides the foundation for rapid analysis of some of the basic processes involved in human genome.

Animals↗

PubMed2004

Génolevures: comparative genomics and molecular evolution of hemiascomycetous yeasts.

The Génolevures online database (http://cbi.labri.fr/Genolevures/) provides data and tools to facilitate comparative genomic studies on hemiascomycetous yeasts. Now, four complete genome sequences recently determined (Candida glabrata, Kluyveromyces lactis, Debaryomyces hansenii, Yarrowia lipolytica) have been added to the partial sequences of 13 species previously analysed by a random approach. The database also includes the reference genome Saccharomyces cerevisiae. Data are presented with a focus on relations between genes and genomes: conservation of genes and gene families, speciation, chromosomal reorganization and synteny. The Génolevures site includes a community area for specific studies by members of the international community.

Computational Biology↗

PubMed2004

The Pfam protein families database.

Pfam is a large collection of protein families and domains. Over the past 2 years the number of families in Pfam has doubled and now stands at 6190 (version 10.0). Methodology improvements for searching the Pfam collection locally as well as via the web are described. Other recent innovations include modelling of discontinuous domains allowing Pfam domain definitions to be closer to those found in structure databases. Pfam is available on the web in the UK (http://www.sanger.ac.uk/Software/Pfam/), the USA (http://pfam.wustl.edu/), France (http://pfam.jouy.inra.fr/) and Sweden (http://Pfam.cgb.ki.se/).

Animals↗

PubMed2003

An automated program to screen databases for members of protein families.

We have developed a program, ScreenTransporter (ST), to screen for potential members of recognized transporter families. This program uses Blastpgp as the engine to search a nonredundant database, NRDB90, based on an adjustable E-value cut-off as well as adjustable protein size criteria. Additional parameters can be integrated in later versions. ST is convenient for easily obtaining nonredundant members of transporter families starting from several homologous query sequences. The program can be applied to any protein family.

Computational Biology↗

PubMed2003

Bioinformatic strategies for better understanding of immune function.

Novartis Foundation sponsored a Symposium which brought together a group of experimental immunologists, theoretical immunologists, and bioinformaticians to discuss the new field of immunoinformatics. The discussion focused on immunological databases, antigen processing and presentation, immunogenomics, host-pathogen interactions, and mathematical modelling of the immune system. A main conclusion of the meeting is the critical role played by immunoinformatics in current immunology research. In particular, immunoinformatics provides a foundation for the emerging fields of systems immunology and immunogenomics.

Allergy and Immunology↗