ields to graphically represent various types of hierarchical relationships, including evolutionary relationships between species, divergent patterns between subpopulations and evolutionary relationships between genes. These trees are generally rooted and semi-labeled, i.e., they descend from a single node called the root, bifurcate at lower nodes and end at terminal nodes, called tips or leaves; the leaves are labeled by the names of the species, subpopulations or genes being studied. In biological studies the latter are called operational taxonomic units (OTU's). Traditionally, trees were inferred form morphological similarities among the OTU's. To build an evolutionary species tree, or phylogenetic tree, two species which shared the most characteristics were classified as `siblings' and assumed to share a common ancestor which is not the ancestor of any other species. Such `siblings' are said to be homologous, and it is this basic homo

High-throughput structural proteomics is expected to generate considerable amounts of data on the progress of structure determination for many proteins. For each protein this includes information about cloning, expression, purification, biophysical characterization and structure determination via NMR spectroscopy or X-ray crystallography. It will be essential to develop specifications and ontologies for standardizing this information to make it amenable to retrospective analysis. To this end we created the SPINE database and analysis system for the Northeast Structural Genomics Consortium. SPINE, which is available at bioinfo.mbb.yale.edu/ nesg or nesg.org, is specifically designed to enable distributed scientific collaboration via the Internet. It was designed not just as an information repository but as an active vehicle to standardize proteomics data in a form that would enable systematic data mining. The system features an intuitive user interface for interactive retrieval and modification of expression construct data, query forms designed to track global project progress and external links to many other resources. Currently the database contains experimental data on 985 constructs, of which 740 are drawn from Methanobacterium thermoautotrophicum, 123fromSaccharomyces cerevisiae, 93fromCaenorhabditis elegans and the remainder from other organisms. We developed a comprehensive set of data mining features for each protein, including several related to experimental progress (e.g. expression level, solubility and crystallization) and 42 based on the underlying protein sequence (e.g. amino acid composition, secondary structure and occurrence of low complexity regions). We demonstrate in detail the application of a particular machine learning approach, decision trees, t...

Abstract (2 sentences) An important aspect of structural genomics is connecting coordinate data with whole-genome information related to phylogenetic occurrence, protein function, gene expression, and protein-protein interactions. Integrative database analysis can highlight certain folds and structural features that stand out against the general population of proteins in particular ways. Individual bits of genomic data need to be put in a context to be meaningful. For instance, the isolated fact that yeast gene YBR191w is expressed at a level of 65 copies per cell in GeneChip experiments is, by itself, meaningless. However, if one can connect this measurement to those of other genes and an overall functional classification, one can determine that this gene codes for a ribosomal protein and that ribosomal proteins have amongst the highest levels of expression in yeast. The same logic applies to structure. Coordinates by themselves just specify shape and are not of intrinsic biological value, unless they can be related to other information. In the past, for "singlemolecule" experiments, formal integration was unnecessary; one got the whole picture through reading the literature. However, this is impossible for all ~18,000 proteins in the worm. Thus,

BACKGROUND: The recent flood of data from genome sequencing and functional genomics has given rise to new field, bioinformatics, which combines elements of biology and computer science. OBJECTIVES: Here we propose a definition for this new field and review some the research that is being pursued, particularly in relation to transcriptional regulatory systems. METHODS: Our definition is as follows: Bioinformatics is conceptualizing biology in terms of macromolecules (in the sense of physical-chemistry) and then applying "informatics" techniques (derived from disciplines such as applied maths, computer science, and statistics) to understand and organize the information associated with these molecules, on a large-scale. RESULTS & CONCLUSIONS: Analyses in bioinformatics predominantly focus on three types of large datasets available in molecular biology: macromolecular structures, genome sequences, and the results of functional genomics experiments (eg expression data). Additional information includes the text of scientific papers and "relationship data" from metabolic pathways, taxonomy trees, and proteinprotein interaction networks. Bioinformatics employs a wide range of computational topics including sequence and structural alignment, database design and data mining, macromolecular geometry, phylogenetic tree construction, prediction of protein structure and function, gene finding, and expression data clustering. The emphasis is on approaches that integrate a variety of computational techniques and heterogeneous data sources. Finally, bioinformatics is a practical discipline. We survey some representative applications, such as finding homologues, designing drugs, and performing large-scale censuses. Additional information pertinent to the review is available over the w...

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A flood of data means that many of the challenges in biology are now challenges in computing. Bioinformatics, the application of computational techniques to analyse the information associated with biomolecules on a large-scale, has now firmly established itself as a discipline in molecular biology, and encompasses a wide range of subject areas from structural biology, genomics to gene expression studies. In this review we provide an introduction and overview of the current state of the field. We discuss the main principles that underpin bioinformatics analyses, look at the types of biological information and databases that are commonly used, and finally examine some of the studies that are being conducted, particularly with reference to transcription regulatory systems. 2. Introduction Biological data are flooding in at an unprecedented rate (1). For example as of August 2000, the GenBank repository of nucleic acid sequences contained 8,214,000 entries (2) and the SWISS-PROT databas...

We conducted a structural genomics analysis of the folds and structural superfamilies in the first 20 completely sequenced genomes by focusing on the patterns of fold usage and trying to identify structural characteristics of typical and atypical folds. We assigned folds to sequences using PSI-blast, run with a systematic protocol to reduce the amount of computational overhead. On average, folds could be assigned to about a fourth of the ORFs in the genomes and about a fifth of the amino acids in the proteomes. More than 80% of all the folds in the SCOP structural classification were identified in one of the 20 organisms, with worm and E. coli having the largest number of distinct folds. Folds are particularly effective at comprehensively measuring levels of gene duplication, because they group together even very remote homologues. Using folds, we find the average level of duplication varies depending on the complexity of the organism, ranging from 2.4 in M. genitalium to 32 for the worm, values significantly higher than those observed based purely on sequence similarity. We rank the common folds in the 20 organisms, finding that the top three are the P-loop NTP hydrolase, the ferrodoxin fold, and the TIM-barrel, and discuss in detail the many factors that affect and bias these rankings. We also identify atypical folds that are "unique" to one of the organisms in our study and compare the characteristics of these folds with the most common ones. We find that common folds tend be more multifunctional and associated with more regular, "symmetrical " structures than the unique ones. In addition, many of the unique folds are associated with proteins involved in cell defense (e.g., toxins). We analyze specific patterns of fold occurrence in the genomes by associating some of...

We present a prototype of a new database tool, GeneCensus, which focuses on comparing genomes globally, in terms of the collective properties of many genes, rather than in terms of the attributes of a single gene (e.g. sequence similarity for a particular ortholog). The comparisons are presented in a visual fashion over the web at GeneCensus.org. The system concentrates on two types of comparisons: (i) trees based on the sharing of generalized protein families between genomes, and (ii) whole pathway analysis in terms of activity levels. For the trees, we have developed a module (TreeViewer) that clusters genomes in terms of the folds, superfamilies, or orthologs-- all can be considered as generalized "families " or "protein parts "-- they share, and compares the resulting trees side-by-side with those built from sequence similarity of individual genes (e.g. a traditional tree built on ribosomal similarity). We also include comparisons to trees built on whole-genome dinucleotide or codon composition. For pathway comparisons, we have implemented a module (PathwayPainter) that graphically depicts, in selected metabolic pathways, the fluxes or expression levels of the associated enzymes (i.e. generalized "activities"). One can, consequently, compare organisms (and organism states) in terms of representations of these systemic quantities. Development of this module involved compiling, calculating and standardizing flux and expression information from many different sources. We illustrate pathway analysis for enzymes involved in central metabolism. We are able to show that, to some degree, flux and expression fluctuations have characteristic values in different sections of the central metabolism and that control points in this system (e.g. hexokinase, pyruvate kinase, phosphofructokinase, isocitrate dehydrogenase, and citric synthase) tend to be especially variable in flux and expression. Both the TreeViewer and PathwayPainter modules connect to other information sources related to individual-gene or organism properties (e.g. a single-gene structural annotation viewer).

In recent years, the analysis of orthologous genes based on phylogenetic profiles has received popularity in bioinfomatics. We propose a new method to extract organism groups and their hierarchy from phylogenetic profiles using the independent component analysis (ICA). The method involves first finding independent axes in the projected space from the multivariate data matrix representing phylogenetic profiles for a number of orthologous genes. Then the extracted axes are correlated with major organism groups, according to the extent of affiliaion of axes scores for all the genes to specific organisms. The ICA was applied to the phylogenetic profiles created for 2875 orthologs in 77 organisms by using the KEGG/GENES database. The 9 extracted components out of 18 predefined components well represented the organism groups as categorized in KEGG. Furthermore, we performed the cluster analysis and obtained the hierarchy of organism groups.

Based on searches for disabled homologs to known proteins, we have identified a large population of pseudogenes in four sequenced eukaryotic genomes---the worm, yeast, fly and human (chromosomes 21 and 22 only). Each of our nearly 2500 pseudogenes is characterized by one or more disablements middomain, such as premature stops and frameshifts. Here, we perform a comprehensive survey of the amino acid and nucleotide composition of these pseudogenes in comparison to that of functional genes and intergenic DNA. We show that pseudogenes invariably have an amino acid composition intermediate between genes and translated intergenic DNA. Although the degree of intermediacy varies among the four organisms, in all cases, it is most evident for amino acid types that differ most in occurrence between genes and intergenic regions. The same intermediacy also applies to codon frequencies, especially in the worm and human. Moreover, the intermediate composition of pseudogenes applies even though the composition of the genes in the four organisms is markedly different, showing a strong correlation with the overall A/T content of the genomic sequence. Pseudogenes can be divided into `ancient' and `modern' subsets, based on the level of sequence identity with their closest matching homolog (within the same genome). Modern pseudogenes usually have a much closer sequence composition to genes than ancient pseudogenes. Collectively, our results indicate that the composition of pseudogenes that are under no selective constraints progressively drifts from that of coding DNA towards non-coding DNA. Therefore, we propose that the degree to which pseudogenes approach a random sequence composition may be useful in dating different sets of pseudogenes, as well as to assess the rate at which intergen...