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Rapid mining of large genomic and metagenomic data sets for modular polyketide synthases, non-ribosomal peptide synthetases and hybrid polyketide synthase/non-ribosomal peptide synthetase biosynthetic gene clusters has been achieved using the generic computer program packages ClustScan and CompGen. These program packages perform the annotation with the hierarchical structuring into polypeptides, modules and domains, as well as storage and graphical presentations of the data. This aims to achieve the most accurate predictions of the activities and specificities of catalytically active domains that can be made with present knowledge, leading to a prediction of the most likely chemical structures produced by these enzymes. The program packages also allow generation of novel clusters by homologous recombination of the annotated genes in silico. ClustScan and CompGen were used to construct a custom database of known compounds (CSDB) and of predicted entirely novel recombinant products (r-CSDB) that can be used for in silico screening with computer-aided drug design technology. The use of these programs has been exemplified by analysing genomic sequences from terrestrial prokaryotes and eukaryotic microorganisms, a marine metagenomic data set and a newly discovered example of a 'shared metabolic pathway' in marine-microbial endosymbiosis.

Peptide mass fingerprinting is a term which describes technique which utilizes ESI or MALDI MS followed by tandem mass spectrometry sequencing. This technique has become a cornerstone for protein identification. Today, applications using peptide mass fingerprinting in biomedical analyses are a major driving force behind its rapid development. However, efficient and accurate analyses of generally big protein tandem mass spectrometry data sets require robust software. In terms of final goal, which is data interpretation, the role of software and underlying algorithms is at least equally important as the technique itself, a fact which is often neglected. High-throughput mass spectrometry instruments can readily generate hundreds of thousands of spectra. This fact combined with the ever growing size of genomic databases imposes tremendous demands for potential successful software solutions. In fact, it is the process of comparing large-scale mass spectrometry data with large databases that remains the toughest bottleneck in proteomics. Here we present a completely novel approach based on natural language processing which is not just another improvement of existing approaches, but represents a paradigm shift. It doesn't rely on peak intensity for database peptide matching and it uses newly developed concept of microbial proteome fingerprints for strain/species identification. Since this new algorithm doesn't rely on sequence alignment but instead utilizes a concept of singular proteome fingerprints rather than sets of unrelated peptides, it proposes an elegant solution for this most troubling step in proteome analyses. Abandoning BLAST and other alignment based methods, results in far superior processing speed, accuracy and sensitivity. The above mentioned algorithm can be used to analyse not only proteomes but also metaproteomes coming from mixed microbe communities as in the case presented – human urine samples taken from a hospital. The method itself is completely generic, not developed with any specific platform in mind, which makes it highly versatile, able to turn any existing device into highly efficient metaproteome analyser without significant costs related to purchase of new equipment.

Samples were collected from sea sediments at seven sites in the northern Adriatic Sea that included six sites next to industrial complexes and one from a tourist site (recreational beach). The samples were assayed for alkanes and polycyclic aromatic hydrocarbons. The composition of the hydrocarbon samples suggested that industrial pollution was present in most cases. A sample from one site was also grown aerobically under crude oil enrichment in order to evaluate the response of indigenous bacterial populations to crude oil exposure. Analysis of 16S rRNA gene sequences showed varying microbial biodiversity depending on the level of pollution – ranging from low (200 detected genera) to high (1000+ genera) biodiversity, with lowest biodiversity observed in polluted samples. This indicated that there was considerable biodiversity in all sediment samples but it was severely restricted after exposure to crude oil selection pressure. Phylogenetic analysis of putative alkB genes showed high evolutionary diversity of the enzymes in the samples and suggested great potential for bioremediation and bioprospecting. The first systematic analysis of bacterial communities from sediments of the northern Adriatic Sea is presented, and it will provide a baseline assessment that may serve as a reference point for ecosystem changes and hydrocarbon degrading potential – a potential that could soon gain importance due to plans for oil exploitation in the area.

Peptide mass fingerprinting is an analytical technique for protein identification developed in 1993. Since then, it emerged as a dominant method in proteomics, along with Edman Degradation. Generally, the method can be divided in two distinct parts, first part being accurate measurement of protein fragment masses with a mass spectrometer such as MALDI-TOF or ESI-TOF and second part which utilizes computer programs to compare these masses to either a database containing known protein sequences or even the genomes. Mass-spectrometry part has constantly been improved by onset of newer Mass Spectrometry devices with drastic improvements in accuracy. Computer program analysis part has been lagging behind. It’s not uncommon that a single proteome analysis capturing thousands of proteins gets done in several hours, while the computer based database search and protein identification part takes days. Today, when Mass-Spectrometry based proteomic assays are becoming standard to identify bacteria at the genus and species level in many clinical microbiology laboratories, it is prime time to find solutions which would bridge this gap between computer and spectrometer. In our lab, we are developing one such solution by replacing numerous unrelated single protein searches with something we call a “suspect species fingerprint”.

The recent achievement of synthesising a functioning bacterial chromosome marks a coming of age for engineering living organisms. In the future this should allow the construction of novel organisms to help solve the problems facing the human race, including health care, food, energy and environmental protection. In this minireview, the current state of the field is described and the role of synthetic biology in biotechnology in the short and medium term is discussed. It is particularly aimed at the needs of food technologists, nutritionists and other biotechnologists, who might not be aware of the potential significance of synthetic biology to the research and development in their fields. The potential of synthetic biology to produce interesting new polyketide compounds is discussed in detail.

The rapid progress in DNA sequencing technology is resulting in exponentially growing microbial genome sequences, but the increase in laboratory-derived information on protein function is much slower. In many cases, standard bioinformatics methods can assign genes to a family, but the exact function of the encoded protein remains unknown. Moreover, the generation of high quality DNA sequence is disproportionately time consuming and expensive. The dramatic fall in sequencing costs means that genome sequencing is tending to become an experimental technique, which is used as part of the strategy for tackling a research problem, rather than an aim in itself. It is, therefore, important to develop methods for the structural and functional automatic annotation for the analysis of lower quality sequence starting from DNA reads generated, for example, by the 454 Genome Sequencer FLX instrument. We have developed the bioinformatics expert system for the structural and functional automatic annotation of microbial genomes and metagenomes that allows simple browsing of DNA reads. The expert system consists of structural and functional annotation that runs on a server as a bioinformatics "black box" and the Web based graphical user interface for sequence interrogation. Unlike most other systems, the initial functional analysis is carried out on reads before assembly. The reads are translated in silico and the resulting protein sequences scanned using HMM-profiles. Depending on the aims of the analysis, it is possible to use a generic library of profiles (e.g. corresponding to the classification in the KEGG database) or a custom databases of choice (e.g. to detect particular classes of enzymes in metagenomic data). The reads are subsequently assembled (e.g. using the Newbler whole-genome sequence assembler). The assembled sequences can be used for "gene of interest" (GOl) analysis by BLAST and/or InterproScan that are also integrated within the expert system. The user can browse the sequences for GOls with the Solr search engine, which is implemented within the graphical user interface. An example of a recently sequenced microbial genome will be used to demonstrate the functionality of the expert system. The aim is to provide a flexible intuitive system for browsing microbial genomic sequences and metagenomic sequences, which allows the user to "Google" the data.

Modular polyketide synthases (PKS) and non-ribosomal peptide synthetases (NRPS) consist of large polypeptides, which can have more than 10000 amino acids. The polypeptides usually contain several modules each divided into domains. The fundamental structure of each module is similar, but they differ in detail according to the nature of the extender unit that they incorporate. A large number of PKS and NRPS clusters are known, which provides a lot of dana for testing evolutionary hypotheses about codon usage. Both types of clusters are present in actinomycetes, which have a high G+C-content and a highly biased codon usage. Many NRPS clusters are also present in Bacillus species and PKS clusters are common in myxobacteria. Comparison of the clusters in these distantly related bacteria with the similar clusters in actinomycetes shows whether the observed codon usage phenomena are determined by the evolutionary pressures on the proteins or caused by the host organisms. Use of common codons for an amino acid is associated with high efficiencies of translation, which can be estimated using the codon adaptation index (CAI). The CAI was calculated in sliding windows for the modular proteins and showed characteristic short regions of slow translation at conserved positions in modules, which may be associated with allowing time for protein folding. Selective pressures acting on the modules and domains were estimated using the ratio of non-synonymous/synonymous codons in sliding windows. This confirmed that most of the sequences had low ratios suggesting strong purifying selection.

INTRODUCTION Functional "-omics" describes the techniques that map interactions between biological macromolecules described in various "-omes" such as the genome, transcriptome, proteome and metabolome. Amongst these omics, nutritional genomics is the most recent. METHODOLOGY The American National Institute of Health has set a goal for individual human genome sequencing to cost no more than 1, 000 USD. At this price, it is realistic that individual genomes will be routinely screened for genes responsible for hereditary diseases, including those influenced by nutrition. RESULTS AND DISCUSSION Nutrigenomics is the study of how individual genetic differences can affect the way we respond to nutrients in the foods we eat. Nutrigenomics has received much attention because of the potential for preventing or treating chronic disease including certain cancers, through dietary changes. The conceptual basis for this new branch of genomics can best be summarized by the five doctrines of nutrigenomics: 1/ under certain circumstances and in some individuals, diet can be a serious risk factor for a number of diseases ; 2/ common dietary chemicals can act on the human genome, either directly or indirectly, to alter gene expression or structure ; 3/ the degree to which diet influences the balance between health and disease may depend on an individual's genetic makeup ; 4/ some diet-regulated genes are likely to play a role in the onset, incidence, progression, and/or severity of chronic diseases ; 5/ dietary intervention based on knowledge of nutritional requirement, nutritional status and genotype can be used to prevent, mitigate or cure chronic disease. CONCLUSION The promise of nutrigenomics is personalized medicine based upon an understanding of our nutritional needs, nutritional and health status and our genotype. Knowledge gained from comparing diet/gene interactions in different populations may provide the information needed to address the larger problem of global malnutrition and disease.

INTRODUCTION Bacterial natural products are important sources of chemical diversity for commercial exploitation by the pharmaceutical industry. In the past few years there has been a lot of interest in generating new compounds as potential drug candidates by manipulating the programming of biosynthetic gene-clusters in vitro. In order to assist this process a novel expert system for in silico drug design and discovery was developed. METHODOLOGY The expert system has a server-client architecture, with analysis being carried out on the server and a Java user interface for the client which can be PC, Mac or Linux. RESULTS AND DISCUSSION This expert system consists of two suites of programs for semi-automatic DNA sequence analysis (ClustScan) and for the generation of novel gene-clusters by virtual homeologous recombination (CompGen). ClustScan and CompGen are used to generate two specialised databases. The CSDB is a ClustScan database of well-characterised polyketide and nonribosomal peptide natural products. The database contains 170 well annotated natural product gene-clusters. Conversely, r-CSDB is a virtual compound database for molecular modelling studies developed by the use of the CompGen program package and contains more than 20.000 novel compounds. In silico studies are only useful if it is possible to generate strains producing them. Continuing progress in synthetic biology will improve methods to achieve that. CONCLUSION A major issue for the pharmaceutical industry is maintaining a continuous supply of promising new leads for drug development. We propose that recombinatorial biosynthesis offers a new and exciting strategy whereby large and chemically diverse libraries of polyketides can first be screened in silico and then generated in the laboratory for further new lead development. Given that many polyketides are used clinically as antimicrobials, this new expert system comes at an important time when ever increasing numbers of pathogens are becoming resistant to our current antibiotic armoury.

Bacterial natural products are important sources of chemical diversity for commercial exploitation by the pharmaceutical industry. In the past few years there has been a lot of interest in generating new compounds as potential drug candidates by manipulating the programming of biosynthetic gene-clusters in vitro. In order to assist this process a novel expert system for in silico drug design and discovery was developed (3DIS). Te expert system has server-client architecture, with analysis being carried out on the server and a Java user interface for the client which can be a PC, Mac or Linux. 3DIS consist of two program packages: for semi-automatic DNA sequence analysis (ClustScan) and for the generation of novel gene-clusters by virtual homeologous recombination (CompGen). ClustScan and CompGen were used to generate two specialised databases. CSDB is a ClustScan database of well characterised polyketide and nonribosomal peptide natural products. Te database contains 170 uniformly annotated natural product gene-clusters. In contrast, r-CSDB is a virtual compound database for molecular modelling studies produced by the CompGen program package and contains more than 11, 796 novel compounds. In silico studies are only useful if it is possible to generate strains producing them. Continuing progress in synthetic biology will improve methods to achieve that. A major issue for the pharmaceutical industry is maintaining a continuous supply of promising new leads for drug development. We propose that recombinatorial biosynthesis ofers a new and exciting strategy whereby large and chemically diverse libraries of polyketides can frst be screened in silico and then gener-ated in the laboratory for further new lead development. Given that many polyketides are used clinically as antimicrobials, the 3DIS comes at an important time when with ever increasing numbers of pathogens are becoming resistant to our current antibiotic armamentarium.

Traditionally genome sequencing was an expensive activity. It was carried out by specialised institutes, with the aim of producing a generic genomic annotation, which would then be used as a genetic map for many other research groups interested in the organism. Sequencing institutes maintained a bioinformatics team to carry out the annotation. The dramatic fall in sequencing costs means that genome sequencing is tending to become an experimental technique, which is used as part of the strategy for tackling a research problem, rather than an aim in itself. Thus, the aim of each genome project is different and not satisfied by the provision of a generic annotation. Individual research laboratories or even most biotechnology SMEs do not have the experience and infrastructure in bioinformatics to effectively solve the problems posed by the analysis of genome sequences. Although sequencing service providers can undertake an assembly of the sequence and provide some basic standard annotation, both processes are error-prone and the results need to be critically evaluated. The low cost of genome sequencing is leading to an explosion of sequence information available in the web resulting in problems with search strategies, which may make the recognition of the most relevant information nearly impossible. We see this situation as a market chance for bioinformatics service providers and have started developing a novel expert system for genomic sequence analyses. Our strategy can be described as a bioinformatics "black box design". The user is only interested in whether the system can solve his problems and is not interested in the details of the programs that do the work for him ("black boxes"). Thus, the major emphasis is in designing a human interface that is intuitive and flexible enough for the user to specify the particular problem that he wants to solve. This has obvious analogies to the general searching for information on the internet and we found ourselves repeatedly asking a single question best put by the title of a recent book from Jeff Jarvis: "What Would Google Do?".

Modular polyketide synthases (PKS) are an attractive target for synthetic biology approaches with the possibility of reusing synthesized components in many constructs. A major stumbling block in the reprogramming of PKS has been the low product yield after most manipulations. Homologous recombination probably plays a major role in the natural evolution of modular PKS and would occur in regions of high sequence similarity, which may well favour better product yield compared to junctions typically selected for in vitro manipulation. However, it is not clear whether recombination between existing clusters can cover a comparable chemical space compared to a fully combinatorial approach. We decided to model homologous recombination between pairs of PKS clusters to determine the potential to generate novel chemical entities. The recombination model assumed a core of sequence identity in a region of high sequence similarity. The ClustScan annotation program developed a data format for modular PKSs that links DNA sequence to module architecture and the chemical structure of the polyketide backbone. This format was used to develop an algorithm to determine whether a recombinant would produce a polyketide product and predict the chemical structure. For ease of use, this was implemented as a program (CompGen) with a graphical user interface. Recombination between 47 well-characterised clusters was modeled. This yielded 20, 187 recombinants that were predicted to produce a product and 11, 796 unique chemical entities. The details of the recombinants were entered into a database (http://bioserv.pbf.hr/cms/). The chemical structures can be exported in a standard SMILE format. These were used to calculate the molecular weights and degree of reduction of the predicted products. These data showed that they covered a significant region of chemical space. The large number of recombinants and their chemical diversity indicate that this is an attractive target for synthetic biology to produce novel lead compounds.

Modular polyketide synthases (PKS) have attracted a lot of attention as a target for creating novel pharmaceutical products. Although there has been success in manipulating decorating enzymes, most attempts to modify the polyketide backbone have resulted in very low product yields making them of no commercial interest. As there is a vast diversity of natural polyketides, it is interesting to examine how modular polyketides evolve in nature. Most attempts to modify PKS in vitro have exchanged single domains. Examination of the ratio of synonomous/non-synonomous codon changes between different PKS modules shows that there is very strong purifying selection. This would explain why the non-natural junctions created by in vitro approaches are so deleterious and suggest a strategy of preserving natural junction sequences, which could be implemented by appropriate bioinformatic methods. The study of the evolution of modular PKS has been dominated by the observation that the ketosynthase domains (KS) form cluster-specific groupings in phylogenetic trees whereas the acyl transferase (AT) domains are grouped according to substrate incorporated. This discordance has sometimes been interpreted as evidence of extensive horizontal gene transfer. Our studies showed that this phenomenon could be more easily explained by a high frequency of gene conversion within clusters. Once this was understood, it was possible to reexamine the mechanisms of modular PKS evolution. We suggest that a major mechanism utilises homologous recombination systems for single cross overs between clusters. This would generate radically new polyketide backbones. Modelling of homologous recombination in silico provides a novel strategy for generating new polyketide backbones with natural junctions, which are likely to synthesise products in reasonable yields. A software package CompGen was developed to model homologous recombination and predict the chemical structures of novel compounds produced by recombinants. Examples of predicted recombinants and their products are presented in the r-CSDB database. Methods are available to allow the efficient production of recombinants in the laboratory.

AMYLOMICS is an acronym for the European project "AMYLOenzymes" captured by targeted metagenOMICS that is funded by EU in the 7th framework program. The aim is to develop new, robust enzymes for the starch and carbohydrate industries. The enzymes should enable the formation of new primary products, such as oligosaccharides of defined sizes, composition and degree of branching, new types of linkages, cyclic or more complex polysaccharides, increased digestive resistance, and secondary sugar derivatives such as substituted starches and polyols. Fundamental to the success of the project will be the development of an efficient platform technology for enzyme screening based on Targeted Metagenomics, which comprises: microbial enrichment techniques, massive parallel 454 sequencing and microarray based sequence capture. The starch and the carbohydrate industries depend greatly on biocatalytic processes in modifying and fractionating polysaccharides and its derived components for the production of industrial and consumer products. There is a constant need for a range of robust enzymes that can be used for the synthesis, fractionation and/or modification of carbohydrates. There is also a need for economical alternatives to the existing tools and techniques, both for the production of novel higher value products and for the optimization and improvement of older processes. A special focus will be on harvesting new carbohydrate modifying genes/enzymes from metagenome libraries obtained from environments extreme in more than one aspect, i.e. thermo-acidophilic and thermo-alkalophilic biotopes. There is usually an advantage of using moderate to high temperatures (60-100°C) in industrial catalytic processes for lowering the viscosity of polysaccharides and increasing enzymatic access. Furthermore, enzymes adapted to these environments are not only thermostable but also often resistant to proteases, organic solvents and extremes of pH. Exploitation of high-throughput sequence data of metagenomes has been hampered by the high yield of short length DNA reads with limited informative content. AMYLOMICS addresses this problem by developing a gene retrieval platform based on massive parallel sequencing and microarray sequence capture techniques. These methodologies combined in the project enable “genome walking” of complex environmental DNA. Consequently, this greatly facilitates the access to the largely unexplored wealth of genes in the environment and greatly improves upon current technologies. The Metagenomic mining platform that will be developed in this project efficiently circumvents the need for labour intensive cloning, construction of large insert gene-libraries and subsequent functional screening. It also evades the limitation of sequence based PCR methods.

Natural products derived from bacteria have traditionally been an important source of new leads for the pharmaceutical industry. The clustering of genes encoding enzymes that biosynthesise these compounds are very interesting evolutionary systems, as they are subject to strong selective pressure and can undergo horizontal gene transfer. Gene clusters that encode modular polyketide synthases (PKSs) can undergo recombination events including duplication and deletion that contribute to their evolution. Modular PKSs are large multifunctional proteins composed of individual catalytic domains that can be grouped into modules, whose properties determine the extender unit that is built into the polyketide at each step. Most evolutionary studies have concentrated on the evolution of single domains within modules, which does not identify whether sequences are orthologues or paralogues. We identified orthologous clusters so that it was possible to examine the evolution of sets of orthologous domains within modules. This analysis was greatly assisted by using the ClustScan computer package (Starcevic et al., Nucleic Acids Res. 36: 6882-6892, 2008) for the efficient annotation of clusters in a hierarchical fashion. Gene conversion was detected by examining phylogenetic trees constructed from the sequences of single domains: ketosynthase (KS), acyl transferase (AT), acyl carrier protein (ACP), ketoreductase (KR), dehydratase (DH) and enoyl reductase (ER). In most cases, phylogenetic trees based on DNA and protein sequences gave essentially the same results. However, for the KS domain, DNA analysis showed over 50% more gene conversion events than the protein analysis ; this was interpreted as being due to strong selection pressure to preserve activity of this highly conserved domain. It was found that gene conversion was common and could affect any of the domains in a module. However, the frequencies of gene conversion varied between domains ranging from 27% for KS and 5% for ACP. To our surprise, this seemed to follow the biosynthetic order of the domains in a module (KS≈AT>DH>ER>KR>ACP). KS seems to be a generic domain with no contribution to specificity of the product and gene conversion between the KS domains of a cluster would explain the previous observation that all KS domains of a cluster group together in phylogenetic trees. AT determines substrate choice and AT domains with the same specificities from different clusters group together. In this case, constraints of function would explain why the gene conversion events do not result in grouping of all AT domains of a cluster. We suggest that a module could in fact be considered an evolutionary unit that drives selection in modular PKSs. Strategies for deducing evolutionary relationships of clusters in the presence of large amount of gene conversion will also be discussed.

Rapid mining of large genomic and metagenomic data sets for modular polyketide synthases, non-ribosomal peptide synthetases and hybrid polyketide synthases/non-ribosomal peptide synthetases biosynthetic gene clusters was achieved using the generic computer program packages ClustScan and CompGen. These program packages perform the annotation with the hierarchical structuring into polypeptides, modules and domains, as well as storage and graphical presentations of the data. This aims to achieve the most accurate predictions of the activities and specificities of catalytically active domains that can be made with present knowledge, leading to a prediction of the most likely chemical structures produced by these enzymes. The program packages also allow generation of novel clusters by homologous recombination of the annotated genes in silico. ClustScan and CompGen were used to construct a custom database of known compounds (CSDB) and of predicted entirely novel recombinant products (r-CSDB), that can be used for in silico screening with computer aided drug design technology. The use of these programs have been exemplified by analysing genomic sequences from terrestrial prokaryotes and eukaryotic microorganisms, a marine metagenomic dataset and a newly discovered example of a "shared metabolic pathway" in marine-microbial endosymbiosis.

The genome sequences of a small number of individuals have already been determined. “ New generation” DNA sequencing technology should lead to rapidly falling prices and the widespread determination of individual genome sequences. A major problem will be the provision of expert advice to interpret the results, both for the individual and their physician. An integrated set of computer programs (ClustScan) was developed initially to annotate modular biosynthetic clusters in bacteria that are important for the production of antibiotics and other pharmaceutically important products. The concept of ClustScan is to use a top-down approach incorporating knowledge in an ordered way (using XML) to produce results in a form useful for Biologists including a special graphical user interface. The versatility of this program package has been demonstrated by annotating biochemical pathways in microbial, invertebrate animal and metagenomic datasets. The open architecture of ClustScan allows easy integration with other programs and updating to incorporate new knowledge. The analysis of an individual human genome requires a number of different sorts of analyses. This includes detection of alleles (both known and novel) of genes as well as determination of haplotypes that are linked to QTLs of medical and pharmacogenomic significance. The results of the analyses must be integrated with existing knowledge to produce a medically useful tool. The concepts used in ClustScan seem ideally suited to generate a novel tool for automatic individual genome annotation, TAGS (Tool for Analysis of Genome Sequences). We would welcome collaboration with clinicians in the development of TAGS.

Enzymes of the shikimic acid pathway catalyse the biosynthesis of many aromatic compounds by a broad range of organisms, including bacteria, fungi, plants and some protozoans. Animals are considered to lack this pathway as evinced by their dietary requirement for shikimate-derived aromatic amino acids, anthranilate and folate. We aim to challenge the universality of this traditional view by annotating genes encoding enzymes of the shikimic acid pathway in the sequenced genome of the starlet sea anemone, Nematostella vectensis. The published DNA sequence of the N. vectensis genome was downloaded from StellaBase version 1.0 and translated into all six reading frames. For profile analyses, HMMER version 2.3.2 and release 20 of the Pfam database were used. Similarity searches were performed using the BLAST service at NCBI. Molecular evidence establishes the horizontal transfer of ancestral genes of the shikimic acid pathway into the Nematostella genome from both bacterial and eukaryotic (dinoflagellate) donors. In addition to dinoflagelate sequences, the published genome comprises a high content of Tenacibaculum-like gene orthologs, including a large 16S rRNA fragment that establishes these orthologs to originate from the Flavobacteriaceae. Bioinformatics analysis reveals four additional genes encoding enzyme orthologs of the shikimate pathway that are from an unrelated bacterial source, raising speculation for the existence of a previously unsuspected symbiont in this sea anemone. These results provide a complementary view of the biogenesis of shikimate-related metabolites in marine Cnidaria as a "shared metabolic adaptation" between an invertebrate host and its microbial consorts.

The rapid progress in DNA sequencing technology will soon make the sequencing of the genomes of individuals commonplace. This will create the problem of analysing the mass of data to extract the relevant information. An integrated set of computer programs, the ClustScan (Cluster Scanner) suite of programs, was developed. ClustScan is an easy to use, rapid annotation tool that takes a top-down approach based on HMM models to annotate genes encoding biosynthetic enzymes forming metabolic pathways. The versatility of this program package has been demonstrated by annotating biochemical pathways in microbial, invertebrate animal and metagenomic datasets. The novelty of ClustScan over existing computer programs is the knowledge-based automatic prediction of activities and specificities of proteins under investigation. The open architecture of ClustScan allows easy integration with other programs, facilitating further analyses of results. The program is easily updated to incorporate new knowledge as well as standard format annotation files for DNA sequences. The analysis of an individual human genome requires a number of different sorts of analyses. This includes detection of mutations (both known and novel) that inactivate genes as well as determination of haplotypes that are linked to QTLs of medical and pharmacogenomic significance. The results of the analyses must be integrated with existing knowledge to produce a medically useful tool. Work is underway to extend ClustScan for comparative genome annotation to generate a novel tool for automatic genome annotation, TAGS (Tool for Analysis of Genome Sequences). We would welcome collaboration with clinicians in the development of TAGS.