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Probiotics are modern-day panacea with claims to alleviate or cure everything from diarrhea to obesity and Alzheimer’s disease. They seem effective for numerous conditions (e.g. antibiotic-associated diarrhea), although these views have been challenged recently. Greater issue is the lack of confirmed efficacy for majority of claims provided by producers. While offering products without adequate clinical research, industry has more simplistic approach for probiotic treatments, that is, ‘one-formula-fits-all.’ In contrast, recent studies showed different efficacies of probiotic mixtures with multiple strains without fully understanding factors behind it. Furthermore, important parameter for prescribing probiotic treatment is the individual native microbiota. Interactions among probiotic strains and microbiota need more research, so there is a long way paved with ‘big data’ and chemometric analysis before it is possible to make full use of probiotics.

Alignment-based methods dominate molecular biology. However, by primarily allowing one-to-one comparisons, these methods are focused on a gene-centered viewpoint and lack the broad context essential to analyze how complex biological systems function and evolve. In actuality, a gene is part of genome where more than one sequence contributes to the functional network and evolutionary trajectory of the cell. The need for conservation of established interactions, is arguably more important to the evolutionary success of species than conservation of individual function. To test whether such contextual information exists, a distributional semantics method - Latent Semantic Analysis (LSA), was applied to thousands of species proteomes. Using natural language processing, Latent Taxonomic Signatures (LTSs) were identified that outperformed existing alignment-based BLAST methods when random protein sequences were being mapped to annotated taxonomy according to GenBank. LTSs are a novel proteome distributed feature, suggesting the existence of evolutionary constraints imposed on individual proteins by their proteome context. Even orphan proteins are exhibiting LTSs, which makes their uniqueness linked to a specific taxonomic level questionable. Unlike more simple bias, LTSs represent a self-similarity pattern, where random sets of species proteins show the same statistical properties of a complete proteome at many scales. Natural language processing and machine learning provide insights not easily discernable using alignment based methods suggestive there is more to species related differences than just translational optimization.

Alignment-based methods allow only one-to-one comparisons; promote gene-centered viewpoint and lack broad insight needed for complex biological systems. In actuality, each gene or a protein is part of conglomerate where more than one sequence contributes to the functional network and evolutionary trajectory of the cell. Conserving these network interactions is arguably more important to the evolutionary success than conservation of sequence integrity of an individual protein. Using alignment-free language model, we encoded sets of randomly selected species’ proteins into distributed vector representations of respective species. These representations captured transitive relations between otherwise unrelated proteins, resulting from conserved interactions within a proteome. This allowed us to discover Latent Taxonomic Signatures, a species-specific difference in the frequency of short amino acid chains occurrence, reflecting constraints imposed on protein evolution by their proteome context. Even orphan proteins exhibited LTSs, allowing us to establish taxonomic relatedness in total absence of alignment-based homology. The alignment-free approach here suggests that difference between species is more than just numbers and sequences, actual semantic properties could be equally important as protein family kinship when proteins evolve as parts of a system.

The MEGGASENSE platform constructs relational databases of DNA or protein sequences. The default functional analysis uses 14 106 hidden Markov model (HMM) profiles based on sequences in the KEGG database. The Solr search engine allows sophisticated queries and a BLAST search function is also incorporated. These standard capabilities were used to generate the SCATT database from the predicted proteome of Streptomyces cattleya. The implementation of a specialised metagenome database (AMYLOMICS) for bioprospecting of carbohydrate-modifying enzymes is described. In addition to standard assembly of reads, a novel ‘functional’ assembly was developed, in which screening of reads with the HMM profiles occurs before the assembly. The AMYLOMICS database incorporates additional HMM profiles for carbohydrate-modifying enzymes and it is illustrated how the combination of HMM and BLAST analyses helps identify interesting genes. A variety of different proteome and metagenome databases have been generated by MEGGASENSE., This work was supported by the European Commission FP7 (265992 to J.Z., S.K.P., O.H.F., R.G., J.D., G.O.H., D.H., A.S.), the Croatian Science Foundation (09/5 to D.H.), the German Academic Exchange Service (DAAD) and the Ministry of Science, Education and Sports, Republic of Croatia (cooperation grant to D.H. and J.C.), and King's College London, UK (to P.F.L.).

RATIONALE One of the most challenging tasks of proteomics is peptide de novo sequencing. 4- Sulfophenyl isothiocyanate (SPITC) peptide derivatization enables acquisition of high- quality tandem mass spectra (MS/MS) for de novo sequencing, but unwanted non-specific reactions and reduced mass spectra (MS) signal intensities still represent the obstacles in high-throughput de novo sequencing. METHODS We developed a SPITC peptide derivatization procedure under acidic conditions (pH ≤5). Derivatized peptides were analyzed by matrix- assisted laser desorption/ionization (MALDI-MS) in negative ion mode followed by MS/MS in positive ion mode. A de novo sequencing tool, named DUST, adjusted to SPITC chemistry, was designed for successful high-throughput peptide de novo sequencing. This high-throughput peptide de novo sequencing was tested on Fusarium delphinoides, an organism with an uncharacterized genome. RESULTS The SPITC derivatization procedure under acidic conditions produced a significantly improved MS dataset in comparison to commonly used derivatization under basic conditions. Signal intensities were 6 to 10 times greater and the over-sulfonation effect measured on lysine- containing peptides was significantly decreased. Furthermore, development of a novel DUST algorithm enabled automated de novo sequencing with the calculated accuracy of 70.6%. CONCLUSIONS The SPITC derivatization and de novo sequencing approach outlined here provides a reliable method for high-throughput peptide de novo sequencing. High-throughput peptide de novo sequencing enabled protein mutation identification and identification of proteins from organisms with non-sequenced genomes.

Microbes living in and on us have been recognized in the last decade as important part of ourselves and have been assigned a status of an organ with a wide range of functions—from metabolizing food to training our immune system. The intraindividual and interindividual variation of normal microbiota is vast and its composition is influenced by various external factors—most notable being diet, antibiotics and medication. External factors can cause a disturbance in microbiota frequently termed as dysbiosis which is often associated with a range of diseases—from inflammatory bowel disease and diabetes to obesity and cancer. Pioneering works in manipulating the microbiota for preventing and treating disease shows huge promise but also open a number of questions—from standardization and safety to personalization and reproducibility.

Izrađene su tri metagenomske baze (knjižnice) podataka pomoću površinskih uzoraka sedimenata iz sjevernog Jadranskog mora. Jedan je uzorak uzet s izrazito onečišćene lokacije, drugi s nezagađene, a treći, prikupljen s onečišćene lokacije, obogaćen je sirovom naftom. Rezultati metagenomskih analiza pohranjeni su u relacijsku bazu podataka REDPET (http://redpet.bioinfo.pbf.hr/REDPET), koja je izrađena na prethodno razvijenoj platformi MEGGASENSE. Baza uključuje taksonomske podatke za procjenu biološke raznolikosti metagenomskih podataka i općenite funkcionalne analize gena provedene pomoću profila skrivenih Markovljevih modela (HMM), temeljenih na bazi podataka KEGG. Razvijen je niz od 22 specijalizirana profila HMM radi otkrivanja gena što kodiraju enzime koji razgrađuju ugljikovodike. Primjenom tih profila je utvrđeno da je metagenomska knjižnica dobivena iz uzoraka obogaćenih naftom sadržavala veći broj gena za aerobnu razgradnju n-alkana. Pomoću potencijalnih alkB i almA gena iz metagenomske knjižnice potvrđena je mogućnost primjene opisanog sustava za procjenu biološke raznolikosti., Three metagenomic libraries were constructed using surface sediment samples from the northern Adriatic Sea. Two of the samples were taken from a highly polluted and an unpolluted site respectively. The third sample from a polluted site had been enriched using crude oil. The results of the metagenome analyses were incorporated in the REDPET relational database (http://redpet.bioinfo.pbf.hr/REDPET), which was generated using the previously developed MEGGASENSE platform. The database includes taxonomic data to allow the assessment of the biodiversity of metagenomic libraries and a general functional analysis of genes using hidden Markov model (HMM) profiles based on the KEGG database. A set of 22 specialised HMM profiles was developed to detect putative genes for hydrocarbon-degrading enzymes. Use of these profiles showed that the metagenomic library generated after selection on crude oil had enriched genes for aerobic n-alkane degradation. The use of this system for bioprospecting was exemplified using potential alkB and almA genes from this library.

RATIONALE We describe a novel negative chemically activated fragmentation/positive chemically activated fragmentation (CAF-/CAF+) technique for protein identification. The technique was used to investigate Lactobacillus brevis adaptation to nutrient deprivation. METHODS The CAF-/CAF+ method enables de novo sequencing of derivate peptides with negative and positive ion mode matrix-assisted laser desorption/ionization (MALDI) tandem mass spectrometry (MS/MS). Peptide sequences obtained from MS/MS spectra were matched against the National Center for Biotechnology Information (NCBI) non-redundant (nr) database and confirmed by the mass spectrometry data of elucidated peptide mass sequences derived from the annotated genome. This improved protein identification method highlighted 36 differentially expressed proteins in the proteome of L. brevis after 75 days of starvation. RESULTS The results revealed the key differences in the metabolic pathways that are responsible for the survival of L. brevis in a hostile environment. Proteomics analysis demonstrated that numerous proteins engaged in glucose and amino-acid catabolizing pathways, glycerolipid metabolizing pathways, and stress-response mechanisms are differentially expressed after long-term starvation. Amino acid and proteomics analysis indicated that starved L. brevis metabolized arginine, glycine, and histidine from dead cells as alternative nutrient sources. The production of lactic acid also varied between the parent cells and the starved cells. CONCLUSIONS Differentially expressed proteins identified exclusively by peptide sequence reading provided promising results for CAF-/CAF+ implementation in a standard proteomics workflow (e.g., biomarker and mutation discovery and biotyping). The practical performance of a reliable de novo sequencing technique in routine proteomics analysis is emphasized in this article. Copyright © 2013 John Wiley & Sons, Ltd. Copyright

The MEGGASENSE platform constructs relational databases of DNA or protein sequences. The default functional analysis uses 14 106 hidden Markov model (HMM) profiles based on sequences in the KEGG database. The Solr search engine allows sophisticated queries and a BLAST search function is also incorporated. These standard capabilities were used to generate the SCATT database from the predicted proteome of Streptomyces cattleya. The implementation of a specialised metagenome database (AMYLOMICS) for bioprospecting of carbohydrate-modifying enzymes is described. In addition to standard assembly of reads, a novel ‘functional’ assembly was developed, in which screening of reads with the HMM profiles occurs before the assembly. The AMYLOMICS database incorporates additional HMM profiles for carbohydrate-modifying enzymes and it is illustrated how the combination of HMM and BLAST analyses helps identify interesting genes. A variety of different proteome and metagenome databases have been generated by MEGGASENSE., Platforma MEGGASENSE služi za izradu relacijskih baza podataka koje sadržavaju nukleotidne ili proteinske sekvencije. Osnovna funkcionalna analiza zasniva se na primjeni 14 106 profila skrivenih Markovljevih modela (HMM), temeljenih na sekvencijama dostupnim u bazi podataka KEGG. Pomoću tražilice Solr mogu se zadati napredni upiti u sprezi s implementiranom pretragom BLAST. Osnovne funkcionalnosti platforme omogućile su izradu baze podataka SCATT, temeljene na predviđenom proteomu bakterije Streptomyces cattleya. U radu je opisana implementacija specijalizirane metagenomske baze podataka (AMYLOMICS) za „bioprospecting“ enzima koji modificiraju ugljikohidrate. Uz standardno slaganje očitanih kratkih sljedova DNA, razvijen je funkcionalni postupak pretraživanja HMM profila u očitanim slijedovima DNA prije slaganja. Baza podataka AMYLOMICS sadržava i dodatne HMM profile enzima za modifikaciju ugljikohidrata. U radu je prikazano kako se kombinacijom analiza HMM i BLAST mogu identificirati ciljani geni. Platforma MEGGASENSE upotrijebljena je za izradu raznih proteomskih i metagenomskih baza podataka.

An in silico model for homoeologous recombination between gene clusters encoding modular polyketide synthases (PKS) or non-ribosomal peptide synthetases (NRPS) was developed. This model was used to analyze recombination between 12 PKS clusters from Streptomyces species and related genera to predict if new clusters might give rise to new products. In many cases, there were only a limited number of recombination sites (about 13 per cluster pair), suggesting that recombination may pose constraints on the evolution of PKS clusters. Most recombination events occurred between pairs of ketosynthase (KS) domains, allowing the biosynthetic outcome of the recombinant modules to be predicted. About 30% of recombinants were predicted to produce polyketides. Four NRPS clusters from Streptomyces strains were also used for in silico recombination. They yielded a comparable number of recombinants to PKS clusters, but the adenylation (A) domains contained the largest proportion of recombination events; this might be a mechanism for producing new substrate specificities. The extreme G + C-content, the presence of linear chromosomes and plasmids, as well as the lack of a mutSL-mismatch repair system should favor production of recombinants in Streptomyces species.

ProteinReader (PT) is an expert software developed for the analysis of spectra produced with patented in-solution CAF-/CAF+ peptide derivatization reagent. PT is developed as client-server application to analyse MS/MS spectra on MALDI TOF/TOF mass spec. Four main advantages of CAF-/CAF+ technology are: Allows to capture same peptide in positive and negative mode of operation additional accuracy control In positive mode of operation obtaining mostly Y-ions while in negative mode obtaining mostly B-ions - eased de novo readout No degradation of the sample due to use of CAF-/CAF+ derivatisation reagent Except unambiguous biotypization (species identification), antibiotic resistance can be easily find by aforementioned method ProteinReader uses two methods for peptide search: database match (NCBInr) and de novo reading. Each peptide can be read with the two spectra, in the positive and the negative ion mode. Testing of more than 200 microorganisms showed positive identification on the species level (strain was also successfully identified if present in NCBInr database). ProteinReader was developed using open source technologies (Java, Eclipse RCP, Linux, Apache Tomcat, Infobright data warehouse) and is designed to work effectively (parallel processing) in a multitier client-server architecture. CAF-/CAF+ patent: http://www.google.com/patents/EP2529233A2?cl=en PBF Section for Bioinformatics: http://proteinreader.com/ IRB Laboratory for system biomedicine: http://www.irb.hr/eng/Research/Divisions-and-Centers/Division-of-Molecular-Medicine/Laboratory-for-system-biomedicine This work was funded by HRZZ (Croatian Science Foundation) research project Clinical proteomics of microorganisms.

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 algortihms 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 softvare 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 intesity for database peptide matching and it uses newly developed concept of microbial proteome fingerptints 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 algortithm 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 analyzer without siginificant costs related to purchase of new equipment. This work was funded by HRZZ (Croatian Science Foundation) research project Clinical proteomics of microorganisms.

Actinomycetes are a very important source of natural products for the pharmaceutical industry and other applications. Most of the strains belong to Streptomyces or related genera, partly because they are particularly amenable to growth in the laboratory and industrial fermenters. It is unlikely that chemical synthesis can fulfil the needs of the pharmaceutical industry for novel compounds so there is a continuing need to find novel natural products. An evolutionary perspective can help this process in several ways. Genome mining attempts to identify secondary metabolite biosynthetic clusters in DNA sequences, which are likely to produce interesting chemical entities. There are often technical problems in assembling the DNA sequences of large modular clusters in genome and metagenome projects, which can be overcome partially using information about the evolution of the domain sequences. Understanding the evolutionary mechanisms of modular clusters should allow simulation of evolutionary pathways in the laboratory to generate novel compounds.

Successful genome mining is dependent on accurate prediction of protein function from sequence. This often involves dividing protein families into functional subtypes (e.g., with different substrates). In many cases, there are only a small number of known functional subtypes, but in the case of the adenylation domains of nonribosomal peptide synthetases (NRPS), there are >500 known substrates. Latent semantic indexing (LSI) was originally developed for text processing but has also been used to assign proteins to families. Proteins are treated as ‘‘documents’’ and it is necessary to encode properties of the amino acid sequence as ‘‘terms’’ in order to construct a term-document matrix, which counts the terms in each document. This matrix is then processed to produce a document-concept matrix, where each protein is represented as a row vector. A standard measure of the closeness of vectors to each other (cosines of the angle between them) provides a measure of protein similarity. Previous work encoded proteins as oligopeptide terms, i.e. counted oligopeptides, but used no information regarding location of oligopeptides in the proteins. A novel tokenization method was developed to analyze information from multiple alignments. LSI successfully distinguished between two functional subtypes in five well-characterized families. Visualization of different ‘‘concept’’ dimensions allows exploration of the structure of protein families. LSI was also used to predict the amino acid substrate of adenylation domains of NRPS. Better results were obtained when selected residues from multiple alignments were used rather than the total sequence of the adenylation domains. Using ten residues from the substrate binding pocket performed better than using 34 residues within 8 Å of the active site. Prediction efficiency was somewhat better than that of the best published method using a support vector machine.

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., Razvojem područja sinteze funkcionalnog bakterijskog kromosoma obilježen je početak novog doba genetičkog inženjerstva. Konstrukcijom novih organizama mogli bi se riješiti neki problemi vezani uz zdravstvo, proizvodnju hrane i energenata te zaštitu okoliša. U ovom su kratkom revijalnom prikazu opisana sadašnja dostignuća na području sintetičke biologije, a raspravlja se i o njezinoj ulozi u razvoju biotehnologije. Prikaz je posebno namijenjen prehrambenim tehnolozima, nutricionistima i ostalim biotehnolozima koji možda nisu svjesni značaja što bi sintetička biologija mogla imati za njihova istraživanja. Detaljno se raspravlja o mogućem utjecaju sintetičke biologije u formiranju potpuno novih poliketida, koji se mogu upotrijebiti za proizvodnju lijekova.

Modular polyketide synthases (PKSs) from Streptomyces and related genera of bacteria produce many important pharmaceuticals. A program called CompGen was developed to carry out in silico homologous recombination between gene clusters encoding PKSs and determine whether recombinants have cluster architectures compatible with the production of polyketides. The chemical structure of recombinant polyketides was also predicted. In silico recombination was carried out for 47 well-characterised clusters. The predicted recombinants would produce 11,796 different polyketide structures. The molecular weights and average degree of reduction of the chemical structures are dispersed around the parental structures indicating that they are likely to include pharmaceutically interesting compounds. The details of the recombinants and the chemical structures were entered in a database called r-CSDB. The virtual compound library is a useful resource for computer-aided drug design and chemoinformatics strategies for finding pharmaceutically relevant chemical entities. A strategy to construct recombinant Streptomyces strains to produce these polyketides is described and the critical steps of mobilizing large biosynthetic clusters and producing new linear cloning vectors are illustrated by experimental data.

Clustscan ("Cluster Scanner") is a semi-automatic DNA sequence analysis tool. It has a 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 machine. Using collapsible tree overviews and zoom functions, it is possible to work with whole genomes or large sets of metagenomic data. Using custom profiles it is possible to quickly identify secondary metabolite clusters. For modular biosynthetic clusters (polyketide synthases and non-ribosomal peptide synthetases), the cluster hierarchical architecture is represented by the program and allows prediction of product chemistry that can be exported in a standard chemical format (SMILES). The user can examine the basis of predictions made and override them if necessary. An open architecture allows easy extension of the scope of the program. The program package allows rapid scanning of large DNA data sets to identify interesting clusters of genes. This chapter reviews the subsequent development of a system published in 2008 [Starcevic et al., 2008] for the semi-automatic annotation of modular biosynthetic gene clusters from different sources, including metagenomes from free-living and symbiotic microorganisms and, in silico prediction of novel chemical structures.

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., Brzo pretraživanje genomskih i metagenomskih skupova podataka, modularnih biosintetskih genskih nakupina poliketid sintaza i sintetaza neribosomalno sintetiziranih peptida, postignuto je primjenom generičkih računalnih programskih paketa ClustScan i CompGen. Ti programski paketi provode anotaciju hijerarhijskim strukturiranjem podataka na polipeptide, module i domene, te pohranu i grafičku prezentaciju tih podataka. Na temelju dosadašnjih spoznaja, nastoji se postići najtočnije moguće predviđanje aktivnosti i specifičnosti katalitički aktivnih domena, što vodi prema predviđanju najvjerojatnijih kemijskih struktura koje ti enzimi mogu sintetizirati. Programski paketi ClustScan i CompGen omogućuju generiranje novih genskih nakupina homolognom rekombinacijom anotiranih gena u uvjetima in silico, a upotrijebljeni su i za konstrukciju vlastitih baza podataka poznatih poliketidnih i peptidnih supstancija (CSDB) te potpuno novih poliketidnih i peptidnih supstancija produkata rekombinacije (r-CSDB). Ti će se produkti rekombinacije moći upotrijebiti za izbor supstancija s potencijalnom biološkom aktivnošću pomoću računalom vođenog dizajna lijekova u uvjetima in silico. Primjenjivost programskih paketa ClustScan i CompGen dokazana je u analizi genomskih sekvencija prokariotskih i eukariotskih mikroorganizama što žive u tlu, analizi metagenomske skupine podataka u uzorku iz morske vode, a i na nedavno opisanom primjeru 'zajedničkog metaboličkoga puta' u mikrobnog endosimbionta morske životinje.

Background Contemporary coral reef research has firmly established that a genomic approach is urgently needed to better understand the effects of anthropogenic environmental stress and global climate change on coral holobiont interactions. Here we present KEGG orthology-based annotation of the complete genome sequence of the scleractinian coral Acropora digitifera and provide the first comprehensive view of the genome of a reef-building coral by applying advanced bioinformatics. Description Sequences from the KEGG database of protein function were used to construct hidden Markov models. These models were used to search the predicted proteome of A. digitifera to establish complete genomic annotation. The annotated dataset is published in ZoophyteBase, an open access format with different options for searching the data. A particularly useful feature is the ability to use a Google-like search engine that links query words to protein attributes. We present features of the annotation that underpin the molecular structure of key processes of coral physiology that include (1) regulatory proteins of symbiosis, (2) planula and early developmental proteins, (3) neural messengers, receptors and sensory proteins, (4) calcification and Ca2+-signalling proteins, (5) plant-derived proteins, (6) proteins of nitrogen metabolism, (7) DNA repair proteins, (8) stress response proteins, (9) antioxidant and redox-protective proteins, (10) proteins of cellular apoptosis, (11) microbial symbioses and pathogenicity proteins, (12) proteins of viral pathogenicity, (13) toxins and venom, (14) proteins of the chemical defensome and (15) coral epigenetics. Conclusions We advocate that providing annotation in an open-access searchable database available to the public domain will give an unprecedented foundation to interrogate the fundamental molecular structure and interactions of coral symbiosis and allow critical questions to be addressed at the genomic level based on combined aspects of evolutionary, developmental, metabolic, and environmental perspectives.

The rapid progress in DNA sequencing technology is resulting in exponential growth in the volume of genome and metagenome sequences. 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. 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. We have developed MEGGASENSE platform for the functional and structural automatic annotation of metagenomes/genomes. The platform allows functional and structural annotation that runs on a server as a bioinformatics "black box" and the Web based graphical user interface for sequence interrogation. The initial functional analysis is carried out on reads before read assembly using in silico translation. However, unlike most other platforms, the resulting protein sequences are scanned using HMMs, which results in more effective detection of genes. Depending on the aims of the analysis, it is possible to use a generic library of profiles or a custom database of choice. The reads are subsequently assembled. The assembled sequences can be used for 'gene of interest' analysis by BLAST within the platform. Finally, the user can browse the sequences with the Solr search engine, which is implemented within the graphical user interface. The utility of the MEGGASENSE will be illustrated with examples including metagenomes composed of novel anaerobic or microaerophilic heterotrophic species from thermophilic habitats that cover a range of physicochemical conditions. Using MEGGASENSE over 350 reads/genes potentially expressing carbohydrate-modifying enzymes were discovered showing identities ranging from 26 to 100% to genes present in GenBank.