Automatic Functional Annotation Research Articles

High-throughput functional annotation and data mining with the Blast2GO suite

Functional genomics technologies have been widely adopted in the biological research of both model and non-model species. An efficient functional annotation of DNA or protein sequences is a major requirement for the successful application of these approaches as functional information on gene products is often the key to the interpretation of experimental results. Therefore, there is an increasing need for bioinformatics resources which are able to cope with large amount of sequence data, produce valuable annotation results and are easily accessible to laboratories where functional genomics projects are being undertaken. We present the Blast2GO suite as an integrated and biologist-oriented solution for the high-throughput and automatic functional annotation of DNA or protein sequences based on the Gene Ontology vocabulary. The most outstanding Blast2GO features are: (i) the combination of various annotation strategies and tools controlling type and intensity of annotation, (ii) the numerous graphical features such as the interactive GO-graph visualization for gene-set function profiling or descriptive charts, (iii) the general sequence management features and (iv) high-throughput capabilities. We used the Blast2GO framework to carry out a detailed analysis of annotation behaviour through homology transfer and its impact in functional genomics research. Our aim is to offer biologists useful information to take into account when addressing the task of functionally characterizing their sequence data.

The c-Myc Target Gene Rcl (C6orf108) Encodes a Novel Enzyme, Deoxynucleoside 5′-monophosphate N-Glycosidase

RCL is a c-Myc target with tumorigenic potential. Genome annotation predicted that RCL belonged to the N-deoxyribosyltransferase family. However, its putative relationship to this class of enzymes did not lead to its precise biochemical function. The purified native or N-terminal His-tagged recombinant rat RCL protein expressed in Escherichia coli exhibits the same enzyme activity, deoxynucleoside 5'-monophosphate N-glycosidase, never before described. dGMP appears to be the best substrate. RCL opens a new route in the nucleotide catabolic pathways by cleaving the N-glycosidic bond of deoxynucleoside 5'-monophosphates to yield two reaction products, deoxyribose 5-phosphate and purine or pyrimidine base. Biochemical studies show marked differences in the terms of the structure and catalytic mechanism between RCL and of its closest enzyme family neighbor, N-deoxyribosyltransferase. The reaction products of this novel enzyme activity have been implicated in purine or pyrimidine salvage, glycolysis, and angiogenesis, and hence are all highly relevant for tumorigenesis.

Resources and Tools for Investigating Biomolecular Networks in Mammals

Molecular databases serve as primary information resources for the analysis of biological networks providing an essential and invaluable treasure for information exploration. Tools for projecting experimental data sets onto known functional information are a major need to support the analysis of samples produced in clinical research. A new concept is the notation of functional modules, i.e. the characterisation of sets of proteins that perform a defined biological function in cooperation. The determination and analysis of functional modules overcome the limitations of the analysis of individual genes and their properties. Although functional modules are not suitable to fully capture systems properties, they have the potential to unify the information generated by different types of experiments. We describe advances related to the problem of integrating heterogeneous data sets into functional modules for mouse and/or human cellular networks based on publicly available data resources, including advances in the design of ontologies for functional classification, problems of automatic protein functional annotation and integration of microarray data.

The fragment transformation method to detect the protein structural motifs

To identify functional structural motifs from protein structures of unknown function becomes increasingly important in recent years due to the progress of the structural genomics initiatives. Although certain structural patterns such as the Asp-His-Ser catalytic triad are easy to detect because of their conserved residues and stringently constrained geometry, it is usually more challenging to detect a general structural motifs like, for example, the betabetaalpha-metal binding motif, which has a much more variable conformation and sequence. At present, the identification of these motifs usually relies on manual procedures based on different structure and sequence analysis tools. In this study, we develop a structural alignment algorithm combining both structural and sequence information to identify the local structure motifs. We applied our method to the following examples: the betabetaalpha-metal binding motif and the treble clef motif. The betabetaalpha-metal binding motif plays an important role in nonspecific DNA interactions and cleavage in host defense and apoptosis. The treble clef motif is a zinc-binding motif adaptable to diverse functions such as the binding of nucleic acid and hydrolysis of phosphodiester bonds. Our results are encouraging, indicating that we can effectively identify these structural motifs in an automatic fashion. Our method may provide a useful means for automatic functional annotation through detecting structural motifs associated with particular functions.

Discovery of immune-related genes expressed in hemocytes of the tarantula spider Acanthoscurria gomesiana

The present study reports the identification of immune related transcripts from hemocytes of the spider Acanthoscurria gomesiana by high throughput sequencing of expressed sequence tags (ESTs). To generate ESTs from hemocytes, two cDNA libraries were prepared: one by directional cloning (primary) and the other by the normalization of the first (normalized). A total of 7584 clones were sequenced and the identical ESTs were clustered, resulting in 3723 assembled sequences (AS). At least 20% of these sequences are putative novel genes. The automatic functional annotation of AS based on Gene Ontology revealed several abundant transcripts related to the following functional classes: hemocyanin, lectin, and structural constituents of ribosome and cytoskeleton. From this annotation, 73 transcripts possibly involved in immune response were also identified, suggesting the existence of several molecular processes not previously described for spiders, such as: pathogen recognition, coagulation, complement activation, cell adhesion and intracellular signaling pathway for the activation of cellular defenses.

Biological Master Games: Using Biologists' Reasoning to Guide Algorithm Development for Integrated Functional Genomics

We review some powerful new algorithms that build on the intuitive biological interpretation techniques for statistical analysis of functional genomics experiments. Although they were originally designed for transcriptomics, we argue that these algorithms are applicable to any type of -omics study (transcriptomics, proteomics, metabolomics). Rank Products (RP), a strictly non-parametric test statistic to detect differentially regulated elements (genes, proteins, metabolites) in genome-wide screens. RP is particularly powerful for noisy data and low numbers of replicates and makes full use of the availability of a large number of parallel measurements that is typical of modern large-scale experiments. Iterative Group Analysis (iGA), a statistical method that makes the transition from regulated single elements to significant classes of elements, and thus provides an automatic functional annotation of an experiment. Graph-based iGA (GiGA), an extension of iGA that combines experimental data with a broad variety of biological annotations to highlight physiologically relevant regions in a given "evidence graph" (e.g., metabolic networks, signaling pathway diagrams, protein interaction maps). The sequential application of these techniques yields an increasingly abstract interpretation of experimental data that is at the same time quantitative, statistically rigorous, and biologically significant. The results can be used either as helpful tools to guide data visualization and exploration, or as the input for downstream computational applications in a systems biology framework.

MIPS bacterial genomes functional annotation benchmark dataset

Any development of new methods for automatic functional annotation of proteins according to their sequences requires high-quality data (as benchmark) as well as tedious preparatory work to generate sequence parameters required as input data for the machine learning methods. Different program settings and incompatible protocols make a comparison of the analyzed methods difficult. The MIPS Bacterial Functional Annotation Benchmark dataset (MIPS-BFAB) is a new, high-quality resource comprising four bacterial genomes manually annotated according to the MIPS functional catalogue (FunCat). These resources include precalculated sequence parameters, such as sequence similarity scores, InterPro domain composition and other parameters that could be used to develop and benchmark methods for functional annotation of bacterial protein sequences. These data are provided in XML format and can be used by scientists who are not necessarily experts in genome annotation. BFAB is available at http://mips.gsf.de/proj/bfab

Automatic annotation of protein function based on family identification.

Although genomes are being sequenced at an impressive rate, the information generated tells us little about protein function, which is slow to characterize by traditional methods. Automatic protein function annotation based on computational methods has alleviated this imbalance. The most powerful current approach for inferring the function of new proteins is by studying the annotations of their homologues, since their common origin is assumed to be reflected in their structure and function. Unfortunately, as proteins evolve they acquire new functions, so annotation based on homology must be carried out in the context of orthologues or subfamilies. Evolution adds new complications through domain shuffling: homology (or orthology) frequently corresponds to domains rather than complete proteins. Moreover, the function of a protein may be seen as the result of combining the functions of its domains. Additionally, automatic annotation has to deal with problems related to the annotations in the databases: errors (which are likely to be propagated), inconsistencies, or different degrees of function specification. We describe a method that addresses these difficulties for the annotation of protein function. Sequence relationships are detected and measured to obtain a map of the sequence space, which is searched for differentiated groups of proteins (similar to islands on the map), which are expected to have a common function and correspond to groups of orthologues or subfamilies. This mapmaking is done by applying a clustering algorithm based on Normalized cuts in graphs. The domain problem is addressed in a simple way: pairwise local alignments are analyzed to determine the extent to which they cover the entire sequence lengths of the two proteins. This analysis determines both what homologues are preferred for functional inheritance and the level of confidence of the annotation. To alleviate the problems associated with database annotations, the information on all the homologues that are grouped together with the query protein are taken into account to select the most representative functional descriptors. This method has been applied for the annotation of the genome of Buchnera aphidicola (specific host Baizongia pistaciae). Human inspection of the annotations allowed an estimation of accuracy of 94%; the different kinds of error that may appear when using this approach are described. Results can be accessed at http://www.pdg.cnb.uam.es/funcut.html. The programs are available upon request, although installation in other systems may be complicated.

Functional information in SWISS-PROT: the basis for large-scale characterisation of protein sequences.

With the rapid growth of sequence databases, there is an increasing need for reliable functional characterisation and annotation of newly predicted proteins. To cope with such large data volumes, faster and more effective means of protein sequence characterisation and annotation are required. One promising approach is automatic large-scale functional characterisation and annotation, which is generated with limited human interaction. However, such an approach is heavily dependent on reliable data sources. The SWISS-PROT protein sequence database plays an essential role here owing to its high level of functional information.

Metacomputing in practice: a distributed compute server for pharmaceutical industry

We describe a distributed high-performance compute server that has been implemented for running compute-intensive applications on a mixture of HPC systems interconnected by Inter-and Intranet. With a practical industrial background, our work focusses on high availability, efficient job load balancing, security, and the easy integration of HPC computing into the daily work-flow at pharmaceutical companies. The work was done in the course of the ESPRIT project P hase A Distributed Pharmaceutical Application Server The client software is implemented in Java. All results are displayed in a web browser and can be forwarded to the next stage of applications used in the drug design cycle. The server software handles the job load balancing between the participating HPC nodes and is capable of managing multi-site applications. Our environment currently supports four key applications that are used in rational drug design and drug target identification. They range from the automatic functional annotation of protein sequences to three-dimensional protein structure prediction tools and protein comparison applications.

A novel method for automatic functional annotation of proteins.

To cope with the increasing amount of sequence data, reliable automatic annotation tools are required. The TrEMBL database contains together with SWISS-PROT nearly all publicly available protein sequences, but in contrast to SWISS-PROT only limited functional annotation. To improve this situation, we had to develop a method of automatic annotation that produces highly reliable functional prediction using the language and the syntax of SWISS-PROT. An algorithm was developed and successfully used for the automatic annotation of a testset of unknown proteins. The predicted information included description, function, catalytic activity, cofactors, pathway, subcellular location, quaternary structure, similarity to other protein, active sites, and keywords. The algorithm showed a low coverage (10%), but a high specificity and reliability. The results can be obtained by anonymous ftp from ftp.ebi.ac.uk/pub/databases/sp_tr_nrdb. The source code is available on request from the authors.