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Functional annotation of the Arabidopsis genome using controlled vocabularies.

Controlled vocabularies are increasingly used by databases to describe genes and gene products because they facilitate identification of similar genes within an organism or among different organisms. One of The Arabidopsis Information Resource's goals is to associate all Arabidopsis genes with terms developed by the Gene Ontology Consortium that describe the molecular function, biological process, and subcellular location of a gene product. We have also developed terms describing Arabidopsis anatomy and developmental stages and use these to annotate published gene expression data. As of March 2004, we used computational and manual annotation methods to make 85,666 annotations representing 26,624 unique loci. We focus on associating genes to controlled vocabulary terms based on experimental data from the literature and use The Arabidopsis Information Resource-developed PubSearch software to facilitate this process. Each annotation is tagged with a combination of evidence codes, evidence descriptions, and references that provide a robust means to assess data quality. Annotation of all Arabidopsis genes will allow quantitative comparisons between sets of genes derived from sources such as microarray experiments. The Arabidopsis annotation data will also facilitate annotation of newly sequenced plant genomes by using sequence similarity to transfer annotations to homologous genes. In addition, complete and up-to-date annotations will make unknown genes easy to identify and target for experimentation. Here, we describe the process of Arabidopsis functional annotation using a variety of data sources and illustrate several ways in which this information can be accessed and used to infer knowledge about Arabidopsis and other plant species.

Arabidopsis↗

FAST-NMR: functional annotation screening technology using NMR spectroscopy.

An abundance of protein structures emerging from structural genomics and the Protein Structure Initiative (PSI) are not amenable to ready functional assignment because of a lack of sequence and structural homology to proteins of known function. We describe a high-throughput NMR methodology (FAST-NMR) to annotate the biological function of novel proteins through the structural and sequence analysis of protein-ligand interactions. This is based on basic tenets of biochemistry where proteins with similar functions will have similar active sites and exhibit similar ligand binding interactions, despite global differences in sequence and structure. Protein-ligand interactions are determined through a tiered NMR screen using a library composed of compounds with known biological activity. A rapid co-structure is determined by combining the experimental identification of the ligand binding site from NMR chemical shift perturbations with the protein-ligand docking program AutoDock. Our CPASS (Comparison of Protein Active Site Structures) software and database are then used to compare this active site with proteins of known function. The methodology is demonstrated using unannotated protein SAV1430 from Staphylococcus aureus.

Amino Acid Sequence↗

Functional annotation of a full-length Arabidopsis cDNA collection.

Full-length complementary DNAs (cDNAs) are essential for the correct annotation of genomic sequences and for the functional analysis of genes and their products. We isolated 155,144 RIKEN Arabidopsis full-length (RAFL) cDNA clones. The 3'-end expressed sequence tags (ESTs) of 155,144 RAFL cDNAs were clustered into 14,668 nonredundant cDNA groups, about 60% of predicted genes. We also obtained 5' ESTs from 14,034 nonredundant cDNA groups and constructed a promoter database. The sequence database of the RAFL cDNAs is useful for promoter analysis and correct annotation of predicted transcription units and gene products. Furthermore, the full-length cDNAs are useful resources for analyses of the expression profiles, functions, and structures of plant proteins.

Arabidopsis↗

DescribePROT Database of Residue-Level Protein Structure and Function Annotations.

DescribePROT is a freely available online database of structural and functional descriptors of proteins at the amino acid level. It provides access to 13 diverse descriptors that include sequence conservation, putative secondary structure, solvent accessibility, intrinsic disorder, and signal peptides, and putative annotations of residues that interact with proteins, peptides and nucleic acids. These data can be used to elucidate protein functions, to support efforts to develop therapeutics, and to develop and evaluate future predictors of protein structure and function. DescribePROT includes 7.8 billion predictions for 1.4 million proteins from 83 complete proteomes of popular model organisms. This information can be downloaded at multiple levels of scope (entire database, specific organisms, and individual proteins) and can be interacted with using a graphical interface that simultaneously displays data on multiple descriptors. We describe the contents of this resource, provide directions on how to use its interface, and offer instructions on how to obtain and interact with the underlying data. Moreover, we briefly discuss plans for a future expansion of this database. DescribePROT is available at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/ .

Databases, Protein↗

The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes.

In this paper, we present the Functional Catalogue (FunCat), a hierarchically structured, organism-independent, flexible and scalable controlled classification system enabling the functional description of proteins from any organism. FunCat has been applied for the manual annotation of prokaryotes, fungi, plants and animals. We describe how FunCat is implemented as a highly efficient and robust tool for the manual and automatic annotation of genomic sequences. Owing to its hierarchical architecture, FunCat has also proved to be useful for many subsequent downstream bioinformatic applications. This is illustrated by the analysis of large-scale experiments from various investigations in transcriptomics and proteomics, where FunCat was used to project experimental data into functional units, as 'gold standard' for functional classification methods, and also served to compare the significance of different experimental methods. Over the last decade, the FunCat has been established as a robust and stable annotation scheme that offers both, meaningful and manageable functional classification as well as ease of perception.

Abstracting and Indexing↗

Comparison of functional annotation schemes for genomes.

In this paper we survey a number of functional classification schemes applicable to genomes. We present the concepts of depth, breadth and resolution as descriptors of the schemes' scope and architecture and compare selected classifications according to these criteria. We also generate a 'Combined Scheme' against which we map six classifications which we believe are representative of the range currently available. The mapping allows the generation of 'FuncWheels', which are graphical representations of hierarchical classification schemes. They are used to illustrate similarities and differences in functional space coverage. This survey highlights many issues related to the design and implementation of gene product functional classifications, which are discussed in the light of emerging 'second-generation' schemes.

Animals↗

Effective function annotation through catalytic residue conservation.

Because of the extreme impact of genome sequencing projects, protein sequences without accompanying experimental data now dominate public databases. Homology searches, by providing an opportunity to transfer functional information between related proteins, have become the de facto way to address this. Although a single, well annotated, close relationship will often facilitate sufficient annotation, this situation is not always the case, particularly if mutations are present in important functional residues. When only distant relationships are available, the transfer of function information is more tenuous, and the likelihood of encountering several well annotated proteins with different functions is increased. The consequence for a researcher is a range of candidate functions with little way of knowing which, if any, are correct. Here, we address the problem directly by introducing a computational approach to accurately identify and segregate related proteins into those with a functional similarity and those where function differs. This approach should find a wide range of applications, including the interpretation of genomics/proteomics data and the prioritization of targets for high-throughput structure determination. The method is generic, but here we concentrate on enzymes and apply high-quality catalytic site data. In addition to providing a series of comprehensive benchmarks to show the overall performance of our approach, we illustrate its utility with specific examples that include the correct identification of haptoglobin as a nonenzymatic relative of trypsin, discrimination of acid-d-amino acid ligases from a much larger ligase pool, and the successful annotation of BioH, a structural genomics target.

Amino Acid Sequence↗

GeneFarm, structural and functional annotation of Arabidopsis gene and protein families by a network of experts.

Genomic projects heavily depend on genome annotations and are limited by the current deficiencies in the published predictions of gene structure and function. It follows that, improved annotation will allow better data mining of genomes, and more secure planning and design of experiments. The purpose of the GeneFarm project is to obtain homogeneous, reliable, documented and traceable annotations for Arabidopsis nuclear genes and gene products, and to enter them into an added-value database. This re-annotation project is being performed exhaustively on every member of each gene family. Performing a family-wide annotation makes the task easier and more efficient than a gene-by-gene approach since many features obtained for one gene can be extrapolated to some or all the other genes of a family. A complete annotation procedure based on the most efficient prediction tools available is being used by 16 partner laboratories, each contributing annotated families from its field of expertise. A database, named GeneFarm, and an associated user-friendly interface to query the annotations have been developed. More than 3000 genes distributed over 300 families have been annotated and are available at http://genoplante-info.infobiogen.fr/Genefarm/. Furthermore, collaboration with the Swiss Institute of Bioinformatics is underway to integrate the GeneFarm data into the protein knowledgebase Swiss-Prot.

Arabidopsis↗

High-throughput functional annotation of novel gene products using document clustering.

Gene products differentially expressed in healthy vs. diseased tissues may be considered drug targets since the change in their expression level can be related to the cause and progression of the disease studied. A significant portion of the proteins produced by these genes will be unknown and consequently their function must be characterised. The experimental elucidation of biochemical function must be supported by computational tools which can help predicting the possible function of a given protein from its amino acid sequence. We have designed a high-throughput system which automatically analyses amino acid sequences deduced from differentially represented cDNA clones. The system attempts to assign a biological function to protein sequences by carrying out searches in sequence databanks and by locating functionally relevant motifs in the query sequences. The results delivered by the various prediction methods consist of the annotations of matching sequences and/or motifs, which are free-format texts written by humans and therefore may describe the same concept with synonymous words. It is desirable to present the results in such a way that the annotations describing the same biological function are grouped together. To this end we devised an algorithm that enables the hierarchical clustering of free-format documents based on their contents. The system is capable of detecting and flagging conflicting annotations, and will speed up the interpretation of the function prediction results.

Algorithms↗

Transitive functional annotation by shortest-path analysis of gene expression data.

Current methods for the functional analysis of microarray gene expression data make the implicit assumption that genes with similar expression profiles have similar functions in cells. However, among genes involved in the same biological pathway, not all gene pairs show high expression similarity. Here, we propose that transitive expression similarity among genes can be used as an important attribute to link genes of the same biological pathway. Based on large-scale yeast microarray expression data, we use the shortest-path analysis to identify transitive genes between two given genes from the same biological process. We find that not only functionally related genes with correlated expression profiles are identified but also those without. In the latter case, we compare our method to hierarchical clustering, and show that our method can reveal functional relationships among genes in a more precise manner. Finally, we show that our method can be used to reliably predict the function of unknown genes from known genes lying on the same shortest path. We assigned functions for 146 yeast genes that are considered as unknown by the Saccharomyces Genome Database and by the Yeast Proteome Database. These genes constitute around 5% of the unknown yeast ORFome.

Cell Nucleus↗

Functional annotation of proteomic sequences based on consensus of sequence and structural analysis.

To maximise the assignment of function of the proteins encoded by a genome and to aid the search for novel drug targets, there is an emerging need for sensitive methods of predicting protein function on a genome-wide basis. GeneAtlas is an automated, high-throughput pipeline for the prediction of protein structure and function using sequence similarity detection, homology modelling and fold recognition methods. GeneAtlas is described in detail here. To test GeneAtlas, a 'virtual' genome was used, a subset of PDB structures from the SCOP database, in which the functional relationships are known. GeneAtlas detects additional relationships by building 3D models in comparison with the sequence searching method PSI-BLAST. Functionally related proteins with sequence identity below the twilight zone can be recognised correctly.

Consensus Sequence↗

Comparative analysis of chloroplast genomes: functional annotation, genome-based phylogeny, and deduced evolutionary patterns.

All protein sequences from 19 complete chloroplast genomes (cpDNA) have been studied using a new computational method able to analyze functional correlations among series of protein sequences contained in complete proteomes. First, all open reading frames (ORFs) from the cpDNAs, comprising a total of 2266 protein sequences, were compared against the 3168 proteins from Synechocystis PCC6803 complete genome to find functionally related orthologous proteins. Additionally, all cpDNA genomes were pairwise compared to find orthologous groups not present in cyanobacteria. Annotations in the cluster of othologous proteins database and CyanoBase were used as reference for the functional assignments. Following this protocol, new functional assignments were made for ORFs of unknown function and for ycfs (hypothetical chloroplast frames), which still lack a functional assignment. Using this information, a matrix of functional relationships was derived from profiles of the presence and/or absence of orthologous proteins; the matrix included 1837 proteins in 277 orthologous clusters. A factor analysis study of this matrix, followed by cluster analysis, allowed us to obtain accurate phylogenetic reconstructions and the detection of genes probably involved in speciation as phylogenetic correlates. Finally, by grouping common evolutionary patterns, we show that it is possible to determine functionally linked protein networks. This has allowed us to suggest putative associations for some unknown ORFs.

Bacterial Proteins↗

SNPannotator: automated functional annotation of genetic variants and linked proxies.

SUMMARY: Genome-wide association studies (GWASs) have identified thousands of genetic variants associated with complex traits and diseases. However, explaining the mechanisms underlying phenotypic variation remains challenging. Here, we introduce SNPannotator, an automated post-GWAS analysis software package designed to streamline the interpretation of GWAS findings. Our pipeline implements a multi-step process that identifies proxy variants in high linkage disequilibrium (LD) with associated lead variants, then queries comprehensive resources (including Ensembl, the GTEx Portal, the eQTL Catalog, and STRING DB) for genomic position, deleteriousness, regulatory annotations, clinical significance, trait associations, expression (eQTLs) and splicing quantitative trait loci (sQTLs), and functional enrichment analyses and compiles the results into user-friendly reports. This package is implemented in the R programming language and includes auxiliary functions for variant lookup and LD exploration. SNPannotator provides a practical framework for efficiently deriving biologically meaningful insights from GWAS data and for assisting researchers in prioritizing candidate variants for functional validation. AVAILABILITY AND IMPLEMENTATION: The SNPannotator package is available from the Comprehensive R Archive Network (CRAN) at https://cran.r-project.org/web/packages/SNPannotator. The development version and tutorial is available on GitHub (https://github.com/omicslaboratory/SNPannotator). The online version of the package is available at https://omicslab.org/snpannotator.

Software↗

Toward a functional annotation of the human genome using artificial transcription factors.

We have developed a novel, high-throughput approach to collecting randomly perturbed gene-expression profiles from the human genome.A human 293 cell library that stably expresses randomly chosen zinc-finger transcription factors was constructed, and the expression profile of each cell line was obtained using cDNA microarray technology.Gene expression profiles from a total of 132 cell lines were collected and analyzed by (1) a simple clustering method based on expression-profile similarity, and (2) the shortest-path analysis method. These analyses identified a number of gene groups, and further investigation revealed that the genes that were grouped together had close biological relationships. The artificial transcription factor-based random genome perturbation method thus provides a novel functional genomic tool for annotation and classification of genes in the human genome and those of many other organisms.

Antigens, Neoplasm↗

BABELOMICS: a systems biology perspective in the functional annotation of genome-scale experiments.

We present a new version of Babelomics, a complete suite of web tools for functional analysis of genome-scale experiments, with new and improved tools. New functionally relevant terms have been included such as CisRed motifs or bioentities obtained by text-mining procedures. An improved indexing has considerably speeded up several of the modules. An improved version of the FatiScan method for studying the coordinate behaviour of groups of functionally related genes is presented, along with a similar tool, the Gene Set Enrichment Analysis. Babelomics is now more oriented to test systems biology inspired hypotheses. Babelomics can be found at http://www.babelomics.org.

Animals↗

An integrated, functionally annotated gene map of the DXS8026-ELK1 interval on human Xp11.3-Xp11.23: potential hotspot for neurogenetic disorders.

Human chromosome Xp11.3-Xp11.23 encompasses the map location for a growing number of diseases with a genetic basis or genetic component. These include several eye disorders, syndromic and nonsyndromic forms of X-linked mental retardation (XLMR), X-linked neuromuscular diseases and susceptibility loci for schizophrenia, type 1 diabetes, and Graves' disease. We have constructed an approximately 2.7-Mb high-resolution physical map extending from DXS8026 to ELK1, corresponding to a genetic distance of approximately 5.5 cM. A combination of chromosome walking and sequence-tagged site (STS)-content mapping resulted in an integrated framework and transcript map, precisely positioning 10 polymorphic microsatellites (one of which is novel), 16 ESTs, and 12 known genes (RP2, PCTK1, UHX1, UBE1, RBM10, ZNF157, SYN1, ARAF1, TIMP1, PFC, ELK1, UXT). The composite map is currently anchored with 89 STSs to give an average resolution of approximately 1 STS every 30 kb. By a combination of EST database searches and in silico detection of UniGene clusters within genomic sequence generated from this template map, we have mapped several novel genes within this interval: a Na+/H+ exchanger (SLC9A7), at least two zincfinger transcription factors (KIAA0215 and Hs.68318), carbohydrate sulfotransferase-7 (CHST7), regucalcin (RGN), inactivation-escape-1 (INE1), the human ortholog of mouse neuronal protein 15.6, and four putative novel genes. Further genomic analysis enabled annotation of the sequence interval with 20 predicted pseudogenes and 21 UniGene clusters of unknown function. The combined PAC/BAC transcript map and YAC scaffold presented here clarifies previously conflicting data for markers and genes within the Xp11.3-Xp11.23 interval and provides a powerful integrated resource for functional characterization of this clonally unstable, yet gene-rich and clinically significant region of proximal Xp.

Chromosome Mapping↗

BABELOMICS: a suite of web tools for functional annotation and analysis of groups of genes in high-throughput experiments.

We present Babelomics, a complete suite of web tools for the functional analysis of groups of genes in high-throughput experiments, which includes the use of information on Gene Ontology terms, interpro motifs, KEGG pathways, Swiss-Prot keywords, analysis of predicted transcription factor binding sites, chromosomal positions and presence in tissues with determined histological characteristics, through five integrated modules: FatiGO (fast assignment and transference of information), FatiWise, transcription factor association test, GenomeGO and tissues mining tool, respectively. Additionally, another module, FatiScan, provides a new procedure that integrates biological information in combination with experimental results in order to find groups of genes with modest but coordinate significant differential behaviour. FatiScan is highly sensitive and is capable of finding significant asymmetries in the distribution of genes of common function across a list of ordered genes even if these asymmetries were not extreme. The strong multiple-testing nature of the contrasts made by the tools is taken into account. All the tools are integrated in the gene expression analysis package GEPAS. Babelomics is the natural evolution of our tool FatiGO (which analysed almost 22,000 experiments during the last year) to include more sources on information and new modes of using it. Babelomics can be found at http://www.babelomics.org.

Binding Sites↗

Enhanced functional annotation of protein sequences via the use of structural descriptors.

In order to circumvent limitations of sequence based methods in the process of making functional predictions for proteins, we have developed a methodology that uses a sequence-to-structure-to-function paradigm. First, an approximate three-dimensional structure is predicted. Then, a three-dimensional descriptor of the functional site, termed a Fuzzy Functional Form, or FFF, is used to screen the structure for the presence of the functional site of interest (Fetrow et al., 1998; Fetrow and Skolnick, 1998). Previously, a disulfide oxidoreductase FFF was developed and applied to predicted structures obtained from a small structural database. Here, using a substantially larger structural database, we expand the analysis of the disulfide oxidoreductase FFF to the B. subtilis genome. To ascertain the performance of the FFF, its results are compared to those obtained using both the sequence alignment method BLAST and three local sequence motif databases: PRINTS, Prosite, and Blocks. The FFF method is then compared in detail to Blocks and it is shown that the FFF is more flexible and sensitive in finding a specific function in a set of unknown proteins. In addition, the estimated false positive rate of function prediction is significantly lower using the FFF structural motif, rather than the standard sequence motif methods. We also present a second FFF and describe a specific example of the results of its whole-genome application to D. melanogaster using a newer threading algorithm. Our results from all of these studies indicate that the addition of three-dimensional structural information adds significant value in the prediction of biochemical function of genomic sequences.

Algorithms↗