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PubMed2005

A rigorous method for multigenic families' functional annotation: the peptidyl arginine deiminase (PADs) proteins family example.

BACKGROUND: large scale and reliable proteins' functional annotation is a major challenge in modern biology. Phylogenetic analyses have been shown to be important for such tasks. However, up to now, phylogenetic annotation did not take into account expression data (i.e. ESTs, Microarrays, SAGE, ...). Therefore, integrating such data, like ESTs in phylogenetic annotation could be a major advance in post genomic analyses. We developed an approach enabling the combination of expression data and phylogenetic analysis. To illustrate our method, we used an example protein family, the peptidyl arginine deiminases (PADs), probably implied in Rheumatoid Arthritis. RESULTS: the analysis was performed as follows: we built a phylogeny of PAD proteins from the NCBI's NR protein database. We completed the phylogenetic reconstruction of PADs using an enlarged sequence database containing translations of ESTs contigs. We then extracted all corresponding expression data contained in EST database This analysis allowed us 1/To extend the spectrum of homologs-containing species and to improve the reconstruction of genes' evolutionary history. 2/To deduce an accurate gene expression pattern for each member of this protein family. 3/To show a correlation between paralogous sequences' evolution rate and pattern of tissular expression. CONCLUSION: coupling phylogenetic reconstruction and expression data is a promising way of analysis that could be applied to all multigenic families to investigate the relationship between molecular and transcriptional evolution and to improve functional annotation.

Animals↗

PubMed2002

Assessing the impact of comparative genomic sequence data on the functional annotation of the Drosophila genome.

BACKGROUND: It is widely accepted that comparative sequence data can aid the functional annotation of genome sequences; however, the most informative species and features of genome evolution for comparison remain to be determined. RESULTS: We analyzed conservation in eight genomic regions (apterous, even-skipped, fushi tarazu, twist, and Rhodopsins 1, 2, 3 and 4) from four Drosophila species (D. erecta, D. pseudoobscura, D. willistoni, and D. littoralis) covering more than 500 kb of the D. melanogaster genome. All D. melanogaster genes (and 78-82% of coding exons) identified in divergent species such as D. pseudoobscura show evidence of functional constraint. Addition of a third species can reveal functional constraint in otherwise non-significant pairwise exon comparisons. Microsynteny is largely conserved, with rearrangement breakpoints, novel transposable element insertions, and gene transpositions occurring in similar numbers. Rates of amino-acid substitution are higher in uncharacterized genes relative to genes that have previously been studied. Conserved non-coding sequences (CNCSs) tend to be spatially clustered with conserved spacing between CNCSs, and clusters of CNCSs can be used to predict enhancer sequences. CONCLUSIONS: Our results provide the basis for choosing species whose genome sequences would be most useful in aiding the functional annotation of coding and cis-regulatory sequences in Drosophila. Furthermore, this work shows how decoding the spatial organization of conserved sequences, such as the clustering of CNCSs, can complement efforts to annotate eukaryotic genomes on the basis of sequence conservation alone.

Animals↗

PubMed2006

Aetiology-specific patterns in end-stage heart failure patients identified by functional annotation and classification of microarray data.

BACKGROUND: The objective of the present study was to use gene expression profiling, functional annotations and classification to identify aetiology-specific biological processes and potential molecular markers for different aetiologies of end-stage heart failure. METHODS AND RESULTS: Individual left ventricular myocardial samples from eleven coronary artery disease and nine dilated cardiomyopathy transplant patients were co-hybridized with pooled RNA from four non-failing hearts on custom-made arrays of 7000 human genes. Significance analysis identified differential expression of 153 and 147 genes, respectively, in coronary artery disease or dilated cardiomyopathy versus non-failing hearts. Analysis of Gene Ontology biological process annotations indicated aetiology-specific patterns, primarily related to genes involved in catabolism and in regulation of protein kinase activity. Gene expression classifiers were obtained and used for class prediction of random samples of coronary artery diseased and dilated cardiomyopathic hearts. Best classifiers frequently included matrix metalloproteinase 3, fibulin 1, ATP-binding cassette, sub-family B member 1 and iroquois homeobox protein 5. CONCLUSION: Combining functional annotation from microarray data and classification analysis constitutes a potent strategy to identify disease-specific biological processes and gene expression markers in e.g. end-stage coronary artery disease and dilated cardiomyopathy.

Adult↗

PubMed2006

JAFA: a protein function annotation meta-server.

With the high number of sequences and structures streaming in from genomic projects, there is a need for more powerful and sophisticated annotation tools. Most problematic of the annotation efforts is predicting gene and protein function. Over the past few years there has been considerable progress in automated protein function prediction, using a diverse set of methods. Nevertheless, no single method reports all the information possible, and molecular biologists resort to 'shopping around' using different methods: a cumbersome and time-consuming practice. Here we present the Joined Assembly of Function Annotations, or JAFA server. JAFA queries several function prediction servers with a protein sequence and assembles the returned predictions in a legible, non-redundant format. In this manner, JAFA combines the predictions of several servers to provide a comprehensive view of what are the predicted functions of the proteins. JAFA also offers its own output, and the individual programs' predictions for further processing. JAFA is available for use from http://jafa.burnham.org.

Internet↗

PubMed2005

Functional annotation from predicted protein interaction networks.

MOTIVATION: Progress in large-scale experimental determination of protein-protein interaction networks for several organisms has resulted in innovative methods of functional inference based on network connectivity. However, the amount of effort and resources required for the elucidation of experimental protein interaction networks is prohibitive. Previously we, and others, have developed techniques to predict protein interactions for novel genomes using computational methods and data generated from other genomes. RESULTS: We evaluated the performance of a network-based functional annotation method that makes use of our predicted protein interaction networks. We show that this approach performs equally well on experimentally derived and predicted interaction networks, for both manually and computationally assigned annotations. We applied the method to predicted protein interaction networks for over 50 organisms from all domains of life, providing annotations for many previously unannotated proteins and verifying existing low-confidence annotations. AVAILABILITY: Functional predictions for over 50 organisms are available at http://bioverse.compbio.washington.edu and datasets used for analysis at http://data.compbio.washington.edu/misc/downloads/nannotation_data/. SUPPLEMENTARY INFORMATION: A supplemental appendix gives additional details not in the main text. (http://data.compbio.washington.edu/misc/downloads/nannotation_data/supplement.pdf).

Chromosome Mapping↗

PubMed2006

Structural and functional analysis of Rv3214 from Mycobacterium tuberculosis, a protein with conflicting functional annotations, leads to its characterization as a phosphatase.

The availability of complete genome sequences has highlighted the problems of functional annotation of the many gene products that have only limited sequence similarity with proteins of known function. The predicted protein encoded by open reading frame Rv3214 from the Mycobacterium tuberculosis H37Rv genome was originally annotated as EntD through sequence similarity with the Escherichia coli EntD, a 4'-phosphopantetheinyl transferase implicated in siderophore biosynthesis. An alternative annotation, based on slightly higher sequence identity, grouped Rv3214 with proteins of the cofactor-dependent phosphoglycerate mutase (dPGM) family. The crystal structure of this protein has been solved by single-wavelength anomalous dispersion methods and refined at 2.07-Angstroms resolution (R = 0.229; R(free) = 0.245). The protein is dimeric, with a monomer fold corresponding to the classical dPGM alpha/beta structure, albeit with some variations. Closer comparisons of structure and sequence indicate that it most closely corresponds with a broad-spectrum phosphatase subfamily within the dPGM superfamily. This functional annotation has been confirmed by biochemical assays which show negligible mutase activity but acid phosphatase activity with a pH optimum of 5.4 and suggests that Rv3214 may be important for mycobacterial phosphate metabolism in vivo. Despite its weak sequence similarity with the 4'-phosphopantetheinyl transferases (EntD homologues), there is little evidence to support this function.

Amino Acid Sequence↗

PubMed2003

OntoBlast function: From sequence similarities directly to potential functional annotations by ontology terms.

OntoBlast allows one to find information about potential functions of proteins by presenting a weighted list of ontology entries associated with similar sequences from completely sequenced genomes identified in a BLAST search. It combines, in a single analysis step, the search for sequence similarities in several species with the association of information stored in ontologies. From each identified ontology term a list of genes, which share the functional annotation, can be retrieved. The OntoBlast function is an integral part of the 'Ontologies TO GenomeMatrix' tool which provides an alternative entry point from ontology terms to the Genome-Matrix database. OntoBlast's web interface is accessible on the 'Ontologies TO GenomeMatrix Gate' page at http://functionalgenomics.de/ontogate/.

Animals↗

PubMed2006

Combining evidence, biomedical literature and statistical dependence: new insights for functional annotation of gene sets.

BACKGROUND: Large-scale genomic studies based on transcriptome technologies provide clusters of genes that need to be functionally annotated. The Gene Ontology (GO) implements a controlled vocabulary organised into three hierarchies: cellular components, molecular functions and biological processes. This terminology allows a coherent and consistent description of the knowledge about gene functions. The GO terms related to genes come primarily from semi-automatic annotations made by trained biologists (annotation based on evidence) or text-mining of the published scientific literature (literature profiling). RESULTS: We report an original functional annotation method based on a combination of evidence and literature that overcomes the weaknesses and the limitations of each approach. It relies on the Gene Ontology Annotation database (GOA Human) and the PubGene biomedical literature index. We support these annotations with statistically associated GO terms and retrieve associative relations across the three GO hierarchies to emphasise the major pathways involved by a gene cluster. Both annotation methods and associative relations were quantitatively evaluated with a reference set of 7397 genes and a multi-cluster study of 14 clusters. We also validated the biological appropriateness of our hybrid method with the annotation of a single gene (cdc2) and that of a down-regulated cluster of 37 genes identified by a transcriptome study of an in vitro enterocyte differentiation model (CaCo-2 cells). CONCLUSION: The combination of both approaches is more informative than either separate approach: literature mining can enrich an annotation based only on evidence. Text-mining of the literature can also find valuable associated MEDLINE references that confirm the relevance of the annotation. Eventually, GO terms networks can be built with associative relations in order to highlight cooperative and competitive pathways and their connected molecular functions.

Algorithms↗

PubMed2026

Microbiome Datahub: an open-access platform integrating environmental metadata, taxonomy, and functional annotation for comprehensive metagenome-assembled genome datasets.

BACKGROUND: Metagenome-assembled genomes (MAGs) provide crucial insights into the genomic diversity of uncultured microbes. However, MAG datasets deposited in public repositories such as INSDC are often difficult to reuse due to heterogeneous quality, inconsistent taxonomic and functional annotations, and insufficiently curated environmental metadata. While secondary MAG databases such as MGnify, IMG/M, and SPIRE provide standardized resources, they reconstruct MAGs de novo from public metagenomic reads and therefore do not represent the original MAGs reported in publications. RESULTS: To address this gap, we developed Microbiome Datahub, an open-access platform that systematically aggregates and re-annotates original MAGs from INSDC. We collected 214,427 MAGs, predicted genes by DFAST, performed quality assessment with CheckM, standardized taxonomic assignments with GTDB-Tk, inferred 27 phenotypic traits using Bac2Feature, assigned proteins to MBGD ortholog clusters and KEGG Orthology IDs using PZLAST, and annotated environmental metadata with the Metagenome and Microbes Environmental Ontology. Across these MAGs, the average completeness was 80.5% and contamination 1.8%; notably, the most frequent values were >95% completeness and <1% contamination, indicating that the majority of MAGs are of high quality. Comparative analyses showed that Microbiome Datahub provides phylogenetically and environmentally diverse MAGs: while the majority originated from vertebrate gut environments, a substantial number were also recovered from other habitats such as groundwater, including nearly 10,000 MAGs from the Patescibacteria. Inference of 27 phenotypic traits, including optimum growth temperature, further revealed ecological differentiation across phyla. Protein clustering revealed 56 million identity 40% clusters, with the majority unique compared with MGnify and GlobDB, and ~19% of proteins unassigned to MBGD ortholog clusters, underscoring their novelty. CONCLUSIONS: Microbiome Datahub integrates MAG genome sequences, gene and protein predictions, quality metrics, environmental and taxonomic annotations, ortholog cluster assignments, and phenotype predictions, all accessible via a web interface, API, and bulk downloads. By combining original MAGs with curated metadata and functional annotations, Microbiome Datahub constitutes a comprehensive and reusable resource that will accelerate microbiome and microbial genomics research. Video Abstract.

Metagenome↗

PubMed2003

Protein family classification and functional annotation.

With the accelerated accumulation of genomic sequence data, there is a pressing need to develop computational methods and advanced bioinformatics infrastructure for reliable and large-scale protein annotation and biological knowledge discovery. The Protein Information Resource (PIR) provides an integrated public resource of protein informatics to support genomic and proteomic research. PIR produces the Protein Sequence Database of functionally annotated protein sequences. The annotation problems are addressed by a classification-driven and rule-based method with evidence attribution, coupled with an integrated knowledge base system being developed. The approach allows sensitive identification, consistent and rich annotation, and systematic detection of annotation errors, as well as distinction of experimentally verified and computationally predicted features. The knowledge base consists of two new databases, sequence analysis tools, and graphical interfaces. PIR-NREF, a non-redundant reference database, provides a timely and comprehensive collection of all protein sequences, totaling more than 1,000,000 entries. iProClass, an integrated database of protein family, function, and structure information, provides extensive value-added features for about 830,000 proteins with rich links to over 50 molecular databases. This paper describes our approach to protein functional annotation with case studies and examines common identification errors. It also illustrates that data integration in PIR supports exploration of protein relationships and may reveal protein functional associations beyond sequence homology.

Amino Acid Motifs↗

PubMed2005

Improving functional annotation of non-synonomous SNPs with information theory.

Automated functional annotation of nsSNPs requires that amino-acid residue changes are represented by a set of descriptive features, such as evolutionary conservation, side-chain volume change, effect on ligand-binding, and residue structural rigidity. Identifying the most informative combinations of features is critical to the success of a computational prediction method. We rank 32 features according to their mutual information with functional effects of amino-acid substitutions, as measured by in vivo assays. In addition, we use a greedy algorithm to identify a subset of highly informative features. The method is simple to implement and provides a quantitative measure for selecting the best predictive features given a set of features that a human expert believes to be informative. We demonstrate the usefulness of the selected highly informative features by cross-validated tests of a computational classifier, a support vector machine (SVM). The SVM's classification accuracy is highly correlated with the ranking of the input features by their mutual information. Two features describing the solvent accessibility of "wild-type" and "mutant" amino-acid residues and one evolutionary feature based on superfamily-level multiple alignments produce comparable overall accuracy and 6% fewer false positives than a 32-feature set that considers physiochemical properties of amino acids, protein electrostatics, amino-acid residue flexibility, and binding interactions.

Analysis of Variance↗

PubMed2006

Functional annotation and kinetic characterization of PhnO from Salmonella enterica.

Phosphorus is an essential nutrient for all living organisms. Under conditions of inorganic phosphate starvation, genes from the Pho regulon are induced, allowing microorganisms to use phosphonates as a source of phosphorus. The phnO gene was previously annotated as a transcriptional regulator of unknown function due to sequence homology with members of the GCN5-related N-acyltransferase family (GNAT). PhnO can now be functionally annotated as an aminoalkylphosphonic acid N-acetyltransferase which is able to acetylate a range of aminoalkylphosphonic acids. Studies revealed that PhnO proceeds via an ordered, sequential kinetic mechanism with AcCoA binding first followed by aminoalkylphosphonate. Attack by the amine on the thioester of AcCoA generates the tetrahedral intermediate that collapses to generate the products. The enzyme also requires a divalent metal ion for activity, which is the first example of this requirement for a GNAT family member.

Acetyltransferases↗

PubMed2004

POLYVIEW: a flexible visualization tool for structural and functional annotations of proteins.

UNLABELLED: The POLYVIEW visualization server can be used to generate protein sequence annotations, including secondary structures, relative solvent accessibilities, functional motifs and polymorphic sites. Two-dimensional graphical representations in a customizable format may be generated for both known protein structures and predictions obtained using protein structure prediction servers. POLYVIEW may be used for automated generation of pictures with structural and functional annotations for publications and proteomic on-line resources. AVAILABILITY: http://polyview.cchmc.org.

Algorithms↗

PubMed2005

Molecular processes during fat cell development revealed by gene expression profiling and functional annotation.

BACKGROUND: Large-scale transcription profiling of cell models and model organisms can identify novel molecular components involved in fat cell development. Detailed characterization of the sequences of identified gene products has not been done and global mechanisms have not been investigated. We evaluated the extent to which molecular processes can be revealed by expression profiling and functional annotation of genes that are differentially expressed during fat cell development. RESULTS: Mouse microarrays with more than 27,000 elements were developed, and transcriptional profiles of 3T3-L1 cells (pre-adipocyte cells) were monitored during differentiation. In total, 780 differentially expressed expressed sequence tags (ESTs) were subjected to in-depth bioinformatics analyses. The analysis of 3'-untranslated region sequences from 395 ESTs showed that 71% of the differentially expressed genes could be regulated by microRNAs. A molecular atlas of fat cell development was then constructed by de novo functional annotation on a sequence segment/domain-wise basis of 659 protein sequences, and subsequent mapping onto known pathways, possible cellular roles, and subcellular localizations. Key enzymes in 27 out of 36 investigated metabolic pathways were regulated at the transcriptional level, typically at the rate-limiting steps in these pathways. Also, coexpressed genes rarely shared consensus transcription-factor binding sites, and were typically not clustered in adjacent chromosomal regions, but were instead widely dispersed throughout the genome. CONCLUSIONS: Large-scale transcription profiling in conjunction with sophisticated bioinformatics analyses can provide not only a list of novel players in a particular setting but also a global view on biological processes and molecular networks.

3T3-L1 Cells↗

PubMed2023

Pervasive correlations between causal disease effects of proximal SNPs vary with functional annotations and implicate stabilizing selection.

The genetic architecture of human diseases and complex traits has been extensively studied, but little is known about the relationship of causal disease effect sizes between proximal SNPs, which have largely been assumed to be independent. We introduce a new method, LD SNP-pair effect correlation regression (LDSPEC), to estimate the correlation of causal disease effect sizes of derived alleles between proximal SNPs, depending on their allele frequencies, LD, and functional annotations; LDSPEC produced robust estimates in simulations across various genetic architectures. We applied LDSPEC to 70 diseases and complex traits from the UK Biobank (average N=306K), meta-analyzing results across diseases/traits. We detected significantly nonzero effect correlations for proximal SNP pairs (e.g., -0.37±0.09 for low-frequency positive-LD 0-100bp SNP pairs) that decayed with distance (e.g., -0.07±0.01 for low-frequency positive-LD 1-10kb), varied with allele frequency (e.g., -0.15±0.04 for common positive-LD 0-100bp), and varied with LD between SNPs (e.g., +0.12±0.05 for common negative-LD 0-100bp) (because we consider derived alleles, positive-LD and negative-LD SNP pairs may yield very different results). We further determined that SNP pairs with shared functions had stronger effect correlations that spanned longer genomic distances, e.g., -0.37±0.08 for low-frequency positive-LD same-gene promoter SNP pairs (average genomic distance of 47kb (due to alternative splicing)) and -0.32±0.04 for low-frequency positive-LD H3K27ac 0-1kb SNP pairs. Consequently, SNP-heritability estimates were substantially smaller than estimates of the sum of causal effect size variances across all SNPs (ratio of 0.87±0.02 across diseases/traits), particularly for certain functional annotations (e.g., 0.78±0.01 for common Super enhancer SNPs)-even though these quantities are widely assumed to be equal. We recapitulated our findings via forward simulations with an evolutionary model involving stabilizing selection, implicating the action of linkage masking, whereby haplotypes containing linked SNPs with opposite effects on disease have reduced effects on fitness and escape negative selection.

Journal Article↗

PubMed2023

Pervasive correlations between causal disease effects of proximal SNPs vary with functional annotations and implicate stabilizing selection.

Journal Article↗

PubMed2004

Profiling and functional annotation of mRNA gene expression in pediatric rhabdomyosarcoma and Ewing's sarcoma.

Using Affymetrix oligonucleotide microarrays, we analyzed mRNA gene expression patterns of 12 primary pediatric rhabdomyosarcomas (RMS) and 11 Ewing's sarcomas (EWS), which belong to the small round blue cell tumors (SRBCTs). Diagnostic classification of these cancers is frequently complicated by the highly similar appearance in routine histology, and additional molecular markers could significantly improve tumor classification. A combination of three independent statistical approaches (t-test, SAM, k-nearest neighborhood analysis) resulted in 101 highly significant probe sets that clearly discriminate between EWS and RMS. We identified novel marker transcripts that have not been previously associated with either RMS or EWS yet, including CITED2, glypican 3 (GPC3), and cyclin D1 (CCND1). Expression levels for selected candidate genes were validated by quantitative real-time reverse-transcription PCR. Furthermore, to identify biologically meaningful trends, functional annotations were assigned to 946 genes differentially expressed between EWS and RMS (t-test). Genes involved in protein biosynthesis (n = 28) and complex assembly (n = 9), lipid metabolism (n = 23), energy generation (n = 22), and mRNA processing (n = 11) were expressed significantly higher in EWS. Thus, functional annotation of tumor-specific genes reveals detailed insights into tumor biology and differentiation-specific expression patterns and gives important clues related to the possible cellular origin of these pediatric tumors. Supplementary material for this article is available at the International Journal of Cancer website at http://www.interscience.wiley.com/jpages/0020-7136/suppmat/index.html.

Biomarkers, Tumor↗

PubMed2006

The Mouse Functional Genome Database (MfunGD): functional annotation of proteins in the light of their cellular context.

MfunGD (http://mips.gsf.de/genre/proj/mfungd/) provides a resource for annotated mouse proteins and their occurrence in protein networks. Manual annotation concentrates on proteins which are found to interact physically with other proteins. Accordingly, manually curated information from a protein-protein interaction database (MPPI) and a database of mammalian protein complexes is interconnected with MfunGD. Protein function annotation is performed using the Functional Catalogue (FunCat) annotation scheme which is widely used for the analysis of protein networks. The dataset is also supplemented with information about the literature that was used in the annotation process as well as links to the SIMAP Fasta database, the Pedant protein analysis system and cross-references to external resources. Proteins that so far were not manually inspected are annotated automatically by a graphical probabilistic model and/or superparamagnetic clustering. The database is continuously expanding to include the rapidly growing amount of functional information about gene products from mouse. MfunGD is implemented in GenRE, a J2EE-based component-oriented multi-tier architecture following the separation of concern principle.

Animals↗