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PubMed2002

Sequence conserved for subcellular localization.

The more proteins diverged in sequence, the more difficult it becomes for bioinformatics to infer similarities of protein function and structure from sequence. The precise thresholds used in automated genome annotations depend on the particular aspect of protein function transferred by homology. Here, we presented the first large-scale analysis of the relation between sequence similarity and identity in subcellular localization. Three results stood out: (1) The subcellular compartment is generally more conserved than what might have been expected given that short sequence motifs like nuclear localization signals can alter the native compartment; (2) the sequence conservation of localization is similar between different compartments; and (3) it is similar to the conservation of structure and enzymatic activity. In particular, we found the transition between the regions of conserved and nonconserved localization to be very sharp, although the thresholds for conservation were less well defined than for structure and enzymatic activity. We found that a simple measure for sequence similarity accounting for pairwise sequence identity and alignment length, the HSSP distance, distinguished accurately between protein pairs of identical and different localizations. In fact, BLAST expectation values outperformed the HSSP distance only for alignments in the subtwilight zone. We succeeded in slightly improving the accuracy of inferring localization through homology by fine tuning the thresholds. Finally, we applied our results to the entire SWISS-PROT database and five entirely sequenced eukaryotes.

Amino Acid Sequence↗
PubMed2003

Small envelope protein E of SARS: cloning, expression, purification, CD determination, and bioinformatics analysis.

AIM: To obtain the pure sample of SARS small envelope E protein (SARS E protein), study its properties and analyze its possible functions. METHODS: The plasmid of SARS E protein was constructed by the polymerase chain reaction (PCR), and the protein was expressed in the E coli strain. The secondary structure feature of the protein was determined by circular dichroism (CD) technique. The possible functions of this protein were annotated by bioinformatics methods, and its possible three-dimensional model was constructed by molecular modeling. RESULTS: The pure sample of SARS E protein was obtained. The secondary structure feature derived from CD determination is similar to that from the secondary structure prediction. Bioinformatics analysis indicated that the key residues of SARS E protein were much conserved compared to the E proteins of other coronaviruses. In particular, the primary amino acid sequence of SARS E protein is much more similar to that of murine hepatitis virus (MHV) and other mammal coronaviruses. The transmembrane (TM) segment of the SARS E protein is relatively more conserved in the whole protein than other regions. CONCLUSION: The success of expressing the SARS E protein is a good starting point for investigating the structure and functions of this protein and SARS coronavirus itself as well. The SARS E protein may fold in water solution in a similar way as it in membrane-water mixed environment. It is possible that beta-sheet I of the SARS E protein interacts with the membrane surface via hydrogen bonding, this beta-sheet may uncoil to a random structure in water solution.

Circular Dichroism↗
PubMed2004

Gene recognition based on nucleotide distribution of ORFs in a hyper-thermophilic crenarchaeon, Aeropyrum pernix K1.

The 2694 ORFs originally annotated as potential genes in the genome of Aeropyrum pernix can be categorized into three clusters (A, B, C), according to their nucleotide composition at three codon positions. Coding potential was found to be responsible for the phenomenon of three clusters in a 9-dimensional space derived from the nucleotide composition of ORFs: ORFs assigned to cluster A are coding ones, while those assigned to clusters B and C are non-coding ORFs. A "codingness" index called the AZ score is defined based on a clustering method used to recognize protein-coding genes in the A. pernix genome. The criterion for a coding or non-coding ORF is based on the AZ score. ORFs with AZ > 0 or AZ < 0 are coding or non-coding, respectively. Consequently, 620 out of 632 ORFs with putative functions based on the original annotation are contained in cluster A, which have positive AZ scores. In addition, all 29 ORFs encoding putative or conserved proteins newly added in RefSeq annotation also have positive AZ scores. Accordingly, the number of re-recognized protein-coding genes in the A. pernix genome is 1610, which is significantly less than 2694 in the original annotation and also much less than 1841 in the RefSeq annotation curated by NCBI staff. Annotation information of re-recognized genes and their AZ scores are available at: http://tubic.tju.edu.cn/Aper/.

Aeropyrum↗
PubMed2005

Gene and alternative splicing annotation with AIR.

Designing effective and accurate tools for identifying the functional and structural elements in a genome remains at the frontier of genome annotation owing to incompleteness and inaccuracy of the data, limitations in the computational models, and shifting paradigms in genomics, such as alternative splicing. We present a methodology for the automated annotation of genes and their alternatively spliced mRNA transcripts based on existing cDNA and protein sequence evidence from the same species or projected from a related species using syntenic mapping information. At the core of the method is the splice graph, a compact representation of a gene, its exons, introns, and alternatively spliced isoforms. The putative transcripts are enumerated from the graph and assigned confidence scores based on the strength of sequence evidence, and a subset of the high-scoring candidates are selected and promoted into the annotation. The method is highly selective, eliminating the unlikely candidates while retaining 98% of the high-quality mRNA evidence in well-formed transcripts, and produces annotation that is measurably more accurate than some evidence-based gene sets. The process is fast, accurate, and fully automated, and combines the traditionally distinct gene annotation and alternative splicing detection processes in a comprehensive and systematic way, thus considerably aiding in the ensuing manual curation efforts.

Alternative Splicing↗
PubMed2005

Comparison of Mycobacterium tuberculosis isocitrate dehydrogenases (ICD-1 and ICD-2) reveals differences in coenzyme affinity, oligomeric state, pH tolerance and phylogenetic affiliation.

BACKGROUND: M.tb icd-1 and M.tb icd-2, have been identified in the Mycobacterium tuberculosis genome as probable isocitrate dehydrogenase (ICD) genes. Earlier we demonstrated that the two isoforms can elicit B cell response in TB patients and significantly differentiate TB infected population from healthy, BCG-vaccinated controls. Even though immunoassays suggest that these proteins are closely related in terms of antigenic determinants, we now show that M.tb icd-1 and M.tb icd-2 code for functional energy cycle enzymes and document the differences in their biochemical properties, oligomeric assembly and phylogenetic affiliation. RESULTS: Functionally, both M.tb ICD-1 and ICD-2 proteins are dimers. Zn+2 can act as a cofactor for ICD-1 apart from Mg+2, but not for ICD-2. ICD-1 has higher affinity for metal substrate complex (Km (isocitrate) with Mg++: 10 microM +/- 5) than ICD-2 (Km (isocitrate) with Mg++: 20 microM +/- 1). ICD-1 is active across a wider pH range than ICD-2, retaining 33-35% activity in an acidic pH upto 5.5. Difference in thermal behaviour is also observed with ICD-2 being active across wider temperature range (20 degrees C to 40 degrees C) than ICD-1 (optimum temperature 40 degrees C). The isozymes are NADP+ dependent with distinct phylogenetic affiliations; unlike M.tb ICD-2 that groups with bacterial ICDs, M.tb ICD-1 exhibits a closer lineage to eukaryotic NADP+ dependent ICDs. CONCLUSION: The data provide experimental evidence to show that the two open reading frames, Rv3339c (ICD-1) and Rv0066c (ICD-2), annotated as probable ICDs are functional TCA cycle enzymes with identical enzymatic function but different physio-chemical and kinetic properties. The differences in biochemical and kinetic properties suggest the possibility of differential expression of the two ICDs during different stages of growth, despite having identical metabolic function.

Bacterial Proteins↗
PubMed2025

BTS: a scalable Bayesian Tissue Score for prioritizing GWAS variants and their functional contexts across >1000s of omics datasets.

MOTIVATION: statistics from genome-wide association studies (GWAS) are widely used in fine-mapping and colocalization analyses to identify causal variants and their enrichment in functional contexts, such as affected cell types and genomic features. With the expansion of functional genomic (FG) datasets, which now include hundreds of thousands of tracks across various cell and tissue types, it is critical to establish scalable algorithms integrating thousands of diverse FG annotations with GWAS results. RESULTS: We propose BTS (Bayesian Tissue Score), a novel, highly efficient algorithm uniquely designed for (i) identifying affected cell types and functional elements (context-mapping) and (ii) fine-mapping potentially causal variants in a context-specific manner using large collections of cell type-specific FG annotation tracks. BTS leverages GWAS summary statistics and annotation-specific Bayesian models to analyze genome-wide annotation tracks, including enhancers, open chromatin, and histone marks. We evaluated BTS on GWAS summary statistics for immune and cardiovascular traits, such as Inflammatory Bowel Disease (IBD), Rheumatoid Arthritis (RA), Systemic Lupus Erythematosus (SLE), and Coronary Artery Disease (CAD). Our results demonstrate that BTS is over 100&#xd7; more efficient in estimating functional annotation effects and context-specific variant fine-mapping compared to existing methods. Importantly, this large-scale Bayesian approach prioritizes both known and novel annotations, cell types, genomic regions, and variants and provides valuable biological insights into the functional contexts of these diseases. AVAILABILITY AND IMPLEMENTATION: Docker image is available at https://hub.docker.com/r/wanglab/bts with preinstalled BTS R package (https://bitbucket.org/wanglab-upenn/BTS-R) and BTS GWAS summary statistics analysis pipeline (https://bitbucket.org/wanglab-upenn/bts-pipeline).

Genome-Wide Association Study↗
PubMed2005

Improvements to GALA and dbERGE II: databases featuring genomic sequence alignment, annotation and experimental results.

We describe improvements to two databases that give access to information on genomic sequence similarities, functional elements in DNA and experimental results that demonstrate those functions. GALA, the database of Genome ALignments and Annotations, is now a set of interlinked relational databases for five vertebrate species, human, chimpanzee, mouse, rat and chicken. For each species, GALA records pairwise and multiple sequence alignments, scores derived from those alignments that reflect the likelihood of being under purifying selection or being a regulatory element, and extensive annotations such as genes, gene expression patterns and transcription factor binding sites. The user interface supports simple and complex queries, including operations such as subtraction and intersections as well as clustering and finding elements in proximity to features. dbERGE II, the database of Experimental Results on Gene Expression, contains experimental data from a variety of functional assays. Both databases are now run on the DB2 database management system. Improved hardware and tuning has reduced response times and increased querying capacity, while simplified query interfaces will help direct new users through the querying process. Links are available at http://www.bx.psu.edu/.

Animals↗
PubMed2005

A 7872 cDNA microarray and its use in bovine functional genomics.

The strategy used to create and annotate a 7872 cDNA microarray from cattle placenta and spleen cDNA sequences is described. This microarray contains approximately 6300 unique genes, as determined by BLASTN and TBLASTX similarity search against the human and mouse UniGene and draft human genome sequence databases (build 34). Sequences on the array were annotated with gene ontology (GO) terms, thereby facilitating data analysis and interpretation. A total of 3244 genes were annotated with GO terms. The array is rich in sequences encoding transcription factors, signal transducers and cell cycle regulators. Current research being conducted with this array is described, and an overview of planned improvements in our microarray platform for cattle functional genomics is presented.

Animals↗
PubMed2005

Evaluation of BioCreAtIvE assessment of task 2.

BACKGROUND: Molecular Biology accumulated substantial amounts of data concerning functions of genes and proteins. Information relating to functional descriptions is generally extracted manually from textual data and stored in biological databases to build up annotations for large collections of gene products. Those annotation databases are crucial for the interpretation of large scale analysis approaches using bioinformatics or experimental techniques. Due to the growing accumulation of functional descriptions in biomedical literature the need for text mining tools to facilitate the extraction of such annotations is urgent. In order to make text mining tools useable in real world scenarios, for instance to assist database curators during annotation of protein function, comparisons and evaluations of different approaches on full text articles are needed. RESULTS: The Critical Assessment for Information Extraction in Biology (BioCreAtIvE) contest consists of a community wide competition aiming to evaluate different strategies for text mining tools, as applied to biomedical literature. We report on task two which addressed the automatic extraction and assignment of Gene Ontology (GO) annotations of human proteins, using full text articles. The predictions of task 2 are based on triplets of protein--GO term--article passage. The annotation-relevant text passages were returned by the participants and evaluated by expert curators of the GO annotation (GOA) team at the European Institute of Bioinformatics (EBI). Each participant could submit up to three results for each sub-task comprising task 2. In total more than 15,000 individual results were provided by the participants. The curators evaluated in addition to the annotation itself, whether the protein and the GO term were correctly predicted and traceable through the submitted text fragment. CONCLUSION: Concepts provided by GO are currently the most extended set of terms used for annotating gene products, thus they were explored to assess how effectively text mining tools are able to extract those annotations automatically. Although the obtained results are promising, they are still far from reaching the required performance demanded by real world applications. Among the principal difficulties encountered to address the proposed task, were the complex nature of the GO terms and protein names (the large range of variants which are used to express proteins and especially GO terms in free text), and the lack of a standard training set. A range of very different strategies were used to tackle this task. The dataset generated in line with the BioCreative challenge is publicly available and will allow new possibilities for training information extraction methods in the domain of molecular biology.

Computational Biology↗
PubMed2006

PhenoGO: assigning phenotypic context to gene ontology annotations with natural language processing.

Natural language processing (NLP) is a high throughput technology because it can process vast quantities of text within a reasonable time period. It has the potential to substantially facilitate biomedical research by extracting, linking, and organizing massive amounts of information that occur in biomedical journal articles as well as in textual fields of biological databases. Until recently, much of the work in biological NLP and text mining has revolved around recognizing the occurrence of biomolecular entities in articles, and in extracting particular relationships among the entities. Now, researchers have recognized a need to link the extracted information to ontologies or knowledge bases, which is a more difficult task. One such knowledge base is Gene Ontology annotations (GOA), which significantly increases semantic computations over the function, cellular components and processes of genes. For multicellular organisms, these annotations can be refined with phenotypic context, such as the cell type, tissue, and organ because establishing phenotypic contexts in which a gene is expressed is a crucial step for understanding the development and the molecular underpinning of the pathophysiology of diseases. In this paper, we propose a system, PhenoGO, which automatically augments annotations in GOA with additional context. PhenoGO utilizes an existing NLP system, called BioMedLEE, an existing knowledge-based phenotype organizer system (PhenOS) in conjunction with MeSH indexing and established biomedical ontologies. More specifically, PhenoGO adds phenotypic contextual information to existing associations between gene products and GO terms as specified in GOA. The system also maps the context to identifiers that are associated with different biomedical ontologies, including the UMLS, Cell Ontology, Mouse Anatomy, NCBI taxonomy, GO, and Mammalian Phenotype Ontology. In addition, PhenoGO was evaluated for coding of anatomical and cellular information and assigning the coded phenotypes to the correct GOA; results obtained show that PhenoGO has a precision of 91% and recall of 92%, demonstrating that the PhenoGO NLP system can accurately encode a large number of anatomical and cellular ontologies to GO annotations. The PhenoGO Database may be accessed at the following URL: http://www.phenoGO.org

Computational Biology↗
PubMed2003

Gene structure prediction in syntenic DNA segments.

The accurate prediction of higher eukaryotic gene structures and regulatory elements directly from genomic sequences is an important early step in the understanding of newly assembled contigs and finished genomes. As more new genomes are sequenced, comparative approaches are becoming increasingly practical and valuable for predicting genes and regulatory elements. We demonstrate the effectiveness of a comparative method called pattern filtering; it utilizes synteny between two or more genomic segments for the annotation of genomic sequences. Pattern filtering optimally detects the signatures of conserved functional elements despite the stochastic noise inherent in evolutionary processes, allowing more accurate annotation of gene models. We anticipate that pattern filtering will facilitate sequence annotation and the discovery of new functional elements by the genetics and genomics communities.

Algorithms↗
PubMed2004

Computational identification of transcription factor binding sites by functional analysis of sets of genes sharing overrepresented upstream motifs.

BACKGROUND: Transcriptional regulation is a key mechanism in the functioning of the cell, and is mostly effected through transcription factors binding to specific recognition motifs located upstream of the coding region of the regulated gene. The computational identification of such motifs is made easier by the fact that they often appear several times in the upstream region of the regulated genes, so that the number of occurrences of relevant motifs is often significantly larger than expected by pure chance. RESULTS: To exploit this fact, we construct sets of genes characterized by the statistical overrepresentation of a certain motif in their upstream regions. Then we study the functional characterization of these sets by analyzing their annotation to Gene Ontology terms. For the sets showing a statistically significant specific functional characterization, we conjecture that the upstream motif characterizing the set is a binding site for a transcription factor involved in the regulation of the genes in the set. CONCLUSIONS: The method we propose is able to identify many known binding sites in S. cerevisiae and new candidate targets of regulation by known transcription factors. Its application to less well studied organisms is likely to be valuable in the exploration of their regulatory interaction network.

5' Flanking Region↗
PubMed2006

DBMODELING: a database applied to the study of protein targets from genome projects.

Genome sequencing efforts are providing us with complete genetic blueprints for hundreds of organisms. We are now faced with assigning, understanding, and modifying the functions of proteins encoded by these genomes. DBMODELING is a relational database of annotated comparative protein structure models and their metabolic pathway characterization, when identified. This procedure was applied to complete genomes such as Mycobacterium tuberculosis and Xylella fastidiosa. The main interest in the study of metabolic pathways is that some of these pathways are not present in humans, which makes them selective targets for drug design, decreasing the impact of drugs in humans. In the database, there are currently 1,116 proteins from two genomes. It can be accessed by any researcher at http://www.biocristalografia.df.ibilce.unesp.br/tools/. This project confirms that homology modeling is a useful tool in structural bioinformatics and that it can be very valuable in annotating genome sequence information, contributing to structural and functional genomics, and analyzing protein-ligand docking.

Animals↗
PubMed2005

Large-scale identification of proteins expressed in mouse embryonic stem cells.

A protein subset expressed in the mouse embryonic stem (ES) cell line, E14-1, was characterized by mass spectrometry-based protein identification technology and data analysis. In total, 1790 proteins including 365 potential nuclear and 260 membrane proteins were identified from tryptic digests of total cell lysates. The subset contained a variety of proteins in terms of physicochemical characteristics, subcellular localization, and biological function as defined by Gene Ontology annotation groups. In addition to many housekeeping proteins found in common with other cell types, the subset contained a group of regulatory proteins that may determine unique ES cell functions. We identified 39 transcription factors including Oct-3/4, Sox-2, and undifferentiated embryonic cell transcription factor I, which are characteristic of ES cells, 88 plasma membrane proteins including cell surface markers such as CD9 and CD81, 44 potential proteinaceous ligands for cell surface receptors including growth factors, cytokines, and hormones, and 100 cell signaling molecules. The subset also contained the products of 60 ES-specific and 41 stemness genes defined previously by the DNA microarray analysis of Ramalho-Santos et al. (Ramalho-Santos et al., Science 2002, 298, 597-600), as well as a number of components characteristic of differentiated cell types such as hematopoietic and neural cells. We also identified potential post-translational modifications in a number of ES cell proteins including five Lys acetylation sites and a single phosphorylation site. To our knowledge, this study provides the largest proteomic dataset characterized to date for a single mammalian cell species, and serves as a basic catalogue of a major proteomic subset that is expressed in mouse ES cells.

Animals↗
PubMed2006

Ontology-driven approaches to analyzing data in functional genomics.

Ontologies are fundamental knowledge representations that provide not only standards for annotating and indexing biological information, but also the basis for implementing functional classification and interpretation models. This chapter discusses the application of gene ontology (GO) for predictive tasks in functional genomics. It focuses on the problem of analyzing functional patterns associated with gene products. This chapter is divided into two main parts. The first part overviews GO and its applications for the development of functional classification models. The second part presents two methods for the characterization of genomic information using GO. It discusses methods for measuring functional similarity of gene products, and a tool for supporting gene expression clustering analysis and validation.

Cluster Analysis↗
PubMed2002

Dictionary-driven protein annotation.

Computational methods seeking to automatically determine the properties (functional, structural, physicochemical, etc.) of a protein directly from the sequence have long been the focus of numerous research groups. With the advent of advanced sequencing methods and systems, the number of amino acid sequences that are being deposited in the public databases has been increasing steadily. This has in turn generated a renewed demand for automated approaches that can annotate individual sequences and complete genomes quickly, exhaustively and objectively. In this paper, we present one such approach that is centered around and exploits the Bio-Dictionary, a collection of amino acid patterns that completely covers the natural sequence space and can capture functional and structural signals that have been reused during evolution, within and across protein families. Our annotation approach also makes use of a weighted, position-specific scoring scheme that is unaffected by the over-representation of well-conserved proteins and protein fragments in the databases used. For a given query sequence, the method permits one to determine, in a single pass, the following: local and global similarities between the query and any protein already present in a public database; the likeness of the query to all available archaeal/ bacterial/eukaryotic/viral sequences in the database as a function of amino acid position within the query; the character of secondary structure of the query as a function of amino acid position within the query; the cytoplasmic, transmembrane or extracellular behavior of the query; the nature and position of binding domains, active sites, post-translationally modified sites, signal peptides, etc. In terms of performance, the proposed method is exhaustive, objective and allows for the rapid annotation of individual sequences and full genomes. Annotation examples are presented and discussed in Results, including individual queries and complete genomes that were released publicly after we built the Bio-Dictionary that is used in our experiments. Finally, we have computed the annotations of more than 70 complete genomes and made them available on the World Wide Web at http://cbcsrv.watson.ibm.com/Annotations/.

Algorithms↗
PubMed2004

Enzyme classification by ligand binding.

The problem of assigning a biochemical function to newly discovered proteins has been traditionally approached by expert enzymological analysis, sequence analysis, and structural modeling. In recent years, the appearance of databases containing protein-ligand interaction data for large numbers of protein classes and chemical compounds have provided new ways of investigating proteins for which the biochemical function is not completely understood. In this work, we introduce a method that utilizes ligand-binding data for functional classification of enzymes. The method makes use of the existing Enzyme Commission (EC) classification scheme and the data on interactions of small molecules with enzymes from the BRENDA database. A set of ligands that binds to an enzyme with unknown biochemical function serves as a query to search a protein-ligand interaction database for enzyme classes that are known to interact with a similar set of ligands. These classes provide hypotheses of the query enzyme's function and complement other computational annotations that take advantage of sequence and structural information. Similarity between sets of ligands is computed using point set similarity measures based upon similarity between individual compounds. We present the statistics of classification of the enzymes in the database by a cross-validation procedure and illustrate the application of the method on several examples.

5'-Nucleotidase↗
PubMed2006

Annotation of unknown yeast ORFs by correlation analysis of microarray data and extensive literature searches.

Changes in the expression of genes were used to elucidate the metabolic pathways and regulatory mechanisms that respond to environmental or genetic modifications. Results from previously published chemostat datasets were merged with novel data generated in the present study. ORFs displaying significant changes in expression that correlated with those of other ORFs were analysed using GO mapping tools and supplemented by literature information. The strategy developed was used to propose annotations for ORFs of unknown function. The following ORFs were assigned functions as a result of this study: YMR090w, YGL157w, YGR243w, YLR327c, YER121w, YFR017c, YGR067c, YKL187c, YGR236c (SPG1), YMR107w (SPG4), YMR206w, YER067w, YJL103c, YNL175C (NOP13) YJL200C, YDL070C (FMP16) and YGR173W.

Algorithms↗