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Biomedical subjects

Vladimir B Bajic

Publications and source records attributed to Vladimir B Bajic.

At least 19 recordsLinked to original sources
PubMed2007

Promoter profiling and coexpression data analysis identifies 24 novel genes that are coregulated with AMPA receptor genes, GRIAs.

We identified a set of transcriptional elements that are conserved and overrepresented within the promoters of human, mouse, and rat GRIAs by comparing these promoters against a collection of 10,741 gene promoters. Cells regulate functional groups of genes by coordinating the transcriptional and/or posttranscriptional mRNA levels of interacting genes. As such, it is expected that functional groups of genes share the same transcriptional features within their promoters. We found 47 genes whose promoters contain the same combination of transcriptional elements that are overrepresented within the promoters of the GRIA gene family. Coexpressed genes may be transcriptionally coregulated, which in turn suggests that these genes may play complementary roles within a particular functional context. Using microarray expression data, we found 24 (of the 47) genes that share not only a similar promoter profile with GRIAs but also a well-correlated gene expression profile and, thus, we believe these to be coregulated with GRIAs.

Animals↗
PubMed2006

KBERG: KnowledgeBase for Estrogen Responsive Genes.

Estrogen has a profound impact on human physiology affecting transcription of numerous genes. To decipher functional characteristics of estrogen responsive genes, we developed KnowledgeBase for Estrogen Responsive Genes (KBERG). Genes in KBERG were derived from Estrogen Responsive Gene Database (ERGDB) and were analyzed from multiple aspects. We explored the possible transcription regulation mechanism by capturing highly conserved promoter motifs across orthologous genes, using promoter regions that cover the range of [-1200, +500] relative to the transcription start sites. The motif detection is based on ab initio discovery of common cis-elements from the orthologous gene cluster from human, mouse and rat, thus reflecting a degree of promoter sequence preservation during evolution. The identified motifs are linked to transcription factor binding sites based on the TRANSFAC database. In addition, KBERG uses two established ontology systems, GO and eVOC, to associate genes with their function. Users may assess gene functionality through the description terms in GO. Alternatively, they can gain gene co-expression information through evidence from human EST libraries via eVOC. KBERG is a user-friendly system that provides links to other relevant resources such as ERGDB, UniGene, Entrez Gene, HomoloGene, GO, eVOC and GenBank, and thus offers a platform for functional exploration and potential annotation of genes responsive to estrogen. KBERG database can be accessed at http://research.i2r.a-star.edu.sg/kberg.

Animals↗
PubMed2006

EGASP: the human ENCODE Genome Annotation Assessment Project.

BACKGROUND: We present the results of EGASP, a community experiment to assess the state-of-the-art in genome annotation within the ENCODE regions, which span 1% of the human genome sequence. The experiment had two major goals: the assessment of the accuracy of computational methods to predict protein coding genes; and the overall assessment of the completeness of the current human genome annotations as represented in the ENCODE regions. For the computational prediction assessment, eighteen groups contributed gene predictions. We evaluated these submissions against each other based on a 'reference set' of annotations generated as part of the GENCODE project. These annotations were not available to the prediction groups prior to the submission deadline, so that their predictions were blind and an external advisory committee could perform a fair assessment. RESULTS: The best methods had at least one gene transcript correctly predicted for close to 70% of the annotated genes. Nevertheless, the multiple transcript accuracy, taking into account alternative splicing, reached only approximately 40% to 50% accuracy. At the coding nucleotide level, the best programs reached an accuracy of 90% in both sensitivity and specificity. Programs relying on mRNA and protein sequences were the most accurate in reproducing the manually curated annotations. Experimental validation shows that only a very small percentage (3.2%) of the selected 221 computationally predicted exons outside of the existing annotation could be verified. CONCLUSION: This is the first such experiment in human DNA, and we have followed the standards established in a similar experiment, GASP1, in Drosophila melanogaster. We believe the results presented here contribute to the value of ongoing large-scale annotation projects and should guide further experimental methods when being scaled up to the entire human genome sequence.

Alternative Splicing↗
PubMed2006

Performance assessment of promoter predictions on ENCODE regions in the EGASP experiment.

BACKGROUND: This study analyzes the predictions of a number of promoter predictors on the ENCODE regions of the human genome as part of the ENCODE Genome Annotation Assessment Project (EGASP). The systems analyzed operate on various principles and we assessed the effectiveness of different conceptual strategies used to correlate produced promoter predictions with the manually annotated 5' gene ends. RESULTS: The predictions were assessed relative to the manual HAVANA annotation of the 5' gene ends. These 5' gene ends were used as the estimated reference transcription start sites. With the maximum allowed distance for predictions of 1,000 nucleotides from the reference transcription start sites, the sensitivity of predictors was in the range 32% to 56%, while the positive predictive value was in the range 79% to 93%. The average distance mismatch of predictions from the reference transcription start sites was in the range 259 to 305 nucleotides. At the same time, using transcription start site estimates from DBTSS and H-Invitational databases as promoter predictions, we obtained a sensitivity of 58%, a positive predictive value of 92%, and an average distance from the annotated transcription start sites of 117 nucleotides. In this experiment, the best performing promoter predictors were those that combined promoter prediction with gene prediction. The main reason for this is the reduced promoter search space that resulted in smaller numbers of false positive predictions. CONCLUSION: The main finding, now supported by comprehensive data, is that the accuracy of human promoter predictors for high-throughput annotation purposes can be significantly improved if promoter prediction is combined with gene prediction. Based on the lessons learned in this experiment, we propose a framework for the preparation of the next similar promoter prediction assessment.

Computational Biology↗
PubMed2006

Transcriptional network dynamics in macrophage activation.

Transcriptional regulatory networks govern cell differentiation and the cellular response to external stimuli. However, mammalian model systems have not yet been accessible for network analysis. Here, we present a genome-wide network analysis of the transcriptional regulation underlying the mouse macrophage response to bacterial lipopolysaccharide (LPS). Key to uncovering the network structure is our combination of time-series cap analysis of gene expression with in silico prediction of transcription factor binding sites. By integrating microarray and qPCR time-series expression data with a promoter analysis, we find dynamic subnetworks that describe how signaling pathways change dynamically during the progress of the macrophage LPS response, thus defining regulatory modules characteristic of the inflammatory response. In particular, our integrative analysis enabled us to suggest novel roles for the transcription factors ATF-3 and NRF-2 during the inflammatory response. We believe that our system approach presented here is applicable to understanding cellular differentiation in higher eukaryotes.

Algorithms↗
PubMed2006

Genome-wide analysis of mammalian promoter architecture and evolution.

Mammalian promoters can be separated into two classes, conserved TATA box-enriched promoters, which initiate at a well-defined site, and more plastic, broad and evolvable CpG-rich promoters. We have sequenced tags corresponding to several hundred thousand transcription start sites (TSSs) in the mouse and human genomes, allowing precise analysis of the sequence architecture and evolution of distinct promoter classes. Different tissues and families of genes differentially use distinct types of promoters. Our tagging methods allow quantitative analysis of promoter usage in different tissues and show that differentially regulated alternative TSSs are a common feature in protein-coding genes and commonly generate alternative N termini. Among the TSSs, we identified new start sites associated with the majority of exons and with 3' UTRs. These data permit genome-scale identification of tissue-specific promoters and analysis of the cis-acting elements associated with them.

3' Untranslated Regions↗
PubMed2006

Pseudo-messenger RNA: phantoms of the transcriptome.

The mammalian transcriptome harbours shadowy entities that resist classification and analysis. In analogy with pseudogenes, we define pseudo-messenger RNA to be RNA molecules that resemble protein-coding mRNA, but cannot encode full-length proteins owing to disruptions of the reading frame. Using a rigorous computational pipeline, which rules out sequencing errors, we identify 10,679 pseudo-messenger RNAs (approximately half of which are transposon-associated) among the 102,801 FANTOM3 mouse cDNAs: just over 10% of the FANTOM3 transcriptome. These comprise not only transcribed pseudogenes, but also disrupted splice variants of otherwise protein-coding genes. Some may encode truncated proteins, only a minority of which appear subject to nonsense-mediated decay. The presence of an excess of transcripts whose only disruptions are opal stop codons suggests that there are more selenoproteins than currently estimated. We also describe compensatory frameshifts, where a segment of the gene has changed frame but remains translatable. In summary, we survey a large class of non-standard but potentially functional transcripts that are likely to encode genetic information and effect biological processes in novel ways. Many of these transcripts do not correspond cleanly to any identifiable object in the genome, implying fundamental limits to the goal of annotating all functional elements at the genome sequence level.

Animals↗
PubMed2006

Complex Loci in human and mouse genomes.

Mammalian genomes harbor a larger than expected number of complex loci, in which multiple genes are coupled by shared transcribed regions in antisense orientation and/or by bidirectional core promoters. To determine the incidence, functional significance, and evolutionary context of mammalian complex loci, we identified and characterized 5,248 cis-antisense pairs, 1,638 bidirectional promoters, and 1,153 chains of multiple cis-antisense and/or bidirectionally promoted pairs from 36,606 mouse transcriptional units (TUs), along with 6,141 cis-antisense pairs, 2,113 bidirectional promoters, and 1,480 chains from 42,887 human TUs. In both human and mouse, 25% of TUs resided in cis-antisense pairs, only 17% of which were conserved between the two organisms, indicating frequent species specificity of antisense gene arrangements. A sampling approach indicated that over 40% of all TUs might actually be in cis-antisense pairs, and that only a minority of these arrangements are likely to be conserved between human and mouse. Bidirectional promoters were characterized by variable transcriptional start sites and an identifiable midpoint at which overall sequence composition changed strand and the direction of transcriptional initiation switched. In microarray data covering a wide range of mouse tissues, genes in cis-antisense and bidirectionally promoted arrangement showed a higher probability of being coordinately expressed than random pairs of genes. In a case study on homeotic loci, we observed extensive transcription of nonconserved sequences on the noncoding strand, implying that the presence rather than the sequence of these transcripts is of functional importance. Complex loci are ubiquitous, host numerous nonconserved gene structures and lineage-specific exonification events, and may have a cis-regulatory impact on the member genes.

Animals↗
PubMed2006

Mice and men: their promoter properties.

Using the two largest collections of Mus musculus and Homo sapiens transcription start sites (TSSs) determined based on CAGE tags, ditags, full-length cDNAs, and other transcript data, we describe the compositional landscape surrounding TSSs with the aim of gaining better insight into the properties of mammalian promoters. We classified TSSs into four types based on compositional properties of regions immediately surrounding them. These properties highlighted distinctive features in the extended core promoters that helped us delineate boundaries of the transcription initiation domain space for both species. The TSS types were analyzed for associations with initiating dinucleotides, CpG islands, TATA boxes, and an extensive collection of statistically significant cis-elements in mouse and human. We found that different TSS types show preferences for different sets of initiating dinucleotides and cis-elements. Through Gene Ontology and eVOC categories and tissue expression libraries we linked TSS characteristics to expression. Moreover, we show a link of TSS characteristics to very specific genomic organization in an example of immune-response-related genes (GO:0006955). Our results shed light on the global properties of the two transcriptomes not revealed before and therefore provide the framework for better understanding of the transcriptional mechanisms in the two species, as well as a framework for development of new and more efficient promoter- and gene-finding tools.

Animals↗
PubMed2006

Dragon Promoter Mapper (DPM): a Bayesian framework for modelling promoter structures.

UNLABELLED: Dragon Promoter Mapper (DPM) is a tool to model promoter structure of co-regulated genes using methodology of Bayesian networks. DPM exploits an exhaustive set of motif features (such as motif, its strand, the order of motif occurrence and mutual distance between the adjacent motifs) and generates models from the target promoter sequences, which may be used to (1) detect regions in a genomic sequence which are similar to the target promoters or (2) to classify other promoters as similar or not to the target promoter group. DPM can also be used for modelling of enhancers and silencers. AVAILABILITY: http://defiant.i2r.a-star.edu.sg/projects/BayesPromoter/ CONTACT: vlad@sanbi.ac.za SUPPLEMENTARY INFORMATION: Manual for using DPM web server is provided at http://defiant.i2r.a-star.edu.sg/projects/BayesPromoter/html/manual/manual.htm.

Algorithms↗
PubMed2005

Detection and preliminary analysis of motifs in promoters of anaerobically induced genes of different plant species.

BACKGROUND AND AIMS: Plants can suffer from oxygen limitation during flooding or more complete submergence and may therefore switch from Kreb's cycle respiration to fermentation in association with the expression of anaerobically inducible genes coding for enzymes involved in glycolysis and fermentation. The aim of this study was to clarify mechanisms of transcriptional regulation of these anaerobic genes by identifying motifs shared by their promoter regions. METHODS: Statistically significant motifs were detected by an in silico method from 13 promoters of anaerobic genes. The selected motifs were common for the majority of analysed promoters. Their significance was evaluated by searching for their presence in transcription factor-binding site databases (TRANSFAC, PlantCARE and PLACE). Using several negative control data sets, it was tested whether the motifs found were specific to the anaerobic group. KEY RESULTS: Previously, anaerobic response elements have been identified in maize (Zea mays) and arabidopsis (Arabidopsis thaliana) genes. Known functional motifs were detected, such as GT and GC motifs, but also other motifs shared by most of the genes examined. Five motifs detected have not been found in plants hitherto but are present in the promoters of animal genes with various functions. The consensus sequences of these novel motifs are 5'-AAACAAA-3', 5'-AGCAGC-3', 5'-TCATCAC-3', 5'-GTTT(A/C/T)GCAA-3' and 5'-TTCCCTGTT-3'. CONCLUSIONS: It is believed that the promoter motifs identified could be functional by conferring anaerobic sensitivity to the genes that possess them. This proposal now requires experimental verification.

Anaerobiosis↗
PubMed2005

Promoter modeling: the case study of mammalian histone promoters.

MOTIVATION: Histone proteins play important roles in chromosomal functions. They are significantly evolutionarily conserved across species, which suggests similarity in their transcription regulation. The abundance of experimental data on histone promoters provides an excellent background for the evaluation of computational methods. Our study addresses the issue of how well computational analysis can contribute to unveiling the biologically relevant content of promoter regions for a large number of mammalian histone genes taken across several species, and suggests the consensus promoter models of different histone groups. RESULTS: This is the first study to unveil the detailed promoter structures of all five mammalian histone groups and their subgroups. This is also the most comprehensive computational analysis of histone promoters performed to date. The most exciting fact is that the results correlate very well with the biologically known facts and experimental data. Our analysis convincingly demonstrates that computational approach can significantly contribute to elucidation of promoter content (identification of biologically relevant signals) complementing tedious wet-lab experiments. We believe that this type of analysis can be easily applied to other functional gene classes, thus providing a general framework for modelling promoter groups. These results also provide the basis to hunt for genes co-regulated with histone genes across mammalian genomes.

Algorithms↗
PubMed2005

Integration of text- and data-mining using ontologies successfully selects disease gene candidates.

Genome-wide techniques such as microarray analysis, Serial Analysis of Gene Expression (SAGE), Massively Parallel Signature Sequencing (MPSS), linkage analysis and association studies are used extensively in the search for genes that cause diseases, and often identify many hundreds of candidate disease genes. Selection of the most probable of these candidate disease genes for further empirical analysis is a significant challenge. Additionally, identifying the genes that cause complex diseases is problematic due to low penetrance of multiple contributing genes. Here, we describe a novel bioinformatic approach that selects candidate disease genes according to their expression profiles. We use the eVOC anatomical ontology to integrate text-mining of biomedical literature and data-mining of available human gene expression data. To demonstrate that our method is successful and widely applicable, we apply it to a database of 417 candidate genes containing 17 known disease genes. We successfully select the known disease gene for 15 out of 17 diseases and reduce the candidate gene set to 63.3% (+/-18.8%) of its original size. This approach facilitates direct association between genomic data describing gene expression and information from biomedical texts describing disease phenotype, and successfully prioritizes candidate genes according to their expression in disease-affected tissues.

Anatomy↗
PubMed2005

Dragon Plant Biology Explorer. A text-mining tool for integrating associations between genetic and biochemical entities with genome annotation and biochemical terms lists.

We introduce a tool for text mining, Dragon Plant Biology Explorer (DPBE) that integrates information on Arabidopsis (Arabidopsis thaliana) genes with their functions, based on gene ontologies and biochemical entity vocabularies, and presents the associations as interactive networks. The associations are based on (1) user-provided PubMed abstracts; (2) a list of Arabidopsis genes compiled by The Arabidopsis Information Resource; (3) user-defined combinations of four vocabulary lists based on the ones developed by the general, plant, and Arabidopsis GO consortia; and (4) three lists developed here based on metabolic pathways, enzymes, and metabolites derived from AraCyc, BRENDA, and other metabolism databases. We demonstrate how various combinations can be applied to fields of (1) gene function and gene interaction analyses, (2) plant development, (3) biochemistry and metabolism, and (4) pharmacology of bioactive compounds. Furthermore, we show the suitability of DPBE for systems approaches by integration with "omics" platform outputs. Using a list of abiotic stress-related genes identified by microarray experiments, we show how this tool can be used to rapidly build an information base on the previously reported relationships. This tool complements the existing biological resources for systems biology by identifying potentially novel associations using text analysis between cellular entities based on genome annotation terms. Thus, it allows researchers to efficiently summarize existing information for a group of genes or pathways, so as to make better informed choices for designing validation experiments. Last, DPBE can be helpful for beginning researchers and graduate students to summarize vast information in an unfamiliar area. DPBE is freely available for academic and nonprofit users at http://research.i2r.a-star.edu.sg/DRAGON/ME2/.

Arabidopsis↗
PubMed2004

Computational method for discovery of estrogen responsive genes.

Estrogen has a profound impact on human physiology and affects numerous genes. The classical estrogen reaction is mediated by its receptors (ERs), which bind to the estrogen response elements (EREs) in target gene's promoter region. Due to tedious and expensive experiments, a limited number of human genes are functionally well characterized. It is still unclear how many and which human genes respond to estrogen treatment. We propose a simple, economic, yet effective computational method to predict a subclass of estrogen responsive genes. Our method relies on the similarity of ERE frames across different promoters in the human genome. Matching ERE frames of a test set of 60 known estrogen responsive genes to the collection of over 18,000 human promoters, we obtained 604 candidate genes. Evaluating our result by comparison with the published microarray data and literature, we found that more than half (53.6%, 324/604) of predicted candidate genes are responsive to estrogen. We believe this method can significantly reduce the number of testing potential estrogen target genes and provide functional clues for annotating part of genes that lack functional information.

Computational Biology↗
PubMed2004

Discovery of estrogen receptor alpha target genes and response elements in breast tumor cells.

BACKGROUND: Estrogens and their receptors are important in human development, physiology and disease. In this study, we utilized an integrated genome-wide molecular and computational approach to characterize the interaction between the activated estrogen receptor (ER) and the regulatory elements of candidate target genes. RESULTS: Of around 19,000 genes surveyed in this study, we observed 137 ER-regulated genes in T-47D cells, of which only 89 were direct target genes. Meta-analysis of heterogeneous in vitro and in vivo datasets showed that the expression profiles in T-47D and MCF-7 cells are remarkably similar and overlap with genes differentially expressed between ER-positive and ER-negative tumors. Computational analysis revealed a significant enrichment of putative estrogen response elements (EREs) in the cis-regulatory regions of direct target genes. Chromatin immunoprecipitation confirmed ligand-dependent ER binding at the computationally predicted EREs in our highest ranked ER direct target genes, NRIP1, GREB1 and ABCA3. Wider examination of the cis-regulatory regions flanking the transcriptional start sites showed species conservation in mouse-human comparisons in only 6% of predicted EREs. CONCLUSIONS: Only a small core set of human genes, validated across experimental systems and closely associated with ER status in breast tumors, appear to be sufficient to induce ER effects in breast cancer cells. That cis-regulatory regions of these core ER target genes are poorly conserved suggests that different evolutionary mechanisms are operative at transcriptional control elements than at coding regions. These results predict that certain biological effects of estrogen signaling will differ between mouse and human to a larger extent than previously thought.

Binding Sites↗
PubMed2004

Dragon TF Association Miner: a system for exploring transcription factor associations through text-mining.

We present Dragon TF Association Miner (DTFAM), a system for text-mining of PubMed documents for potential functional association of transcription factors (TFs) with terms from Gene Ontology (GO) and with diseases. DTFAM has been trained and tested in the selection of relevant documents on a manually curated dataset containing >3000 PubMed abstracts relevant to transcription control. On our test data the system achieves sensitivity of 80% with specificity of 82%. DTFAM provides comprehensive tabular and graphical reports linking terms to relevant sets of documents. These documents are color-coded for easier inspection. DTFAM complements the existing biological resources by collecting, assessing, extracting and presenting associations that can reveal some of the not so easily observable connections among the entities found which could explain the functions of TFs and help decipher parts of gene transcriptional regulatory networks. DTFAM summarizes information from a large volume of documents saving time and making analysis simpler for individual users. DTFAM is freely available for academic and non-profit users at http://research.i2r.a-star.edu.sg/DRAGON/TFAM/.

Gene Expression Regulation↗
PubMed2004

ERGDB: Estrogen Responsive Genes Database.

ERGDB is an integrated knowledge database dedicated to genes responsive to estrogen. Genes included in ERGDB are those whose expression levels are experimentally proven to be either up-regulated or down-regulated by estrogen. Genes included are identified based on publications from the PubMed database and each record has been manually examined, evaluated and selected for inclusion by biologists. ERGDB aims to be a unified gateway to store, search, retrieve and update information about estrogen responsive genes. Each record contains links to relevant databases, such as GenBank, LocusLink, Refseq, PubMed and ATCC. The unique feature of ERGDB is that it contains information on the dependence of gene reactions on experimental conditions. In addition to basic information about the genes, information for each record includes gene functional description, experimental methods used, tissue or cell type, gene reaction, estrogen exposure time and the summary of putative estrogen response elements if the gene's promoter sequence was available. Through a web interface at http://sdmc.i2r.a-star.edu.sg/ergdb/ cgi-bin/explore.pl users can either browse or query ERGDB. Access is free for academic and non-profit users.

Animals↗