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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.

This study describes comprehensive polling of transcription start and termination sites and analysis of previously unidentified full-length complementary DNAs derived from the mouse genome. We identify the 5' and 3' boundaries of 181,047 transcripts with extensive variation in transcripts arising from alternative promoter usage, splicing, and polyadenylation. There are 16,247 new mouse protein-coding transcripts, including 5154 encoding previously unidentified proteins. Genomic mapping of the transcriptome reveals transcriptional forests, with overlapping transcription on both strands, separated by deserts in which few transcripts are observed. The data provide a comprehensive platform for the comparative analysis of mammalian transcriptional regulation in differentiation and development.

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.

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/.

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.

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.

This paper presents two efficient models for predicting transcription termination (TT) in human DNA. A neural network, self-organizing map, was used for finding features from a human polyadenylation (polyA) sites dataset. We derived prediction models related to different polyA signals. A program, "Dragon PolyAtt", for predicting TT regions was designed for the two most frequent polyA sites "AAUAAA" and "AUUAAA". In our tests, Dragon PolyAtt predicts TT regions with a sensitivity of 48.4% (13.6%) and specificity of 74% (79.1%) when searching for polyA signal "AAUAAA" ("AUUAAA"). Both tests were done on human chromosome 21. Results of Dragon PolyAtt system are substantially better than those obtained by the well-known "polyadq" program.

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 models complement laboratory experimentation for efficient identification of MHC-binding peptides and T-cell epitopes. Methods for prediction of MHC-binding peptides include binding motifs, quantitative matrices, artificial neural networks, hidden Markov models, and molecular modelling. Models derived by these methods have been successfully used for prediction of T-cell epitopes in cancer, autoimmunity, infectious disease, and allergy. For maximum benefit, the use of computer models must be treated as experiments analogous to standard laboratory procedures and performed according to strict standards. This requires careful selection of data for model building, and adequate testing and validation. A range of web-based databases and MHC-binding prediction programs are available. Although some available prediction programs for particular MHC alleles have reasonable accuracy, there is no guarantee that all models produce good quality predictions. In this article, we present and discuss a framework for modelling, testing, and applications of computational methods used in predictions of T-cell epitopes.

Promoter prediction programs (PPPs) are important for in silico gene discovery without support from expressed sequence tag (EST)/cDNA/mRNA sequences, in the analysis of gene regulation and in genome annotation. Contrary to previous expectations, a comprehensive analysis of PPPs reveals that no program simultaneously achieves sensitivity and a positive predictive value >65%. PPP performances deduced from a limited number of chromosomes or smaller data sets do not hold when evaluated at the level of the whole genome, with serious inaccuracy of predictions for non-CpG-island-related promoters. Some PPPs even perform worse than, or close to, pure random guessing.

We present a comprehensive database, Information for the Coordinates of Exons (ICE), of genomic splice sites (SSs) for 10,803 human genes. ICE contains 91,846 pairs of donor acceptor sites, supported by the alignment of "full-length" human mRNAs (including transcript variants) on human genomic sequences. ICE represents the largest collection of human SSs known to date and provides a significant resource to both molecular biologists and bioinformaticians alike. A user can visualize and extract genomic sequences around SSs of the donor acceptor pairs and can also visualize the primary structure of individual genes. We list in this article the 22 most frequently found canonical and noncanonical splice sites. The top four most represented donor acceptor pairs (GT-AG, GC-AG, AT-AC, and GT-GG) accounted for 99.16% of our data set. In addition, we calculated the SS matrix models for the three most common donor acceptor pairs. The database is focused on providing SSs and surrounding sequence information, associated SS and sequence characteristics, and relation to overall transcript structure. It allows targeted search and presents evidence for the gene structure., (Copyright 2004 Elsevier Inc.)

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/.

We analyzed an extended core promoter regions covering [-70,+60] segment relative to the transcription start site of human promoters contained in the Eukaryotic Promoter Database. The analysis was made by using the Match program ver. 1.9 with an optimized setting and the TRANSFAC Professional database ver. 7.2. This analysis revealed that the most common transcription factor binding site in the examined collection of core promoters appears to be initiator (characterized by GEN_INI), which is expected. The other less obvious sites found were Spz1, E2F-1, ZF5, and C/EBP. The 'cap' site was also in this most common group. Over-representation of these sites relative to the non-promoter background data ranged from 0.3167 to 32.1645. These sites were characterized by being present in more than 60% of promoter sequences. Interestingly, the TATA-box has been found in only 11.63% of all examined promoters. The study is complemented by separate analyses of promoter groups having different GC content. These additional analyses revealed that the most common promoter elements found also include AP-2, CdxA, Pax-2, SRY, STAT1 and STAT5A. It was also observed that a number of promoter elements show strong preference either for the GC-rich or the GC-poor core promoters.

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.

Antimicrobial peptides (AMPs) are important components of the innate immune system of many species. These peptides are found in eukaryotes, including mammals, amphibians, insects and plants, as well as in prokaryotes. Other than having pathogen-lytic properties, these peptides have other activities like antitumor activity, mitogen activity, or they may act as signaling molecules. Their short length, fast and efficient action against microbes and low toxicity to mammals have made them potential candidates as peptide drugs. In many cases they are effective against pathogens that are resistant to conventional antibiotics. They can serve as natural templates for the design of novel antimicrobial drugs. Although there are vast amounts of data on natural AMPs, they are not available through one central resource. We have developed a comprehensive database (ANTIMIC, http://research.i2r. a-star.edu.sg/Templar/DB/ANTIMIC/) of known and putative AMPs, which contains approximately 1700 of these peptides. The database is integrated with tools to facilitate efficient extraction of data and their analysis at molecular level, as well as search for new AMPs. These tools include BLAST, PDB structure viewer and the Antimic profile module.

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.

Recent work using expression profiling to computationally predict the estrogen receptor (ER) status of breast tumors has revealed that certain tumors are characterized by a high prediction uncertainty ('low-confidence'). We analyzed these 'low-confidence' tumors and determined that their 'uncertain' prediction status arises as a result of widespread perturbations in multiple genes whose expression is important for ER subtype discrimination. Patients with 'low-confidence' ER+ tumors exhibited a significantly worse overall survival (P=0.03) and shorter time to distant metastasis (P=0.004) compared with their 'high-confidence' ER+ counterparts, indicating that the 'high-' and 'low-confidence' binary distinction is clinically meaningful. We then discovered that elevated expression of the ERBB2 receptor is significantly correlated with a breast tumor exhibiting a 'low-confidence' prediction, and this association was subsequently validated across multiple independently derived breast cancer expression datasets employing a variety of different array technologies and patient populations. Although ERBB2 signaling has been proposed to inhibit the transcriptional activity of ER, a large proportion of the perturbed genes in the 'low-confidence'/ERBB2+ samples are not known to be estrogen responsive, and a recently described bioinformatic algorithm (DEREF) was used to demonstrate the absence of potential estrogen-response elements (EREs) in their promoters. We propose that a significant portion of ERBB2's effects on ER+ breast tumors may involve ER-independent mechanisms of gene activation, which may contribute to the clinically aggressive behavior of the 'low-confidence' breast tumor subtype.

We present an advanced system for recognition of gene starts in mammalian genomes. The system makes predictions of gene start location by combining information about CpG islands, transcription start sites (TSSs), and signals downstream of the predicted TSSs. The system aims at predicting a region that contains the gene start or is in its proximity. Evaluation on human chromosomes 4, 21, and 22 resulted in Se of over 65% and in a ppv of approximately 78%. The system makes on average one prediction per 177000 nucleotides on the human genome, as judged by the results on chromosome 21. Comparison of abilities to predict TSS with the two other systems on human chromosomes 4, 21, and 22 reveals that our system has superior accuracy and overall provides the most confident predictions.

We present a unique program for identification of estrogen response elements (EREs) in genomic DNA and related analyses. The detection algorithm was tested on several large datasets and makes one prediction in 13 300 nt while achieving a sensitivity of 83%. Users can further investigate selected regions around the identified ERE patterns for transcription factor binding sites based on the TRANSFAC database. It is also possible to search for candidate human genes with a match for the identified EREs and their flanking regions within EPD annotated promoters. Additionally, users can search among the extended promoter regions of approximately 11 000 human genes for those that have a high degree of similarity to the identified ERE patterns. Dragon ERE Finder version 2 is freely available for academic and non-profit users (http://sdmc.lit.org.sg/ERE-V2/index).

Recognition of gene starts is a difficult and yet unsolved problem. We present a program, Dragon Gene Start Finder (DGSF), which assesses the gene start in mammalian genomes and predicts a region which should overlap with the first exon of the gene or be in its proximity. The program has been rigorously tested on human chromosomes 4, 21 and 22, and in a strand specific search achieves an overall sensitivity of approximately 65% and a positive predictive value of approximately 78%. The sensitivity for the CpG-island related promoters is >88%. DGSF is free for academic and non-profit users at http://sdmc.lit.org.sg/promoter/dragonGSF1_0/genestart.htm; the download version of the program integrated within the TRANSPLORER package can be obtained from Biobase GmbH, at http://www.biobase.de/.

FIE2 (5' end Information Extraction v2) is a web-based program for easy identification and extraction of nucleotide sequence around the start of genes (promoter region) and their translation initiation site (TIS). Using information provided by the National Center for Biotechnology Information's (NCBI's) LocusLink, FIE2 identifies the 5'-most end of a gene on its respective chromosome based on alignment of a selected set of mRNAs representative of the gene. FIE2 then uses currently available human genome sequence information to extract the desired sequences. The accuracy of the information extracted is therefore limited by the accuracy and completeness of the sequence annotation and sequence alignment provided by LocusLink. In addition, multiple TIS positions are also occasionally presented, for example, as a result of multiple alignments of transcript variants. One of the key criteria of FIE2 is that it should extract only the correct information or attempt no extraction at all. To date, the authors are not aware of any publicly available web-based tool that uses the human genomic sequence to extract pertinent promoter- and TIS-region information in this fashion. FIE2 is freely available at http://sdmc.lit.org.sg/FIE2.0.

Persistent organic pollutants, such as polychlorinated biphenyls (PCBs), are a global problem. We demonstrate enhanced depletion of PCBs using root-associated microbes, which can use plant secondary metabolites, such as phenylpropanoids. Using a "rhizosphere metabolomics" approach, we show that phenylpropanoids constitute 84% of the secondary metabolites exuded from Arabidopsis roots. Phenylpropanoid-utilizing microbes are more competitive and are able to grow at least 100-fold better than their auxotrophic mutants on roots of plants that are able to synthesize or overproduce phenylpropanoids, such as flavonoids. Better colonization of the phenylpropanoid-utilizing strain in a gnotobiotic system on the roots of flavonoid-producing plants leads to almost 90% removal of PCBs in a 28-d period. Our work complements previous approaches to engineer soil microbial populations based on opines produced by transgenic plants and used by microbes carrying opine metabolism genes. The current approach based on plant natural products can be applied to contaminated soils with pre-existing vegetation. This strategy is also likely to be applicable to improving the competitive abilities of biocontrol and biofertilization strains.

This paper introduces a new computer system for recognition of functional transcription start sites (TSSs) in RNA polymerase II promoter regions of vertebrates. This system allows scanning complete vertebrate genomes for promoters with significantly reduced number of false positive predictions. It can be used in the context of gene finding through its recognition of the 5' end of genes. The implemented recognition model uses a composite-hierarchical approach, artificial intelligence, statistics, and signal processing techniques. It also exploits the separation of promoter sequences into those that are C+G-rich or C+G-poor. The system was evaluated on a large and diverse human sequence-set and exhibited several times higher accuracy than several publicly available TSS-finding programs. Results obtained using human chromosome 22 data showed even greater specificity than the evaluation set results. The system has been implemented in the Dragon Promoter Finder package, which can be accessed at http://sdmc.krdl.org.sg:8080/promoter/.

Promiscuous T-cell epitopes make ideal targets for vaccine development. We report here a computational system, MULTIPRED, for the prediction of peptide binding to the HLA-A2 supertype. It combines a novel representation of peptide/MHC interactions with a hidden Markov model as the prediction algorithm. MULTIPREDis both sensitive and specific, and demonstrates high accuracy of peptide-binding predictions for HLA-A*0201, *0204, and *0205 alleles, good accuracy for *0206 allele, and marginal accuracy for *0203 allele. MULTIPREDreplaces earlier requirements for individual prediction models for each HLA allelic variant and simplifies computational aspects of peptide-binding prediction. Preliminary testing indicates that MULTIPRED can predict peptide binding to HLA-A2 supertype molecules with high accuracy, including those allelic variants for which no experimental binding data are currently available.

Dragon Promoter Finder (DPF) locates RNA polymerase II promoters in DNA sequences of vertebrates by predicting Transcription Start Site (TSS) positions. DPF's algorithm uses sensors for three functional regions (promoters, exons and introns) and an Artificial Neural Network (ANN). Results on a large and diverse evaluation set indicate that DPF exhibits a superior predicting ability for TSS location compared to three other promoter-finding programs.

FIE (5'-end Information Extraction) is a web-based program designed primarily to extract the sequence of the regions around the 5'-end and around the translation initiation sites for a particular gene, based on information provided by LocusLink.