Position Of Transcription Start Site Research Articles

Identifying promoter sequence architectures via a chunking-based algorithm using non-negative matrix factorisation.

Core promoters are stretches of DNA at the beginning of genes that contain information that facilitates the binding of transcription initiation complexes. Different functional subsets of genes have core promoters with distinct architectures and characteristic motifs. Some of these motifs inform the selection of transcription start sites (TSS). By discovering motifs with fixed distances from known TSS positions, we could in principle classify promoters into different functional groups. Due to the variability and overlap of architectures, promoter classification is a difficult task that requires new approaches. In this study, we present a new method based on non-negative matrix factorisation (NMF) and the associated software called seqArchR that clusters promoter sequences based on their motifs at near-fixed distances from a reference point, such as TSS. When combined with experimental data from CAGE, seqArchR can efficiently identify TSS-directing motifs, including known ones like TATA, DPE, and nucleosome positioning signal, as well as novel lineage-specific motifs and the function of genes associated with them. By using seqArchR on developmental time courses, we reveal how relative use of promoter architectures changes over time with stage-specific expression. seqArchR is a powerful tool for initial genome-wide classification and functional characterisation of promoters. Its use cases are more general: it can also be used to discover any motifs at near-fixed distances from a reference point, even if they are present in only a small subset of sequences.

TSSUNet-MB – ab initio identification of σ70 promoter transcription start sites in Escherichia coli using deep multitask learning

MotivationComputational promoter prediction (CPP) tools designed to classify prokaryotic promoter regions usually assume that a transcription start site (TSS) is located at a predefined position within each promoter region. Such CPP tools are sensitive to any positional shifting of the TSS in a windowed region, and they are unsuitable for determining the boundaries of prokaryotic promoters. ResultsTSSUNet-MB is a deep learning model developed to identify the TSSs of σ70 promoters. Mononucleotide and bendability were used to encode input sequences. TSSUNet-MB outperforms other CPP tools when assessed using the sequences obtained from the neighborhood of real promoters. TSSUNet-MB achieved a sensitivity of 0.839 and specificity of 0.768 on sliding sequences, while other CPP tool cannot maintain both sensitivities and specificities in a compatible range. Furthermore, TSSUNet-MB can precisely predict the TSS position of σ70 promoter-containing regions with a 10-base accuracy of 77.6%. By leveraging the sliding window scanning approach, we further computed the confidence score of each predicted TSS, which allows for more accurately identifying TSS locations. Our results suggest that TSSUNet-MB is a robust tool for finding σ70 promoters and identifying TSSs.

Genome-wide transcription start site mapping in the facultative intracellular pathogen Brucella melitensis by Capping-seq

Brucella melitensis (B. melitensis) is an important facultative intracellular bacterium that causes global zoonotic diseases. Continuous intracellular survival and replication are the main obstruction responsible for the accessibility of prevention and treatment of brucellosis. Bacteria respond to complex environment by regulating gene expression. Many regulatory factors function at loci where RNA polymerase initiates messenger RNA synthesis. However, limited gene annotation is a current obstacle for the research on expression regulation in bacteria. To improve annotation and explore potential functional sites, we proposed a novel genome-wide method called Capping-seq for transcription start site (TSS) mapping in B. melitensis. This technique combines capture of capped primary transcripts with Single Molecule Real-Time (SMRT) sequencing technology. We identified 2,369 TSSs at single nucleotide resolution by Capping-seq. TSSs analysis of Brucella transcripts showed a preference of purine on the TSS positions. Our results revealed that −35 and −10 elements of promoter contained consensus sequences of TTGNNN and TATNNN, respectively. The 5′ ends analysis showed that 57% genes are associated with more than one TSS and 47% genes contain long leader regions, suggested potential complex regulation at the 5′ ends of genes in B. melitensis. Moreover, we identified 52 leaderless genes that are mainly involved in the metabolic processes. Overall, Capping-seq technology provides a unique solution for TSS determination in prokaryotes. Our findings develop a systematic insight into the primary transcriptome characterization of B. melitensis. This study represents a critical basis for investigating gene regulation and pathogenesis of Brucella.

Genome-Wide TSS Distribution in Three Related Clostridia with Normalized Capp-Switch Sequencing.

ABSTRACTTranscription initiation is a tightly regulated process that is crucial for many aspects of prokaryotic physiology. High-throughput transcription start site (TSS) mapping can shed light on global and local regulation of transcription initiation, which in turn may help us understand and predict microbial behavior. In this study, we used Capp-Switch sequencing to determine the TSS positions in the genomes of three model solventogenic clostridia: Clostridium acetobutylicum ATCC 824, C. beijerinckii DSM 6423, and C. beijerinckii NCIMB 8052. We first refined the approach by implementing a normalization pipeline accounting for gene expression, yielding a total of 12,114 mapped TSSs across the species. We further compared the distributions of these sites in the three strains. Results indicated similar distribution patterns at the genome scale, but also some sharp differences, such as for the butyryl-CoA synthesis operon, particularly when comparing C. acetobutylicum to the C. beijerinckii strains. Lastly, we found that promoter structure is generally poorly conserved between C. acetobutylicum and C. beijerinckii. A few conserved promoters across species are discussed, showing interesting examples of how TSS determination and comparison can improve our understanding of gene expression regulation at the transcript level.IMPORTANCE Solventogenic clostridia have been employed in industry for more than a century, initially being used in the acetone-butanol-ethanol (ABE) fermentation process for acetone and butanol production. Interest in these bacteria has recently increased in the context of green chemistry and sustainable development. However, our current understanding of their genomes and physiology limits their optimal use as industrial solvent production platforms. The gene regulatory mechanisms of solventogenesis are still only partly understood, impeding efforts to increase rates and yields. Genome-wide mapping of transcription start sites (TSSs) for three model solventogenic Clostridium strains is an important step toward understanding mechanisms of gene regulation in these industrially important bacteria.

Divergently Transcribed ncRNAs in Escherichia coli: Refinement of the Transcription Starts Assumes Functional Diversification.

Non-coding regulatory RNAs (ncRNAs) comprise specialized group of essential genetically encoded biological molecules involved in the wide variety of cellular metabolic processes. The progressive increase in the number of newly identified ncRNAs and the defining of their genome location indicate their predominant nesting in intergenic regions and expression under the control of their own regulatory elements. At the same time, the regulation of ncRNA’s transcription cannot be considered in isolation from the processes occurring in the immediate genetic environment. A number of experimental data indicate the notable impact of positional regulation of gene expression mediated by dynamic temporal DNA rearrangements accompanying transcription events in the vicinity of neighboring genes. This issue can be perceived as particularly significant for divergently transcribed ncRNAs being actually subjected to double regulatory pressure. Based on available results of RNAseq experiments for Escherichia coli, we screened out divergent ncRNAs and the adjacent genes for the exact positions of transcription start sites (TSSs) and relative efficiency of RNA production. This analysis revealed extension or shortening of some previously annotated ncRNAs resulting in modified secondary structure, confirmed stable expression of four ncRNAs annotated earlier as putative, and approved the possibility of expression of divergently transcribed ncRNAs containing repetitive extragenic palindromic (REP) elements. The biogenesis of secreted ncRNAs from divergently transcribed ffs, chiX, ralA, and ryhB is discussed taking into account positions of TSSs. Refinement of TSSs for the neighboring genes renders some ncRNAs as true antisense overlapping with 5′UTR of divergently transcribed mRNAs.

Prediction of Rice Transcription Start Sites Using TransPrise: A Novel Machine Learning Approach.

As the interest in genetic resequencing increases, so does the need for effective mathematical, computational, and statistical approaches. One of the difficult problems in genome annotation is determination of precise positions of transcription start sites. In this paper, we present TransPrise-an efficient deep learning tool for predicting positions of eukaryotic transcription start sites. TransPrise offers significant improvement over existing promoter-prediction methods. To illustrate this, we compared predictions of TransPrise with the TSSPlant approach for well-annotated genome of Oryza sativa. Using a computer with a graphics processing unit, the run time of TransPrise is 250min on a genome of 374Mb long.We provide the full basis for the comparison and encourage users to freely access a set of our computational tools to facilitate and streamline their own analyses. The ready-to-use Docker image with all the necessary packages, models, and code as well as the source code of the TransPrise algorithm are available at http://compubioverne.group/ . The source code is ready to use and to be customized to predict TSS in any eukaryotic organism.

TransPrise: a novel machine learning approach for eukaryotic promoter prediction.

As interest in genetic resequencing increases, so does the need for effective mathematical, computational, and statistical approaches. One of the difficult problems in genome annotation is determination of precise positions of transcription start sites. In this paper we present TransPrise—an efficient deep learning tool for prediction of positions of eukaryotic transcription start sites. Our pipeline consists of two parts: the binary classifier operates the first, and if a sequence is classified as TSS-containing the regression step follows, where the precise location of TSS is being identified. TransPrise offers significant improvement over existing promoter-prediction methods. To illustrate this, we compared predictions of TransPrise classification and regression models with the TSSPlant approach for the well annotated genome of Oryza sativa. Using a computer equipped with a graphics processing unit, the run time of TransPrise is 250 minutes on a genome of 374 Mb long. The Matthews correlation coefficient value for TransPrise is 0.79, more than two times larger than the 0.31 for TSSPlant classification models. This represents a high level of prediction accuracy. Additionally, the mean absolute error for the regression model is 29.19 nt, allowing for accurate prediction of TSS location. TransPrise was also tested in Homo sapiens, where mean absolute error of the regression model was 47.986 nt. We provide the full basis for the comparison and encourage users to freely access a set of our computational tools to facilitate and streamline their own analyses. The ready-to-use Docker image with all necessary packages, models, code as well as the source code of the TransPrise algorithm are available at (http://compubioverne.group/). The source code is ready to use and customizable to predict TSS in any eukaryotic organism.

SNP and indel frequencies at transcription start sites and at canonical and alternative translation initiation sites in the human genome.

Single-nucleotide polymorphisms (SNPs) are the most common form of genetic variation in humans and drive phenotypic variation. Due to evolutionary conservation, SNPs and indels (insertion and deletions) are depleted in functionally important sequence elements. Recently, population-scale sequencing efforts such as the 1000 Genomes Project and the Genome of the Netherlands Project have catalogued large numbers of sequence variants. Here, we present a systematic analysis of the polymorphisms reported by these two projects in different coding and non-coding genomic elements of the human genome (intergenic regions, CpG islands, promoters, 5’ UTRs, coding exons, 3’ UTRs, introns, and intragenic regions). Furthermore, we were especially interested in the distribution of SNPs and indels in direct vicinity to the transcription start site (TSS) and translation start site (CSS). Thereby, we discovered an enrichment of dinucleotides CpG and CpA and an accumulation of SNPs at base position −1 relative to the TSS that involved primarily CpG and CpA dinucleotides. Genes having a CpG dinucleotide at TSS position -1 were enriched in the functional GO terms “Phosphoprotein”, “Alternative splicing”, and “Protein binding”. Focusing on the CSS, we compared SNP patterns in the flanking regions of canonical and alternative AUG and near-cognate start sites where we considered alternative starts previously identified by experimental ribosome profiling. We observed similar conservation patterns of canonical and alternative translation start sites, which underlines the importance of alternative translation mechanisms for cellular function.

Transcription-driven chromatin repression of Intragenic transcription start sites.

Progression of RNA polymerase II (RNAPII) transcription relies on the appropriately positioned activities of elongation factors. The resulting profile of factors and chromatin signatures along transcription units provides a “positional information system” for transcribing RNAPII. Here, we investigate a chromatin-based mechanism that suppresses intragenic initiation of RNAPII transcription. We demonstrate that RNAPII transcription across gene promoters represses their function in plants. This repression is characterized by reduced promoter-specific molecular signatures and increased molecular signatures associated with RNAPII elongation. The conserved FACT histone chaperone complex is required for this repression mechanism. Genome-wide Transcription Start Site (TSS) mapping reveals thousands of discrete intragenic TSS positions in fact mutants, including downstream promoters that initiate alternative transcript isoforms. We find that histone H3 lysine 4 mono-methylation (H3K4me1), an Arabidopsis RNAPII elongation signature, is enriched at FACT-repressed intragenic TSSs. Our analyses suggest that FACT is required to repress intragenic TSSs at positions that are in part characterized by elevated H3K4me1 levels. In sum, conserved and plant-specific chromatin features correlate with the co-transcriptional repression of intragenic TSSs. Our insights into TSS repression by RNAPII transcription promise to inform the regulation of alternative transcript isoforms and the characterization of gene regulation through the act of pervasive transcription across eukaryotic genomes.

Genetic dissection of the BRCA2 promoter and transcriptional impact of DNA variants.

Promoter mutations may affect transcription and can be associated with human diseases. However, the promoters of the breast cancer (BC) genes are not regularly screened. Our goal was to investigate the BRCA2 promoter in order to study a possible correlation between impaired transcription and disease. The proximal and core promoter of the BRCA2 gene was sequenced in 95 high-risk BC patients. A BRCA2-promoter insert [-938 to + 312 from the transcription start site (TSS)] was generated and cloned into the firefly luciferase vector pGL4.10. Promoter variants and deletions were introduced by site-directed mutagenesis and quantified by Dual-Luciferase assays and semi-quantitative RT-PCR. Three different variants were detected in high-risk BC patients: rs3092989, rs206118, and rs563971900. Functional mapping of 13 overlapping deletions revealed four down-regulating segments (TSS positions): -59_-10del/µdel3 (16% of activity of the wild-type construct), -104_-55del/µdel4 (62%), -239_-190del/µdel7 (39%), -464_-415/µdel12 (78%), suggesting the presence therein of putative transcriptional activator motifs. Additionally, six microdeletions rendered luciferase overexpression: +32_+81del/µdel1 (356%), -14_+36del/µdel2 (180%), -194_-145del/µdel6 (154%), -284_-235del/µdel8 (168%), -329_-280del/µdel9 (111%), and -509_-460del/µdel13 (139%), which is indicative of repressor elements. Functional assays of 15 promoter variants (including those detected in patients) showed that ten of them significantly altered expression with seven up-regulating (113-163%) and three down-regulating (rs551887850_G, rs570548398_T, rs55880202_T; 72-83%) SNPs. Eight of them were located in an ENCODE-DNase Hypersensitive Cluster (TSS - 185 to + 105) where most active transcriptional motifs are known to be placed. BRCA2 expression is highly sensitive to promoter variations as most of them induced relevant changes. Moreover, we mapped critical regions of the BRCA2 promoter that may constitute potential targets for regulatory variants. Three SNPs moderately decreased luciferase activity, but confirmation of its potential pathogenicity requires further analysis. These data reinforce the need to screen the promoter regions of breast cancer genes with a view to discovering novel deleterious mutations.

TSSAR: TSS annotation regime for dRNA-seq data

BackgroundDifferential RNA sequencing (dRNA-seq) is a high-throughput screening technique designed to examine the architecture of bacterial operons in general and the precise position of transcription start sites (TSS) in particular. Hitherto, dRNA-seq data were analyzed by visualizing the sequencing reads mapped to the reference genome and manually annotating reliable positions. This is very labor intensive and, due to the subjectivity, biased.ResultsHere, we present TSSAR, a tool for automated de novo TSS annotation from dRNA-seq data that respects the statistics of dRNA-seq libraries. TSSAR uses the premise that the number of sequencing reads starting at a certain genomic position within a transcriptional active region follows a Poisson distribution with a parameter that depends on the local strength of expression. The differences of two dRNA-seq library counts thus follow a Skellam distribution. This provides a statistical basis to identify significantly enriched primary transcripts.We assessed the performance by analyzing a publicly available dRNA-seq data set using TSSAR and two simple approaches that utilize user-defined score cutoffs. We evaluated the power of reproducing the manual TSS annotation. Furthermore, the same data set was used to reproduce 74 experimentally validated TSS in H. pylori from reliable techniques such as RACE or primer extension. Both analyses showed that TSSAR outperforms the static cutoff-dependent approaches.ConclusionsHaving an automated and efficient tool for analyzing dRNA-seq data facilitates the use of the dRNA-seq technique and promotes its application to more sophisticated analysis. For instance, monitoring the plasticity and dynamics of the transcriptomal architecture triggered by different stimuli and growth conditions becomes possible.The main asset of a novel tool for dRNA-seq analysis that reaches out to a broad user community is usability. As such, we provide TSSAR both as intuitive RESTful Web service ( http://rna.tbi.univie.ac.at/TSSAR) together with a set of post-processing and analysis tools, as well as a stand-alone version for use in high-throughput dRNA-seq data analysis pipelines.

EPD and EPDnew, high-quality promoter resources in the next-generation sequencing era

The Eukaryotic Promoter Database (EPD), available online at http://epd.vital-it.ch, is a collection of experimentally defined eukaryotic POL II promoters which has been maintained for more than 25 years. A promoter is represented by a single position in the genome, typically the major transcription start site (TSS). EPD primarily serves biologists interested in analysing the motif content, chromatin structure or DNA methylation status of co-regulated promoter subsets. Initially, promoter evidence came from TSS mapping experiments targeted at single genes and published in journal articles. Today, the TSS positions provided by EPD are inferred from next-generation sequencing data distributed in electronic form. Traditionally, EPD has been a high-quality database with low coverage. The focus of recent efforts has been to reach complete gene coverage for important model organisms. To this end, we introduced a new section called EPDnew, which is automatically assembled from multiple, carefully selected input datasets. As another novelty, we started to use chromatin signatures in addition to mRNA 5′tags to locate promoters of weekly expressed genes. Regarding user interfaces, we introduced a new promoter viewer which enables users to explore promoter-defining experimental evidence in a UCSC genome browser window.

Systematic clustering of transcription start site landscapes.

Genome-wide, high-throughput methods for transcription start site (TSS) detection have shown that most promoters have an array of neighboring TSSs where some are used more than others, forming a distribution of initiation propensities. TSS distributions (TSSDs) vary widely between promoters and earlier studies have shown that the TSSDs have biological implications in both regulation and function. However, no systematic study has been made to explore how many types of TSSDs and by extension core promoters exist and to understand which biological features distinguish them. In this study, we developed a new non-parametric dissimilarity measure and clustering approach to explore the similarities and stabilities of clusters of TSSDs. Previous studies have used arbitrary thresholds to arrive at two general classes: broad and sharp. We demonstrated that in addition to the previous broad/sharp dichotomy an additional category of promoters exists. Unlike typical TATA-driven sharp TSSDs where the TSS position can vary a few nucleotides, in this category virtually all TSSs originate from the same genomic position. These promoters lack epigenetic signatures of typical mRNA promoters and a substantial subset of them are mapping upstream of ribosomal protein pseudogenes. We present evidence that these are likely mapping errors, which have confounded earlier analyses, due to the high similarity of ribosomal gene promoters in combination with known G addition bias in the CAGE libraries. Thus, previous two-class separations of promoter based on TSS distributions are motivated, but the ultra-sharp TSS distributions will confound downstream analyses if not removed.

Characteristics of Core Promoter Types with respect to Gene Structure and Expression in Arabidopsis thaliana

It is now well known that vertebrates use multiple types of core promoter to accomplish differentiated tasks in Pol II-dependent transcription. Several transcriptional characteristics are known to be associated with core types, including distribution patterns of transcription start sites (TSSs) and selection between tissue-specific and constitutive expression profiles. However, their relationship to gene structure is poorly understood. In this report, we carried a comparative analysis of three Arabidopsis core types, TATA, GA, and Coreless, with regard to gene structure. Our genome-wide investigation was based on the peak TSS positions in promoters that had been identified in a large-scale experimental analysis. This analysis revealed that the types of core promoter are related with the room for promoters that is measured as the distance from the TSS to the end of the upstream gene, the distance from the TSS to the start position of the coding sequence (CDS), and the number and species of the cis-regulatory elements. Of these, it was found that the distance from the TSS to the CDS has a tight, inverse correlation to the expression level, and thus the observed relationship to the core type appears to be indirect. However, promoter length and preference of cis-elements are thought to be a direct reflection of core type-specific transcriptional initiation mechanisms.

Unamplified cap analysis of gene expression on a single-molecule sequencer

We report the development of a simplified cap analysis of gene expression (CAGE) protocol adapted for single-molecule sequencers that avoids second strand synthesis, ligation, digestion, and PCR. HeliScopeCAGE directly sequences the 3' end of cap trapped first-strand cDNAs. As with previous versions of CAGE, we better define transcription start sites (TSS) than known models, identify novel regions of transcription and alternative promoters, and find two major classes of TSS signal, sharp peaks and broad regions. However, using this protocol, we observe reproducible evidence of regulation at the much finer level of individual TSS positions. The libraries are quantitative over 5 orders of magnitude and highly reproducible (Pearson's correlation coefficient of 0.987). We have also scaled down the sample requirement to 5 μg of total RNA for a standard HeliScopeCAGE library and 100 ng for a low-quantity version. When the same RNA was run as 5-μg and 100-ng versions, the 100 ng was still able to detect expression for ∼60% of the 13,468 loci detected by a 5-μg library using the same threshold, allowing comparative analysis of even rare cell populations. Testing the protocol for differential gene expression measurements on triplicate HeLa and THP-1 samples, we find that the log fold change compared to Illumina microarray measurements is highly correlated (0.871). In addition, HeliScopeCAGE finds differential expression for thousands more loci including those with probes on the array. Finally, although the majority of tags are 5' associated, we also observe a low level of signal on exons that is useful for defining gene structures.

Hormone receptor-like in 96 and Broad-Complex modulate phenobarbital induced transcription of cytochrome P450 CYP6D1 in Drosophila S2 cells

Phenobarbital (PB) is a prototypical inducer for studies of xenobiotic responses in animals. In mammals, the nuclear receptors constitutive androstane receptor (CAR) and pregnane X receptor (PXR) have been identified as key transcription factors regulating PB induced transcription of xenobiotic responsive genes. In insects, much less is known about the transcription factors involved in regulating PB induced transcription, although CAR and PXR have a single orthologue hormone receptor-like in 96 (HR96) in Drosophila melanogaster. Using dual luciferase reporter assays in Drosophila Schneider (S2) cells, constructs containing variable lengths of the promoter of the PB inducible cytochrome P450 CYP6D1 were evaluated in the presence and absence of PB. The promoter region between -330 and -280 (relative to the position of transcription start site, +1) was found to be critical for PB induction. Putative binding sites for Drosophila Broad-Complex (BR-C) and deformed (Dfd) were identified within this promoter region using TFsearch. RNA interference (RNAi) treatment of S2 cells in conjunction with CYP6D1 promoter assays showed that suppression of Drosophila HR96 and BR-C transcription in S2 cells resulted in a significant decrease and increase, respectively, of PB induction. Effects of HR96 and BR-C in mediating PB induction were PB specific and PB dependent. This represents new functional evidence that Drosophila HR96 and BR-C can act as an activator and repressor, respectively, in regulating PB induced transcription in insects.

THE PREDICTION OF BACTERIAL TRANSCRIPTION START SITES USING SVMS

Identifying promoters is the key to understanding gene expression in bacteria. Promoters lie in tightly constrained positions relative to the transcription start site (TSS). In this paper, we address the problem of predicting transcription start sites in Escherichia coli. Knowing the TSS position, one can then predict the promoter position to within a few base pairs, and vice versa. The accepted method for promoter prediction is to use a pair of position weight matrices (PWMs), which define conserved motifs at the sigma-factor binding site. However this method is known to result in a large number of false positive predictions, thereby limiting its usefulness to the experimental biologist. We adopt an alternative approach based on the Support Vector Machine (SVM) using a modified mismatch spectrum kernel. Our modifications involve tagging the motifs with their location, and selectively pruning the feature set. We quantify the performance of several SVM models and a PWM model using a performance metric of area under the detection-error tradeoff (DET) curve. SVM models are shown to outperform the PWM on a biologically realistic TSS prediction task. We also describe a more broadly applicable peak scoring technique which reduces the number of false positive predictions, greatly enhancing the utility of our results.

DoOP: Databases of Orthologous Promoters, collections of clusters of orthologous upstream sequences from chordates and plants

DoOP (http://doop.abc.hu/) is a database of eukaryotic promoter sequences (upstream regions) aiming to facilitate the recognition of regulatory sites conserved between species. The annotated first exons of human and Arabidopsis thaliana genes were used as queries in BLAST searches to collect the most closely related orthologous first exon sequences from Chordata and Viridiplantae species. Up to 3000 bp DNA segments upstream from these first exons constitute the clusters in the chordate and plant sections of the Database of Orthologous Promoters. Release 1.0 of DoOP contains 21 061 chordate clusters from 284 different species and 7548 plant clusters from 269 different species. The database can be used to find and retrieve promoter sequences of a given gene from various species and it is also suitable to see the most trivial conserved sequence blocks in the orthologous upstream regions. Users can search DoOP with either sequence or text (annotation) to find promoter clusters of various genes. In addition to the sequence data, the positions of the conserved sequence blocks derived from multiple alignments, the positions of repetitive elements and the positions of transcription start sites known from the Eukaryotic Promoter Database (EPD) can be viewed graphically.

The Eukaryotic Promoter Database EPD: the impact of in silico primer extension.

The Eukaryotic Promoter Database (EPD) is an annotated non-redundant collection of eukaryotic POL II promoters, experimentally defined by a transcription start site (TSS). There may be multiple promoter entries for a single gene. The underlying experimental evidence comes from journal articles and, starting from release 73, from 5' ESTs of full-length cDNA clones used for so-called in silico primer extension. Access to promoter sequences is provided by pointers to TSS positions in nucleotide sequence entries. The annotation part of an EPD entry includes a description of the type and source of the initiation site mapping data, links to other biological databases and bibliographic references. EPD is structured in a way that facilitates dynamic extraction of biologically meaningful promoter subsets for comparative sequence analysis. Web-based interfaces have been developed that enable the user to view EPD entries in different formats, to select and extract promoter sequences according to a variety of criteria and to navigate to related databases exploiting different cross-references. Tools for analysing sequence motifs around TSSs defined in EPD are provided by the signal search analysis server. EPD can be accessed at http://www.epd. isb-sib.ch.