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FirstEF / First Exon Finder
A 5' terminal exon and promoter prediction program. FirstEF consists of different discriminant functions structured as a decision tree. The probabilistic models are optimized to find potential first donor sites and CpG-related and non-CpG-related promoter regions based on discriminant analysis. For every potential first donor site (GT) and an upstream promoter region, FirstEF decides whether or not the intermediate region can be a potential first exon, based on a set of quadratic discriminant functions.
PlantPAN / Plant Promoter Analysis Navigator
Provides an informative resource for detecting transcription factor binding sites (TFBSs), corresponding transcription factors (TFs), and other important regulatory elements (CpG islands and tandem repeats) in a promoter or a set of plant promoters. Additionally, TFBSs, CpG islands, and tandem repeats in the conserve regions between similar gene promoters are also identified. The current PlantPAN release (version 2.0) contains 16 960 TFs and 1143 TF binding site matrices among 76 plant species. In addition to updating of the annotation information, adding experimentally verified TF matrices, and making improvements in the visualization of transcriptional regulatory networks, several new features and functions are incorporated. These features include: (i) comprehensive curation of TF information (response conditions, target genes, and sequence logos of binding motifs, etc.), (ii) co-expression profiles of TFs and their target genes under various conditions, (iii) protein-protein interactions among TFs and their co-factors, (iv) TF-target networks, and (v) downstream promoter elements.
A program for annotating miRNA promoters in human, as well as other species. PROmiRNA uses deepCAGE data from the FANTOM4 Consortium and integrated cage tag counts and other promoter features, such as CpG content, conservation and TATA box affinity, to score the potential of a candidate region to be a promoter. Given a list of genomic regions of interest, in the form of a gff file, PROmiRNA returns the most probable promoter locations, together with the posterior probabilities calculated by the model.
Enables the user to customize the procedure to a specific problem. MADAP is a flexible one-dimensional clustering tool for the inference of promoters from mRNA 5′ end profiles obtained from the mapping of full-length cDNAs to the genome sequence. It uses internally normal distributions and was designed to model non-contiguous distributions of any shape. MADAP is versatile enough to interpret data from any source in terms of a finite number of clusters characterized by center positions, volume and extension.
A Java GUI with multiple graphical representations ('Views') of enhancer alignments that displays motifs, as IUPAC consensus sequences or position frequency matrices, in the context of phylogenetic conservation to facilitate cis-regulatory element discovery. Thresholds of phylogenetic conservation and motif stringency can be altered dynamically to facilitate detailed analysis of enhancer architecture. Views can be exported to vector graphics programs to generate high-quality figures for publication. Twine can be extended via Java plugins to manipulate alignments and analyze sequences.
A sequence-based machine learning model which identifies transcription start sites (TSSs) with high accuracy and resolution for multiple spatial distribution patterns along the genome, including broadly distributed TSS patterns which have previously been difficult to characterize. TIPR predicts not only the locations of TSSs, but also the expected spatial initiation pattern each TSS will form along the chromosome-a novel capability for TSS prediction algorithms. As spatial initiation patterns are associated with spatiotemporal expression patterns and gene function, this capability has the potential to improve gene annotations and our understanding of the regulation of transcription initiation. The high nucleotide-resolution of this model locates TSSs within 10 nucleotides or less on average.
A web application for identifying promoter regions and annotating regulatory features in user-input sequences. The GPMiner system has a gene group analysis function for analyzing the co-occurrence of TFBSs with statistical measures in a set of co-expressed genes. This function uses a practical platform to examine co-expression genes of microarray data in transcriptional regulation networks. Furthermore, the GPMiner system has a user-friendly input/output interface, and has numerous advantages in mammalian promoter analysis. The proposed system incorporates an SVM with nucleotide composition over-represented hexamer nucleotides and DNA stability for mammalian proximal promoter identification and mines regulatory elements, including TSSs, TFBSs, CpG islands, tandem repeats, the TATA box, CCAAT box, GC box, statistically over-represented sequence patterns, GC content (GC%) and DNA stability. Evaluated by benchmark cross-validation, the predictive sensitivity and specificity of GPMiner are roughly 80%.
Mines for regulons, promoters and transcription factor binding sites (TFBSs) in sequenced bacterial genome. PePPER uses an approach in which all available information on prokaryotic regulons and TFBSs is used to identify regulons in any query bacterium. The software allows uploading of un-annotated data, which is then processed automatically. It can be used to pinpoint a wide range of putative regulons and their cognate TFBSs in any bacterial genome on the basis of existing knowledge.
MCatch / MatrixCatch
Identifies composite regulatory elements in promoters using a library of known examples. MatrixCatch is a methodology that complements the lack of knowledge on sequence variation of each DNA binding sites (BS) in composite elements (CEs) by recruiting data collected for respective BSs separately from each other. The software may serve as a basis for more sophisticated combinatorial analysis of promoters, enhancers or other regulatory regions, thereby helping to understand complex transcriptional regulation of genes and reconstruct complete hierarchical regulatory models.
NNPP / Neural Network Promoter Prediction
Allows to predict variable spacing between functional binding sites, which is known to play a key role in the transcription initiation process. The Neural Network Promoter Prediction is a method that finds eukaryotic and prokaryotic promoters in a DNA sequence. This resource is a part of the Berkeley Drosophila Genome Project. The basis of the NNPP program is a time-delay neural network that consists mainly of two feature layers, one for recognizing a TATA-box and one for recognizing the "Initiator", which is the region spanning the transcription start site.
Predicts both TATA and TATA-less promoters in sequences of a wide spectrum of plant genomes. TSSPlant was developed by using large promoter collections from Plant Promoter Databases (ppdb) and PlantProm DB. It utilizes eighteen significant compositional and signal features of plant promoter sequences, that feed the artificial neural network-based model trained by the backpropagation algorithm. TSSPlant achieves significantly higher accuracy compared to the next best promoter prediction program for both TATA promoters and TATA-less promoters.
CONPRO / CONsensus PROmoter
Predicts promoters in the upstream region of genes. For CONPRO the e transcription start site (TSS) is calculated as mean of the individual predictions. It is able to identify promoters for over 70% of such genes, and 90% of all predictions are true promoters. This software can align the coding region sequence to the genomic sequence, and uses GENSCAN to generate a gene model that overlaps the alignment anchor, and searches for candidate promoters in the region.
Locates the promoter region on the reference genomic sequence and validates the transcription start site location. PromAn allows users to perform multiple alignment using more than two orthologous sequences; it also allows to integrate, visualize results and filter out false positive transcription factors predictions with matrix, conservation scores and biological knowledge of the user. PromAn integrates experimentally-based databases such as DBTSS and EPD as well as promoter, first exon or exonic map prediction programs, such as EF, Eponine and GenScan.
Allows analysis of a set of genes for the presence of transcription factor binding sites. PAINT provides an interaction matrix informing users about a candidate transcriptional regulatory network. This tool includes: (1) modules permitting visualization and analysis of the resulting set of candidate network connections; (2) other modules to find promoter sequences for binding sites of known transcription factors; and (3) a database containing promoter sequences concerning known or predicted genes about mouse genome.
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