The AthaMap database generates a map of predicted transcription factor binding sites (TFBS) for the whole Arabidopsis thaliana genome. AthaMap has now been extended to include data on post-transcriptional regulation. A total of 403 173 genomic positions of small RNAs have been mapped in the A. thaliana genome. These identify 5772 putative post-transcriptionally regulated target genes. AthaMap tools have been modified to improve the identification of common TFBS in co-regulated genes by subtracting post-transcriptionally regulated genes from such analyses. Furthermore, AthaMap was updated to the TAIR7 genome annotation, a graphic display of gene analysis results was implemented, and the TFBS data content was increased. AthaMap is freely available at http://www.athamap.de/.
The AthaMap database generates a map of cis-regulatory elements for the Arabidopsis thaliana genome. AthaMap contains more than 7.4 × 106 putative binding sites for 36 transcription factors (TFs) from 16 different TF families. A newly implemented functionality allows the display of subsets of higher conserved transcription factor binding sites (TFBSs). Furthermore, a web tool was developed that permits a user-defined search for co-localizing cis-regulatory elements. The user can specify individually the level of conservation for each TFBS and a spacer range between them. This web tool was employed for the identification of co-localizing sites of known interacting TFs and TFs containing two DNA-binding domains. More than 1.8 × 105 combinatorial elements were annotated in the AthaMap database. These elements can also be used to identify more complex co-localizing elements consisting of up to four TFBSs. The AthaMap database and the connected web tools are a valuable resource for the analysis and the prediction of gene expression regulation at .
The AthaMap database generates a map of cis-regulatory elements for the whole Arabidopsis thaliana genome. This database has been extended by new tools to identify common cis-regulatory elements in specific regions of user-provided gene sets. A resulting table displays all cis-regulatory elements annotated in AthaMap including positional information relative to the respective gene. Further tables show overviews with the number of individual transcription factor binding sites (TFBS) present and TFBS common to the whole set of genes. Over represented cis-elements are easily identified. These features were used to detect specific enrichment of drought-responsive elements in cold-induced genes. For identification of co-regulated genes, the output table of the colocalization function was extended to show the closest genes and their relative distances to the colocalizing TFBS. Gene sets determined by this function can be used for a co-regulation analysis in microarray gene expression databases such as Genevestigator or PathoPlant. Additional improvements of AthaMap include display of the gene structure in the sequence window and a significant data increase. AthaMap is freely available at .
The AthaMap database generates a map of potential transcription factor binding sites (TFBS) and small RNA target sites in the Arabidopsis thaliana genome. The database contains sites for 115 different transcription factors (TFs). TFBS were identified with positional weight matrices (PWMs) or with single binding sites. With the new web tool ‘Gene Identification’, it is possible to identify potential target genes for selected TFs. For these analyses, the user can define a region of interest of up to 6000 bp in all annotated genes. For TFBS determined with PWMs, the search can be restricted to high-quality TFBS. The results are displayed in tables that identify the gene, position of the TFBS and, if applicable, individual score of the TFBS. In addition, data files can be downloaded that harbour positional information of TFBS of all TFs in a region between −2000 and +2000 bp relative to the transcription or translation start site. Also, data content of AthaMap was increased and the database was updated to the TAIR8 genome release.
Database URL: http://www.athamap.de/gene_ident.php
The number of online databases and web-tools for gene expression analysis in Arabidopsis thaliana has increased tremendously during the last years. These resources permit the database-assisted identification of putative cis-regulatory DNA sequences, their binding proteins, and the determination of common cis-regulatory motifs in coregulated genes. DNA binding proteins may be predicted by the type of cis-regulatory motif. Further questions of combinatorial control based on the interaction of DNA binding proteins and the colocalization of cis-regulatory motifs can be addressed. The database-assisted spatial and temporal expression analysis of DNA binding proteins and their target genes may help to further refine experimental approaches. Signal transduction pathways upstream of regulated genes are not yet fully accessible in databases mainly because they need to be manually annotated. This review focuses on the use of the AthaMap and PathoPlant® databases for gene expression regulation analysis and discusses similar and complementary online databases and web-tools. Online databases are helpful for the development of working hypothesis and for designing subsequent experiments.
Bioinformatics; databases; gene expression; plants; transcription; web-server.
Gene expression is controlled mainly by the binding of transcription factors to regulatory sequences. To generate a genomic map for regulatory sequences, the Arabidopsis thaliana genome was screened for putative transcription factor binding sites. Using publicly available data from the TRANSFAC database and from publications, alignment matrices for 23 transcription factors of 13 different factor families were used with the pattern search program Patser to determine the genomic positions of more than 2.4 × 106 putative binding sites. Due to the dense clustering of genes and the observation that regulatory sequences are not restricted to upstream regions, the prediction of binding sites was performed for the whole genome. The genomic positions and the underlying data were imported into the newly developed AthaMap database. This data can be accessed by positional information or the Arabidopsis Genome Initiative identification number. Putative binding sites are displayed in the defined region. Data on the matrices used and on the thresholds applied in these screens are given in the database. Considering the high density of sites it will be a valuable resource for generating models on gene expression regulation. The data are available at http://www.athamap.de.
Plant endogenous non-coding short small RNAs (20–24 nt), including microRNAs (miRNAs) and a subset of small interfering RNAs (ta-siRNAs), play important role in gene expression regulatory networks (GRNs). For example, many transcription factors and development-related genes have been reported as targets of these regulatory small RNAs. Although a number of miRNA target prediction algorithms and programs have been developed, most of them were designed for animal miRNAs which are significantly different from plant miRNAs in the target recognition process. These differences demand the development of separate plant miRNA (and ta-siRNA) target analysis tool(s). We present psRNATarget, a plant small RNA target analysis server, which features two important analysis functions: (i) reverse complementary matching between small RNA and target transcript using a proven scoring schema, and (ii) target-site accessibility evaluation by calculating unpaired energy (UPE) required to ‘open’ secondary structure around small RNA’s target site on mRNA. The psRNATarget incorporates recent discoveries in plant miRNA target recognition, e.g. it distinguishes translational and post-transcriptional inhibition, and it reports the number of small RNA/target site pairs that may affect small RNA binding activity to target transcript. The psRNATarget server is designed for high-throughput analysis of next-generation data with an efficient distributed computing back-end pipeline that runs on a Linux cluster. The server front-end integrates three simplified user-friendly interfaces to accept user-submitted or preloaded small RNAs and transcript sequences; and outputs a comprehensive list of small RNA/target pairs along with the online tools for batch downloading, key word searching and results sorting. The psRNATarget server is freely available at http://plantgrn.noble.org/psRNATarget/.
MicroRNAs (miRNAs) regulate genes post transcription by pairing with messenger RNA (mRNA). Variants such as single nucleotide polymorphisms (SNPs) in miRNA regulatory regions might result in altered protein levels and disease. Genome-wide association studies (GWAS) aim at identifying genomic regions that contain variants associated with disease, but lack tools for finding causative variants. We present a computational tool that can help identifying SNPs associated with diseases, by focusing on SNPs affecting miRNA-regulation of genes. The tool predicts the effects of SNPs in miRNA target sites and uses linkage disequilibrium to map these miRNA-related variants to SNPs of interest in GWAS. We compared our predicted SNP effects in miRNA target sites with measured SNP effects from allelic imbalance sequencing. Our predictions fit measured effects better than effects based on differences in free energy or differences of TargetScan context scores. We also used our tool to analyse data from published breast cancer and Parkinson's disease GWAS and significant trait-associated SNPs from the NHGRI GWAS Catalog. A database of predicted SNP effects is available at http://www.bigr.medisin.ntnu.no/mirsnpscore/. The database is based on haplotype data from the CEU HapMap population and miRNAs from miRBase 16.0.
In animals, RNA binding proteins (RBPs) and microRNAs (miRNAs) post-transcriptionally regulate the expression of virtually all genes by binding to RNA. Recent advances in experimental and computational methods facilitate transcriptome-wide mapping of these interactions. It is thought that the combinatorial action of RBPs and miRNAs on target mRNAs form a post-transcriptional regulatory code. We provide a database that supports the quest for deciphering this regulatory code. Within doRiNA, we are systematically curating, storing and integrating binding site data for RBPs and miRNAs. Users are free to take a target (mRNA) or regulator (RBP and/or miRNA) centric view on the data. We have implemented a database framework with short query response times for complex searches (e.g. asking for all targets of a particular combination of regulators). All search results can be browsed, inspected and analyzed in conjunction with a huge selection of other genome-wide data, because our database is directly linked to a local copy of the UCSC genome browser. At the time of writing, doRiNA encompasses RBP data for the human, mouse and worm genomes. For computational miRNA target site predictions, we provide an update of PicTar predictions.
MicroRNAs (miRNAs) regulate several biological processes through post-transcriptional gene silencing. The efficiency of binding of miRNAs to target transcripts depends on the sequence as well as intramolecular structure of the transcript. Single Nucleotide Polymorphisms (SNPs) can contribute to alterations in the structure of regions flanking them, thereby influencing the accessibility for miRNA binding.
The entire human genome was analyzed for SNPs in and around predicted miRNA target sites. Polymorphisms within 200 nucleotides that could alter the intramolecular structure at the target site, thereby altering regulation were annotated. Collated information was ported in a MySQL database with a user-friendly interface accessible through the URL: .
The database has a user-friendly interface where the information can be queried using either the gene name, microRNA name, polymorphism ID or transcript ID. Combination queries using 'AND' or 'OR' is also possible along with specifying the degree of change of intramolecular bonding with and without the polymorphism. Such a resource would enable researchers address questions like the role of regulatory SNPs in the 3' UTRs and population specific regulatory modulations in the context of microRNA targets.
Using bioinformatic methods, 83 novel Arabidopsis miRNAs have been predicted. Putative target mRNAs have been identified for most of the candidate genes.
A class of eukaryotic non-coding RNAs termed microRNAs (miRNAs) interact with target mRNAs by sequence complementarity to regulate their expression. The low abundance of some miRNAs and their time- and tissue-specific expression patterns make experimental miRNA identification difficult. We present here a computational method for genome-wide prediction of Arabidopsis thaliana microRNAs and their target mRNAs. This method uses characteristic features of known plant miRNAs as criteria to search for miRNAs conserved between Arabidopsis and Oryza sativa. Extensive sequence complementarity between miRNAs and their target mRNAs is used to predict miRNA-regulated Arabidopsis transcripts.
Our prediction covered 63% of known Arabidopsis miRNAs and identified 83 new miRNAs. Evidence for the expression of 25 predicted miRNAs came from northern blots, their presence in the Arabidopsis Small RNA Project database, and massively parallel signature sequencing (MPSS) data. Putative targets functionally conserved between Arabidopsis and O. sativa were identified for most newly identified miRNAs. Independent microarray data showed that the expression levels of some mRNA targets anti-correlated with the accumulation pattern of their corresponding regulatory miRNAs. The cleavage of three target mRNAs by miRNA binding was validated in 5' RACE experiments.
We identified new plant miRNAs conserved between Arabidopsis and O. sativa and report a wide range of transcripts as potential miRNA targets. Because MPSS data are generated from polyadenylated RNA molecules, our results suggest that at least some miRNA precursors are polyadenylated at certain stages. The broad range of putative miRNA targets indicates that miRNAs participate in the regulation of a variety of biological processes.
Regulation of post-transcriptional gene expression by microRNAs (miRNA) has so far been validated for only a few mRNA targets. Based on the large number of miRNA genes and the possibility that one miRNA might influence gene expression of several targets simultaneously, the quantity of ribo-regulated genes is expected to be much higher. Here, we describe the web tool MicroInspector that will analyse a user-defined RNA sequence, which is typically an mRNA or a part of an mRNA, for the occurrence of binding sites for known and registered miRNAs. The program allows variation of temperature, the setting of energy values as well as the selection of different miRNA databases to identify miRNA-binding sites of different strength. MicroInspector could spot the correct sites for miRNA-interaction in known target mRNAs. Using other mRNAs, for which such an interaction has not yet been described, we discovered frequently potential miRNA binding sites of similar quality, which can now be analysed experimentally. The MicroInspector program is easy to use and does not require specific computer skills. The service can be accessed via the MicroInspector web server at .
MicroRNAs (miRNAs) are small, non-coding RNAs that play critical roles in post-transcriptional gene regulation. In plants, mature miRNAs pair with complementary sites on mRNAs and subsequently lead to cleavage and degradation of the mRNAs. Many miRNAs target mRNAs that encode transcription factors; therefore, they regulate the expression of many downstream genes. In this study, we carry out a survey of Arabidopsis microRNA genes in response to UV-B radiation, an important adverse abiotic stress. We develop a novel computational approach to identify microRNA genes induced by UV-B radiation and characterize their functions in regulating gene expression. We report that in A. thaliana, 21 microRNA genes in 11 microRNA families are upregulated under UV-B stress condition. We also discuss putative transcriptional downregulation pathways triggered by the induction of these microRNA genes. Moreover, our approach can be directly applied to miRNAs responding to other abiotic and biotic stresses and extended to miRNAs in other plants and metazoans.
integration of heterogeneous data; microRNA gene; UV-B responsive
MicroRNA (miRNA) target hubs are genes that can be simultaneously targeted by a comparatively large number of miRNAs, a class of non-coding RNAs that mediate post-transcriptional gene repression. Although the details of target hub regulation remain poorly understood, recent experiments suggest that pairs of miRNAs can cooperate if their binding sites reside in close proximity. To test this and other hypotheses, we established a novel approach to investigate mechanisms of collective miRNA repression. The approach presented here combines miRNA target prediction and transcription factor prediction with data from the literature and databases to generate a regulatory map for a chosen target hub. We then show how a kinetic model can be derived from the regulatory map. To validate our approach, we present a case study for p21, one of the first experimentally proved miRNA target hubs. Our analysis indicates that distinctive expression patterns for miRNAs, some of which interact cooperatively, fine-tune the features of transient and long-term regulation of target genes. With respect to p21, our model successfully predicts its protein levels for nine different cellular functions. In addition, we find that high abundance of miRNAs, in combination with cooperativity, can enhance noise buffering for the transcription of target hubs.
microRNAs (miRNAs) are small endogenous RNA molecules that are implicated in many biological processes through post-transcriptional regulation of gene expression. The DIANA-microT Web server provides a user-friendly interface for comprehensive computational analysis of miRNA targets in human and mouse. The server has now been extended to support predictions for two widely studied species: Drosophila melanogaster and Caenorhabditis elegans. In the updated version, the Web server enables the association of miRNAs to diseases through bibliographic analysis and provides insights for the potential involvement of miRNAs in biological processes. The nomenclature used to describe mature miRNAs along different miRBase versions has been extensively analyzed, and the naming history of each miRNA has been extracted. This enables the identification of miRNA publications regardless of possible nomenclature changes. User interaction has been further refined allowing users to save results that they wish to analyze further. A connection to the UCSC genome browser is now provided, enabling users to easily preview predicted binding sites in comparison to a wide array of genomic tracks, such as single nucleotide polymorphisms. The Web server is publicly accessible in www.microrna.gr/microT-v4.
microRNAs (miRNAs) are important post-transcriptional regulators, but the extent of this regulation is uncertain, both with regard to the number of miRNA genes and their targets. Using an algorithm based on intragenomic matching of potential miRNAs and their targets coupled with support vector machine classification of miRNA precursors, we explore the potential for regulation by miRNAs in three plant genomes: Arabidopsis thaliana, Populus trichocarpa, and Oryza sativa. We find that the intragenomic matching in conjunction with a supervised learning approach contains enough information to allow reliable computational prediction of miRNA candidates without requiring conservation across species. Using this method, we identify ∼1,200, ∼2,500, and ∼2,100 miRNA candidate genes capable of extensive base-pairing to potential target mRNAs in A. thaliana, P. trichocarpa, and O. sativa, respectively. This is more than five times the number of currently annotated miRNAs in the plants. Many of these candidates are derived from repeat regions, yet they seem to contain the features necessary for correct processing by the miRNA machinery. Conservation analysis indicates that only a few of the candidates are conserved between the species. We conclude that there is a large potential for miRNA-mediated regulatory interactions encoded in the genomes of the investigated plants. We hypothesize that some of these interactions may be realized under special environmental conditions, while others can readily be recruited when organisms diverge and adapt to new niches.
microRNAs (miRNAs) are small RNA molecules that regulate gene expression by complementary basepairing to mRNAs. In plants, this base-pairing is almost perfect along the whole length of miRNAs. This long stretch of complementarity makes it relatively easy to make computational predictions of the targets for known miRNAs. To predict novel miRNA genes, we take advantage of this and reverse the target prediction: instead of predicting targets for known miRNAs, we predict novel miRNA candidates for all known mRNAs. Because matching between target and miRNA candidates is integral to the method, it is possible to achieve good predictions without having to rely on evolutionary conservation, as most other current methods do. This means that we can predict new miRNAs that are specific to an organism. Interestingly, this could help explain the difference between species that have very similar protein-coding genes, but highly different phenotypes. Furthermore, it turns out that many of these new miRNA candidates derive from genomic repeat regions such as transposons, which points to a possible active role for repeats/transposons in the regulation of gene expression.
MicroRNA (miRNAs) play essential roles in post-transcriptional gene regulation in animals and plants. Several existing computational approaches have been developed to complement experimental methods in discovery of miRNAs that express restrictively in specific environmental conditions or cell types. These computational methods require a sufficient number of characterized miRNAs as training samples, and rely on genome annotation to reduce the number of predicted putative miRNAs. However, most sequenced genomes have not been well annotated and many of them have a very few experimentally characterized miRNAs. As a result, the existing methods are not effective or even feasible for identifying miRNAs in these genomes.
Aiming at identifying miRNAs from genomes with a few known miRNA and/or little annotation, we propose and develop a novel miRNA prediction method, miRank, based on our new random walks- based ranking algorithm. We first tested our method on Homo sapiens genome; using a very few known human miRNAs as samples, our method achieved a prediction accuracy greater than 95%. We then applied our method to predict 200 miRNAs in Anopheles gambiae, which is the most important vector of malaria in Africa. Our further study showed that 78 out of the 200 putative miRNA precursors encode mature miRNAs that are conserved in at least one other animal species. These conserved putative miRNAs are good candidates for further experimental study to understand malaria infection.
Availability: MiRank is programmed in Matlab on Windows platform. The source code is available upon request.
In plants, short RNAs including ∼ 21-nt microRNA (miRNA) and 21-nt trans-acting siRNA (ta-siRNA) compose a ‘miRNA → ta-siRNA → target gene’ cascade pathway that regulates gene expression at the posttranscriptional level. In this cascade, biogenesis of ta-siRNA clusters requires 21-nt intervals (i.e. phasing) and miRNA (phase-initiator) cleavage sites on its TAS transcript. Here, we report a novel web server, pssRNAMiner, which is developed to identify both the clusters of phased small RNAs as well as the potential phase-initiator. To detect phased small RNA clusters, the pssRNAMiner maps input small RNAs against user-specified transcript/genomic sequences, and then identifies phased small RNA clusters by evaluating P-values of hypergeometric distribution. To identify potential phase-initiators, pssRNAMiner aligns input phase-initiators with transcripts of TAS candidates using the Smith–Waterman algorithm. Potential cleavage sites on TAS candidates are further identified from complementary regions by weighting the alignment expectation and its distance to detected phased small RNA clusters. The pssRNAMiner web server is freely available at http://bioinfo3.noble.org/pssRNAMiner/.
MicroRNAs (miRNAs) are important regulators of gene expression and have been implicated in development, differentiation and pathogenesis. Hundreds of miRNAs have been discovered in mammalian genomes. Approximately 50% of mammalian miRNAs are expressed from introns of protein-coding genes; the primary transcript (pri-miRNA) is therefore assumed to be the host transcript. However, very little is known about the structure of pri-miRNAs expressed from intergenic regions. Here we annotate transcript boundaries of miRNAs in human, mouse and rat genomes using various transcription features. The 5' end of the pri-miRNA is predicted from transcription start sites, CpG islands and 5' CAGE tags mapped in the upstream flanking region surrounding the precursor miRNA (pre-miRNA). The 3' end of the pri-miRNA is predicted based on the mapping of polyA signals, and supported by cDNA/EST and ditags data. The predicted pri-miRNAs are also analyzed for promoter and insulator-associated regulatory regions.
We define sets of conserved and non-conserved human, mouse and rat pre-miRNAs using bidirectional BLAST and synteny analysis. Transcription features in their flanking regions are used to demarcate the 5' and 3' boundaries of the pri-miRNAs. The lengths and boundaries of primary transcripts are highly conserved between orthologous miRNAs. A significant fraction of pri-miRNAs have lengths between 1 and 10 kb, with very few introns. We annotate a total of 59 pri-miRNA structures, which include 82 pre-miRNAs. 36 pri-miRNAs are conserved in all 3 species. In total, 18 of the confidently annotated transcripts express more than one pre-miRNA. The upstream regions of 54% of the predicted pri-miRNAs are found to be associated with promoter and insulator regulatory sequences.
Little is known about the primary transcripts of intergenic miRNAs. Using comparative data, we are able to identify the boundaries of a significant proportion of human, mouse and rat pri-miRNAs. We confidently predict the transcripts including a total of 77, 58 and 47 human, mouse and rat pre-miRNAs respectively. Our computational annotations provide a basis for subsequent experimental validation of predicted pri-miRNAs.
MicroRNAs (miRNAs) are endogenously small non-coding RNAs which are key post-transcriptional regulators of gene expression. Deregulation of miRNAs is common in human tumorigenesis. We report that miRNA-205 is significantly down-regulated in glioma cell lines and tissue specimens. Ectopic expression of miRNA-205 induces apoptosis, cell cycle arrest, impairs cell viability, clonability and invasive properties of glioma cells. We further demonstrate that miRNA-205 can specifically suppress expression of VEGF-A by directly interacting with the putative miRNA-205 binding site at the 3′-UTR. Identification of VEGF-A as a direct target for miRNA-205 may imply that miRNA-205 is a novel target for glioma therapy. Taken together, the present study for the first time provides evidence that miRNA-205 is a glioma-specific tumor suppressor by targeting VEGF-A.
glioma; miRNA-205; VEGF-A
MicroRNAs (miRNAs) are involved in various biological processes by suppressing gene expression. A recent work has indicated that host miRNAs are also capable of regulating viral gene expression by targeting the virus genomes. To investigate regulatory relationships between host miRNAs and related viruses, we present a novel database, namely ViTa, to curate the known virus miRNA genes and the known/putative target sites of human, mice, rat and chicken miRNAs. Known miRNAs are obtained from miRBase. Virus data are collected and referred from ICTVdB, VBRC and VirGen. Experimentally validated miRNA targets on viruses were derived from literatures. Then, miRanda and TargetScan are utilized to predict miRNA targets within virus genomes. ViTa also provides the virus annotations, virus-infected tissues and tissue specificity of host miRNAs. This work also facilitates the comparisons between subtypes of viruses, such as influenza viruses, human liver viruses and the conserved regions between viruses. Both textual and graphical web interfaces are provided to facilitate the data retrieves in the ViTa database. The database is now freely available at .
MicroRNAs (miRNAs) are small RNAs widely present in animals and plants and involved in post-transcriptional regulation of gene transcripts. In this study we identified and validated 58 miRNAs from an EST dataset of Spodoptera litura based on the computational and experimental analysis of sequence conservation and secondary structure of miRNA by comparing the miRNA sequences in the miRbase. RT-PCR was conducted to examine the expression of these miRNAs and stem-loop RT-PCR assay was performed to examine expression of 11 mature miRNAs (out of the 58 putative miRNA) that showed significant changes in different tissues and stages of the insect development. One hundred twenty eight possible target genes against the 11 miRNAs were predicted by using computational methods. Binding of one miRNA (sli-miR-928b) with the three possible target mRNAs was confirmed by Southern blotting, implying its possible function in regulation of the target genes.
MicroRNAs (miRNA) are ∼21 nucleotide-long non-coding small RNAs, which function as post-transcriptional regulators in eukaryotes. miRNAs play essential roles in regulating plant growth and development. In recent years, research into the mechanism and consequences of miRNA action has made great progress. With whole genome sequence available in such plants as Arabidopsis thaliana, Oryza sativa, Populus trichocarpa, Glycine max, etc., it is desirable to develop a plant miRNA database through the integration of large amounts of information about publicly deposited miRNA data. The plant miRNA database (PMRD) integrates available plant miRNA data deposited in public databases, gleaned from the recent literature, and data generated in-house. This database contains sequence information, secondary structure, target genes, expression profiles and a genome browser. In total, there are 8433 miRNAs collected from 121 plant species in PMRD, including model plants and major crops such as Arabidopsis, rice, wheat, soybean, maize, sorghum, barley, etc. For Arabidopsis, rice, poplar, soybean, cotton, medicago and maize, we included the possible target genes for each miRNA with a predicted interaction site in the database. Furthermore, we provided miRNA expression profiles in the PMRD, including our local rice oxidative stress related microarray data (LC Sciences miRPlants_10.1) and the recently published microarray data for poplar, Arabidopsis, tomato, maize and rice. The PMRD database was constructed by open source technology utilizing a user-friendly web interface, and multiple search tools. The PMRD is freely available at http://bioinformatics.cau.edu.cn/PMRD. We expect PMRD to be a useful tool for scientists in the miRNA field in order to study the function of miRNAs and their target genes, especially in model plants and major crops.
Transcription Factors (TFs) and microRNAs (miRNAs) are key players for gene expression regulation in higher eukaryotes. In the last years, a large amount of bioinformatic studies were devoted to the elucidation of transcriptional and post-transcriptional (mostly miRNA-mediated) regulatory interactions, but little is known about the interplay between them.
Here we describe a dynamic web-accessible database, CircuitsDB, supporting a genome-wide transcriptional and post-transcriptional regulatory network integration, for the human and mouse genomes, based on a bioinformatic sequence-analysis approach. In particular, CircuitsDB is currently focused on the study of mixed miRNA/TF Feed-Forward regulatory Loops (FFLs), i.e. elementary circuits in which a master TF regulates an miRNA and together with it a set of Joint Target protein-coding genes. The database was constructed using an ab-initio oligo analysis procedure for the identification of the transcriptional and post-transcriptional interactions. Several external sources of information were then pooled together to obtain the functional annotation of the proposed interactions. Results for human and mouse genomes are presented in an integrated web tool, that allows users to explore the circuits, investigate their sequence and functional properties and thus suggest possible biological experiments.
We present CircuitsDB, a web-server devoted to the study of human and mouse mixed miRNA/TF Feed-Forward regulatory circuits, freely available at: http://biocluster.di.unito.it/circuits/
MicroRNAs are small, non-protein coding RNA molecules known to regulate the expression of genes by binding to the 3′UTR region of mRNAs. MicroRNAs are produced from longer transcripts which can code for more than one mature miRNAs. miRGen 2.0 is a database that aims to provide comprehensive information about the position of human and mouse microRNA coding transcripts and their regulation by transcription factors, including a unique compilation of both predicted and experimentally supported data. Expression profiles of microRNAs in several tissues and cell lines, single nucleotide polymorphism locations, microRNA target prediction on protein coding genes and mapping of miRNA targets of co-regulated miRNAs on biological pathways are also integrated into the database and user interface. The miRGen database will be continuously maintained and freely available at http://www.microrna.gr/mirgen/.