Group of Computational Epigenetic Research
Human Histone Modification Database (HHMD), a specialized and comprehensive database for human histone modifications, focuses on integrating useful histone modifications information from experimental data and providing up-to-date data that is essential for understanding these modifications at a systematic level. The current release of HHMD incorporates 45 types of histone modifications in human. To facilitate the study of the relationships between histone modifications and DNA methylation, we integrate a data of genome-wide DNA methylation. We also provide a comprehensive resource of histone modification regulation in 58 human cancer types. We develop HisModView to facilitate the users to browse histone modifications in the context of existing human genome annotations.
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CpG_MI provides a useful information-theoretic tool to distinguish and extract the CpG-rich segments (CpG islands) from the random segments in the bulk genomes with remarkable consistency. CpG_MI identifies CpG islands by calculating the amount of accumulative mutual information of the distances between two neighboring CpGs from 1 bp to 50 bp. CpG dinucleotides densities in all species genome are implicated in the dialog box of species. Due to CpG dinucleotide densities differ from species to species, the corresponding species should be selected first for CpG_MI. Then you can identify CpG islands by three approaches:(I) inputing the start and end coordinate positions of a chromosome, or (II) pasting one sequence in FASTA format, or (III) uploading a fasta sequence file. The output of CpG_MI includes the CpG islands identified together with corresponding genome coordinate, length, the number of CpGs, G+C content and CpG O/E of the CpG islands.
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QDMR (Quantitative Differentially Methylated Regions) is a quantitative approach to quantify methylation difference and identify DMRs from genome-wide methylation profiles by adapting Shannon entropy. The platform-free and species-free nature of QDMR makes it potentially applicable to various methylation data. This approach provides an effective tool for the high-throughput identification of the functional regions involved in epigenetic regulation.
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Gbrowse2, a highly customizable genome visualizer.
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DiseaseMeth, the human disease methylation database. DiseaseMeth is a web based resource focused on the aberrant methylomes of human diseases. Until recently, bulks of large-scale data are avaible and are increasingly grown, from which more information can be mined to gain further information towards human diseases. Our mission is to provide a curated set of methylation information datasets and tools in the human genome, to support and promote research in this area. Especially, we provide a genome-scale landscape to show human methylaton information in a scalable and flexible manner.
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CpG_MPs is proposed for identification and analysis of CpG methylation patterns of genomic regions from high-throughput bisulfite sequencing data. It may identify the unmethylated and methylated regions for a single sample, the conserved and differential methylation regions with different methylation patterns for paired or multiple samples. It includes four main modules as follows: I) Normalization of methylation level of cytosines following guanines; II) Identification of methylated and unmethylated regions for a single sample based on the method of hotspots extension; III) Identification of conserved and differential methylation regions with the corresponding methylation patterns by changes of combinatorial methylation patterns for multiple samples; IV) Extraction of sequence features and visualization of these potentially functional regions. CpG_MPs provide a user-friendly tool for experimental researchers and bioinformaticians to systematicly investigate the biological function of genomic methylation patterns.
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MImprintDB, a database for the imprinted genes of mammals. Imprinted genes are epigenetically modified genes whose expression is determined according to their parent of origin. They are involved in embryonic development, and imprinting dysregulation is linked to cancer, obesity, diabetes, and behavioral disorders such as autism and bipolar disease.Moreover, imprinted genes are targets through which environmental factors can influence gene expression. For these reasons, it is critically important to identify imprinted genes, as well as the cis-acting regulatory elements involved in the establishment and maintenance of imprinting. To date, most efforts to identify imprinted genes have been experimental, and some databases have been constructed.Each has his strong point. While there is no such a database who contains both the information of gene and the material of disease. So, MImprintDB appeared. Now seven of the mammals have included in MImprintDB.From this database you could find the gene which is imprinted,you could see the map of the gene with our tools,you can also find it's related disease.In the map you would see the status of DNA methylation,histone modification,Repeat,ncRNA,SNP and imprint control regions,of a specific location or a given gene. More simply,if you did not known the location,you may just click a particular strip of the chromosomes. ALL the data used in MImprintDB can be downloaded for the further research from this site. If you have any suggestions,please leave message for us or write to us.We pleased that you submitting your experiment data to our database. Have other question, please see the page of help.
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