We calculated average DNMRs for each gene (DNMR-average) based on the four background DNMRs. We set the DNMR-average as default because they shows the best correlation with number of rare SNVs in ExAC. Except for this update, we also added a parameter in the "Prioritize" page to allow users to filter their variants according to frequencies in human genetic variation databases (ExAC, ESP6500, UK10K and 1000G). In addition, we added two parameters to determine the relative risk for LoF and missense variants for the TADA program.
Several updates of mirDNMR.
Jul 15th. 2016
(1) In the "Browse" functionality, all genes were regrouped to 200 bins to ensure that each bin contain relatively equal number of genes. After the adjustment, it will be more easy to get genes of a given DNMR range.
(2) To make the database more user-friendly and easy to use, we added several statements in the top to explain the usage for each functionalities. The tutorial page were also updated.
An overall update of mirDNMR.
Jun 14th. 2016
We adjusted the overall arrangement of mirDNMR to make the web interface more user-friendly. We also provided a "Feedback" page which was important for our future update.
Currently, mirDNMR contains four main functions for users.
(1) Browse four different background gene-based DNMRs, including DNMR-GC, DNMR-SC, DNMR-MF and DNMR-DM.
(2) Search four different background gene-based DNMRs and variant frequencies in human genetic variation databases (ExAC, ESP6500, UK10K, 1000G, dbSNP) for single gene, exon or genomic position (GRCH37/hg19).
(3) Prioritize candidate genes based on DNM burden compared with four different background DNMRs using TADA, Binomial test or Poisson test.
(4) Filter gene list based on four different background DNMRs and distribution of different types of variants in human genetic variation databases.
A plug-in for Gene ontology (GO), KEGG pathway and protein-protein interaction (PPI) analysis was incorporated in mirDNMR.
May 10th. 2016
For gene list generated by the four functionalities in mirDNMR, user can easily get GO, KEGG pathway and PPI annotation result just by click on the button on the top of result page.
A tool for filtering gene list with custom range of background DNMR was incorporated in mirDNMR.
Apr 15th. 2016
To assist gene prioritization, we developed a convenient tool to filter gene list. Users can get started by inputing a gene list and restricting the range of background DNMR. In the result page, genes remained after filtering process are listed by ascending order of DNMRs. Meanwhile, the RVIS scores and the distribution of the counts of LoF, missense and synonymous variants in human genetic variation databases in both rare (AF<0.001) and common (AF≥0.001) form are shown.
ExAC databases were incorporated in mirDNMR.
Mar 29th. 2016
The Exome Aggregation Consortium (ExAC, version r0.3.1) contains a wide variety of large-scale sequencing data spans 60,706 unrelated individuals, including African American, African American, East Asian, Finnish, Non-Finnish European, South Asian and Others.
Variant frequencies of coding region in normal population were collected and incorporated into mirDNMR database.
Mar 1st. 2016
Variant frequency in normal population is important for mutation pathogenicity prioritization. Therefore, we collected variant frequency information from dbSNP (build 147), ESP6500 (ESP6500SI-V2), 1000G (phase 3), UK10K and annotated by ANNOVAR. These data were incorporated into mirDNMR database so that user can search variant frequency easily. It is noted that user could search variant frequency and associated information by a simple input of gene, exon, or a single locus.
TADA were incorporated in mirDNMR.
Dec 28th. 2015
The TADA model combine multiple types of rare variants for candidate gene prioritization. With the incorporation of TADA, users could prioritize candidate genes more accurately.
A tool for assessing the mutation burden for each gene was incorporated in mirDNMR
Dec 22th. 2015
Based on the background DNMRs provided in mirDNMR, we developed a tool for analysing mutation burden for each gene as well as prioritizing candidate genes with an incorporation of Binomial test or Poisson test.
Background gene-based DNMRs were available for users to browse and search.
Nov 18th. 2015
The background gene-based DNMRs were calculated by 4 methods based on GC content (DNMR-GC), sequence context (DNMR-SC), multiple factors (DNMR-MF) and DNA methylation level (DNMR-DM). We incorporated these data into mirDNMR database so that use could browse and search freely.
The mirDNMR database was basically constructed and tested to be usable on different platforms.
Nov 11th. 2015
(1) mirDNMR was constructed under an Apache/PHP/MySQL environment on the Red Hat Enterprise 5.5 Linux operating system, using Perl/Java/R languages;
(2) mirDNMR has been successfully tested with Microsoft Internet Explorer 11.0, Firefox 38, Google Chrome 45, and Safari 5.1 under different versions of MacOS, Microsoft Windows and Linux.
Background gene-based DNMRs were collected.
Oct 13th. 2015
DNMRs vary widely across the whole genome due to different gene length, GC content, recombination rate, sequence context, epigenetic modification, etc. Background DNMR help us identify candidate genes more accurately. Therefore, we collected background gene-based DNMRs calculated by several methods based on GC content (DNMR-GC), sequence context (DNMR-SC), multiple factors (DNMR-MF) and DNA methylation level (DNMR-DM).