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Database Commons - mirDNMR

mirDNMR

Citations: 1

z-index 0.87

Short name mirDNMR
Full name
Description a gene-centered database named mirDNMR for collection of background DNMRs (de novo germline mutation) and variant frequencies in human genetic variation databases including ExAC (r0.3.1), ESP6500 (ESP6500SI-V2), UK10K, the 1000 Genomes (Phase 3) and dbSNP (Build 147)
URL https://www.wzgenomics.cn/mirdnmr/
Year founded 2016
Last update & version 2017-01-01    
Availability Free to all users
University/Institution hosted Wenzhou Medical University
Address University-town
City Wenzhou
Province/State Zhejiang
Country/Region China
Contact name Jinyu Wu
Contact email iamwujy@gmail.com
Data type(s)
Major organism(s)
Keyword(s)
  • De novo germline mutation (DNM)
Publication(s)
  • mirDNMR: a gene-centered database of background de novo mutation rates in human. [PMID: 27799474]

    Yi Jiang, Zhongshan Li, Zhenwei Liu, Denghui Chen, Wanying Wu, Yaoqiang Du, Liying Ji, Zi-Bing Jin, Wei Li, Jinyu Wu
    Nucleic acids research 2017:45(D1)
    1 Citations (Google Scholar as of 2017-02-20)

    Abstract: De novo germline mutations (DNMs) are the rarest genetic variants proven to cause a considerable number of sporadic genetic diseases, such as autism spectrum disorders, epileptic encephalopathy, schizophrenia, congenital heart disease, type 1 diabetes, and hearing loss. However, it is difficult to accurately assess the cause of DNMs and identify disease-causing genes from the considerable number of DNMs in probands. A common method to this problem is to identify genes that harbor significantly more DNMs than expected by chance, with accurate background DNM rate (DNMR) required. Therefore, in this study, we developed a novel database named mirDNMR for the collection of gene-centered background DNMRs obtained from different methods and population variation data. The database has the following functions: (i) browse and search the background DNMRs of each gene predicted by four different methods, including GC content (DNMR-GC), sequence context (DNMR-SC), multiple factors (DNMR-MF) and local DNA methylation level (DNMR-DM); (ii) search variant frequencies in publicly available databases, including ExAC, ESP6500, UK10K, 1000G and dbSNP and (iii) investigate the DNM burden to prioritize candidate genes based on the four background DNMRs using three statistical methods (TADA, Binomial and Poisson test). As a case study, we successfully employed our database in candidate gene prioritization for a sporadic complex disease: intellectual disability. In conclusion, mirDNMR (https://www.wzgenomics.cn/mirdnmr/) can be widely used to identify the genetic basis of sporadic genetic diseases. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

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Rate of accessibility:
HTTP status codeDate requested
200 OK2018-11-20
200 OK2018-11-16
200 OK2018-11-13
200 OK2018-11-09
200 OK2018-11-06
200 OK2018-11-02
200 OK2018-10-30
200 OK2018-10-26
200 OK2018-10-23
200 OK2018-10-19
200 OK2018-10-16
200 OK2018-10-12
200 OK2018-10-09
200 OK2018-10-05
200 OK2018-10-02
200 OK2018-09-28
200 OK2018-09-25
200 OK2018-09-21
200 OK2018-09-18
200 OK2018-09-14
200 OK2018-09-11
200 OK2018-09-07
200 OK2018-09-04
200 OK2018-08-31
200 OK2018-08-28
200 OK2018-08-24
200 OK2018-08-21
200 OK2018-08-17
200 OK2018-08-14
200 OK2018-08-10
200 OK2018-08-07
200 OK2018-08-03
200 OK2018-07-31
200 OK2018-07-27
200 OK2018-07-24
200 OK2018-07-20
200 OK2018-07-17
200 OK2018-07-13
200 OK2018-07-10
200 OK2018-07-06
200 OK2018-07-03
200 OK2018-06-29
200 OK2018-06-26
200 OK2018-06-22
200 OK2018-06-19
200 OK2018-06-15
200 OK2018-06-12
200 OK2018-06-08
200 OK2018-06-05
200 OK2018-06-01
200 OK2018-05-29
200 OK2018-05-25
200 OK2018-05-22
200 OK2018-05-18
200 OK2018-05-15
200 OK2018-05-11
200 OK2018-05-08
200 OK2018-05-04
200 OK2018-05-01
200 OK2018-04-27
200 OK2018-04-24
200 OK2018-04-20
200 OK2018-04-17
200 OK2018-04-13
200 OK2018-04-10
200 OK2018-04-06
200 OK2018-04-03
200 OK2018-02-27
200 OK2018-02-23
200 OK2018-02-20
200 OK2018-02-16
200 OK2018-02-13
200 OK2018-02-09
200 OK2018-02-06
200 OK2018-02-02
200 OK2018-01-30
200 OK2018-01-26
200 OK2018-01-23
200 OK2018-01-19
200 OK2018-01-16
200 OK2018-01-12
200 OK2018-01-09
200 OK2018-01-05
200 OK2018-01-02
200 OK2017-12-29
200 OK2017-12-26
200 OK2017-12-22
200 OK2017-12-19
200 OK2017-12-15
200 OK2017-12-12
200 OK2017-12-08
200 OK2017-12-05
200 OK2017-12-01
200 OK2017-11-28
200 OK2017-11-24
200 OK2017-11-21
200 OK2017-11-17
200 OK2017-11-14
200 OK2017-11-10
200 OK2017-11-07
200 OK2017-11-03
200 OK2017-10-31
200 OK2017-10-27
200 OK2017-10-24
200 OK2017-10-20
200 OK2017-10-17
200 OK2017-10-13
200 OK2017-10-10
-1 Failed2017-10-06
-1 Failed2017-10-03
-1 Failed2017-09-29
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-1 Failed2017-09-08
200 OK2017-09-05
200 OK2017-09-01
200 OK2017-08-29
200 OK2017-08-25
200 OK2017-08-22
200 OK2017-08-18
200 OK2017-08-15
200 OK2017-08-11
200 OK2017-08-08
200 OK2017-08-04
200 OK2017-08-01
200 OK2017-07-28
200 OK2017-07-25
200 OK2017-07-21
200 OK2017-07-18
200 OK2017-07-14
200 OK2017-07-04
200 OK2017-06-30
200 OK2017-06-27
200 OK2017-06-23
200 OK2017-06-20
200 OK2017-06-16
200 OK2017-06-13
200 OK2017-06-09
200 OK2017-06-06
200 OK2017-06-02
200 OK2017-05-30
200 OK2017-05-26
200 OK2017-05-23
200 OK2017-05-19
200 OK2017-05-16
200 OK2017-05-12
200 OK2017-05-09
200 OK2017-05-05
200 OK2017-05-02
200 OK2017-04-28
200 OK2017-04-25
200 OK2017-04-21
200 OK2017-04-18
200 OK2017-04-14
200 OK2017-04-11
-1 Failed2017-04-07
-1 Failed2017-04-04
-1 Failed2017-03-31
-1 Failed2017-03-28
-1 Failed2017-03-24
-1 Failed2017-03-21
-1 Failed2017-03-17
-1 Failed2017-03-14
-1 Failed2017-03-10
-1 Failed2017-03-07
-1 Failed2017-03-03
-1 Failed2017-02-28
-1 Failed2017-02-24
-1 Failed2017-02-21

Tags

DNA Phenotype
Homo sapiens
De novo germline mutation (DNM)

Record metadata

  • Created on: 2017-02-20
  • Curated by:
    • Lina Ma [2017-06-15]
    • Shixiang Sun [2017-02-20]
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