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Making Big Data More Accessible
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Making Big Data More Accessible

Making Big Data More Accessible
News

Making Big Data More Accessible

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When a group of researchers in the Undiagnosed Disease Network at Baylor College of Medicine realized they were spending days combing through databases searching for information regarding gene variants, they decided to do something about it. By creating MARRVEL (Model organism Aggregated Resources for Rare Variant ExpLoration) they are now able to help not only their own lab but also researchers everywhere search databases all at once and in a matter of minutes. 


This collaborative effort among Baylor, the Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital (NRI) and Harvard Medical School is described in the latest online edition of the American Journal of Human Genetics.


Big data search engine


“One big problem we have is that tens of thousands of human genome variants and phenotypes are spread throughout a number of databases, each one with their own organization and nomenclature that aren’t easily accessible,” said Julia Wang, an M.D./Ph.D. candidate in the Medical Scientist Training Program at Baylor and a McNair Student Scholar in the Bellen lab, as well as first author on the publication. “MARRVEL is a way to assess the large volume of data, providing a concise summary of the most relevant information in a rapid user-friendly format.”


MARRVEL displays information from OMIM, ExAC, ClinVar, Geno2MP, DGV, and DECIPHER, all separate databases to which researchers across the globe have contributed, sharing tens of thousands of human genome variants and phenotypes. Since there is not a set standard for recording this type of information, each one has a different approach and searching each database can yield results organized in different ways. Similarly, decades of research in various model organisms, from mouse to yeast, are also stored in their own individual databases with different sets of standards.


Dr. Zhandong Liu, assistant professor in pediatrics – neurology at Baylor, a member of the NRI and co-corresponding author on the publication, explains that MARRVEL acts similar to an internet search engine.


“This program helps to collate the information in a common language, drawing parallels and putting it together on one single page. Our program curates model organism specific databases to concurrently display a concise summary of the data,” Liu said.


This article has been republished from materials provided by Baylor College of Medicine. Note: material may have been edited for length and content. For further information, please contact the cited source.

Reference

Julia Wang, Rami Al-Ouran, Yanhui Hu et al.  MARRVEL: Integration of Human and Model Organism Genetic Resources to Facilitate Functional Annotation of the Human Genome. The American Journal of Human Genetics, 2017; DOI: 10.1016/j.ajhg.2017.04.010

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