Scientists develop free, online genetic research tool

"This work actually started mainly because of the demand of MU scientists," said Jianlin Cheng, an associate professor of computer science in the MU College of Engineering. "RNA sequencing is the means by which researchers use modern sequencing techniques to study RNA, or ribonucleic acid. The process has increased the speed that researchers can note the differences in gene expression among genomes -- but it comes at a cost. Often, scientists must sift through incredibly large amounts of data to get to usable results. RNAMiner has cut that time drastically."

Cheng and doctoral students Jilong Li and Jie Hou partnered with members of the MU Center for Botanical Interaction Studies, the Division of Biological Sciences, the Department of Chemistry, the Department of Biochemistry, the MU Informatics Institute and the Bond Life Sciences Center to analyze vast genomic data sets and to formulate the design of RNAMiner.

The website was created to be user-friendly and allows users to upload data, analyze it through as many as five steps against the complete genomes of five species: human, mouse, Drosophila melanogaster (a type of fly), TAIR10 arabidopsis (a small flowering plant) and Clostridium perfringens (a type of bacterium). Genomic data for any species is welcome for upload to grow the database.

On average, two gigabytes of data takes approximately 10 hours for the servers to process and analyze. Most researchers get results within a couple of hours, Cheng said.

"To use our pipeline, you don't have to know about computing tools," Cheng said. "You just need to upload files and select several parameters, and it will automatically give those results. Using this raw data, we can compress that basically hundreds of thousands of times, even one million times, and make the connections needed for our collaborators to identify the genes that cause diseases or certain traits of plants and do some experiments to verify their findings."

The paper accompanying the website's creation, "From Gigabyte to Kilobyte: A Bioinformatics Protocol for Mining Large RNA-Seq Transcriptomics Data," recently was published in PLoS One and was funded in part by the National Institutes of Health (RO1GM093123), and Cheng's National Science Foundation CAREER Award (DBI1149224). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.

The website is free to use and can be found here: http://calla.rnet.missouri.edu/rnaminer/index.html