A group of scientists at the University of Missouri has introduced what they are calling a game changer in the world of biological research—an online, free service called RNAMiner that has been developed to handle large data sets, which could lead to faster results in the study of plant and animal genomics.
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, including human, mouse, Drosophila melanogaster (a type of fly), TAIR10 arabidopsis (a small flowering plant) and Clostridium perfringens (a type of bacterium).
The university noted that genomic data for any species is welcome for upload to grow the database, and pointed out that 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.
“Organizations that generate and process a lot of genomics and transcriptomic data such as universities, non-profit research laboratories, hospitals, and biotechnology and pharmaceutical companies will find this service useful, Jianlin Cheng,” an associate professor of computer science in the MU College of Engineering, told eWEEK. “They can use the service to analyze the data for improving biomedical research, drug design, and health care.”
Cheng noted users just need to upload files and select several parameters, and it will automatically give those results, and using this raw data, the platform can compress that basically hundreds of thousands of times, even one million times.
The software can then help scientists make the connections needed for collaborators to identify the genes that cause diseases or certain traits of plants and do some experiments to verify their findings.
“Several years ago, my students and I started to collaborate with a number of research groups in multiple departments such as College of Medicine, Division of Biological Sciences, Division of Plant Sciences, Division of Animal Sciences, and Department of Biochemistry to analyze their genomics and transcriptomic data,” Cheng explained. “We saw an enormous amount of big omics data is generated routinely at high speed at [the university] and across the world, which needs an automated and easy-to-use tool to analyze. Therefore, we used our experience to develop the tool to meet the need.”
When the data analysis is finished, the user will receive an email with a link, which they then can click to view and download the data analysis results.
“A lot of biological data, particularly genomics and transcriptomic data, is generated by next-generation high-throughput DNA sequencing equipment,” Cheng said. “Analyzing these data can take a lot of human resource and effort. Therefore, automated, fast analysis of these data provided by computers is useful for researchers and health practitioners to make sense of the data to improve biomedical research and health care.”
