University of Utah researchers have launched Bam.iobio, which university officials say is the first app of its kind to allow scientists to analyze genome sequence data on their web browser, interactively, and in real-time, without having to rely on terabytes of storage and vast sources of computing power.

The resource, developed by a team led by Gabor Marth, co-director of the USTAR Center for Genetic Discovery and human genetics professor at the University of Utah, appears online in Nature Methods. The app analyzes BAM (binary alignment/map) sequence alignment files, data generated from sequencing machines with detailed information on sequence quality and coverage.

"Ordinarily BAM interpretation takes hours or even longer to complete, and is encoded in an only machine-readable format," university officials said. "For the first time, Bam.iobio puts this type of detailed inspection into the hands of the researcher. Data are expressed in a graphic interface that is easy-to-use and intuitive, enabling scientists to focus in on specific regions or genes of interest, if desired. The responsive format empowers the user to customize analysis parameters on the fly, returning query results in real-time."

Bam.iobio will be the first of a series of apps to be developed using the IOBIO operating system, which uses immediate visual feedback to make understanding complex genetic datasets more intuitive and analysis more interactive. The next phase of the project, to occur within the next three months, will be a release of software libraries to developers so they can create their own apps based on the IOBIO platform. In the future, the team plans to develop apps for interactive, custom gene annotation, for examining local ancestry in a patient genome, and other interactive genome analyses.

“The ability to generate data on the scale of the entire genome at ever-decreasing costs has taken the community by surprise,” Marth said. “We want to make this data really useful to the researchers. We’re eliminating the need for expensive computational and hardware investments that before had limited this type of analysis to typically being done in institutional-scale data centers.”

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