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SoyKB and iPlant streamline complex bioinformatics analysis

FEATURE | AUGUST 13, 2014 | BY AMBER HARMON

Original Story Link

Even though next-generation sequencing (NGS) — with millions or

Image courtesy US National Science Foundation/Thinkstock.

billions of DNA nucleotides sequenced in parallel — is much less costly compared to first-generation sequencing, it still remains too expensive for many labs. NGS platform start-up costs can easily surpass hundreds of thousands of dollars, and individual sequencing reactions can cost thousands per genome.

To garner accurate information, the data analysis can be time-consuming and require special knowledge of bioinformatics. Even so, this high-throughput computational analysis is the backbone of novel discoveries in the life sciences, as well as in other domains including anthropology, social sciences, and plant sciences.

“Using next-generation sequencing you’re getting a snapshot of everything that is happening in a given genome up to that point,” says Trupti Joshi, assistant research professor in computer science and core faculty at the Informatics Institute at the University of Missouri (MU), Columbia, US.

Joshi manages SoyKB (Soybean Knowledge Base), a free online data resource infrastructure that was developed as part of the Obama administration’s $200 million Big Data Research and Development Initiative. Joshi’s team is working with the iPlant Collaborative and XSEDE (Extreme Science and Engineering Discovery Environment) teams to integrate SoyKB data resources and analysis tools.

In addition to integrating SoyKB — which already includes many built-in informatics tools — with existing iPlant tools, the MU team is developing additional toolsets that will also be available to the iPlant community. “Right now we are building the infrastructure so that we can submit jobs — RNA-seq analysis is just one example — to iPlant Atmosphere.” Joshi says three to four different analysis capabilities will be available in a couple months.

SoyKB includes the tens of thousands of genes in the soybean genome, experimental data related to gene expressions, fast-neutron mutation data, and soybean lines GWAS (genome-wide association studies) data. SoyKB is unique in that it includes ‘multi-omics’ experimental data that might otherwise be irrelevant (thrown out) by a particular researcher at a particular time. By making all research data available, experiments take on an increasingly important role in the bigger picture, and enable future researchers to narrow their own results.

Researchers may want to look at soybeans that have a high-oil content, for example, or a high-protein content. Or, they may want to focus on soybean lines that are more drought, disease, or insect resistant. Scientists can access data on particular genomic variations directly in SoyKB, using tools to quickly query and isolate items of interest.

“One of the biggest advantages here is that iPlant is an integrated environment,” says Mats Rynge, who is part of XSEDE’s Extended Collaborative Support Service Workflow Community Applications team. “The iPlant team clearly understands the science and can tailor their services and setup to a biologist.”

More than 19,000 users take part in the iPlant Collaborative, and about 2,500 of them use Atmosphere — iPlant’s cloud service that is fully integrated with user management and theData Store (570 terabytes). “Atmosphere is one of the nicest academic cloud implementations available,” says Rynge. “I would say it is on par with Amazon in terms of user interface; really well done.”

Rynge is developing a SoyKB submit infrastructure and Pegasus workflows for scientists to pull data from the data store, analyze it, and deposit the results back in the data store — all with the click of a button. The ultimate goal is to make the workflows general enough to be mapped to other infrastructures, which future sequencing groups can use as a starting point.

As NGS techniques continue to amass more data than labs and researchers can handle on their own, high-performance computing and infrastructures capable of presenting, analyzing, and storing data will remain critical resources for complex bioinformatics analysis. After all, with 50,000 to 70,000 genes in a single soybean, looking at thousands of soybean genomes can produce several gigabytes of data for each soybean line.

The progress of SoyKB as part of the Big Data Initiative was presented at the IEEE International Conference on Bioinformatics and Biomedicine, December 2013, in Shanghai, China. The US National Science Foundation funds the ongoing project.

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SoyKB: Leading the convergence of wet and dry science in the era of Big Data

Yaya Cui, an investigator in plant sciences at the Bond Life Sciences Center examines data on fast neuron soybean mutants that are represented on the SoyKB database.

The most puzzling scientific mysteries may be solved at the same machine you’re likely reading this sentence.

In the era of “Big Data” many significant scientific discoveries — the development of new drugs to fight diseases, strategies of agricultural breeding to solve world-hunger problems and figuring out why the world exists — are being made without ever stepping foot in a lab.

Developed by researchers at the Bond Life Sciences Center, SoyKB.org allows international researchers, scientists and farmers to chart the unknown territory of soybean genomics together — sometimes continents away from one another — through that data.

Digital solutions to real-world questions

As part of the Obama Administration’s $200 million “Big Data” Initiative, SoyKB (Soy Knowledge Base) was born.

The digital infrastructure changes the way researchers conduct their experiments dramatically, according to plant scientists like Gary Stacey, Bond LSC researcher,  professor of soybean biotechnology and professor of plant sciences and biochemistry.

“It’s very powerful,” Stacey said. “Humans can only look at so many lines in an excel spreadsheet — then it just kind of blurs. So we need these kinds of tools to be able to deal with this high-throughput data.”

The website, managed by Trupti Joshi, an assistant research professor in computer science at MU’s College of Engineering, enables researchers to develop important scientific questions and theories.

“There are people that during their entire career, don’t do any bench work or wet science, they just look at the data,” Stacey said.

The Gene Pathway Viewer available on SoyKB, shows different signaling pathways and points to the function of specific genes so that researchers can develop improvements for badly performing soybean lines.

“It’s much easier to grasp this whole data and narrow it down to basically what you want to focus on,” Joshi said.

A 3D-protein modeling tool lends itself especially to drug design. A pharmaceutical company could test the hypothesis and in some situations, the proposed drug turns out to yield the expected results — formulated solely by data analysis.

The Big Data initiative drives a blending of “wet science” — conducting experiments in the lab and gathering original data — and “dry science” — using computational methods.

Testament of the times?

“Oh, absolutely,” Joshi said.

Collaboration between the “wet” and “dry” sciences

Before SoyKB, data from numerous experiments would be gathered and disregarded, with only the desired results analyzed. The website makes it easy to dump all of the data gathered to then be repurposed by other researchers.

“With these kinds of databases now, all the data is put there so something that’s not valuable to me may be valuable to somebody else,” Stacey said,

Joshi said infrastructure like SoyKB is becoming more necessary in all realms of scientific discovery.

“(SoyKB) has turned out to be a very good public resource for the soybean community to cross reference that and check the details of their findings,” she said.

Computer science prevents researchers having to reinvent the wheel with their own digital platforms. SoyKB has a translational infrastructure with computational methods and tools that can be used for many disciplines like health sciences, animal sciences, physics and genetic research.

“I think there’s more and more need for these types of collaborations,” Joshi said. “It can be really difficult for biologists to handle the large scope of data by themselves and you really don’t want to spend time just dealing with files — You want to focus more on the biology, so these types of collaborations work really well.

It’s a win-win situation for everyone,” she said.

The success of SoyKB was perhaps catalyzed by Joshi. She adopted the website and the compilation of data in its infant stages as her PhD dissertation.

Joshi is unique because she has both a biology degree and a computer science background. Stacey said Joshi, who has “had a foot in each camp,” serves as an irreplaceable translator.

Most recently, the progress of SoyKB as part of the Big Data Initiative was presented at the International Conference on Bioinformatics and Biomedicine Dec. 2013 in Shanghai. The ongoing project is funded by NSF grants.

Original Story at http://decodingscience.missouri.edu/

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– MU Researchers Develop Free Online Database for Soybean Studies

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