Video Tip of the Week: Enzyme Portal and User-Centered Design

This week’s video tip of the week introduces you to Enzyme Portal, an interface to explore data about these important proteins, from the EBI. In the video, Jenny Cham–one of the authors of the paper below–takes you through the main features of their newly designed resource.

I learned about the new effort from this blog post at BMC: Designing better web experiences for bioinformatics. In this post, the team talks about the backstory and the philosophy of user-centered design that they employed to create the site. They also note that the article describes not only their experience, but also offers guidance for people who might be building resources of their own.

The resource they deliver provides categorized and integrated information about the proteins, genes, EC numbers, structure, pathways, disease relationships, small molecules, and the literature. So from that perspective it might sound similar to other resources. But their re-organization of that data into the easy tab navigation, and the quick way to switch among species, is easier than some other resources I’ve used. I do like the quick access to the graphical representations like you can see on this page: http://www.ebi.ac.uk/enzymeportal/search/P09104/reactionsPathways . And I like that they link to Reactome, which is one of my preferred pathway resources. But there are links to many other useful tools and resources as well–exactly the ones I’d expect to need when seeking out more details.

In the paper I liked their summary of the “challenges” associated with applying user-centered design (UCD) to bioinformatics. I have seen some of the resistance to this first-hand, beginning over 15 years ago when a friend of mine was trying really hard to encourage usability and design for bioinformatics tools (right Michael?). And getting very little support for that. Alas. I hope people begin to appreciate this at some point….

So have a look and think about how you are using this tool. And offer them feedback–I’m sure they’d want your input. If you are creating tools for end-users, think about ways you might incorporate some of their strategies. A lot of tools I’ve seen could benefit from a bit more thought about how it’s going to be used by people who don’t write the code.

Quick link:

Enzyme Portal at EBI: http://www.ebi.ac.uk/enzymeportal/

Reference:

de Matos, P., Cham, J., Cao, H., Alcántara, R., Rowland, F., Lopez, R., & Steinbeck, C. (2013). The Enzyme Portal: A case study in applying user-centred design methods in bioinformatics BMC Bioinformatics, 14 (1) DOI: 10.1186/1471-2105-14-103

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Video analysis: Detecting text every which way

Jan. 3, 2013 — Software that detects and extracts text from within video frames, making it searchable, is set to make a vast resource even more valuable.

As video recording technology improves in performance and falls in price, ever-more events are being captured within video files. If all of this footage could be searched effectively, it would represent an invaluable information repository. One option to help catalogue large video databases is to extract text, such as street signs or building names, from the background of each recording. Now, a method that automates this process has been developed by a research team at the National University of Singapore, which also included Shijian Lu at the A*STAR Institute for Infocomm Research.

Previous research into automated text detection within images has focused mostly on document analysis. Recognizing background text within the complex scenes typically captured by video is a much greater challenge: it can come in any shape or size, be partly occluded by other objects, or be oriented in any direction.

The multi-step method for automating text recognition developed by Lu and co-workers overcomes these challenges, particularly the difficulties associated with multi-oriented text. Their method first processes video frames using 'masks' that enhance the contrast between text and background. The researchers developed a process to combine the output of two known masks to enhance text pixels without generating image noise. From the contrast-enhanced image, their method then searches for characters of text using an algorithm called a Bayesian classifier, which employs probabilistic models to detect the edges of each text character.

Even after identifying all characters in an image, a key challenge remains, explains Lu. The software must detect how each character relates to its neighbors to form lines of text -- which might run in any orientation within the captured scene. Lu and his co-workers overcame this problem using a so-called 'boundary growing' approach. The software starts with one character and then scans its surroundings for nearby characters, growing the text box until the end of the line of text is found. Finally, the software eliminates false-positive results by checking that identified 'text boxes' conform to certain geometric rules.

Tests using sample video frames confirmed that the new method is the best yet at identifying video text, especially for text not oriented horizontally within the image, says Lu. However, there is still room for refinement, such as adapting the method to identify text not written in straight lines. "Document analysis methods achieve more than 90% character recognition," Lu adds. "The current state-of-the-art for video text is around 67-75%. There is a demand for improved accuracy."

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The above story is reprinted from materials provided by The Agency for Science, Technology and Research (A*STAR).

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Journal Reference:

Palaiahnakote Shivakumara, Rushi Padhuman Sreedhar, Trung Quy Phan, Shijian Lu, Chew Lim Tan. Multioriented Video Scene Text Detection Through Bayesian Classification and Boundary Growing. IEEE Transactions on Circuits and Systems for Video Technology, 2012; 22 (8): 1227 DOI: 10.1109/TCSVT.2012.2198129

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Video games lead to new paths to treat cancer, other diseases

ScienceDaily (Feb. 16, 2012) — Anqi Zou never thought she would thank video gamers for showing her the way to exciting discoveries in molecular biology.

But here she is, acknowledging that the technology she uses to show the inner workings of cells was originally perfected to create realistic images on gaming screens worldwide.

No matter. Sam Cho and his students are using graphics processing units -- also called GPUs or graphics cards -- to explore the biomolecular processes in the cell and take on challenges, including a cure for cancer.

"We have hijacked the same technology that creates the detailed gaming scenes on your computer screen to perform molecular-dynamic simulations," Cho said.

Zou is helping Cho push the limits of GPU-optimized cell simulations. This Mathematical Business and Computational Science major is comparing the data provided by GPU and non-GPU simulations.

"Because of the powerful computational ability of these GPU devices that are usually used for gaming, I couldn't help registering for Dr. Cho's GPU programming course," she said. "Halfway through the semester, I was much impressed by the computational performance of the GPUs, and I approached Dr. Cho about working on a research project related to GPU programming."

For his most recent published study, Cho, an assistant professor of physics and computer science, simulated the folding and unfolding of a critical RNA molecule component of the human telomerase enzyme. This enzyme lengthens DNA strands during cell division. It's what makes tumors continue to grow.

Knowing how human telomerase works could lead to cancer therapies that essentially obliterate tumors, Cho said.

His research findings appear in the Journal of the American Chemical Society.

Now, Cho and his research assistants are looking at a much larger cell system -- the bacterial ribosome -- to see what they can uncover through GPU-optimized molecular dynamic simulations. The graphics cards were donated by Nvidia, the company that invented the GPU; Cho developed a new GPU programming course so he could teach Wake students how to use the cards.

The benefit of the GPU-optimized simulations is that they are much quicker to perform. The ribosome simulation, for example, would take more than 40 years on a standard computer. Using GPUs, Cho and his students will see results in a few months.

The end goal is to map the ribosome's functions so researchers can develop antibiotics to specifically kill bacteria.

And that would be an amazing accomplishment, thanks in large part to videogamers, Cho said.

"If it wasn't for gamers who kept buying these GPUs, the prices wouldn't have dropped, and we couldn't have used them for science," he said.

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The above story is reprinted from materials provided by Wake Forest University, via Newswise. The original article was written by Alicia Roberts.

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Journal Reference:

Shi Biyun, Samuel S. Cho, D. Thirumalai. Folding of Human Telomerase RNA Pseudoknot Using Ion-Jump and Temperature-Quench Simulations. Journal of the American Chemical Society, 2011; 133 (50): 20634 DOI: 10.1021/ja2092823

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Disclaimer: This article is not intended to provide medical advice, diagnosis or treatment. Views expressed here do not necessarily reflect those of ScienceDaily or its staff.

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