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Johns Hopkins Study Finds Ludesi Analysis Service Outperforms Other 2D Gel Software

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While the popular conception might be that 2D gel imaging is lagging behind other protein-separation approaches like liquid chromatography, researchers at Johns Hopkins University are trying to change that notion.
 
Jennifer Van Eyk, director of the Hopkins National Heart, Lung, and Blood Institute Proteomics Center, said her group has found that 2D gel-imaging software — and Ludesi’s service in particular — is cheaper, faster, and less prone to error than doing the work in-house.
 
Hopkins’ bioinformatics team, led by Steven Elliott, recently conducted a study that compared several 2D gel-analysis software packages and found that Ludesi’s approach, which it offers as a service through its headquarters in Lund, Sweden, came out on top.
 
The Hopkins team presented a poster on the study, co-authored with several Ludesi researchers, at the PepTalk conference in San Diego in January, and has written a paper outlining the results that it plans to submit shortly to the journal Proteomics.
 
Van Eyk noted that while the study found that Ludesi’s software offers advantages over its rivals’, the real focus of the paper is to learn how different image-analysis methods affect 2D gel-electrophoresis studies.
 
The Hopkins team also found advantages to farming out its 2D gel analysis to Ludesi rather than doing it in-house.
 
“We do a lot of gels here, and in itself people think that’s a lot of work, but actually it’s the data analysis that turns out to be the time crunch,” Van Eyk said. Doing the image analysis themselves is “extremely laborious,” she said, “and no one can do it for many hours in a day.”
 
In addition, she said that the service-based approach reduces some of the variability that results from in-house analysis. When one person does the work, inevitably, that individual has to do “a huge amount of manual editing, and that already says [one] will introduce error because one person has to enter gel by gel to get the right spots circled and edited,” she said.
 
“If [one is] doing large data sets, even the same person who is well-trained [is apt to err]. These things aren’t as automated as everyone says they are; you will have variation between people doing it,” Van Eyk added.
 
Ludesi CEO Ola Forsstrom-Olsson told BioInform this week that “it’s all about keeping the error rate in check,” something that his shop touts.
 
Forsstrom-Olsson said that the lower error rate of the company’s software is also able to reduce the time-consuming process of correcting false hits.
 
Van Eyk noted that accuracy is particularly important in studying regulatory protein changes, which can be very small. “The more your analysis errors go down, the more you can find those subtle changes and be confident around them,” she said.
 
In the PepTalk poster, the Hopkins team contrasted Ludesi’s spot detection and segmentation with Cy-labeled serum gels against GE Healthcare’s DeCyder and Nonlinear Dynamics’ Progenesis and PG240 with SameSpots.
 

“With proteins, you really want to find the small changes.”

“Ludesi provided the highest overall correctness for the serum gels analyzed based on spot detection matching and segmentation,” according to the poster. “This will improve the ability of the 2D gel platform to track protein changes.”
 
In the report, a graph on software comparison results shows, for example, that in spot matching correctness, Ludesi hovers around 100 percent, in contrast to DeCyder by GE Healthcare at about 80 percent.
 
A spokesperson for GE Healthcare declined to comment
 
Other comparisons such as estimated number of correct regulated proteins found are also made in the report, contrasted against multiple vendors.
 
’Blew Us Out of the Water’
 
When Ludesi first knocked on Johns Hopkins door, Van Eyk said that she and her group were skeptical. However, after reviewing the data set Ludesi analyzed for the team, “I have to admit they blew us out of the water; the data came back beautiful and consistent.”
 
This is key, she said, because “some of the samples we analyze are so priceless. We spend all this time on data generation … we needed to spend time and care on [more important matters].”
 
Van Eyk pointed out that often in gels, two spots will be very close, so defining their borders is key, a study parameter the Johns Hopkins group considered primary. According to the Hopkins poster, not only did Ludesi outperform other methods in spot matching, but also spot segmentation correctness, sometimes by greater than two-to-one.

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