Genetic Engineering & Biotechnology News (GEN) is the world's most widely read biotech publication. It provides the R&D; community with critical information on the tools, technologies, and trends that drive the biotech industry.
Issue link: http://gen.epubxp.com/i/155541
OMICS rithm to build networks. Drawing upon roughly a quarter-million physical interactions reported from experiments and data from a given NGS experiment, this PCST-based method identifes highly probable networks. Every interaction—including the quarter of a million he's already gathered—is given a probability based on reliability factors such as the experimental method used and number of times reported. The number of possible network connections is huge. PCST winnows them down. "You collect as many prizes—high confdence interactions—as you can in the fnal network," he said. "But if you just do that, you still get a hairball"—or a dense usable network. So, "you tell it one more thing. You say every time you use an edge to connect something, you have to pay a price for the edge, and the price goes up the less reliable it is," Dr. Fraenkel continued. "High-confdence edges are cheap; low-confdence edges are expensive. You ask it to collect as many prizes as possible while paying as little as possible for those edges. That forces the algorithm to decide whether or not to connect something to a bunch of connections to get to other data points—that whole chain of connections has to be really high confdence." Dr. Fraenkel and his colleagues have set up a website with links to several tools including its PCST tool. "You can upload a list of genes and press a button and it sends an email back when it's solved the problem," he said. Deciphering Sample Heterogeneity Sample heterogeneity is a signifcant challenge when analyzing NGS data. The problem is well captured in a 2011 PLOS One paper from conference speaker Ting Gong, Ph.D., assistant professor of molecular carcinogenesis at MD Anderson Center and her colleagues. "RNA prepared from heterogeneous tissue samples might contain only a fraction of the total cell subpopulation of interest. Consequently, the expression signal of any gene detected directly from a complex sample is a convolution of expressions of all present cell types," Dr. Gong et al. wrote. "If tissues or cells are used without consideration of such a mixing phenomenon, measurement of differential gene expression will certainly be confounded by the heterogeneous cell populations." She has since extended a version of her computational method, frst used with microarrays, for use with NGS data. Speaking at "NGX", Dr. Gong discussed a statistical pipeline for distinguishing heterogeneous tissues and cell types based on RNA-Seq data. The method works by generating gene signatures by analyzing data from the ''pure'' samples—or training data—and applying these signatures to estimate the mixing fractions for the complex samples. "We tested our methods on several wellcontrolled benchmark datasets with known mixing fractions of pure cell or tissue types and mRNA expression profling data from samples collected in a clinical trial. Accurate agreement between predicted and actual mixing fractions was observed," Dr. Gong noted in a published excerpt from this study. "In addition, our method was able to predict mixing fractions for more than ten species of circulating cells and to provide accurate estimates for relatively rare cell types." Dr. Gong's group has released an opensource R software package, dubbed DeconRNASeq, for other researchers to use. A Critical Mass Fostering collaboration and providing tools able to span research and the clinic remain pressing needs, said Toby Bloom, Ph.D., deputy scientifc director of informatics at the New York Genome Center (NYGC). Her talk provided a glimpse into emerging needs. "We have two goals here at NYGC," she said. "One is to have a genome center that is really focused on clinical and on bringing genomics into the clinic. The second is to provide help for collaboration and sharing of research projects and data and analysis across participating organizations." The center, she said, is providing both computational work and data storage for participating organizations and their collaborators. "Some will choose to keep their data here. Some will choose to keep their data locally, See Next-Gen Sequencing on page 29 Impressive – but can you repeat it? Of course you can: Cellvento™ CHO-200 for consistent high performance An achievement is only truly meaningful when it can be repeated. That's the simple formula we apply to our cell culture media at EMD Millipore. Cellvento™ CHO-200 is specially designed for consistently high performance in fed-batch applications. This chemically defined, non-animal origin product gives you the confidence you need to succeed – and it's backed up by expert regulatory support so you won't stumble over approvals later on. Ask your business partner about ways to perform impressively time and again, or find out more at www.emdmillipore.com/cellvento al, ternation rocess In t BioP 7-19 Visit us a tember 1 ston, Sep Bo EMD Millipore is a division of Merck KGaA, Darmstadt, Germany EMD Millipore and the M logo are trademarks of Merck KGaA, Darmstadt, Germany. © 2013 EMD Millipore Corporation, Billerica, MA, USA. All rights reserved. Genetic Engineering & Biotechnology News | GENengnews.com | September 1, 2013 | 27