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:: Posted by American Biotechnologist on 08-24-2011
Some of the technical challenges in qPCR research can be traced back to the reverse transcription step and the synthesis of cDNA for qPCR analysis.
Limitations in the maximum amount of template RNA that can be added to an RT reaction make it tougher to detect low-abundant target genes and limits the amount of data that can be obtained from a typical 20ul RT reaction.
:: Posted by American Biotechnologist on 08-18-2011
For the first time ever, scientists are using computers and genomic information to predict new uses for existing medicines.
A National Institutes of Health-funded computational study analyzed genomic and drug data to predict new uses for medicines that are already on the market. A team led by Atul J. Butte, M.D., Ph.D., of Stanford University, Palo Alto, Calif., reports its results in two articles in the Aug. 17 online issue of Science Translational Medicine.
Butte’s group focused on 100 diseases and 164 drugs. They created a computer program to search through the thousands of possible drug-disease combinations to find drugs and diseases whose gene expression patterns essentially cancelled each other out. For example, if a disease increased the activity of certain genes, the program tried to match it with one or more drugs that decreased the activity of those genes.
Below is a talk that Dr. Buttes gave recently at Packard Children’s Hospital where he explained some of the amazing work done in his lab.
:: Posted by American Biotechnologist on 08-10-2011
In the past, we’ve discussed the importance of selecting appropriate reference genes for your qPCR experiment (also see point 7 of the MIQE guideline checklist). This means that it is important to select genes that do NOT exhibit any changes in expression under the treatment conditions you are studying. This is easier said than done!
“Once upon a time” everyone used either beta actin, 18s, or gapdh as reference genes. Their expression never changes, right? Wrong! So which genes should you choose? If you try to figure it out using previous papers, how do you know that they’ve chosen the correct genes? If you run a few genes side-by-side and try to compare their expression both under treatment and control, which one should you set as the baseline and which one can you say is for sure moving (it’s all relative isn’t it)?
One of my twitter friends told me that she uses six reference genes in her qPCR experiments. I used to use two. That got me thinking…how many reference genes does the “average” lab use? Please help satisfy my curiosity by participating in the poll below!
In honor of this launch, we invite you to review some of the resources (including technical notes, review articles and video tutorials) that we have posted on high resolution melt analysis. Feel free to to click on any of the links below for further details:
:: Posted by American Biotechnologist on 07-26-2011
George Church is one of the founders of the human genome project and continues to play an important role in the personal genome project, stem cell research and biofuel research. In this video, Dr. Sriram Kosuri, a Postdoctoral fellow in George’s lab at Harvard University discusses the Church lab’s approach to solving problems and developing technologies.