Affymetrix launches Affymetrix University an education effort

Affymetrix  launched Affymetrix University, a series of courses that will be held throughout Europe and North America.

from Affy website

Santa Clara-based Affymetrix  said the courses give biologists a better understanding of how to design their microarray experiments successfully with appropriate quality control, and how to apply statistical methods to interpret biological results more effectively.

for more details check the Affymetrix website  

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Microarray based Bio Detection Technologies

DNA microarray detection of antimicrobial resistance genes in diverse bacteria

Study published at http://cat.inist.fr/?aModele=afficheN&cpsidt=17459830
High throughput genotyping is essential for studying the spread of multiple antimicrobial resistance. A test oligonucleotide microarray designed to detect 94 antimicrobial resistance genes was constructed and successfully used to identify antimicrobial resistance genes in control strains. The microarray was then used to assay 51 distantly related bacteria, including Gram-negative and Gram-positive isolates, resulting in the identification of 61 different antimicrobial resistance genes in these bacteria. These results were consistent with their known gene content and resistance phenotypes. Microarray results were confirmed by polymerase chain reaction and Southern blot analysis. These results demonstrate that this approach could be used to construct a microarray to detect all sequenced antimicrobial resistance genes in nearly all bacteria.

New non-parametric analyis algorithm for Detecting Differentially Expressed Genes with Replicated Microarray Data

Previous nonparametric statistical methods on constructing the test and null statistics require having at least 4 arrays under each condition. In this paper, we provide an improved method of constructing the test and null statistics which only requires 2 arrays under one condition if the number of arrays under the other condition is at least 3. The conventional testing method defines the rejection region by controlling the probability of Type I error. In this paper, we propose to determine the critical values (or the cut-off points) of the rejection region by directly controlling the false discovery rate. Simulations were carried out to compare the performance of our proposed method with several existing methods. Finally, our proposed method is applied to the rat data of Pan et al. (2003). It is seen from both simulations and the rat data that our method has lower false discovery rates than those from the significance analysis of microarray (SAM) method of Tusher et al. (2001) and the mixture model method (MMM)of Pan et al. (2003).

study published by

Shunpu Zhang (2006) “An Improved Nonparametric Approach for Detecting Differentially Expressed Genes with Replicated Microarray Data,” Statistical Applications in Genetics and Molecular Biology: Vol. 5 : Iss. 1, Article 30.
Available at: http://www.bepress.com/sagmb/vol5/iss1/art30

Online Microarray tools

Open source was always the favourite with scientists, Now with companies liek Google and IBM pushing the concept of software as a service educational institutions and non profit organisation alike can offer there efficiencies and expertise to scores of scientists cost effectively

for a start take a look at the online microarray analysis tool offered at European Bioinformatics Institute

http://www.embl-ebi.ac.uk/expressionprofiler/

Microarray for Catharanthus Roseus

In the recently concluded Bio-Asia 2007 meeting  Ocimum Biosolutions has entered into an accord with a scientist for developing microarray  on the medicinal plant Catheranthus Roseus. 

 

Catharanthus roseus is known as the common or Madagascar periwinkle, though its name and classification may be contradictory in some literature because this plant was formerly classified as the species Vinca rosea, Lochnera rosea and Ammocallis rosea. Furthermore, lesser periwinkle (Vinca minor) may also be called common periwinkle. Both species are also known as myrtle.

Western researchers finally noticed the plant in the 1950’s when they learned of a tea Jamaicans were drinking to treat diabetes. They discovered the plant contains a motherlode of useful alkaloids (70 in all at last count). Some, such as catharanthine, leurosine sulphate, lochnerine, tetrahydroalstonine, vindoline and vindolinine lower blood sugar levels (thus easing the symptoms of diabetes). Others lower blood pressure, others act as hemostatics (arrest bleeding) and two others, vincristine and vinblastine, have anticancer properties. Periwinkles also contain the alkaloids reserpine and serpentine, which are powerful tranquilizers.

Carnegie Mellon U. Transforms DNA Microarrays With Standard Internet Communications Protocol

 Source: Carnegie Mellon University December 2005

A standard Internet protocol that checks errors made during email transmissions has now inspired a revolutionary method to transform DNA microarray analysis, a common technology used to understand gene activation. The new method, which blends experiment and computation, strengthens DNA microarray analysis, according to its Carnegie Mellon University inventor, who has published his findings in the December issue of Nature Biotechnology with collaborators at the Hebrew University in Israel. Ziv Bar Joesph

The innovative method combines a new experimental procedure and a new algorithm to identify gene activation captured by DNA microarray analysis with greater sensitivity and specificity. The work holds great promise for vastly improving research on health and disease, according to Ziv Bar-Joseph, assistant professor of computer science and biological sciences at Carnegie Mellon.

“We are very excited about introducing this versatile, powerful method to the research community because it can be used to study a wide range of complex, dynamic systems more comprehensively,” said Bar-Joseph, who also is a member of the Center for Automated Learning and Discovery at the School of Computer Science. “Such systems under study include stress and drug response, cancer and embryo development.”

DNA microarray analysis — a multimillion-dollar-a-year industry — identifies gene activation in living, complex biological systems. DNA microarrays monitor the behavior of thousands of genes over time by detecting changes in the expression of as many as 30,000 different genes on one small chip. The technique has been used to study some of the most important biological systems, including how cells normally divide (the cell cycle) and immune responses to disease and infection.

“Ultimately, we think that the addition of this method to standard DNA microarray analysis will make it more accurate and cost-effective,” Bar-Joseph added.

“While DNA microarrays are very powerful, they present a sampling problem,” Bar-Joseph said. “DNA microarrays only take static snapshots of gene activity over time. In between these snapshots, genes could be activated and we just don’t see them turning on. Our protocol will offer greater overall sensitivity in detecting the expression of any gene, even if a gene turns on when no microarray sampling takes place.”

Bar-Joseph’s procedure is based on a “check-sum” protocol initially developed to ensure that email messages sent via the Internet don’t become garbled in transmission. In the standard Internet check-sum protocol, bits of information that begin as one value (0 or 1) may inadvertently flip to the opposite value as they move from one computer to the next in the form of an email. This data loss, ascribed to noise in the communication channel, is checked by counting the number of 1’s in the message. If this number is odd, then the last bit is set to 1; otherwise it is set to 0. By comparing the number of 1’s on the sending end with the value of the last bit on the receiving end, the recipient’s computer can determine whether the message was accurately received. If not, the recipient’s computer asks the sender’s computer to forward the message again.

Bar-Joseph’s method carries out a similar analysis of the microarray snapshots by “checking” the sum of a set of DNA microarray data points over time (a time series experiment) against the “summary” of the temporal response. If the two sets of results are equal, then what is captured by the DNA microarray time series is real. If the time series results produce a lower value than the microarray summary, the protocol indicates that the researchers have missed a gene’s activation somewhere in their time series.

Just as important, according to Bar-Joseph, is whether a DNA microarray summary value exceeds its time sequence value. If that’s the case, then researchers have likely identified gene activity that should be attributed to changes taking place during an experiment — adding a chemical or changing the temperature, for instance. This aspect of the method provides scientists with the specificity they need to weed out such introduced gene activation from fundamental gene activation pathways that form the hallmark of processes like cancer or immunity. To prove the effectiveness of this new method, Bar-Joseph studied the human cell division cycle. Considered one of the most important biological systems, the cell cycle plays a major role in cancer. Using their new method, Bar-Joseph and his colleagues identified many new human genes that were not previously found to be participants in this system.

“This new set of gene discoveries opens the way to new and more accurate models of the cell cycle system, which in turn can lead to new targets for cancer drugs,” said Bar-Joseph.

The new method also overcomes synchronization loss, a vexing problem for scientists who study hundreds or thousands of cells over time, according to Bar-Joseph. Large groups of living cells that start out together at the same biological point in time eventually become asynchronized in their activities, he noted.

“You can compare a group of cells starting out in an experiment like a group of marathoners at the starting line. Over time, some marathoners will be far ahead on the track, while others will fall back.” After the race begins, finding one marathoner among the thousands is difficult. Similarly, with asynchronous cells, trying to sort out a single cell response is virtually impossible. But Bar-Joseph has incorporated mathematical tools in his method that can detect genes affected by such asynchrony in a population of cells.

standardization in microarray analysis software industry

scouting for the right software for the microarray analysis software , kept me thinkng why despite these software being used by scores or scientists no one has come forward to create what can be called as a standard for such software, the confusion rains in this field as one company’s software data do not work with another one and vice versa, For an industry like biology and drug discovery  that is trying to benefit from the knowledge of mathematics statitics and chemistry physics inability to port data across platform is a serious roadblock. there are standards such as MIAMe and MAGE but these are just data standards, not for softwares, I believe ther should be  something similar to ISO standards, SEI CMI etc.

majority of the newsgroup and forums are used by graduate and at times senor researchers to find out which is the best software to be used, I thought of starting a wiki page where researchers can post their comments and rate the products and compare the features against each other,

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