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  

Microarrays in daily life

Accurate assessment of a calf’s future performance may soon be possible by using microarrays.

By 2010, less than three years away,
Australia’s largest integrated beef research program, the Beef Cooperative Research Centre (CRC) anticipates cattle breeders may be able to get an accurate assessment of a bull or a dam’s future performance within a few months of its birth

Professor John Gibson, Beef CRC Adaptation and Cattle Welfare Research Leader, says microarray technology has enabled the entire 23,000-odd separate genes of the bovine genome to be printed on one microarray plate the size of a microscope slide. 

“Research overseas indicates that how an animal expresses its genes in early life provides an accurate picture of its gene expression at breeding age.” 

This leads to the prospect of microarrays being printed that carry genes of commercial interest, which could be then used to predict the breeding performance of young animals well before they reach breeding age.

 Prof. Gibson observes that this would help breeders quickly eliminate genetically dud bulls and cows early in their life, without the costs of feeding and progeny testing now required to determine the duds. 

Online Data sharing for scientists

Brent Edwards director of the Starkey Hearing Research Center in Berkeley, California, who blogs on innovation in science is writing his blog about an article on Nature magazine on online data sharing. Brent comments about the potential of new online data sharing sites such as Swivel and IBM’s Many Eyes . Accoding to the Nature reprt some scientists are already using these new tools to share sequence and microarray data. The potential value from scientists openly sharing their data is huge, possibly akin to the value provided by open-source software development.

Once data are uploaded to these sites (which are still being tested), people can reanalyse the numbers, mix them with other data and visualize them in different ways. Swivel focuses on letting users combine data sets, with some basic ways to present the results such as scatter graphs and bar charts. Many Eyes allows users to generate more complicated graphs such as network diagrams, which depict nodes and connections within networks, and treemaps, which display data as groups of nested rectangles

Despite the availability of many software solutions at the dispoal of scientists many of them still write their own code for bioinformatics and statistical analysis, perhaps the next frontier that might help the comunity could be the development of Firefox like software, that offers some basic functions free of cost, additional function can be bought or acquired free of cost as add ons form researchers, such a move would benefit researchers and students alike,

There are sure many more data sharing website like http://www.gotomyfiles.com, http://www.xdrive.com, http://www.ibackup.com, but these are more of a data storage sites, and these does not offer the level of document collaboration features required by a life science researcher

Then there is few other sites like microsofts foldershare and others that offer features such as remote PC access gotomyPC VNC and webex are a few exmaple of this stable. some of these also allows to by pass even a firewall such as foldershare and can pose serious security risks to data and pc if handled improperly

its not so much of junk DNA- University of Oxford Scientists discoveres Cancer cure with it

 Junk DNA is not junk after all

Recently, scientists at the University of Oxford have discovered that ‘junk’ genetic material can switch off cancer tumours, preventing them from growing.

By using RNA to switch off a gene involved in controlling cell division, Oxford University scientists may have found a role for RNA in developing new cancer therapies. RNA is the mirror image of DNA, and is used to pass on instructions to the cell to build the proteins that run every body function.

The Human Genome Project found that human DNA carries approximately 34,000 genes that produce proteins. The remaining majority of the genome constituted what was considered to be junk DNA as it had no obvious function. However, this is set to change.

‘‘There has been a quiet revolution taking place in biology in past few years,’’ said Dr Alexandre Akoulitchev, a Senior Research Fellow at Oxford. ‘‘Scientists have begun to see ‘junk’ DNA as having an important function. The variety of RNA types produced from this so called ‘junk’ is staggering and the functional implications are huge.”

Akoulitchev studied the RNA that regulates a gene called DHFR. This gene produces an enzyme that controls the production of molecules called tetrahydrofolate and thymine that cells need to divide rapidly.

“Switching off the DHFR gene could help prevent ordinary cells from developing into cancerous tumour cells, by slowing down their replication. In fact, one of the first anti-cancer drugs, Methotrexate, acts by binding and inhibiting the enzyme produced by this gene. Targeting the gene itself would cut the enzyme out of the picture altogether. Understanding how we can use RNA to switch off or inhibit DHFR and other genes may have important therapeutic implications for developing new anti-cancer treatments.”

This research was funded by The Wellcome Trust and the Medical Research Council.

Original paper: Repression of the human dihydrofolate reductase gene by a non-coding interfering transcript was published in Nature on 22nd January 2006.

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.

Genetically Guided Treatment For Cancer

Two critical characteristics of breast cancer that are important to treatment can be identified by measuring gene expression in the tumor, a research team led by scientists at The University of Texas M. D. Anderson Cancer Center reports in Lancet Oncology online.

Researchers developed and validated a new genomic microarray test that identifies whether a tumor’s growth is fueled by the female hormone estrogen and the role of a growth factor receptor known as HER-2 that makes a tumor vulnerable to a specific drug.

“This is one important step towards personalized diagnosis and treatment planning based on an integrated genomic test of an individual tumor,” said senior author W. Fraser Symmans, M.D., associate professor in the M. D. Anderson Department of Pathology.

The Lancet Oncology paper results are the latest in an effort by the research team to develop a single test to quickly and efficiently determine the characteristics and vulnerabilities of a patient’s breast cancer and ultimately to guide treatment.

About 70 percent of breast cancers are estrogen-receptor positive and another 15 to 25 percent are human epidermal growth factor receptor-2 (HER-2) positive. Each receptor status requires different types of treatment.

“This moves us closer to developing an integrated single genomic test that could estimate the risk of cancer relapse after surgery, determine the ER and HER2 receptor status, and also gauge the sensitivity of the tumor to hormone therapy and chemotherapy,” says Lajos Pusztai, M.D., Ph.D., associate professor in the M. D. Anderson Department of Breast Medical Oncology, and team leader with Symmans.

Last fall, the group published a study showing that a genomic microarray test can also predict a patient’s response to chemotherapy. They also presented a paper in December showing that another genomic index predicts how an ER-positive patient will respond to hormonal therapy.

The study was funded by the National Cancer Institute, the Breast Cancer Research Foundation and the Goodwin Foundation.

Co-authors with Symmans and Pusztai are: first author Yun Gong, M.D., and Nour Sneige, M.D., of the M. D. Anderson Department of Pathology; Kai Yan, Keith Anderson, and Kenneth Hess, of the M. D. Anderson Department of Biostatistics; Feng Lin, M.D., Vicente Valero, M.D., Daniel Booser, M.D., Jaime Mejia, M.D., and Gabriel Hortobagyi, M.D., of the M. D. Anderson Department of Breast Medical Oncology; Christos Sotiriou, M.D., Ph.D., Institut Jules Bordet, Brussels, Belgium; Fabrice Andre, M.D., of Institut Gustave Roussy, Villejuif, France; Frankie Holmes, M.D., John Pippen Jr., M.D., and Svetislava Vukelja, M.D., of U.S. Oncology-Texas Oncology; Henry Gomez, M.D., of the Instituto Nacional de Enfermedades Neoplasicas, Lima, Peru; and Luis Barajas, M.D., Departmento de Ginecologia Oncologica, Instituto Mexicano del Seguro Social, Guadalajara, Mexico.

Contact: Scott Merville
University of Texas M. D. Anderson Cancer Center

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

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