nodalpoint.org

HI HexiRPA000010
DN (Velculescu VE, 1997) Characterization of the Yeast Transcriptome @Cell #20060730
DA 2006.07.30
CP Cell. 1997 Jan 24;88(2):243-51.
TI Characterization of the yeast transcriptome.
AU Velculescu VE, Zhang L, Zhou W, Vogelstein J, Basrai MA, Bassett DE Jr, Hieter P, Vogelstein B, Kinzler KW.
IN Program in Human Genetics and Molecular Biology, The Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA.
AB We have analyzed the set of genes expressed from the yeast genome, herein called the transcriptome, using serial analysis of gene expression. Analysis of 60,633 transcripts revealed 4,665 genes, with expression levels ranging from 0.3 to over 200 transcripts per cell. Of these genes, 1981 had known functions, while 2684 were previously uncharacterized. The integration of positional information with gene expression data allowed for the generation of chromosomal expression maps identifying physical regions of transcriptional activity and identified genes that had not been predicted by sequence information alone. These studies provide insight into global patterns of gene expression in yeast and demonstrate the feasibility of genome-wide expression studies in eukaryotes.

The paper described a framework for comparisons across gene expression microarray platforms and laboratories, which including: 1) Affymetrix; 2) Agilent; 3) Applied Biosystems (ABI); 4) Amersham (now GE Healthcare); 5) cDNA arrays provided by the Cepko laboratory (academic cDNA); 6) Compugen (now Sigma-Genosys); 7) Mergen; 8) long oligonuceotide arrays from the Microarray Core facility at Massachusetts General Hospital (MGH long oligo); 9) MWG BioTech (now Ocimum Biosolutions); 10) Operon. As a result, the commercial platform ABI has the best performace, where the academic cDNA from Harvard poorest.

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Molecular biomarkers associated with disease and disease predisposition may be used for diagnostic purposes in the early detection and characterization of various disorders. Microarray and SNP data have been used extensively based upon their respectively high resolution of gene expression and polymorphism. And, while diagnostic, pharmacogenomic, and research uses for such biomarkers have proliferated, methods for their identification have standardized. Biomind has developed software which sifts through large, complex microarray datasets to accurately identify biomarkers implicit in clinical disease data. The software uses machine learning algorithms which integrate the Gene Ontology (GO) and Protein Information Resource (PIR).

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Remember SARS? It came, there was a wealth of "end of the world is nigh" TV documentaries, some guys in Canada sequenced it over a weekend...and then it was yesterdays news.

The latest BMC Genomics contains an article (link) which claims that the clinical severity of SARS correlates with the expression of around 52 signature gene transcripts from whole blood samples.

I am no expert in the array analysis methods that they've used or medicine in general, but it looks interesting.

Good afternoon,

I wonder if anyone has had positive experiences with any open-source pathway analysis programs? I am looking for something more than Netaffx for my Affymetrix data. The free trial of Ingenuity Systems' pathway analysis software founded on a hand-curated database has been very impressive, but the software is really expensive.

An article describing MILANO is now published in BMC Bioinformatics: http://www.biomedcentral.com/1471-2105/6/12/
MILANO (http://milano.md.huji.ac.il) is a web based tool for automatic literature searches on lists of genes. It helps in identifying significant genes out of a list of, e.g. upregulated genes from a microarray experiment, by cross-searching the genes with user-provided terms.

I have developed a website hoping that it is useful to the students and microarray researchers. The site contains Introduction to microarrays, microarray software links, microarray protocols, microarray publications and links to other microarray sites. Please have a look and send me if u have any suggestions
URL: http://www.dnamicroarrays.info

Computer scientists must find this kind of report hilarious: Gene name errors can be introduced inadvertently when using Excel in bioinformatics. It turns out that Excel's automatic data type conversion changes Riken identifiers of the form nnnnnnnEnn (where n denotes a digit) into floating point values e.g. "2310009E13" is converted to "2.31E+13". Before everyone starts laughing at poor Excel users, note that this is not an Excel specific problem. The real problem is that most biological file formats do not have schemas. Update: The Register has picked up the story: Excel ate my DNA.

I was intrigued by the headline - Scientists decipher 21,000 genes. It's often hard to know what's been done from such headlines - decipher? Sequence? Analyse?

Anyways, turns out that the Japan Biological Information Research Centre (great name!) have produced an integrated database for 21 037 cDNA clusters with a very wide ranging annotation. It's well worth a look, though I got "server busy" several times. They also have this stupid front page which redirects you after 15 seconds for no apparent reason.