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Friday, February 26, 2010

AGBT 2010 - Shuro Sen - NHGRI

Transcriptome Profiling of ClinSeq Particpants by Massively Parallel Short-Read DNA Sequencing

[No Microphone - I may not get much from this talk. Mostly I will be pulling from Slides, I think]

* cohort of 1,000 individuals
* initial focus on Cardiovascular disease
* Consent for follow up
* transcriptome, exome + few genomes
* application of large-scale medical sequencing in a clinical research setting.
* concurrent "Omes" from same individual
* move on to other diseases in the long term

* started with sanger
* now moved to Illumina

* published marker paper on this topic last Sept in Genome Research

* transcriptome component of ClinSeq
* demonstrate use of RNA-seq in clinical research
* better than SAGE or Microarray

Transcriptome + Exome
* gene expression
* splicing
* gene fusions
* etc

* hardening of arteries
* Looking for biomarkers for calcification
* can look for it by CT scan (in example, arteries look like bone.. [Ouch!]

*4 people w high calcification, 4 with low calcification
* two RNA sources: LCLs and whole blood
* emphasis on uniform cell culture conditions
* repeated EBV transformation from same individual (see noise)
* RNA Fragmentation (Covaris S2)
* PCR amplification 12 cycles
* two PE 51bp lanes Illumina

Differential gene expression
* Expression vs Statistical Significance.
* "upside down volcano plot"
* found about 100 genes that were differently expressed and significant
* Looking at those 100 in detail
* Many of these genes are noise.
* more sequencing reads to improve statistical depth

Discussing his bet hits - but not giving names of genes.

[Kind of silly to take notes on random unnamed genes. Take home message is that some of the genes were found that were known in the process -but obviously not all of them. TFs, TKs and something associated with rheumatoid arthitis. This might be a good time for me to rant about how picking any random list of proteins will give you things that you think are promising. All gene hit sets are "interesting" at first, and useless when not validated... but that's obvious, no?]

Coming up
* analysis of next 8 subjects
* follow up
* sequence more subjects for rare variants
* integrated analysis of genome and transcriptome dat to uncover SNV loci underlying differential expression. ("integrating multiple omes")



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