Question: Next Gen Sequencing Vs Microarray Advanced Applications
0
gravatar for Travis
8.4 years ago by
Travis2.8k
USA
Travis2.8k wrote:

Hi all,

I was wondering if anyone could recommend a good review highlighting the state of the NGS field for applications like classifier generation (prognostics and diagnostics), SNP association studies etc. My main area of interest is cancer.

Obviously NGS has been shown to have many strengths over microarrays and has proven itself in areas like transcript discovery but in areas like classifier generation it may not yet be so strong due to inter-individual variation/noise & the number of samples required to correct this etc.

I am new to the field and would just like a good overview of where the strengths and weaknesses are and what remains to be done before these more advanced applications are possible.

ANY input would be greatly appreciated.

Thanks a lot in advance,

tb34

molecular next-gen snp sequencing • 3.0k views
ADD COMMENTlink modified 8.4 years ago by Jeremy Leipzig18k • written 8.4 years ago by Travis2.8k
1
gravatar for Jeremy Leipzig
8.4 years ago by
Philadelphia, PA
Jeremy Leipzig18k wrote:

My understanding is that RNA-Seq NGS has better dynamic range and less noise than expression microarrays. The ERANGE paper from Mortazavi looks at this in detail.

Because microarrays have no units, normalization is a mess. There are still papers coming out dealing with this issue.

You need replicates regardless of which route you choose.

Microarrays are easier to process bioinformatic sense because the probes are essentially digital, there is no alignment step. They are cheaper, but even that advantage is disappearing.

In terms of genotyping, microarrays are convenient means of finding what you expect to see. They are certainly no better at detecting rare variants or SNPs.

ADD COMMENTlink written 8.4 years ago by Jeremy Leipzig18k

Are there any publications where people have attempted e.g. multivariate prognostic classifier generation with NGS?

Obvious examples (Mammaprint) exist for microarrays but I don't know if anything similar has been attempted with NGS...

ADD REPLYlink written 8.4 years ago by Travis2.8k

That's just a genotyping snp microarray - you won't see anything you aren't already looking for. NGS is sequencing, so that allows you to do actual discovery. You should read "Application of second-generation sequencing to cancer genomics." http://bib.oxfordjournals.org/content/11/5/524.abstract

ADD REPLYlink written 8.4 years ago by Jeremy Leipzig18k

Jeremy, I think you're confusing GWAS-prediction of breast cancer susceptibility with prognostic prediction based on gene expression in tumors. See van 't Veer Nature 2002, van 't Veer NEJM 2002 for details.

ADD REPLYlink written 8.4 years ago by David Quigley11k

David, ok thx for pointing that out. That should keep the discussion on expression microarray vs RNA-Seq issue.

ADD REPLYlink written 8.4 years ago by Jeremy Leipzig18k

Thanks for the responses and I'll take a look at the manuscript when I get to the office.

I guess I am primarily more concerened about classifier generation previously performed with expression arrays. Has antone tried it yet? Is expense vs microarrays still perhaps an issue? Or data volumes? I think Mammaprint for example initially was developed with 70 patients...

ADD REPLYlink written 8.4 years ago by Travis2.8k

Thanks for the responses and I'll take a look at the manuscript when I get to the office. I guess I am primarily more concerened about classifier generation previously performed with expression arrays. Has anyone tried it yet? Is expense vs microarrays still perhaps an issue? Or data volumes? I think Mammaprint for example initially was developed with 70 patients..

ADD REPLYlink written 8.4 years ago by Travis2.8k

Hi Travis, I am also interested in this application. Do you have any plan for this application now?

ADD REPLYlink written 8.1 years ago by Junfeng320
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