Question

Expression Analysis With 454 Cdna Data

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13.7 years ago

Litali ▴ 10

How can I perform expression analysis of 454 data?

rna gene • 3.3k views

ADD COMMENT • link updated 13.1 years ago by Michael 54k • written 13.7 years ago by Litali ▴ 10

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Would you mind explaining a little bit more: what exactly do you need, what type of data you have, setup, software, etc.

ADD REPLY • link 13.7 years ago by Paulo Nuin ★ 3.7k

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454 cDNA data, I have contigs assembled with the 454 Assembler. I have different experimetns(say different time points) , each run in other time and different size of lanes. Now I want to compare the expression of different genes between the conditions (how many reads each contig received? normalized over the number of total reads in this condition?)

ADD REPLY • link 13.7 years ago by Litali ▴ 10

score 2 · Answer 1 · 2010-09-03

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13.7 years ago

Chris Miller 22k

TopHat and Cufflinks are often good tools for working with RNAseq data.

In order to give a better answer, though, we're going to need more information about what your data consists of and what your goals are.

ADD COMMENT • link 13.7 years ago by Chris Miller 22k

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454 cDNA data, I have contigs assembled with the 454 Assembler. I have different experimetns(say different time points) , each run in other time and different size of lanes. Now I want to compare the expression of different genes between the conditions (how many reads each contig received? normalized over the number of total reads in this condition?)

ADD REPLY • link 13.7 years ago by Litali ▴ 10

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I think firs of all you need to have your contigs/isotigs annotated (to do so you can use TopHat or Cufflinks if you have a reference genome) and then analyze the expression level of all the genes you've detected in the annotation step and then check it they are differentially expressed. I think the key point is if you have a reference genome or not.

ADD REPLY • link 13.3 years ago by Marina Manrique ★ 1.3k

Ram · Answer 2 · 2010-09-04

Here are some good review papers for you to start with, that will help you find out what's possible and what's not. Also, you can make your live with these data much easier using R & Bioconductor.

Some good resources for R where mentioned here.

I also recommend R-packages DESeq and edgeR.

Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nature reviews. Genetics. 2009;10(1):57-63. Available at: http://www.ncbi.nlm.nih.gov/pubmed/19015660.
Pepke S, Wold B, Mortazavi A. Computation for ChIP-seq and RNA-seq studies. Nature methods. 2009;6(11 Suppl):S22-32. Available at: http://www.ncbi.nlm.nih.gov/pubmed/19844228.