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I want to learn more about the data analysis and statistics on transcriptome sequencing data. I would like to read some important papers of the field and books and maybe some MOOCS, if they are available.

More precisely I have data of differentially expressed genes across different groups of individuals and I want to test, if the genes are more expressed in one group are the genes also more polymorphic?

Any ideas?

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there are several tools available for this analysis. Tophat-Cufflinks is a popular combo that people use. For analysis, again, there are several tools. –  WYSIWYG Mar 19 at 13:54
    
Are you studying a species that currently have its genome sequenced? –  biojl Mar 19 at 16:20
    
No, it does not have a published genome sequence, de novo transcriptome assemly was used and cuffdiff was used to get the information about differentially expressed genes between two treatments. Now I want to statistically test if the genes, that are more expressed in one treatment also exhibit more polymorphism. Also, the data is very overdispersed. –  Piret Avila Mar 20 at 20:58
    
@PiretAvila. You would need different sample isolates (biological replicates) to study polymorphism. –  WYSIWYG Mar 21 at 5:21
    
Yes, I have many biological replicates for each treatment. So you see, my problem is not obtaining the data, but analyzing it. I have differential gene expression values for genes that were expressed differently in two treatments (log2(FPKMy/FPKMx)) and I know how many SNPs each of these genes have. I want to know if the genes that were more expressed in one treatment (log2(FPKMy/FPKMx) is positive), are they on average more polymorphic. What would be the best statistical method? I used GLM's (hurdle and zero inflated neg. bin.), since there are a lot of sites with no polymorphism. –  Piret Avila Mar 21 at 7:33

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