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?


closed as too broad by kmm Nov 13 '14 at 13:52

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  • $\begingroup$ there are several tools available for this analysis. Tophat-Cufflinks is a popular combo that people use. For analysis, again, there are several tools. $\endgroup$ – WYSIWYG Mar 19 '14 at 13:54
  • $\begingroup$ Are you studying a species that currently have its genome sequenced? $\endgroup$ – biojl Mar 19 '14 at 16:20
  • $\begingroup$ 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. $\endgroup$ – Piret Avila Mar 20 '14 at 20:58
  • $\begingroup$ @PiretAvila. You would need different sample isolates (biological replicates) to study polymorphism. $\endgroup$ – WYSIWYG Mar 21 '14 at 5:21
  • $\begingroup$ 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. $\endgroup$ – Piret Avila Mar 21 '14 at 7:33