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|02-13-2017, 07:20 AM||#1|
Join Date: Feb 2017
Bio-statistics for metatranscriptomic
I am trying to identified differentially expressed (DE) genes of several bacterial metatranscriptomes.
To sum up the experimental plan. We have 2 bacterial communities (named E and U) inoculated under 3 different conditions A, B and C (reference condition). I collected both DNA and RNA from time 7 and 9 and also the experiment was performed in duplicate.
We sequenced RNA (each 24 samples) and metagenome (from pooled of DNA of several samples).
I assembled and annotated metagenome (141 921 contigs) and use it as reference to map RNA reads with bowtie2.
I performed DESeq2 analysis on those result and I obtained a large part of DE gene. So I used edgeR to change the normalization way (I had tested TMM, RLE and upperquartile methods) and I obtained around 10 000 DE genes for each comparison of condition (AvsC BvsC and AvsB).
The problem is that I don't know what is the next step. With a small list of DE gene I would blast the sequence on NCBI and try to identified from which bacterial species, this gene came from and try to identified metabolism pathwas by hand. But with a too large number of DE gene, I don't know how to do.
Do you think that I have to find another way of normalization to reduce the number of DE gene? Or do you have any idea of software I could use to group those gene by "category" or "class" to go throught the metabolic pathway?
Thank's for your help.
|differentially expressed, metatranscriptomic, normalisation, pathway, rnaseq|