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Hi guys, I performed RNA-seq analysis of bacterial transcriptome in four different stressed conditions, mapping the reads on its own genome available in NCBI. Then I used FeatureCounts for reads counting and finally I performed differential analysis with NOISeq R package, because of the absence of replicates.
Before that, my tutor submitted the analysis to a famous company requiring a de novo assembly (they used the trinity pipeline for assembly and differential analysis).
I used the same fastq files, and finally I found a larger number of DE genes, but my results are the opposite of company's results. How is it possible? I know that mapping is better than assembly when a reference genome is available and above all I know that the trinity pipeline have some problems for differential analysis, because it uses DESeq or edgeR after quantification by RSEM.
What do you think about?
Before that, my tutor submitted the analysis to a famous company requiring a de novo assembly (they used the trinity pipeline for assembly and differential analysis).
I used the same fastq files, and finally I found a larger number of DE genes, but my results are the opposite of company's results. How is it possible? I know that mapping is better than assembly when a reference genome is available and above all I know that the trinity pipeline have some problems for differential analysis, because it uses DESeq or edgeR after quantification by RSEM.
What do you think about?