Help in analyzing RNA Seq data

[scifiction]

I have final gene expression data (RNA Sequencing), four treatments in duplicate. I want to analyze this data for cumulative frequency distribution and heatmaps. How should I proceed? This is first time I am dealing with such a data.

[Persistent LABS]

Hi scifiction,
If you are comfortable in R, you can use DESeq2 package and perform differential expression analysis. In that case, this link might help you: https://bioconductor.org/packages/re...doc/DESeq2.pdf

There is another web-server designed for small RNA data analysis. But I guess you can run differential gene expression analysis for your count data under "DE Analysis" tab. Here is the link: https://oasis.dzne.de/small_rna_de.php
It will give you heatmap and other diagnostic plots.

[scifiction]

Thanks a lot

[katiadt]

assembly vs mapping

Hi guys,
I'm new here! I'm a PhD student engaged in a transcriptomic project. I have one control sample and four different stressed, all sequenced using HiSeq2000, without replicates. At a first moment, my tutor submitted the analysis to a ver big and famous company, requesting de novo assembly and differential expression analysis. The assembly was not good for me, because there was a low number of assembled transcripts (686, while actually there are 1936 annotated genes) and a very low number of DE genes was found. So, I tried to perform again the analysis using Trinity, as the company done, and I couldn't assemble the transcriptome: I found more than 15.000 trasncripts and many were too short. Furthermore, when I performed a blast against all NCBI database, I found many and many contaminants (i.e. human sequences). So, I tried with other programs (i.e. TransAByss, Velvet-Oases and Rockhopper) but they didn't work correctly. So I leave my tutor's idea of de novo assembly, because the genome of this bacterial strain became available in NCBI and its annotation too. I mapped the reads with STAR using its own genome; I was surprised because the percentages of mapped reads were low. I performed counting with FeatureCounts and did differential analysis with NOISeq (due to the absence of replicates in the experimental design). At a first moment, I used all mapped reads, but I was in a saturation condition, so I found the right compromise using only 10 million of mapped reads from each bam file. About one million resulted unassigned (NoFeatures).
Finally, I found many DE genes, but I'm frustrated because my results are the opposite of company's results. I did inspect them by an experienced researcher and he told me that I performed the analysis properly.
What should I think?What should I do? I quarrel with my tutor every day and I'm about to leave the PhD course because the situation is unbearable. Please help me

[katiadt]

If your replicates are not so good, you can use edgeR that is more robust.

I ask another type of help: 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?