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Old 10-04-2016, 12:10 PM   #1
scifiction
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Default Help in analyzing RNA Seq data

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.
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Old 10-06-2016, 03:47 AM   #2
Persistent LABS
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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.
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Old 08-04-2017, 08:34 PM   #3
scifiction
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Thanks a lot
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Old 08-22-2017, 03:46 AM   #4
katiadt
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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?
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