http://bioinfogp.cnb.csic.es/tools/venny/index.html

http://www.biovenn.nl/index.php

What you want to do with venn diagrams, precisely? Intersect the lists of up and down regulated genes? I can say by hearth: the intersection is null as they are defined to be so. I mean for down you have log2 fold change < 0, for up it is > 0, so the two set are disjoint.

But your question may be misleading. What is the utility of saying that you have RNA-seq reads? or that you want to do venn diagrams with RNA-seq data? You don't do venn diagrams on RNA-seq data, you do it on lists. So you could do it for differentially expressed genes in different contrasts, and in this case it also make sense to compare up with down and viceversa (but not within the same contrast as I was telling before). I usually go to 

http://bioinformatics.psb.ugent.be/webtools/Venn/

Hello Matteo Brilli,

Thank you for your answer. I guess my question was not very clear. I have the normalized read counts from RNA-seq data. They are in tab-delimited/TSV file with all the gene IDS in the first column and the read counts in the subsequent columns. Now these read counts are from a treatment and a control sample (in separate columns, ofcourse) and in three replicates each. I want to:

1. determine how many genes are up/down regulated in my treatment vs control samples. I have no idea how to do that. I was trying to divide the treatment read counts with control read counts in Excel and filter out the values that are less than 2, so that I get 2-fold upregulated genes. I am not sure if this is the right way.

2. represent the same in a graph (venn diagram came to my mind), showing the number of up/downregulated genes in the treatment and in the control samples, with lower than cut off genes in the intersection.

Thanks again for your time and help.

Raghu

Hello Raghuram Sir,

As I understand your question, you have read counts of the control and treatment samples but not the list of differentially expressed genes. Therefore, first, you have to perform the differential expression which can be done using Cuffdiff, DESeq or any other program. However, these programs are Linux based. I have no idea about any windows based program for differential expression studied. After performing the differential expression analysis you will get the list of genes upregulated/downregulated in the treatment vs control with the level of significance in term of P and Q values. Now with this list you can make venn diagram using the program 'Venny' . This Venny program is online tool and very simple.

All the Best

Thank you Rajeev. You have filled in the missing gaps in my understanding. This will help me in analyzing my data. Thanks again.

Raghu.

DESeq2 is not linux based and can be run on windows too. It is an R-cran package therefore it runs where R can run. So yes, from the matrix with raw read counts per transcript in all replicates, you can identify the differentially expressed genes using DESeq2. You can follow the examples for that package to start. Venn diagrams are not helping in this context. Check how to name the replicates/provide a design matrix such that the program can recognize them. Selecting genes with a fold change above a certain threshold can be risky, as it depends on the variability of the gene expression data. For instance, you might observe a similar fold change also for a gene in the replicates. In those cases, a fold change of two among untreated and treated will not mean differential expression.

Thank you Matteo Brilli. I found the same with DESeq2, that I can run it in Windows as well. Also, thanks for your insightful explanation, this will be of much help to me in my analysis.