Question: Calculating isoforms abundance using RNAseq data
gravatar for iditk
4.1 years ago by
United States
iditk0 wrote:

good morning,

I'm interested in calculating isoform abundance in a certain tissue, and be able to compare the abundance between human patients / mouse.  

From what I can tell my best option is : (1) to use methods (such as RSEM) that quantify the isoforms using known annotations of the genome and isoforms, because both genomes should be well annotated. (2) then use a method based on splicing events (such as MISO) or isoforms (such as EBSeq/cuffdiff) to compare the abundance. In this step both options looks problematic. Any recommendation for such a method, that will work well with quantification methods? or other thoughts? I have access to raw data if it matters. 



rna-seq • 2.4k views
ADD COMMENTlink modified 3.4 years ago by Mikael Huss4.6k • written 4.1 years ago by iditk0

Have you trying using MATS? Or if you don't mind having alternative exon expression instead of alternative isoforms, then you can try DEXSeq

ADD REPLYlink written 4.1 years ago by Sam2.2k

haven't tried DEXseq or MATS yet, but I would prefer working with isoforms, not alternative exons.

ADD REPLYlink written 4.1 years ago by iditk0

Each exon would be annotated by its gene name

so beyond all the isoforms which are already there in reference gtf, if you get alternative exon usage then that would denote alternate isoform also

ADD REPLYlink written 4.1 years ago by Manvendra Singh2.0k
gravatar for Mikael Huss
3.4 years ago by
Mikael Huss4.6k
Mikael Huss4.6k wrote:

You could try Kallisto for quantification and Sleuth for DE analysis. Like CuffDiff or RSEM/ebSeq, Sleuth can do proper DE testing on isoform abundance, but it also allows using covariates in a linear model, like limma/DESeq/edgeR. It's also possible to use Sleuth with Sailfish or Salmon if you prefer that.

ADD COMMENTlink written 3.4 years ago by Mikael Huss4.6k
gravatar for Manvendra Singh
4.1 years ago by
Manvendra Singh2.0k
Berlin, Germany
Manvendra Singh2.0k wrote:

If you are comparing human and mouse then first step would be to take only those RNA-seq reads for mapping on their respective genomes which are uniquely mapable on both genomes.

because for the cross-species analysis, we should be owing to knowledge that there are so many different genomic insertions and deletions between every species.

once you  have mapped the reads in this way, then I would do splicing detection manually in following way, because I personally am disappointed with MATS and MISO, (someone please correct me if I am wrong):

1. modify the gtf file in such a way that each exon of the gene would be represented with different names.

e.g geneA_exon1

geneA_exon2  and so on

another geneB


geneB_exon2  and so on.

now, I would run HT-seq on mapped files providing the modified gtf.

once we have got read counts over each exon, we calculate psi (Reads In/Reads out) for each exon, its like measurement of the reads mapped on exon of interest and reads skipped out to different exon., you can keep threshold of number of reads and foldchange of psi during comparison which would depend on how stringent you want to make this pipeline.

finally level of psi would tell you differential exon usage or differential isoform abundances




ADD COMMENTlink written 4.1 years ago by Manvendra Singh2.0k

Thanks for your answer HTH, I wasn't clear enough. I would work only on human samples or mouse samples (and won't be comparing it). 

Do you run the manual procedure on each exon in a gene? To your knowledge, non of the software available can calculate PSI properly? 

ADD REPLYlink written 4.1 years ago by iditk0

Dear iditk, HTH means "Hope This Helps". My name is Manvendra Singh "Manu" as its written in profile.

I suggested this method because, this way you know what you are doing in each step to calculate psi, where you can play with thresholds to get results less but high confident ones.

No I am not saying that, All tools would be good.

MATS gave me result for 1% of total number of exons in gtf file

One drawback of MISO is that it lacks statistical methods for handling groups of samples.

DEX-seq is better in this case but after validation they got 27 out of 40 detected genes as true positives.

Some tools consider only reads mapping on exon or tarnscript, some consider both the junction reads and exon reads, power of statistics is good in all the tools. 

so its on you which one you want to use for your purpose.


ADD REPLYlink written 4.1 years ago by Manvendra Singh2.0k
gravatar for EagleEye
4.1 years ago by
EagleEye6.2k wrote:

Did you try GESS with MISO which I have explained in other post?  A: How to determine alternative splicing read counts

ADD COMMENTlink written 4.1 years ago by EagleEye6.2k
gravatar for derseb
3.4 years ago by
derseb0 wrote:

Try to calculate the PSI metric for all the exons in your gene and then compare the splicing status of exons across species:

ADD COMMENTlink written 3.4 years ago by derseb0
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