ONT long-read RNA sequencing - data analysis options
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09191919 • 0
@9335d292
Last seen 23 days ago
Spain

Hi,

I am not a bioinformatician, completely new to RNAseq analysis, linux, R script and don't exactly trust myself when doing analysis of RNAseq data but no one else in my lab has experience either! I am doing my PhD and now find myself with 3 datasets to analyse and I would like to get as much out of it, as possible for my capabilities. I do not expect to get all suggestions done but would at least like to try (I do enjoy using R!) It is long-read RNAseq from cDNA, control vs disease, organs brain, lung(model1), lung(model2-reflects different phenotype of disease). I have removed adapters, then mapped reads to genome and transcriptome respectively using Minimap2, this gave me bam files, from this counted reads using R and then did DEseq2 analysis for DE. This worked really well for brain but has not work as well for lung as I get #N/A for many padj (this is a separate problem I am trying to solve..)

My question here is: can you recommend options for analysis I can look into to get the most out of long-read sequencing? For example isoformswitchanalzer. This is the first time long read sequencing is done on these organs in this disease so I don't have a particular question, I have been told that short-read sequencing is probably better if you are just looking at gene expression but we have done long-read so I am interested in what you can look at in long-reads that you can't in short-reads for sequencing.

If I can provide anymore information to help, please let me know. Thank you!!

DESeq2 LongRead DEXSeq IsoformSwitchAnalyzeR RNASeq • 141 views
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@mikelove
Last seen 3 days ago
United States

"This worked really well for brain but has not work as well for lung as I get #N/A for many padj"

See the vignette for the cause of this. You can look at the print out of summary(res) in the console.

"can you recommend options for analysis I can look into to get the most out of long-read sequencing? For example isoformswitchanalzer"

Yes, I would recommend looking at various workflows at the transcript level, including that package. We have a published workflow, where we use transcript counts (these can certainly be from long read):

https://bioconductor.org/packages/release/workflows/vignettes/rnaseqDTU/inst/doc/rnaseqDTU.html

There are other transcript level / splicing / isoform level software in Bioconductor:

https://bioconductor.org/packages/release/BiocViews.html#___DifferentialSplicing

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