I am working with about 400 tumour samples looking for alternative splicing in a specific gene using RNAseq. Should I be aligning the RNAseq libraries to each patient genome (so download 400 genomes), or use the one reference genome i.e. hg19? I have seen papers that use hg19 for tumour data, but was wondering for something sensitive like alternative splicing if I should go the extra mile to account for SNVs and map the reads to their personal genomes? The papers I have read about RNAseq and alternative splicing don't specify which reference genomes they use.
You should align to a single reference genome. That is how the samples become comparable and interpretable.
The annotation for alternative splice variants is extensive. You can still detect novel variants using the single genome, but with some level of error.
I prefer a reference based approach for model organisms because of the errors associated with De novo alignment, which can be exacerbated by other experimental factors.
The annotation of the human genome has improved significantly and it is not clear to me if the error/identification tradeoff in de novo approach is justified for the human genome.
Alternatively, you can do De novo alignment and then a reference guided transcriptome assembly .
Cheers
You should align to a single reference genome. That is how the samples become comparable and interpretable.
The annotation for alternative splice variants is extensive. You can still detect novel variants using the single genome, but with some level of error.
I prefer a reference based approach for model organisms because of the errors associated with De novo alignment, which can be exacerbated by other experimental factors.
The annotation of the human genome has improved significantly and it is not clear to me if the error/identification tradeoff in de novo approach is justified for the human genome.
Alternatively, you can do De novo alignment and then a reference guided transcriptome assembly .
Cheers
As Chinedu Anthony Anene, I would recommend to align to a single reference genome like HG38. Depending on the number of reads you have, you can use somme tools to detect and quantify alternative splice variant (LeafCutter for instance), fusion event. You can eventually use RNASeq data to detect some mutation (check https://science.sciencemag.org/content/364/6444/eaaw0726).
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