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Hi,
I was wondering if there are any new insights that anyone could share on the topic of RNA-Seq read depth. Assuming that the RNA samples are polyA-tail selected, and the sequencing is done with 100 nucleotides, paired-end reads, what number of sequences/sample would be optimal to explore transcript differential expression for a high proportion of the transcriptome (even when the genes are expressed at a low level)?
Are there any relevant article reviews on this topic that you might be aware of? It is clear to me that tissue type (e.g. brain vs liver), RNA preparation protocols, RNA quality (e.g. RIN), sequencing specifications (e.g. paired-end vs single read, read length) and technology, and specific research questions for the RNA-Seq data will all have a great impact on the optimal read-depth and it would be great if some studies have already been performed to address some of these variables.
Thank you,
Alexandra
I was wondering if there are any new insights that anyone could share on the topic of RNA-Seq read depth. Assuming that the RNA samples are polyA-tail selected, and the sequencing is done with 100 nucleotides, paired-end reads, what number of sequences/sample would be optimal to explore transcript differential expression for a high proportion of the transcriptome (even when the genes are expressed at a low level)?
Are there any relevant article reviews on this topic that you might be aware of? It is clear to me that tissue type (e.g. brain vs liver), RNA preparation protocols, RNA quality (e.g. RIN), sequencing specifications (e.g. paired-end vs single read, read length) and technology, and specific research questions for the RNA-Seq data will all have a great impact on the optimal read-depth and it would be great if some studies have already been performed to address some of these variables.
Thank you,
Alexandra