I have transcriptome data of resistant and susceptible genotypes in 3 biological replicates. I have a list of significantly differentially expressed transcripts. I also have their functional annotations. But when I check the fold change value of specific genes (multiple transcripts), one transcript show upregulation, the other shows down-regulation but the gene is the same. What should I do? Ex.
Gene Transcript Log2FC
PAL Contig108 +2.5
PAL Contig1113 -3.6
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Abhishek •
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The levels of a given transcript can be regulated by several different processes. Some of these processes (such as transcript initiation) are shared between transcripts, but others (such as splicing and RNA stability) are not.
Sometimes transcript expression is entirely correlated across a gene: If you have two transcripts from the same promoter and that promoter is upregulated, you will see an increase in the levels of both transcripts.
Sometimes levels of transcripts within a gene are completely independent: If two transcripts from the same gene are transcribed from different promoters, and one of those promoters is upregulated, then the levels of one transcript will go up, and the other will stay the same.
And sometimes the levels of transcripts within a gene are exactly anti-correlated. If you have two transcripts from the same promoter, but are alternatively spliced, a change in splicing will necessarily involve an increase in the quantity of one transcript and a matching decrease in quantity of another.
In short, if you are interested in transcript levels, then don't talk about genes, where as if you are interested in talking about gene activity, then do gene level analysis rather than transcript level analysis.
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Are you interested in differential gene or differential transcript expression? And what pipeline did you use?
I want to see the differential expression of transcripts. I performed EdgeR of assembled transcripts. Selected log2FC more than 2 with p-value and FDR <0.05.
How are you quantifying your transcripts. Differential transcript expression can be assessed using edgeR, but the expression needs to be quantified with the correct tools.
For transcriptome analysis, what would be more appropriate, gene-level analysis or transcript level? If I am not wrong, the transcript and its gene would represent the same function.
Use
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