Monday, 7 December 2015

The key to finding TCR sequences in RNA-seq data

I had previously written a short blog post touching on how I'd tried to mine some PBMC RNA-seq data (from the ENCODE project) for rearranged T-cell receptor genes, to try and open up this huge resource for TCR repertoire analysis. However, I hadn't gotten very far, on account of finding very few TCR sequences per file.

That sets the background for an extremely pleasant surprise this morning, when I found that Scott Brown, Lisa Raeburn and Robert Holt from Vancouver (the latter of whom being notable for producing one of the very earliest high-throughput sequencing TCR repertoire papers) had published a very nice paper doing just that!

This is a lovely example of different groups seeing the same problem and coming up with different takes. I saw an extremely low rate of return when TCR-mining in RNA-seq data from heterogeneous cell types, and gave up on it as a search for needles in a haystack. The Holt group saw the same problem, and simply searched more haystacks!

This paper tidily exemplifies the re-purposing of biological datasets to allow us to ask new biological questions (something that I consider a practical and moral necessity, given the complexity of such data and the time and costs involved in their generation).

Moreover, they do some really nice tricks, like estimating TCR transcript proportions in other data sets based on constant region usage, investigate TCR diversity relative to CD3 expression, testing on simulated RNA-seq data sets as a control, looked for public or known-specificity receptors and inferred possible alpha-beta pairs by checking all each sample's possible combinations for their presence in at least one other sample (somewhat akin to Harlan Robins' pairSEQ approach).

All in all, a very nice paper indeed, and I hope we see more of this kind of data re-purposing in the field at large. Such approaches could certainly be adapted for immunoglobulin genes. I also wonder if, given whole-genome sequencing data from mixed blood cell populations, we might even be able to do a similar analysis on rearranged variable antigen receptors from gDNA.

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