We’re very happy to see this finally out! “Hotspot propensity across mutational processes” with @fmuinos @abel_gonzalezp @nlbigas @bbglab @IRBBarcelona Can mutational hotspots help to study the mutation rate variability at single-nucleotide resolution? -->

The genomic landscape of 2,023 colorectal cancers is out! I’m very grateful to have collaborated with such a great team on this massive project! Check all the findings here -->

Hope you enjoy this as much as we did and stay tuned for new versions of the comic in more languages! 🌍6/6.

This comic is the result of a wonderful collaboration with science illustrator @ClaudiaFlandoli and @S_J_Aitken @aitken_lab @sophtalkssci at @MRC_TU @Cambridge_Uni I’m super happy and thankful to have worked on this! 5/6.

A story that builds upon #LesionSegregation findings from the @LCE_Consortium @S_J_Aitken @mst_paralogue @odom_lab @PaulFlicek @nlbigas @bbglab @DrColinSemple & many others! 4/6.

Join Pol and find the solution to this unexpected discovery in our latest #SciComm comic: What’s wrong with this DNA? 3/6

Pol found a damaged DNA base that was kept unrepaired across cell divisions! 2/6

Just on time for #DNADay let me introduce you to Pol the polymerase. Pol was happily replicating a strand of DNA when something unexpected happened. 1/6

RT @AMathelier: Looking for a postdoc in my group .@NCMMnews @NordicEMBL @UniOslo_MED @unioslo_mn @unioslo_bioinfo to enhance computational….

RT @IRBBarcelona: 🧬Hotspot propensity across mutational processes. 📰@MolSystBiol-@EMBOPress. ✍️@clarnedo @fmuinos @abel_gonzalezp @nlbigas….

RT @bbglab: Can mutational hotspots help to study the mutation rate variability at single-nucleotide resolution?. Follow Claudia's thread o….

RT @MolSystBiol: The propensity of #mutationalsignatures to leave mutational hotspots serves as an estimate of the variability in their mut….

And thank you all for reading!.

As well as funders including @ERC_Research @CienciaGob @FundlaCaixa @BecariosFLC @ContraCancerEs @IRBBarcelona @iCERCA.

Thanks to everyone who supported our work, specially patients, families and researchers who shared and made available all the data used in our study #PCAWG @HartwigMedical @cbioportal @StJude #TARGET and others.

If you want to learn more, check the full version here:

We have shown how hotspot propensity can help to quantify mutation rate variability at nucleotide resolution and the determinants underlying it. We hope this new approach helps those studying and modelling mutagenesis or evolution, both in cancer and beyond!.

Yet, introducing tissue matched CpG methylation into signature 1 models could explain virtually all (80-100%) of its hotspot propensity. This means we can currently understand and predict signature 1 mutation rate variability at base resolution.

The vast majority (94–95%) of signature 17 hotspot propensity remains unexplained, which suggests that local genomic features, most of them still undiscovered, play an important role here. CTCF binding only explains a small fraction of signature 17 hotspots.

What did we find? Known variables affecting mutation rates only explain a fraction of hotspot propensity across mutational signatures, and this fraction is particularly different for signatures 17 and 1.

We calculated how much of the observed hotspot propensity could be explained by known factors affecting mutation rates (large-scale mutation rate variability down to 10 Kbp, genome composition, and trinucleotide mutational probabilities of each signature).