Pedro Madrigal

@pmadrigal.bsky.social

RNA Resources Project Leader at @ebi.embl.org RNAcentral, Rfam

🚀 We're hiring a 𝗕𝗶𝗼𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗰𝘀 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 to run, maintain and optimise pipelines behind Rfam and RNAcentral, including development of LLMs, agent orchestration workflows, and more! Apply by 28 June👇 embl.wd103.myworkdayjobs.com/en-US/EMBL/j... #RNA #Bioinformatics #AI #DataEngineering #job

Bioinformatics Data Engineer (RNA Resources)

About the Team Rfam and RNAcentral are key resources for RNA biology, serving tens of thousands of users every year and widely cited in the scientific literature. We are recruiting a Bioinformatics Da...

embl.wd103.myworkdayjobs.com

Nobel Laureate and Biochemistry Professor Tom Cech will deliver a talk this Wednesday at the World Economic Forum annual meeting in Davos, Switzerland. His message: RNA research is still a big deal. #WEF26 Tune in live ↓ https://bit.ly/4jPzvuN

Tom Cech to Davos: RNA research is 'still a big deal'

The Nobel laureate and CU Boulder professor, recently ranked #1 globally for RNA research, will speak at the World Economic Forum annual meeting in Davos,

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🎉 RNAcentral Release 26 is here! This release introduces our biggest structural change yet: gene-level entries for ncRNAs across 204 organisms. For the first time, you can explore RNA data at the gene level, not just individual sequences. 🧵👇

Integrated prediction of RNA secondary structure jointly with 3D motifs and pseudoknots guided by evolutionary information. @aakaran31.bsky.social and @rivaselenarivas.bsky.social link.springer.com/article/10.1...

All-at-once RNA folding with 3D motif prediction framed by evolutionary information - Nature Methods

Structural RNAs exhibit a vast array of recurrent short three-dimensional (3D) elements found in loop regions involving non-Watson–Crick interactions that help arrange canonical double helices into tertiary structures. Here we present CaCoFold-R3D, a probabilistic grammar that predicts these RNA 3D motifs (also termed modules) jointly with RNA secondary structure over a sequence or alignment. CaCoFold-R3D uses evolutionary information present in an RNA alignment to reliably identify canonical helices (including pseudoknots) by covariation. Here we further introduce the R3D grammars, which also exploit helix covariation that constrains the positioning of the mostly noncovarying RNA 3D motifs. Our method runs predictions over an almost-exhaustive list of over 50 known RNA motifs (‘everything’). Motifs can appear in any nonhelical loop region (including three-way, four-way and higher junctions) (‘everywhere’). All structural motifs as well as the canonical helices are arranged into one single structure predicted by one single joint probabilistic grammar (‘all-at-once’). Our results demonstrate that CaCoFold-R3D is a valid alternative for predicting the all-residue interactions present in a RNA 3D structure. CaCoFold-R3D is fast and easily customizable for novel motif discovery and shows promising value both as a strong input for deep learning approaches to all-atom structure prediction as well as toward guiding RNA design as drug targets for therapeutic small molecules.

link.springer.com