Pedro Madrigal
@pmadrigal.bsky.social
📤 27
📥 65
📝 0
RNA Resources Project Leader at
@ebi.embl.org
RNAcentral, Rfam
reposted by
Pedro Madrigal
BF Francis Ouellette
15 days ago
From
@pmadrigal.bsky.social
@anilthanki.bsky.social
+ friends in
@narjournal.bsky.social
#NARDatabaseIssue
| Expression Atlas in 2026: enabling
#FAIR
and open expression data through community collaboration and integration |
#Bioinformatics
#Database
#Genomics
🧬🖥️🧪🔓 ⬇️
academic.oup.com/nar/advance-...
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Expression Atlas in 2026: enabling FAIR and open expression data through community collaboration and integration
Abstract. Expression Atlas (https://www.ebi.ac.uk/gxa/home) is EMBL-EBI’s comprehensive knowledgebase for gene and protein expression across tissues, cell
https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkaf1238/8376685?login=false
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reposted by
Pedro Madrigal
Cambridge RNA
22 days ago
If you’re into RBPs, miRNAs, RNA regulation, or love cool new tech in biology…You don’t want to miss the talk of
@dmitry-kretov.bsky.social
(
@ulaval.ca
) the creator of RBPscan, a powerful method to quantitatively map RNA–protein interactions inside living cells; Wed 10th at 16:30 online.
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7
5
reposted by
Pedro Madrigal
EMBL-EBI
29 days ago
Join us for an EMBL-EBI
@aibio-uk.bsky.social
community workshop exploring how AI and LLMs can advance FAIR and AI-ready data in the life sciences. Registration is free but essential. Please register by 6 January 2026. Learn more and sign up here:
www.ebi.ac.uk/about/events...
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reposted by
Pedro Madrigal
RNAcentral
about 2 months ago
We've just updated our RNAcentral Online Tutorial!
www.ebi.ac.uk/training/onl...
This tutorial provides an overview of RNAcentral and covers different ways of accessing and using the data. It's aimed at anyone with an interest in non-coding RNAs. As always, we welcome your feedback!
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RNAcentral - Exploring non-coding RNAs
RNAcentral - Exploring non-coding RNAs
https://www.ebi.ac.uk/training/online/courses/rnacentral
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3
reposted by
Pedro Madrigal
Janusz M. Bujnicki
2 months ago
Exciting news for the RNA research community! The Human RNome Project has been launched: a global effort to map all human RNAs and their chemical modifications. Proud to support it and contribute to the article in Genome Biology
doi.org/10.1186/s130...
#RNA
#bioinformatics
#RNAstructure
#modomics
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Unlocking the regulatory code of RNA: launching the Human RNome Project - Genome Biology
The human RNome, the complete set of RNA molecules in human cells, arises through complex processing and includes diverse molecular species. While research traditionally focuses on four canonical nucl...
https://genomebiology.biomedcentral.com/articles/10.1186/s13059-025-03824-y
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19
reposted by
Pedro Madrigal
RNAcentral
3 months ago
🎉 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. 🧵👇
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reposted by
Pedro Madrigal
Elena Rivas
3 months ago
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...
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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.
https://link.springer.com/article/10.1038/s41592-025-02833-w
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