RNAcentral

🎉New RNAcentral paper published in @narjournal.bsky.social! Discover automated literature integration, new expert databases, gene-level entries grouping related transcripts, and more: doi.org/10.1093/nar/...

Major structural change: RNAcentral now groups related transcripts into gene-level entries! Using ML + graph clustering, we built 103,814 human ncRNA genes from 600,225 transcripts. Better reflects biology and enables comparative analyses.

🎉New RNAcentral paper published in @narjournal.bsky.social! Discover automated literature integration, new expert databases, gene-level entries grouping related transcripts, and more: doi.org/10.1093/nar/...

RNAcentral has grown to 45M ncRNA sequences, now includes 52 expert databases, with 10 new databases and major updates to existing sources 📈

🎉 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. 🧵👇

Read more in our blog post: blog.rnacentral.org/2025/10/rnac... or our recent preprint: doi.org/10.1101/2025...

🎉 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. 🧵👇

We built 103,814 human ncRNA genes from 600,225 transcripts using machine learning + graph clustering. The pipeline was trained on Ensembl/GENCODE data and achieved 99.4% accuracy. Most genes are lncRNAs (65,187), followed by antisense lncRNAs (16,790) and pre-miRNAs (8,560).

🎉 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. 🧵👇

1/12 Excited to share our new paper in DATABASE on LitSumm - our system that uses large language models to automatically generate high-quality literature summaries for non-coding RNAs! 🧬📚

12/12 Big thanks to our co-authors @afg781.bsky.social, @antonipetrov.bsky.social, @alexbateman1.bsky.social and others! Read the full paper here: doi.org/10.1093/data... #bioinformatics #LLM #AI

1/12 Excited to share our new paper in DATABASE on LitSumm - our system that uses large language models to automatically generate high-quality literature summaries for non-coding RNAs! 🧬📚

10/12 Some limitations: We can only use open-access papers (highlighting the importance of #OpenAccess!), and LLMs sometimes struggle with complex information synthesis.

1/12 Excited to share our new paper in DATABASE on LitSumm - our system that uses large language models to automatically generate high-quality literature summaries for non-coding RNAs! 🧬📚

8/12 Want to try it yourself? Search for RNAs with summaries at: rnacentral.org/search?q=has...

1/12 Excited to share our new paper in DATABASE on LitSumm - our system that uses large language models to automatically generate high-quality literature summaries for non-coding RNAs! 🧬📚

6/12 The results? We generated >4,600 summaries covering ~28,700 RNA transcripts! Expert evaluation showed 94% were rated good or excellent quality. 📈

1/12 Excited to share our new paper in DATABASE on LitSumm - our system that uses large language models to automatically generate high-quality literature summaries for non-coding RNAs! 🧬📚

4/12 Our solution: Use GPT-4 with carefully designed prompts to read scientific papers and generate accurate summaries, complete with proper citations! 🤖

1/12 Excited to share our new paper in DATABASE on LitSumm - our system that uses large language models to automatically generate high-quality literature summaries for non-coding RNAs! 🧬📚

2/12 Why did we build this? Curation of scientific literature is becoming increasingly challenging. There's a growing gap between publication rates and the number of available curators.