---
title: "RNA sequence features that predict and explain phase separation (LLPS) behavior"
id: "biorxiv-7-sequence-architecture-of-rna-reveals-key-molecular-signatures-driving-rna-phase"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-7-sequence-architecture-of-rna-reveals-key-molecular-signatures-driving-rna-phase"
content_type: "clinical_feed_article"
specialty: "General"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.10.23.761066v1?rss=1"
published_at: "2026-10-24T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# RNA sequence features that predict and explain phase separation (LLPS) behavior
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-7-sequence-architecture-of-rna-reveals-key-molecular-signatures-driving-rna-phase
- **Specialty:** [General](https://medichelpline.com/clinical-feed/general.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.10.23.761066v1?rss=1)
- **Published At:** 2026-10-24T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- The authors constructed an interpretable hybrid computational framework to separate experimentally validated **LLPS**-associated RNAs from background RNAs using sequence-derived descriptors and contextual embeddings. - Each RNA was encoded with 167 handcrafted descriptors capturing composition, k-mer usage, sequence complexity, repeat organization and dinucleotide autocorrelation, plus 640-dimensional embeddings from **RNA-FM**. - Representations were combined via cross-validated stacking and a neural-network meta-learner; the model achieved an AUC-ROC of 0.935, AUC-PR of 0.944 and MCC of 0.782 on the reported dataset. - Statistical feature analysis, ablation experiments and SHAP interpretation pointed to a distributed sequence architecture where altered local **k-mer** usage dominated signals. - Additional sequence signals included elevated low-complexity burden, specific repeat-associated organization and long-range dinucleotide patterns, each making measurable contributions to classifier output. - The authors connected model-derived signatures to experimental biology through case studies on **MALAT1** and **TMPO-AS1**, linking identified patterns to known modes of multivalent RNA–protein assembly. - The study emphasizes interpretability: feature statistics and SHAP values were used to move beyond prediction and generate mechanistic hypotheses for RNA-mediated condensation. - The preprint did not report clinical endpoints, patient data, or experimental protocols for validation in vivo; it focused on computational discovery and interpretation using reviewed, experimentally supported LLPS-associated RNAs. - The framework and supporting analyses are presented as a resource for hypothesis generation about sequence determinants of RNA-driven condensate participation. - The article is a preprint and has not been peer reviewed, as stated by the authors and the hosting platform.
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Sonali Chatterjee Indian Institute of Technology Kharagpur * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Sonali%2BChatterjee%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Chatterjee%20S&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ASonali%2BChatterjee%2B) * [ORCID record for Sonali Chatterjee](http://orcid.org/0000-0002-1319-6941 "Open in new tab") Ranjit Prasad Bahadur Indian Institute of Technology Kharagpur * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Ranjit%2BPrasad%2BBahadur%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Bahadur%20RP&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ARanjit%2BPrasad%2BBahadur%2B) * [ORCID record for Ranjit Prasad Bahadur](http://orcid.org/0000-0002-6705-1713 "Open in new tab") * For correspondence: r.bahadur@bt.iitkgp.ac.in * [Abstract](https://www.biorxiv.org/content/10.64898/2026.10.23.761066v1)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_art/node:5901229/1) * [Info/History](https://www.biorxiv.org/content/10.64898/2026.10.23.761066v1.article-info)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_info/node:5901229/1) * [Metrics](https://www.biorxiv.org/content/10.64898/2026.10.23.761066v1.article-metrics)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_metrics/node:5901229/1) * [Supplementary material](https://www.biorxiv.org/content/10.64898/2026.10.23.761066v1.supplementary-material)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_data/node:5901229/1) * [ Preview PDF](https://www.biorxiv.org/content/10.64898/2026.10.23.761066v1.full.pdf+html)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_pdf/node:5901229/1) ![Loading](https://www.biorxiv.org/sites/all/modules/contrib/panels_ajax_tab/images/loading.gif) ## Abstract Biomolecular condensates are dynamic membraneless assemblies that organize various cellular processes through liquid-liquid phase separation (LLPS). RNA is an active regulator of biomolecular condensates and influence their formation through sequence-encoded features. Despite its established role in condensate formation, no computational framework exists to identify underlying signatures to predict LLPS-associated RNAs. We develop an interpretable hybrid framework to distinguish reviewed, experimentally supported LLPS-associated RNAs from background RNAs. We uncover biologically meaningful sequence properties associated with condensate participation. Each RNA is represented by 167 handcrafted descriptors capturing composition, k-mer usage, sequence complexity, repeat organization and dinucleotide autocorrelation together with 640-dimensional contextual embeddings from RNA-FM. These representations are integrated through cross-validated stacking with neural-network meta-learner. The framework achieves an AUC-ROC of 0.935, AUC-PR of 0.944 and MCC of 0.782. Feature statistics, ablation analysis and SHAP interpretation reveal a distributed sequence architecture dominated by altered local k-mer usage with additional contributions from low-complexity burden, repeat-associated organization and long-range dinucleotide pattern. Biological case studies including MALAT1 and TMPO-AS1 further connect these patterns with experimentally characterized modes of multivalent RNA-protein assembly. Together, the results show how interpretable machine learning can move beyond prediction to identify sequence features that generate mechanistic hypotheses for RNA-mediated condensation. ### Competing Interest Statement The authors have declared no competing interest. ## Funder Information Declared Department of Biotechnology, https://ror.org/03tjsyq23, BT/PR40175/BTIS/137/41/2022 Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a [CC-BY-NC-ND 4.0 International license](http://creativecommons.org/licenses/by-nc-nd/4.0/). bioRxiv and medRxiv thank the following for their generous financial support: > The Chan Zuckerberg Initiative, Cold Spring Harbor Laboratory, the Sergey Brin Family Foundation, California Institute of Technology, Centre National de la Recherche Scientifique, Fred Hutchinson Cancer Center, Imperial College London, Massachusetts Institute of Technology, Stanford University, The University of Edinburgh, University of Washington, and Vrije Universiteit Amsterdam. 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[ Download PDF](https://www.biorxiv.org/content/10.64898/2026.10.23.761066v1.full.pdf) Print/Save Options [Download PDF](https://www.biorxiv.org/content/biorxiv/early/2026/10/24/2026.10.23.761066.full.pdf)Full Text & In-line FiguresXML [More Info](https://www.biorxiv.org/about/FAQ#PrintOptions "More Information on Print/Save Options") [Supplementary Material ](https://www.biorxiv.org/content/10.64898/2026.10.23.761066v1.supplementary-material) [ Email](https://www.biorxiv.org/ "Email this Article") [ Share](https://www.biorxiv.org/) Sequence architecture of RNA reveals key molecular signatures driving RNA phase separation Sonali Chatterjee, Ranjit Prasad Bahadur bioRxiv 2026.10.23.761066; doi: https://doi.org/10.64898/2026.10.23.761066 This article is a preprint and has not been certified by peer review [[what does this mean?](https://www.biorxiv.org/about/FAQ#unrefereed)]. 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