---
title: "Contextualizing novel isoforms from long-read single-cell RNA-seq with CIPI pathway analysis"
id: "biorxiv-12-beyond-the-gene-contextualizing-novel-isoforms-through-pathway-analysis-in-long"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-12-beyond-the-gene-contextualizing-novel-isoforms-through-pathway-analysis-in-long"
content_type: "clinical_feed_article"
specialty: "General"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.10.14.758627v1?rss=1"
published_at: "2026-10-15T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Contextualizing novel isoforms from long-read single-cell RNA-seq with CIPI pathway analysis
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-12-beyond-the-gene-contextualizing-novel-isoforms-through-pathway-analysis-in-long
- **Specialty:** [General](https://medichelpline.com/clinical-feed/general.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.10.14.758627v1?rss=1)
- **Published At:** 2026-10-15T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- Long-read single-cell RNA sequencing reveals thousands of previously unreported **isoforms** and genes, including pseudogenes and transcripts with repetitive motifs. - The authors reported that current annotation-based approaches leave many novel isoforms functionally uncharacterized, limiting prioritization for follow-up experiments. - The paper introduces Centroid-based Isoform Pathway Inference (CIPI), a computational framework that groups novel isoforms with reference genes by similar cell-type expression patterns to infer context. - CIPI constructs expression-based neighborhoods rather than relying only on host-gene annotation or domain similarity; these neighborhoods serve as the basis for downstream pathway analyses. - The study team combined CIPI outputs with pathway databases such as GO, KEGG, and Reactome to infer likely pathway-level roles for individual isoforms. - Authors suggested that this approach enables hypothesis generation and helps prioritize novel isoforms across the discovered isonome for experimental interrogation. - The preprint noted that tools to predict functionality of newly discovered isoforms were previously scarce, and CIPI is presented as a step toward filling that gap. - The manuscript did not provide detailed benchmarking metrics or specific parameter settings for CIPI in the abstract; those details, if present, would be in the full text. - The authors declared no competing interests and listed NIH, Alzheimer’s Association, and BrightFocus as funders.
## Clinical Analysis & Structured Key Points
Beyond the gene: contextualizing novel isoforms through pathway analysis in long-read single-cell RNA sequencing | bioRxiv Skip to main content New Results Beyond the gene: contextualizing novel isoforms through pathway analysis in long-read single-cell RNA sequencing View ORCID Profile Patricia Hayes Doyle , View ORCID Profile Madeline L. Page , View ORCID Profile Mark T. W. Ebbert doi: https://doi.org/10.64898/2026.10.14.758627 Patricia Hayes Doyle University of Kentucky Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Patricia Hayes Doyle Madeline L. Page University of Kentucky Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Madeline L. Page Mark T. W. Ebbert University of Kentucky Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mark T. W. Ebbert For correspondence: mark.ebbert{at}uky.edu Abstract Info/History Metrics Preview PDF Abstract Differential isoform usage has been reportedly associated with distinct contexts associated with cell-type and disease; however, the mechanistic role that individual isoforms play in these contexts is poorly understood. Long-read single-cell RNA sequencing enables greater resolution into genes and isoforms previously difficult to detect via short-read approaches, including pseudogenes and those with repetitive motifs. This technique, combined with isoform-discovery tools such as Bambu, has yielded thousands of newly discovered isoforms and genes for consideration. The functional relevance of these new isoforms, however, remains enigmatic. Tools to prioritize which of these novel isoforms should be interrogated and to predict their functionality remain elusive. To address this, we developed a computational framework, Centroid-based Isoform Pathway Inference (CIPI), which contextualizes novel genes and isoforms relative to reference genes with similar cell-type expression patterns. Rather than relying solely on host-gene annotation or similarity to known protein domains, CIPI constructs expression-based neighborhoods that, combined with pathway analyses such as GO, KEGG, or Reactome, enable inference of pathway-level contextualization of individual isoforms according to their biologically meaningful neighbors. Combining CIPI with pathway analyses provides a biological context to novel isoforms, enabling a hypothesis-generating approach for prioritizing novel isoforms across the isonome. Competing Interest Statement The authors have declared no competing interest. Funder Information Declared National Institutes of Health, https://ror.org/01cwqze88 , 5T32AG078110-02 , R01AG068331 , GM138626 , NS088555 Alzheimer's Association , 2019-AARG-644082 BrightFocus Foundation, https://ror.org/03cvfxv40 , A2020161S 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 . Back to top Previous Next Posted October 15, 2026. Download PDF Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. 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