Skip to content
Open access

SALRR: Scalable Analysis of Long-Read RNA-Seq Enables Comprehensive Transcriptome Profiling in Human Brain

Aug 2026 · bioRxiv · 0 citations · 53 references
Biology

TL;DR

An integrated wet-lab and computational platform designed to overcome barriers to population-scale long-read RNA sequencing, providing a scalable, community-ready framework for isoform-resolved transcriptomics in neurodegeneration, aging, and complex brain disease.

Abstract

Isoform-resolved transcriptomics is fundamental to decoding the molecular complexity of the human brain, yet population-scale long-read RNA sequencing has remained inaccessible due to labor-intensive library preparation, sensitivity to RNA degradation in postmortem tissue, and the absence of integrated, reproducible analysis pipelines. Here we present SALRR (Scalable Analysis of Long-Read RNA-seq), an integrated wet-lab and computational platform designed to overcome these barriers. Automated ONT long-read cDNA library preparation on the Hamilton Microlab NGS STAR platform reduces hands-on time by 67% and enables 24 libraries per operator per day while maintaining performance across RNA integrity values. A modular, Snakemake-based pipeline performs end-to-end processing from ONT signal data to isoform-level quantification, incorporating SIRV spike-in calibration, multi-stage quality control, and stringent isoform validation. Applied to 10 postmortem frontal cortex samples from the North American Brain Expression Consortium, SALRR identified 31,607 high-confidence isoforms from 10,075 genes, including 8,532 novel splice variants absent from GENCODE v49, and complex splicing events systematically missed by short-read sequencing at neurodegeneration-relevant loci, including GBA1, CCNF, CHCHD10, and TREM2. All protocols and code are openly available, providing a scalable, community-ready framework for isoform-resolved transcriptomics in neurodegeneration, aging, and complex brain disease.

Read PDF

Similar papers

Open access Sep 2026

Isocall enables scalable transcript identification from long-read RNA-sequencing data

Isocall, a scalable and deterministic computational method for jointly calling transcripts from multiple PacBio long-read RNA sequencing samples, converts aligned full-length non-concatemer reads into compact per-sample transcript profiles, merges these profiles across samples, and jointly identifies known and novel tr...

E. Dolzhenko, Megan D. Schertzer, Ryan Gossart et al. · 1 citation
Open access Sep 2026

Systematic benchmarking of commercial workflows for isoform-resolved single-nucleus transcriptomics

This study provides a systematic assessment of four commercially available workflows for performing LR snRNA-seq and highlights key methodological trade-offs related to distinct library preparation strategies, thus providing practical guidance for future isoform-resolved transcriptome studies at the single-nucleus leve...

F. Köhler, Anna Delgado-Tejedor, Maik Zehnsdorf et al. · 0 citations
Review Open access Oct 2026

Deciphering transcriptome complexity via long‐read sequencing

Abstract Transcriptomics is moving beyond gene‐level quantification toward isoform‐resolved interrogation of alternative splicing, transcript structural variation, and repeat‐derived transcription. Yet short‐read sequencing remains intrinsically limited in accurately reconstructing full‐length transcripts and resolving...

Chu-Wen Xu, Jia Li, Chen-Xi Yin et al. · 0 citations
Open access Sep 2026

Ultra-fast and scalable high-resolution full-length single-cell RNA sequencing using CHART-seq

Heteroduplex Assay via Recombinant Tn5 provides a rapid, scalable route to full-length single-cell transcript profiling with gene-programme and candidate isoform resolution and results remain exploratory because independent biological replicates were unavailable.

Wen-Yi Zhang, Ai-Qun Chen, Kai-Qiang Ye et al. · 0 citations
Open access Sep 2026

Handling biological replicates in long-read RNA sequencing data by joining or not joining

While isoform identification from long-read RNA sequencing (lrRNA-seq) data has received significant attention, the handling of biologically replicated lrRNA-seq datasets remains less explored. This study defines two strategies for obtaining consensus transcriptomes from multi-sample lrRNA-seq data: Join & Call, where...

Fabian Jetzinger, Alejandro Paniagua, Stanley Cormack et al. · 1 citation
Open access Sep 2026

CoTRA: a comprehensive R/Shiny framework for transparent bulk and single-cell RNA-seq analysis

Bulk RNA-seq and single-cell RNA-seq (scRNA-seq) are widely used to investigate gene-expression changes, but downstream analysis often requires multiple statistical, visualization, and reporting tools, creating fragmented workflows that are difficult to configure and reproduce. We developed CoTRA (Comprehensive Toolbox...

Umair Seemab, Katri Vainionpaa, Ziaurrehman Tanoli et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.