Biology subjects

Rudnick, Z.

Publications and source records attributed to Rudnick, Z..

3 recordsLinked to original sources

bioRxiv2026

Bramble: projection of spliced genomic alignments into transcriptomic space for improved transcript quantification

Accurate transcript abundance estimation is central to many transcriptomic studies. Many current quantification methods rely on reads mapped directly to the transcriptome, but transcriptome alignment can misassign reads from unannotated transcripts to annotated isoforms, leading to biased abundance estimates. We introduce Bramble, a method that projects spliced genomic alignments into transcriptomic coordinates to produce alignments compatible with downstream transcript quantification tools. Across simulated short- and long-read RNA-seq datasets and multiple levels of reference annotation completeness, incorporating Bramble into quantification pipelines consistently improved accuracy and reduced error. These results suggest that genome-derived transcriptomic alignments can improve transcript quantification by preserving compatible alignments to annotated transcripts while filtering alignments likely originating from unannotated transcripts.

genomics↗

bioRxiv2025

SpaTRACE: Spatiotemporal recurrent auto-encoder for reconstructing signaling and regulatory networks from spatiotemporal transcriptomics data

Cell-cell communication and gene regulatory programs jointly coordinate cellular behaviors during development, regeneration, and disease. Recent advances in spatial transcriptomics enable measurement of gene expression with spatial context across developmental trajectories, providing new opportunities to study dynamic signaling and regulatory processes. However, most existing methods analyze either ligand-receptor (LR) signaling or gene regulatory networks (GRNs) separately, rely on curated interaction databases, and often assume steady-state gene expression, limiting their ability to capture temporal regulatory dynamics and discover novel interactions. We present SpaTRACE, a spatiotemporal recurrent autoencoder framework for joint inference of intercellular signaling and gene regulatory networks from spatial transcriptomics data. SpaTRACE models time-lagged dependencies along pseudotime-sampled cellular trajectories using an attention-based encoder-decoder architecture that predicts future target gene expression from upstream intra- and intercellular signals. The learned attention structure enables simultaneous reconstruction of transcription factor-target gene (TF-TG) regulatory interactions, ligand-receptor-target gene (LR-TG) signaling pathways, and ligand-receptor binding relationships without requiring predefined LR databases. Across synthetic benchmarks, SpaTRACE accurately recovers both GRN and signaling interactions and outperforms existing GRN and cell-cell communication inference methods. Applications to mouse midbrain development reveal transcriptional regulators and signaling programs associated with neuronal differentiation, while analysis of axolotl brain regeneration identifies stage-specific signaling dynamics and candidate interactions involved in tissue repair. Availability: Source code and documentation are available at https://github.com/VariaanZhou/SpaTRACE.

bioinformatics↗

bioRxiv2025

StringTie3 Improves Total RNA-seq Assembly by Resolving Nascent and Mature Transcripts

Accurate assembly of rRNA-depleted (total) RNA-seq remains challenging because existing methods often conflate incomplete, nascent RNA with fully processed mature isoforms, leading to misassemblies and quantification errors that skew downstream analyses. Here, we present StringTie3, a major update to the widely used StringTie assembler, specifically designed for total RNA-seq. This new version introduces two key innovations: (1) a nascent mode that models co-transcriptional splicing to separate nascent from mature transcripts, and (2) a refined long-read module that distinguishes genuine polyadenylation sites from poly(A)-priming artifacts. Across short-, long-, and hybrid-read datasets, StringTie3 substantially reduces assembly errors and outperforms existing tools, boosting precision by up to 20% in short-read total RNA-seq and improving sensitivity and precision by as much as 37% and 75%, respectively, in long-read assemblies. In Argonaute knockout experiments, nascent-mode analysis shows that single knockouts predominantly alter nascent transcripts while leaving mature RNA largely unchanged, whereas double or triple knockouts disrupt both fractions. Applying this approach to breast cancer samples shows that, although nascent and mature RNA levels often correlate, certain extracellular matrix and tumor suppressor genes deviate from this pattern, suggesting post-transcriptional regulation. By accurately reconstructing transcriptomes and distinguishing nascent from mature RNA, StringTie3 reveals hidden layers of RNA regulation and provides a powerful framework for investigating transcriptional and post-transcriptional processes in total RNA-seq data.

genomics↗