bioRxiv Science⌕ Search

bioRxiv · 10.64898/2026.08.24.746847

TxNova: recovery of recurrent unannotated intergenic splice loci from existing bulk RNA-seq alignments

Abstract

Background. Reference catalogs such as GENCODE capture most stably expressed mammalian genes but may not include condition-restricted or low-abundance transcripts. Reads supporting unannotated intergenic splice junctions are generally absent from annotation-restricted gene-count matrices; transcript assemblers may reconstruct a subset as novel models, but those models are typically handled separately from the annotated gene-count universe. TxNova is a lightweight command-line tool that directly indexes these unannotated splices from existing BAM files - without an external assembler, gffcompare, or workflow manager - yielding candidate leads for bench validation rather than standalone discovery claims. Methods. TxNova retains CIGAR N junctions from STAR/HISAT2 BAM files that recur in >= 2 samples and are absent from a comprehensive annotation, clusters them into residual loci, and counts each locus alongside annotated genes in a unified matrix. Structure gates - canonical splice motif, same-strand distance, coverage valley, bridging-junction absence, minimum length - remove likely artifacts to yield structure-pass models. An optional contrast filter retains loci detected in treatment but nearly silent in control. Results. A residual splice is a recurrent, unannotated CIGAR N junction that does not overlap any annotated gene body; intronic and antisense channels are out of scope. Across four published mouse treatment arms (GSE221720, GSE166522, GSE157460, GSE193335), harvest catalogs yielded 464, 657, 789, and 594 loci. On GSE221720, 45% of loci (209/464) shared an exact intron with another series, versus 0.074% for excluded junctions; a coordinate-placement control yielded 0/5,000 matches for length-matched intergenic decoys. Masked-gene recovery reached 87.6% (176/201) overall and 99.4% (176/177) among genes with a leak junction - sensitivity rather than precision estimates. An optional contrast provides a presence/absence screen on interval TPM. Residual models are partial reconstructions requiring cloning, RACE, or targeted proteomics for confirmation. Conclusions. TxNova produces a reproducible intergenic residual-locus catalog from existing BAM files, with four downloadable mouse injury/infection catalogs. Cross-series recurrence and coordinate-based null controls support reproducibility above simple placement backgrounds but do not by themselves establish biological validity. The optional contrast step offers a practical screen for candidate leads warranting experimental validation.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Li, Z., James, A., Li, S.. 2026-08-25. TxNova: recovery of recurrent unannotated intergenic splice loci from existing bulk RNA-seq alignments. https://doi.org/10.64898/2026.08.24.746847

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Targeted finetuning enables co-folding models to learn ligand-induced protein conformational states

Advances in protein structure prediction have enabled all-atom protein-ligand co-folding models that predict bound conformations directly from sequence and small-molecule structure. However, these models often fail to generalize to novel binding sites or alternative protein conformational states, limiting their utility for chemical biology and drug discovery. Here we show this limitation reflects training data bias rather than architectural constraints and can be overcome through targeted finetuning. Using ten previously unseen X-ray structures of Werner (WRN) helicase from a drug discovery program, we finetune Boltz-1 to learn both an allosteric binding site and a large conformational change locking the enzyme in an inactive state, while preserving accuracy on the ATP-bound state. The finetuned model generalizes to different chemical series and transfers the conformational logic across RecQ-family helicases in a binding-site sequence-dependent manner. This approach provides a blueprint for adapting foundation models as new structural and mechanistic data emerge, enabling co-folding networks to capture ligand-induced conformational switches and binding poses absent from their training data but central to biological regulation and therapeutic intervention.

bioinformatics↗

Benchmarking single-cell foundation models for aging biology

Single cell foundation models (scFMs) provide representations of cellular states, but their utility across biological questions in aging research remains unclear. We established a benchmark of cellular representations for aging research, evaluating ten general-purpose scFMs, three aging-specific models and conventional methods across five biological questions using more than 2.5 million single cell transcriptomes. Using frozen pretrained representations, Geneformer performed best among scFMs for chronological age prediction and age pseudotime concordance, although 2,000 highly variable genes achieved higher mean performance. Several scFMs captured positive molecular age shifts across three disease contexts, consistent with reported aging-associated changes. SCimilarity performed well for rare cellular state identification across out-of-distribution datasets, exceeding aging specific models and conventional baselines. At the gene level, scGPT showed the highest recovery of reference TF target interactions, including aging-related regulatory hubs. Overall, scFMs supported diverse aging analyses, but performance depended on the biological question, highlighting their utility for rare cellular state identification and regulatory analysis.

bioinformatics↗

CryoMV: Structure-Prior-Guided Modeling and Real-Particle Validation of Continuous Conformational Transitions in Cryo-EM

Continuous protein conformations are essential for understanding fundamental biological processes and supporting drug discovery. Although cryo-EM can resolve individual states at high resolution, recovering continuous heterogeneity from 2D particle images remains challenging. High noise, motion blur, and limited structural priors make it difficult to accurately generate and validate high-resolution continuous conformations using raw particle data. Here, we introduce cryoMV, a framework that integrates structure-prior-guided modeling with real-particle validation for continuous conformational transitions. CryoMV uses reference density maps to establish structural anchors and motion priors, models candidate transition paths between selected conformations, and transfers the learned representation to raw 2D cryo-EM particle images. Each candidate conformation is subsequently evaluated using the estimated particle poses and contrast transfer functions. Supported conformations are reconstructed through raw particle back-projection and assessed using canonical half-maps and Fourier shell correlation. On EMPIAR-10516 and EMPIAR-10345, cryoMV achieves excellent performance in terms of robustness, verifiability, and reconstruction resolution. By incorporating structure-prior modeling and evidence from the raw particles, cryoMV offers an explicit mechanism for assessing whether generated conformations are supported by experimental data and provides a practical approach to reducing model-induced artifacts in continuous cryo-EM heterogeneity analysis.

bioinformatics↗