SpliceCOV: Supervised Filtering of RNA-Seq-Derived Transcript Features Improves Transcriptome Assembly

Background/Objectives: High-throughput RNA sequencing enables transcript discovery and quantification, but the resulting coverage profiles often contain substantial transcriptional noise, making it difficult to distinguish genuine transcript features from spurious signals. These noisy signals can propagate into transcriptome assemblies, increasing the number of false transcript models and reducing annotation precision. Methods: We present SpliceCOV, a supervised framework for filtering RNA-Seq-derived transcript features. Rather than performing general-purpose segmentation of RNA-Seq coverage, SpliceCOV uses coverage- and junction-derived features to distinguish likely genuine splice sites and transcript boundaries from noisy candidates. We benchmarked SpliceCOV against established changepoint methods and evaluated whether SpliceCOV-filtered features improve downstream transcriptome assembly with StringTie. Results: Across human GTEx tissue datasets, SpliceCOV substantially reduced likely false-positive splice sites while retaining most annotated sites. We included segmentation-based methods as baselines to assess whether generic coverage changepoints can support RNA-Seq transcript-feature filtering. Because these methods are unsupervised whereas SpliceCOV is trained using annotation-derived labels, the comparisons are intended to assess suitability for this specific filtering task rather than to provide a general ranking of the underlying algorithms. Under this evaluation framework, SpliceCOV retained more annotation-supported splice sites at comparable levels of filtering. For transcript start and end sites, SpliceCOV reduced the majority of false positives while retaining substantial overlap with reference annotations. When SpliceCOV-filtered splice sites and transcript boundaries were supplied to StringTie, transcript-level precision increased across the evaluated tissues while the number of reference-matching transcripts remained broadly similar. The model also showed transferability across tissues and chromosomes, and could be applied to mouse RNA-Seq without retraining, although downstream assembly performance was lower than in human datasets. Conclusions: SpliceCOV provides a supervised approach for filtering noisy RNA-Seq-derived transcript features before transcriptome assembly. By improving the quality of splice-site and transcript-boundary evidence supplied to downstream assembly, SpliceCOV can increase transcript-level precision while preserving transcript recovery.