Reference-based annotation tools have become standard for cell type assignment in single-cell RNA sequencing, leveraging large-scale atlases to transfer labels to new datasets. However, a substantial fraction of cells often receive uncertain or ambiguous annotations-low confidence scores, competing label probabilities, or high entropy across cell types. These cells are typically treated as technical artifacts and filtered out, yet in perturbation experiments they may represent the biologically interesting deviations that investigators seek to identify. We present AnnQ (Annotation Quantification of cellular identity uncertainty), a Python framework that repurposes annotation uncertainty as a quantitative measure of cellular abnormality. AnnQ extracts uncertainty-aware features from probabilistic cell type assignments-including confidence, confidence gap, admixture ratio, and entropy-and computes an out-of-reference (OOR) score measuring each cell's deviation from a reference population in multivariate uncertainty space. Applying AnnQ to genetic perturbation and drug resistance datasets, we show that OOR scores detect aberrant cellular states that are not resolved by conventional clustering or differential abundance analyses. AnnQ provides a complementary approach for characterizing transitional and abnormal cell states at single-cell resolution. AnnQ is implemented in Python, and its source code and documentation are available on https://github.com/joonan-lab/AnnQ.git.

MOTIVATION: Advances in tumor sequencing enable routine detection of dozens to hundreds of somatic alterations per patient, yet only a minority can be linked to established therapeutic evidence. Curated resources such as OncoKB provide high-quality variant-drug annotations but remain limited in coverage, particularly for rare or low-frequency variants. This coverage gap motivates computational frameworks that can integrate curated, literature-derived, graph-based, and clinical-trial evidence to prioritize therapeutic hypotheses for expert review. RESULTS: We developed the Integrated Drug Annotation Pipeline (IDAP), a modular framework that combines four complementary evidence streams: curated variant-drug associations from OncoKB, literature-derived gene-drug mention counts from PubMed abstracts, graph-based drug prioritization using a TxGNN-derived biomedical knowledge graph, and cancer-specific clinical-trial evidence from ClinicalTrials.gov. Given a cancer type and a MAF file, IDAP generates patient-level reports summarizing detected variants, ranked therapeutic hypotheses, supporting evidence layers, and relevant clinical trials. Evaluated across five cancer types (n = 50 samples), IDAP expanded evidence-linked therapeutic hypotheses beyond curated databases alone. Among patients without OncoKB recommendations (26/50), IDAP identified a median of 87 candidate drugs (range: 2-473). To reduce cross-source scale imbalance, the final ranking used within-sample percentile normalization with fixed bonuses for curated evidence, multi-source support, and trial linkage. Under this revised ranking, 24/50 top-ranked candidates were supported by at least two evidence sources and 44/50 had associated ClinicalTrials metadata. In an exploratory external CIViC comparison, IDAP recovered at least one matched CIViC-supported therapy in 28/41 eligible samples, with 13/41 appearing within the top 10 candidates. These outputs are intended to support evidence triage and translational interpretation rather than direct treatment recommendation. AVAILABILITY AND IMPLEMENTATION: IDAP is freely available at https://github.com/joonan-lab/IDAP-pipeline, with full documentation at https://joonan-lab.github.io/IDAP-pipeline. An archived snapshot of the code used in this study is deposited on Zenodo (DOI: https://doi.org/10.5281/zenodo.19301367).

Variants in cis-regulatory elements link the noncoding genome to human pathology; however, detailed analytic tools for understanding the association between cell-level brain pathology and noncoding variants are lacking. CWAS-Plus, adapted from a Python package for category-wide association testing (CWAS), enhances noncoding variant analysis by integrating both whole-genome sequencing (WGS) and user-provided functional data. With simplified parameter settings and an efficient multiple testing correction method, CWAS-Plus conducts the CWAS workflow 50 times faster than CWAS, making it more accessible and user-friendly for researchers. Here, we used a single-nuclei assay for transposase-accessible chromatin with sequencing to facilitate CWAS-guided noncoding variant analysis at cell-type-specific enhancers and promoters. Examining autism spectrum disorder WGS data (n = 7280), CWAS-Plus identified noncoding de novo variant associations in transcription factor binding sites within conserved loci. Independently, in Alzheimer's disease WGS data (n = 1087), CWAS-Plus detected rare noncoding variant associations in microglia-specific regulatory elements. These findings highlight CWAS-Plus's utility in genomic disorders and scalability for processing large-scale WGS data and in multiple-testing corrections. CWAS-Plus and its user manual are available at https://github.com/joonan-lab/cwas/ and https://cwas-plus.readthedocs.io/en/latest/, respectively.