ArticleNAR cancer2021
UNMASC: tumor-only variant calling with unmatched normal controls.
Article in NAR cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 12 citations in OpenAlex.
- Somatic likelihood tiering: an interpretable post-calling triage protocol for tumor-only whole-exome variant review.Briefings in bioinformatics · 2026Article
- Improved tumor-only variant calling and mutation burden estimation with VarNet-T.Nature communications · 2026Article
- GATES: A Lightweight Tool Automating Pathogenic Variant Discovery From Raw Whole-Exome Sequencing Data.Cancer informatics · 2026Article
- Somatic Mutation Profiling and Therapeutic Landscape of Breast Cancer in the MENA Region.Cells · 2025Article
- ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling.Nature communications · 2025Article
- VOYAGER: an international consortium investigating the role of human papilloma virus and genetics in oral and oropharyngeal cancer risk and survival.medRxiv : the preprint server for health sciences · 2025Article
- Refined variant calling pipeline on RNA-seq data of breast cancer cell lines without matched-normal samples.BMC research notes · 2025Article
- Cancer genomics and bioinformatics in Latin American countries: applications, challenges, and perspectives.Frontiers in oncology · 2025Review
- Precise identification of somatic and germline variants in the absence of matched normal samples.Briefings in bioinformatics · 2024Article
- Pediatric Chordoma: A Tale of Two Genomes.Molecular cancer research : MCR · 2024Article
- Technological and computational approaches to detect somatic mosaicism in epilepsy.Neurobiology of disease · 2023Review
- Detection of mutant antigen-specific T cell receptors against multiple myeloma for T cell engineering.Molecular therapy. Methods & clinical development · 2023Article
- DeepSom: a CNN-based approach to somatic variant calling in WGS samples without a matched normal.Bioinformatics (Oxford, England) · 2023Article
- Systematic assessment and optimizing algorithm of tumor mutational burden calculation and their implications in clinical decision-making.Frontiers in oncology · 2022Article
- Cancer Neoantigens: Challenges and Future Directions for Prediction, Prioritization, and Validation.Frontiers in oncology · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 4 institutions in 1 country.
Funding
Abstract
Despite years of progress, mutation detection in cancer samples continues to require significant manual review as a final step. Expert review is particularly challenging in cases where tumors are sequenced without matched normal control DNA. Attempts have been made to call somatic point mutations without a matched normal sample by removing well-known germline variants, utilizing unmatched normal controls, and constructing decision rules to classify sequencing errors and private germline variants. With budgetary constraints related to computational and sequencing costs, finding the appropriate number of controls is a crucial step to identifying somatic variants. Our approach utilizes public databases for canonical somatic variants as well as germline variants and leverages information gathered about nearby positions in the normal controls. Drawing from our cohort of targeted capture panel sequencing of tumor and normal samples with varying tumortypes and demographics, these served as a benchmark for our tumor-only variant calling pipeline to observe the relationship between our ability to correctly classify variants against a number of unmatched normals. With our benchmarked samples, approximately ten normal controls were needed to maintain 94% sensitivity, 99% specificity and 76% positive predictive value, far outperforming comparable methods. Our approach, called UNMASC, also serves as a supplement to traditional tumor with matched normal variant calling workflows and can potentially extend to other concerns arising from analyzing next generation sequencing data.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.