ArticleGenome medicine2023
De novo identification of expressed cancer somatic mutations from single-cell RNA sequencing data.
Article in Genome medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
8 citing papers in PubMed.
- Exploratory identification of lycorine as a potential inhibitor of the ACP2/YME1L1 prognostic axis in esophageal squamous cell carcinoma: a multi-omics and computational hypothesis.Molecular genetics and genomics : MGG · 2026Article
- BRAF inhibitor resistance in melanoma: from resistance mechanisms to therapeutic innovations.Molecular biomedicine · 2026Review
- LnCeVar 2.0: an updated resource and web tools for genomic variations disrupting ceRNA networks from single-cell/spatial transcriptomics data.Nucleic acids research · 2026Article
- scSNViz: visualization and analysis of cell-specific expressed SNVs.Bioinformatics (Oxford, England) · 2026Article
- Informatics at the Frontier of Cancer Research.Cancer research · 2025Review
- De novo detection of somatic variants in high-quality long-read single-cell RNA sequencing data.Genome research · 2025Article
- De novo detection of somatic variants in high-quality long-read single-cell RNA sequencing data.bioRxiv : the preprint server for biology · 2024Article
- Correction: Genome Med 15, 115 & Genome Med 16, 3.Genome medicine · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
Abstract
Identifying expressed somatic mutations from single-cell RNA sequencing data de novo is challenging but highly valuable. We propose RESA - Recurrently Expressed SNV Analysis, a computational framework to identify expressed somatic mutations from scRNA-seq data. RESA achieves an average precision of 0.77 on three in silico spike-in datasets. In extensive benchmarking against existing methods using 19 datasets, RESA consistently outperforms them. Furthermore, we applied RESA to analyze intratumor mutational heterogeneity in a melanoma drug resistance dataset. By enabling high precision detection of expressed somatic mutations, RESA substantially enhances the reliability of mutational analysis in scRNA-seq. RESA is available at https://github.com/ShenLab-Genomics/RESA .
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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.