ArticleCell genomics2026
Analysis of error profiles of indels and structural variants in deep-sequencing data.
Article in Cell genomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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.
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Who cites it
3 citing papers in PubMed.
- Phage-encoded contingency loci enable bet-hedging against host defence mechanisms.Nature microbiology · 2026Article
- Analysis of error profiles of indels and structural variants in deep-sequencing data.Cell genomics · 2026Article
- Differentiated muscle cells of salamander Pleurodeles waltl re-enter the cell cycle.Histochemistry and cell biology · 2025Article
Corrections and comments
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Authors and funding
21 authors.
Funding
Abstract
Despite extensive studies of the error profiles of SNVs, those of insertions/deletions (indels)/structural variants (SVs) remain elusive. Using ultra-deep sequencing, we show that the error rates of indel/SVs are >100-fold lower than those of SNVs, although repeat indels have high error rates of 1%. We validated this pattern in a cohort of 103 patients with relapsed B cell acute lymphoblastic leukemia (B-ALL). We analyzed repeat indels in 339 cancer driver genes and demonstrated that the number of repeat units is highly predictive of the error rate. We then analyzed minimal residual disease samples from 72 patients with relapsed B-ALL and demonstrated that our approach had positive detections in 61% of cases, outperforming clinical flow cytometry (51% detection). Overall, we established indel and SV error profiles in deep next-generation sequencing (NGS) data, enabling superior tumor detection at very low burdens, which has a significant impact on the clinical diagnosis and monitoring of human cancers and other diseases.
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Registered trials
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