ArticleBMC genomics2022
SCSilicon: a tool for synthetic single-cell DNA sequencing data generation.
Article in BMC genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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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
5 citing papers in PubMed, 6 citations in OpenAlex.
- CRESCENT: a deep learning framework with multi-scale attention for detecting recurrent copy number alterations.Briefings in bioinformatics · 2026Article
- SISTEM: simulation of tumor evolution, metastasis, and DNA-seq data under genotype-driven selection.Bioinformatics (Oxford, England) · 2025Article
- A Systematic Review of the Advances and New Insights into Copy Number Variations in Plant Genomes.Plants (Basel, Switzerland) · 2025Review
- Application and research progress of single cell sequencing technology in leukemia.Frontiers in oncology · 2024Review
- CNAsim: improved simulation of single-cell copy number profiles and DNA-seq data from tumors.Bioinformatics (Oxford, England) · 2023Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundSingle-cell DNA sequencing is getting indispensable in the study of cell-specific cancer genomics. The performance of computational tools that tackle single-cell genome aberrations may be nevertheless undervalued or overvalued, owing to the insufficient size of benchmarking data. In silicon simulation is a cost-effective approach to generate as many single-cell genomes as possible in a controlled manner to make reliable and valid benchmarking.
resultsThis study proposes a new tool, SCSilicon, which efficiently generates single-cell in silicon DNA reads with minimum manual intervention. SCSilicon automatically creates a set of genomic aberrations, including SNP, SNV, Indel, and CNV. Besides, SCSilicon yields the ground truth of CNV segmentation breakpoints and subclone cell labels. We have manually inspected a series of synthetic variations. We conducted a sanity check of the start-of-the-art single-cell CNV callers and found SCYN was the most robust one.
conclusionsSCSilicon is a user-friendly software package for users to develop and benchmark single-cell CNV callers. Source code of SCSilicon is available at https://github.com/xikanfeng2/SCSilicon .
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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.