ArticleBioinformatics (Oxford, England)2020
SCSsim: an integrated tool for simulating single-cell genome sequencing data.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 17 citations in OpenAlex.
- scTCA: a hybrid Transformer-CNN architecture for imputation and denoising of scDNA-seq data.Briefings in bioinformatics · 2024Article
- CoT: a transformer-based method for inferring tumor clonal copy number substructure from scDNA-seq data.Briefings in bioinformatics · 2024Article
- Inferring single-cell copy number profiles through cross-cell segmentation of read counts.BMC genomics · 2024Article
- CNAsim: improved simulation of single-cell copy number profiles and DNA-seq data from tumors.Bioinformatics (Oxford, England) · 2023Article
- SCSilicon: a tool for synthetic single-cell DNA sequencing data generation.BMC genomics · 2022Article
- SCYN: single cell CNV profiling method using dynamic programming.BMC genomics · 2021Article
- Editorial: Unsupervised Learning Models for Unlabeled Genomic, Transcriptomic & Proteomic Data.Frontiers in genetics · 2021Article
- SCSIM: Jointly simulating correlated single-cell and bulk next-generation DNA sequencing data.BMC bioinformatics · 2020Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationAllele dropout (ADO) and unbalanced amplification of alleles are main technical issues of single-cell sequencing (SCS), and effectively emulating these issues is necessary for reliably benchmarking SCS-based bioinformatics tools. Unfortunately, currently available sequencing simulators are free of whole-genome amplification involved in SCS technique and therefore not suited for generating SCS datasets. We develop a new software package (SCSsim) that can efficiently simulate SCS datasets in a parallel fashion with minimal user intervention. SCSsim first constructs the genome sequence of single cell by mimicking a complement of genomic variations under user-controlled manner, and then amplifies the genome according to MALBAC technique and finally yields sequencing reads from the amplified products based on inferred sequencing profiles. Comprehensive evaluation in simulating different ADO rates, variation detection efficiency and genome coverage demonstrates that SCSsim is a very useful tool in mimicking single-cell sequencing data with high efficiency. AVAILABILITY AND IMPLEMENTATION: SCSsim is freely available at https://github.com/qasimyu/scssim. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.