ArticleBMC bioinformatics2017
Pysim-sv: a package for simulating structural variation data with GC-biases.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- MosaicSim: A Novel Mosaic Variant Simulator Reveals Diminishing Returns of Ultra-High Coverage for Mosaic Variant Detection.bioRxiv : the preprint server for biology · 2025Article
- MOV&RSim: computational modelling of cancer-specific variants and sequencing reads characteristics for realistic tumoral sample simulation.BMC bioinformatics · 2025Article
- Article
- Assessing the merits: an opinion on the effectiveness of simulation techniques in tumor subclonal reconstruction.Bioinformatics advances · 2024Article
- Boquila: NGS read simulator to eliminate read nucleotide bias in sequence analysis.Turkish journal of biology = Turk biyoloji dergisi · 2023Article
- Identification of Copy Number Alterations from Next-Generation Sequencing Data.Advances in experimental medicine and biology · 2022Article
- Article
- SimuSCoP: reliably simulate Illumina sequencing data based on position and context dependent profiles.BMC bioinformatics · 2020Article
- A broad survey of DNA sequence data simulation tools.Briefings in functional genomics · 2020Review
- SECNVs: A Simulator of Copy Number Variants and Whole-Exome Sequences From Reference Genomes.Frontiers in genetics · 2020Article
- Autism risk in offspring can be assessed through quantification of male sperm mosaicism.Nature medicine · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundStructural variations (SVs) are wide-spread in human genomes and may have important implications in disease-related and evolutionary studies. High-throughput sequencing (HTS) has become a major platform for SV detection and simulation serves as a powerful and cost-effective approach for benchmarking SV detection algorithms. Accurate performance assessment by simulation requires the simulator capable of generating simulation data with all important features of real data, such GC biases in HTS data and various complexities in tumor data. However, no available package has systematically addressed all issues in data simulation for SV benchmarking.
resultsPysim-sv is a package for simulating HTS data to evaluate performance of SV detection algorithms. Pysim-sv can introduce a wide spectrum of germline and somatic genomic variations. The package contains functionalities to simulate tumor data with aneuploidy and heterogeneous subclones, which is very useful in assessing algorithm performance in tumor studies. Furthermore, Pysim-sv can introduce GC-bias, the most important and prevalent bias in HTS data, in the simulated HTS data.
conclusionsPysim-sv provides an unbiased toolkit for evaluating HTS-based SV detection algorithms.
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