GSR: Simulator - nanosim

Basic Package Attributes
AttributeValue
Title nanosim
Short Description Nanopore sequence read simulator
Long Description NanoSim is a fast and scalable read simulator that captures the technology-specific features of ONT data, and allows for adjustments upon improvement of nanopore sequencing technology.
Keywords Nanopore
Version 3
Project Started 2021
Last Release 8 years, 8 months ago
Homepagehttps://github.com/bcgsc/NanoSim
Citations Yang C, Chu J, Warren RL, Birol I, NanoSim: nanopore sequence read simulator based on statistical characterization., Gigascience, April 1, 2017 [ Abstract, cited in PMC ]
GSR CertificationGSR-certified

Accessibility
Documentation
Application
Support

Last evaluatedSept. 30, 2021 (1421 days ago)
Author verificationThe basic description provided was derived from a website or publications by the GSR team and has not yet been verified by the simulation author. To modify this entry or add more information, propose changes to this simulator.
Detailed Attributes
Attribute CategoryAttribute
Target
Type of Simulated DataSequencing Reads,
VariationsGenotype or Sequencing Error,
Simulation MethodResample Existing Data,
Input
Data Type
File format
Output
Data Type
Sequencing ReadsNanopore,
File FormatFasta or Fastq,
Sample Type
Phenotype
Trait Type
Determinants
Evolutionary Features
Demographic
Population Size Changes
Gene Flow
Spatiality
Life Cycle
Mating System
Fecundity
Natural Selection
Determinant
Models
Recombination
Mutation Models
Events Allowed
Other
InterfaceCommand-line,
Development
Tested PlatformsMac OS X, Linux and Unix,
LanguagePython,
LicenseGNU Public License,
GSR CertificationAccessibility, Documentation, Application, Support,

Number of Primary Citations: 1

Number of Non-Primary Citations: 2

The following 2 publications are selected examples of applications that used nanosim.

2023

Mikalsen AJ, Zola J, Coriolis: enabling metagenomic classification on lightweight mobile devices., Bioinformatics, June 30, 2023 [Abstract]

Pardo-Palacios FJ, Wang D, Reese F, Diekhans M, Carbonell-Sala S, Williams B, Loveland JE, De María M, Adams MS, Balderrama-Gutierrez G, et al., Systematic assessment of long-read RNA-seq methods for transcript identification and quantification., bioRxiv, July 27, 2023 [Abstract]


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