FunctionSIM – A Sequencing Simulator for Functional Metagenomics

FunctionSIM

:: DESCRIPTION

FunctionSIM is a bioinformatics tool to generate microbial DNA sequences. As standalone software it allows users to simulate metagenomic sequence datasets that can be used as standardized test data for planning metagenomic projects or for benchmarking software in functional metagenomic analysis.

::DEVELOPER

Lingling An

:: SCREENSHOTS

FunctionSIM

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • Java

:: DOWNLOAD

 FunctionSIM

:: MORE INFORMATION

UNICOR 2.0 – Species Connectivity and Corridor Network Simulator

UNICOR 2.0

:: DESCRIPTION

UNICOR (UNIversal CORridor Network Simulator) is a species connectivity and corridor identification tool.  UNICOR implements Dijkstra’s shortest path algorithm for any number of landscapes and distributions of species.

::DEVELOPER

the Computational Ecology Lab, The University of Montana

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOsX
  • Python

:: DOWNLOAD

 UNICOR

:: MORE INFORMATION

Citation

Landguth EL, Hand BK, Glassy JM, Cushman SA (2011)
UNICOR: A species connectivity and corridor network simulator.
Ecography. DOI:10.1111/j.1600-0587.2011.07149.x.

popRange – A Spatially & Temporally Explicit Forward Population Genetic Simulator

popRange 1.1.3

:: DESCRIPTION

popRange is a forward genetic simulator, which incorporates large-scale genetic data with stochastic spatially and temporally explicit demographic and selective models.

::DEVELOPER

Kimberly McManus

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/ Linux/ MacOsX
  • R
  • Python

:: DOWNLOAD

 popRange

:: MORE INFORMATION

Citation

Source Code Biol Med. 2015 Apr 11;10:6. doi: 10.1186/s13029-015-0036-4. eCollection 2015.
popRange: a highly flexible spatially and temporally explicit Wright-Fisher simulator.
McManus KF

MANTIS 3.0 – Multilocus ANTIgenic Simulator

MANTIS 3.0

:: DESCRIPTION

MANTIS offers a wide range of functions to simulate and analyse epidemiological time-series, generated under the biological assumptions of the strain theory of host-pathogen systems

::DEVELOPER

EEID (volutionary Ecology of Infectious Disease) – University of Oxford

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows
  • R

:: DOWNLOAD

 MANTIS

:: MORE INFORMATION

Citation:

BMC Bioinformatics. 2015 May 28;16:176. doi: 10.1186/s12859-015-0598-9.
MANTIS: an R package that simulates multilocus models of pathogen evolution.
Lourenço J, Wikramaratna PS, Gupta S

RedLynx v1.1 – Population Genetics Simulator

RedLynx v1.1

:: DESCRIPTION

RedLynx is a population genetics simulator built entirely in JavaScript. It runs entirely withing a web browser and is extremely helpful when teaching population genetics and evolution. It can simulate genetic drift, selection, dominance, mutation, and migration.

::DEVELOPER

Reed A. Cartwright

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

NO

:: MORE INFORMATION

Citation

Ginkgo 4.0.0 – Phylogeographical Evolution Simulator

Ginkgo 4.0.0

:: DESCRIPTION

Ginkgo is a agent-based forward-time simulation to produce gene genealogies and incidence (occurrence) data for multiple populations of multiple species in a spatially-explicit and environmentally-heterogenous framework.

::DEVELOPER

The Holder Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Mac OsX / Windows

:: DOWNLOAD

  Ginkgo

:: MORE INFORMATION

Citation:

Mol Ecol Resour. 2011 Mar;11(2):364-9. doi: 10.1111/j.1755-0998.2010.02926.x. Epub 2010 Nov 10.
Ginkgo: spatially-explicit simulator of complex phylogeographic histories.
Sukumaran J, Holder MT.

SeDuS 1.10 – Segmental Duplication Simulator

SeDuS 1.10

:: DESCRIPTION

SeDuS is a C++ forward-in-time simulator of segmental duplications undergoing concerted evolution.

::DEVELOPER

Evolutionary Genomics Lab

:: SCREENSHOTS

SeDuS

:: REQUIREMENTS

  • Windows/Linux / MacOsX

:: DOWNLOAD

  SeDuS

:: MORE INFORMATION

Citation

SeDuS: Segmental Duplication Simulator.
Hartasánchez DA, Brasó-Vives M, Fuentes-Díaz J, Vallès-Codina O, Navarro A.
Bioinformatics. 2015 Sep 10. pii: btv481.

MCell 4.0.1 – Monte Carlo Simulator of Cellular Microphysiology

MCell 4.0.1

:: DESCRIPTION

MCell (Monte Carlo cell) is a program that uses spatially realistic 3-D cellular models and specialized Monte Carlo algorithms to simulate the movements and reactions of molecules within and between cells—cellular microphysiology.

::DEVELOPER

MCell Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux /Mac OsX / Windows

:: DOWNLOAD

MCell

:: MORE INFORMATION

Citation:

Stiles, JR, and Bartol, TM. (2001).
Monte Carlo methods for simulating realistic synaptic microphysiology using MCell.
In: Computational Neuroscience: Realistic Modeling for Experimentalists, ed. De Schutter, E. CRC Press, Boca Raton, pp. 87-127.

Pysces 0.9.6 – Python Simulator of Cellular Systems

Pysces 0.9.6

:: DESCRIPTION

Pysces is the Python Simulator of Cellular Systems. For a network of coupled reactions it does a stoichiometric matrix analysis, calculates the time course and steady state, and does a complete control analysis.

::DEVELOPER

PySCeS Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 Pysces

:: MORE INFORMATION

Citation:

Brett G. Olivier, Johann M. Rohwer and Jan-Hendrik S. Hofmeyr
Modelling cellular systems with PySCeS
Bioinformatics (2005) 21 (4): 560-561.

COPASI 4.33 – Biochemical Network Simulator

COPASI 4.33

:: DESCRIPTION

COPASI is a software application for simulation and analysis of biochemical networks and their dynamics. COPASI is a stand-alone program that supports models in the SBML standard and can simulate their behavior using ODEs or Gillespie’s stochastic simulation algorithm; arbitrary discrete events can be included in such simulations.

::DEVELOPER

COPASI Team

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows / Linux / Mac OsX

:: DOWNLOAD

COPASI

:: MORE INFORMATION

Citation

COPASI–a COmplex PAthway SImulator.
Hoops S, Sahle S, Gauges R, Lee C, Pahle J, Simus N, Singhal M, Xu L, Mendes P, Kummer U.
Bioinformatics. 2006 Dec 15;22(24):3067-74.

Methods Mol Biol. 2009;500:17-59. doi: 10.1007/978-1-59745-525-1_2.
Computational modeling of biochemical networks using COPASI.
Mendes P, Hoops S, Sahle S, Gauges R, Dada J, Kummer U.