Sunflower 1.1.0 – Model for Natural Selection in Promoters

Sunflower 1.1.0

:: DESCRIPTION

Sunflower is an evolutionary model for promoters, analogous to the commonly used synonymous/nonsynonymous mutation models for protein-coding sequences.

::DEVELOPER

Michael M. Hoffman

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 Sunflower

:: MORE INFORMATION

Citation

Hoffman MM, Birney E. 2010.
An effective model for natural selection in promoters.
Genome Res. 20(5):685-692.

HKA 20100709 – Statistical Test for Natural Selection

HKA 20100709

:: DESCRIPTION

HKA is a computer program that carries out the widely used statistical test for natural selection.This program can handle very large numbers of loci and sample sizes, and conducts tests via coalescent simulation as well as by the conventional chi square approximation.   The simulations can also be used to conduct other tests of natural selection, including tests of Tajima’s D statistic (1989) and the D statistic of Fu and Li (1993).

::DEVELOPER

the Hey lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOsX

:: DOWNLOAD

  HKA

:: MORE INFORMATION

TREESELECT 1.1 – Infer Natural Selection from unusual Population Differentiation

TREESELECT 1.1

:: DESCRIPTION

TreeSelect is a software package for inferring natural selection from unusual population differentiation between closely related populations.

::DEVELOPER

Alkes Price

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 TreeSelect

:: MORE INFORMATION

Citation:

Bhatia et al. (2011),
Genome-wide comparison of African-ancestry populations from CARe and other cohorts reveals signals of natural selection“,
American Journal of Human Genetics, 89(3):368-381.

SFselect – Learning Natural Selection from the Site Frequency Spectrum

SFselect

:: DESCRIPTION

SFselect is a method for classifying genomic regions evolving under positive selection, from those evolving neutrally

::DEVELOPER

Roy Ronen

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 SFselect

:: MORE INFORMATION

Citation

Genetics. 2013 Sep;195(1):181-93. doi: 10.1534/genetics.113.152587. Epub 2013 Jun 14.
Learning natural selection from the site frequency spectrum.
Ronen R, Udpa N, Halperin E, Bafna V.

INSIGHT 1.1 – Inference of Natural Selection from Interspersed Genomically coHerent elemenTs

INSIGHT 1.1

:: DESCRIPTION

INSIGHT is a method for inferring signatures of recent natural selection from patterns of polymorphism and divergence across a collection of short dispersed genomic elements.

::DEVELOPER

Siepel Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

INSIGHT

 :: MORE INFORMATION

Citation

Gronau I, Arbiza L, Mohammed J, Siepel A.
Inference of natural selection from interspersed genomic elements based on polymorphism and divergence.
Mol Biol Evol. 2013 May;30(5):1159-71. doi: 10.1093/molbev/mst019. Epub 2013 Feb 5. PMID: 23386628; PMCID: PMC3697874.

BayeScan 2.1 – Detect Natural Selection from Population-base Genetic Data

BayeScan 2.1

:: DESCRIPTION

BayeScan ( (BAYEsian genome SCAN for outliers) ) aims at identifying candidate loci under natural selection from genetic data, using differences in allele frequencies between populations. BayeScan is based on the multinomial-Dirichlet model. One of the scenarios covered consists of an island model in which subpopulation allele frequencies are correlated through a common migrant gene pool from which they differ in varying degrees. The difference in allele frequency between this common gene pool and each subpopulation is measured by a subpopulation specific FST coefficient. Therefore, this formulation can consider realistic ecological scenarios where the effective size and the immigration rate may differ among subpopulations

::DEVELOPER

Computational and Molecular Population Genetics Lab, University of Bern

:: SCREENSHOTS

:: REQUIREMENTS

:: DOWNLOAD

BayeScan

:: MORE INFORMATION

Citation

Mol Ecol. 2011 Apr;20(7):1450-62. doi: 10.1111/j.1365-294X.2011.05015.x.
Enhanced AFLP genome scans detect local adaptation in high-altitude populations of a small rodent (Microtus arvalis).
Fischer MC, Foll M, Excoffier L, Heckel G.

SnIPRE – Identifying Genes under Natural Selection

SnIPRE

:: DESCRIPTION

SnIPRE (Selection Inference using a Poisson Random Effects model) is a new methodology for identifying genes under natural selection.

:: DEVELOPER

the Bustamante Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • R pacakge

:: DOWNLOAD

   SnIPRE

:: MORE INFORMATION

Citation

PLoS Comput Biol. 2012;8(12):e1002806. doi: 10.1371/journal.pcbi.1002806. Epub 2012 Dec 6.
SnIPRE: selection inference using a Poisson random effects model.
Eilertson KE1, Booth JG, Bustamante CD.

SNPGenie – Estimating Evolutionary parameters to Detect Natural Selection using pooled NGS data

SNPGenie

:: DESCRIPTION

SNPGenie is a program to estimate evolutionary parameters from pooled next-generation sequencing (NGS) data.

::DEVELOPER

Chase W. Nelson

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • Perl

:: DOWNLOAD

 SNPGenie

:: MORE INFORMATION

Citation

SNPGenie: estimating evolutionary parameters to detect natural selection using pooled next-generation sequencing data.
Nelson CW, Moncla LH, Hughes AL.
Bioinformatics. 2015 Jul 29. pii: btv449.

Is it Chance 1.0 – Is Evolution by Natural Selection “just chance?”

Is it Chance 1.0

:: DESCRIPTION

Is It Chance is a very simple little program that demonstrates the power of natural selection to accumulate favorable variations is anything but “mere chance.”.Is It Chance pits chance and selection against each other in a race to match a string of random letters to a sentence typed in by the user, and reveals clearly the difference between natural selection and “mere chance.”

::DEVELOPER

The Queller/Strassmann Research Group at Washington University in St. Louis

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows / Mac

:: DOWNLOAD

 Is It Chance

:: MORE INFORMATION

ADAPTSITE 1.6 – Detect Natural Selection at Single Amino Acid Sites

ADAPTSITE 1.6

:: DESCRIPTION

ADAPTSITE is a method of detecting positive and negative selection at single codon sites.

::DEVELOPER

Yoshiyuki Suzuki

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOSX
  • C Compiler

:: DOWNLOAD

 ADAPTSITE

:: MORE INFORMATION

Citation:

Suzuki Y. and Gojobori T. (1999)
A method for detecting positive selection at single amino acid sites.
Mol. Biol. Evol. 16:1315-1328.