Tag: Selection

ProtEvol 1.0 – Maximum Likelihood Phylogenetic Inference with Selection on Protein Folding Stability

ProtEvol 1.0

:: DESCRIPTION

The program ProtEvol performs two kinds of computation.

  1. It computes the mean-field site-specific amino acid distributions that have minimal differences with respect to the background distribution and that constraint the average stability of the native state of the protein against both unfolding and misfolding. The program also computes an exchangeability matrix derived from an empirical substitution model or from a mutation model that can be used together with the site-specific distributions for applications in phylogenetic inference.
  2. It simulates protein evolution subject to the constraint of selection on the folding stability of the native state of the protein against both unfolding and misfolding.

::DEVELOPER

Unidad de Bioinformatica CBMSO

:: SCREENSHOTS

N/a

:: REQUIREMENTS

  • Windows

:: DOWNLOAD

  ProtEvol

:: MORE INFORMATION

Citation

Maximum likelihood phylogenetic inference with selection on protein folding stability.
Arenas M, Sánchez-Cobos A, Bastolla U.
Mol Biol Evol. 2015 Apr 2. pii: msv085.

forqs 2015 – Forward-in-time Simulation of Recombination, Quantitative traits, and Selection

forqs 2015

:: DESCRIPTION

forqs is a forward-in-time population genetics simulation that tracks individual haplotype chunks as they recombine each generation. forqs also also models quantitative traits and selection on those traits.

::DEVELOPER

Novembre Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOsX

:: DOWNLOAD

 forqs

 :: MORE INFORMATION

Citation

Bioinformatics. 2014 Feb 15;30(4):576-7. doi: 10.1093/bioinformatics/btt712. Epub 2013 Dec 10.
forqs: forward-in-time simulation of recombination, quantitative traits and selection.
Kessner D1, Novembre J.

rvsel 0.1 – Rare Variants Selection with Sequence Data

rvsel 0.1

:: DESCRIPTION

rvsel is an R package for rare variants selection with sequence data. The most outome-related rare variants are selected within a gene or a genetic region. The selection procedure is based on the power set of the subset of the rare variants.

::DEVELOPER

Wang Lab @ Biostatistics Department

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • R

:: DOWNLOAD

 rvsel

:: MORE INFORMATION

Citation

Bioinformatics. 2014 Apr 22. pii: btu207.
A power set-based statistical selection procedure to locate susceptible rare variants associated with complex traits with sequencing data.
Sun H, Wang S.

PCAdapt 201405 – Detect Genes Targetted by Selection

PCAdapt 201405

:: DESCRIPTION

PCAdapt is a software to detect footprints of local adaptation in population genetics data set.

::DEVELOPER

Nicolas Duforet-Frebourg

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/MacOsX
  • C Compiler

:: DOWNLOAD

 PCAdapt

:: MORE INFORMATION

Citation

Mol Biol Evol. 2014 Jun 3. pii: msu182. [Epub ahead of print]
Genome scans for detecting footprints of local adaptation using a Bayesian factor model.
Duforet-Frebourg N1, Bazin E2, Blum MG3.

H-Clust – Tag SNP Selection

H-Clust

:: DESCRIPTION

H-clust is a simple clustering method that can be used to rapidly identify a set of tag SNP’s based upon genotype data. This method does not require haplotype estimation. H-clust consists of two stages. The first stage uses hierarchical clustering to determine the clusters. In the second stage, the tag SNP is chosen by finding the SNP most correlated with all the other SNPs in the cluster. Optionally, the quality of each SNP can be included in the analysis. In this case, both quality and correlation affect the determination of tag SNPs. The input for H-clust is a genotype matrix using 0,1,2 to denote the number of copies of a particular allele. It then computes the similarity matrix based on Pearson’s correlation between allele counts. The distance between two SNPs is one minus the squared correlation. By default, H-clust uses the “complete linkage” method. Hierarchical clustering can be represented as a dendrogram in which any two SNPs diverge at a height equal to their distance. The clusters are obtained by declaring SNPs to be in the same cluster when they converge before a certain cut-off value. In the H-clust program, this cutoff is 1- hcbound, where hcbound is determined by the user. (This is slightly different in the stepwise version, see below.) The second stage of H-clust finds a tag SNP to represent the cluster. This is done by scoring each SNP based on squared correlation and quality. If multiple SNPs are scored equally, then the one in the middle is chosen as the tag SNP.

::DEVELOPER

The Devlin lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 H-Clust

:: MORE INFORMATION

Citation:

Rinald, Bacanu, Devlin, Sonpar, Wasserman and Roeder.
“Characterization of Multilocus Linkage Disequilibrium”
Genet Epidemiol. 2005 Apr;28(3):193-206.

BoNB 1.2 – Biomarker Selection and Classification from Genome-wide SNP data

BoNB 1.2

:: DESCRIPTION

BoNB (Bag of Naïve Bayes), an algorithm for genetic biomarker selection and subjects classification from the simultaneous analysis of genome-wide SNP data. BoNB is based on the Naïve Bayes classification framework, enriched by three main features: bootstrap aggregating of an ensemble of Naïve Bayes classifiers, a novel strategy for ranking and selecting the attributes used by each classifier in the ensemble and a permutation-based procedure for selecting significant biomarkers, based on their marginal utility in the classification process. BoNB is tested on the Wellcome Trust Case-Control study on Type 1 Diabetes and its performance is compared with the ones of both a standard Naïve Bayes algorithm and HyperLASSO, a penalized logistic regression algorithm from the state-of-the-art in simultaneous genome-wide data analysis.

::DEVELOPER

SYSTEMS BIOLOGY AND BIOINFORMATICS GROUP

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • C++ Compiler

:: DOWNLOAD

 BoNB

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2012;13 Suppl 14:S2. doi: 10.1186/1471-2105-13-S14-S2. Epub 2012 Sep 7.
Bag of Naïve Bayes: biomarker selection and classification from genome-wide SNP data.
Sambo F, Trifoglio E, Di Camillo B, Toffolo GM, Cobelli C.

SSCA – Sequences Selection for the Comparative Approach

SSCA

:: DESCRIPTION

SSCA is an algorithm for selecting combinations of RNA homologous sequences suitable for secondary structure predictions with the comparative approach.

::DEVELOPER

EVRY RNA – IBISC

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2007 Nov 28;8:464.
Predicting RNA secondary structure by the comparative approach: how to select the homologous sequences.
Engelen S1, Tahi F.

MCentridFS – Multi-class Centroid Feature Selection

MCentridFS

:: DESCRIPTION

MCentridFS is a software to systematically identify responsive modules or network biomarkers for classifying multi-phenotypes from high-throughput data.

::DEVELOPER

ChenLab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux/ MacOsX
  • MatLab

:: DOWNLOAD

 MCentridFS

:: MORE INFORMATION

Citation

MCentridFS: a tool for identifying module biomarkers for multi-phenotypes from high-throughput data.
Wen Z, Zhang W, Zeng T, Chen L.
Mol Biosyst. 2014 Nov;10(11):2870-5. doi: 10.1039/c4mb00325j.

RADinitio 1.1.1 – Simulation software for the Selection and Optimization of RADseq Experiments

RADinitio 1.1.1

:: DESCRIPTION

RADinitio is a forward simulator for creating population-level RAD data sets, based on a given reference genome. This in silico RADseq library preparation and sequencing process, allows for the exploration of parameters including restriction enzyme selection, library insert size, PCR duplicate distribution, and sequencing coverage.

DEVELOPER

Cresko labs

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

RADinitio

:: MORE INFORMATION

Citation:

Rivera-Colón AG, Rochette NC, Catchen JM.
Simulation with RADinitio improves RADseq experimental design and sheds light on sources of missing data.
Mol Ecol Resour. 2021 Feb;21(2):363-378. doi: 10.1111/1755-0998.13163. Epub 2020 May 20. PMID: 32275349.

BMGE 1.12 – Selection of Phylogenetic Informative Regions from Multiple Sequence Alignments

BMGE 1.12

:: DESCRIPTION

BMGE (Block Mapping and Gathering using Entropy) is a program that selects regions in a multiple sequence alignment that are suited for phylogenetic inference.

::DEVELOPER

BMGE team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/MacOsX/Linux
  • Java

:: DOWNLOAD

 BMGE

:: MORE INFORMATION

Citation

BMGE (Block Mapping and Gathering with Entropy): a new software for selection of phylogenetic informative regions from multiple sequence alignments.
Criscuolo A, Gribaldo S.
BMC Evol Biol. 2010 Jul 13;10:210. doi: 10.1186/1471-2148-10-210.

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