Tag: Selection

EFC-FCBF – Framework for Feature Construction and Selection for Improved Recognition of Antimicrobial Peptides

EFC-FCBF

:: DESCRIPTION

EPC (Evolutionary Feature Construction) is a method for prediction of Antimicrobial Peptides by proposing more complex sequence-based features that are able to capture information about local and distal patterns within a peptide.

::DEVELOPER

Computational Biology lab, George Mason University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • Java
  • BioJava

:: DOWNLOAD

 EFC

:: MORE INFORMATION

Citation

Veltri D, Kamath U, Shehu A.
Improving Recognition of Antimicrobial Peptides and Target Selectivity through Machine Learning and Genetic Programming.
IEEE/ACM Trans Comput Biol Bioinform. 2017 Mar-Apr;14(2):300-313. doi: 10.1109/TCBB.2015.2462364. PMID: 28368808.

Fizzy v1.4 – Feature Selection for Biological Data Formats

Fizzy v1.4

:: DESCRIPTION

Fizzy is a feature subset selection tool that uses FEAST in the background to run feature selection on biological data formats.

::DEVELOPER

Drexel’s EESI Lab.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/ Linux /MacOsX
  • Python

:: DOWNLOAD

 Fizzy

:: MORE INFORMATION

Citation

Fizzy: feature subset selection for metagenomics.
Ditzler G, Morrison JC, Lan Y, Rosen GL.
BMC Bioinformatics. 2015 Nov 4;16:358. doi: 10.1186/s12859-015-0793-8.

FINEMAP 1.4 – Efficient Variable Selection using summary data from Genome-wide Association Studies

FINEMAP 1.4

:: DESCRIPTION

FINEMAP is a computationally efficient program for fine-mapping in genomic regions associated with complex diseases and traits via a shotgun stochastic search algorithm (Hans et al., 2007).

::DEVELOPER

Christian Benner

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • MacOsX / Linux

:: DOWNLOAD

 FINEMAP

:: MORE INFORMATION

Citation

FINEMAP: Efficient variable selection using summary data from genome-wide association studies.
Benner C, Spencer CC, Havulinna AS, Salomaa V, Ripatti S, Pirinen M.
Bioinformatics. 2016 Jan 14. pii: btw018.

Consel 0.20 – Assess Confidence of Phylogenetic Tree Selection

Consel 0.20

:: DESCRIPTION

CONSEL is a program package consists of small programs written in C language. It calculates the probability value (i.e., p-value) to assess the confidence in the selection problem. Although CONSEL is applicable to any selection problem, it is mainly designed for the phylogenetic tree selection. CONSEL does not estimate the phylogenetic tree by itself, but CONSEL does read the output of the other phylogenetic packages, such as Molphy, PAML, PAUP*, TREE-PUZZLE, and PhyML. CONSEL calculates the p-value using several testing procedures; the bootstrap probability, the Kishino-Hasegawa test, the Shimodaira-Hasegawa test, and the weighted Shimodaira-Hasegawa test. In addition to these conventional tests, CONSEL calculates the p-value based on the approximately unbiased test using the multi-scale bootstrap technique.

::DEVELOPER

Hidetoshi Shimodaira

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / WIndows

:: DOWNLOAD

 CONSEL

:: MORE INFORMATION

Citation:

Shimodaira, H. & Hasegawa, M.
CONSEL: for assessing the confidence of phylogenetic tree selection.
Bioinformatics 17, 1246-1247 (2001).

LASSIE – Linear Allele-Specific Selection InferencE

LASSIE

:: DESCRIPTION

ASSIE is a statistical model for inferring selection coefficients associated with coding variants in the human genome.

::DEVELOPER

Siepel Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • Python

:: DOWNLOAD

ASSIE

 :: MORE INFORMATION

Citation

Huang YF, Siepel A.
Estimation of allele-specific fitness effects across human protein-coding sequences and implications for disease.
Genome Res. 2019 Aug;29(8):1310-1321. doi: 10.1101/gr.245522.118. Epub 2019 Jun 27. PMID: 31249063; PMCID: PMC6673719.

SNPPicker 2 – tag SNP Selection Across Multiple Populations

SNPPicker 2

:: DESCRIPTION

SNPPicker is a post-processor to optimize the selection of tag SNPs from common bin-tagging programs. SNPPicker uses a multi-step search strategy in combination with a statistical model to produce optimal genotyping panels. SNPPicker’s algorithm is also designed to optimize tag SNP selection for multi-population panels. It accounts for several assay-specific constraints such as predicted assay failure of SNPs and avoidance of SNPs that are too close.  The latter issue affects one third of all SNPs in the 1000 genomes project pilot 1 data.SNPPicker automates the design of tag SNP genotyping panels by maximizing the likelihood of successfully genotyping the selected SNPs while minimizing the number of tag SNPs to assay. Geno-typing success is a function of two properties:  the genotyping probability of a bin (or cluster of bins) statistically derived from the individual genotyping probability of each SNP; and (for some platforms) the proximity distance between SNPs. The genotyping probabilities currently used by SNPPicker are derived a from pro-spective analysis of the performance of genotyping assay and the probability model can be updated or changed for other platforms. SNP proximity is a strictly enforced constraint

::DEVELOPER

Bioinformatics Program, Division of Biomedical Statistics and Informatics, Mayo Clinic Research

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 SNPPicker

:: MORE INFORMATION

Citation:

BMC Bioinformatics. 2011 May 2;12:129.
SNPPicker: high quality tag SNP selection across multiple populations.
Sicotte H, Rider DN, Poland GA, Dhiman N, Kocher JP.

mRMRe 2.1.0 – Parallelized mRMR Ensemble Feature Selection

mRMRe 2.1.0

:: DESCRIPTION

mRMRe contains a set of function to compute mutual information matrices from continuous, categorical and survival variables. It also contains function to perform feature selection with minimum Redundancy, Maximum Relevance (mRMR) and a new ensemble mRMR technique.

::DEVELOPER

Princess Margaret Bioinformatics and Computational Genomics Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • R

:: DOWNLOAD

 mRMRe

:: MORE INFORMATION

Citation:

Bioinformatics. 2013 Sep 15;29(18):2365-8. doi: 10.1093/bioinformatics/btt383. Epub 2013 Jul 3.
mRMRe: an R package for parallelized mRMR ensemble feature selection.
De Jay N, Papillon-Cavanagh S, Olsen C, El-Hachem N, Bontempi G, Haibe-Kains B.

ModelGenerator 0.851 – Amino Acid & Nucleotide Substitution Model Selection

ModelGenerator 0.851

:: DESCRIPTION

ModelGenerator is a model selection program that selects optimal amino acid and nucleotide substitution models from Fasta or Phylip alignments. ModelGenerator supports 56 nucleotide and 96 amino acid substitution models.

::DEVELOPER

McInerney lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOS
  • Java

:: DOWNLOAD

ModelGenerator

:: MORE INFORMATION

Citation:

Thomas M Keane, Christopher J Creevey ,Melissa M Pentony, Thomas J Naughton and James O McInerney (2006)
Assessment of methods for amino acid matrix selection and their use on empirical data shows that ad hoc assumptions for choice of matrix are not justified,
BMC Evolutionary Biology, 6:29

FastTagger 1.0 – Genome-Wide Tag SNP selection

FastTagger 1.0

:: DESCRIPTION

FastTagger is a software to calculate multi-marker tagging rules and select tag SNPs based on multi-marker LD. FastTagger uses several techniques to reduce running time and memory consumption.

::DEVELOPER

Limsoon Wong Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  •  Linux/ Windows
  • C++ Compiler

:: DOWNLOAD

 FastTagger

:: MORE INFORMATION

Citation:

Guimei Liu, Yue Wang, Limsoon Wong.
FastTagger: An Efficient Algorithm for Genome-Wide Tag SNP selection using multi-marker linkage disequilibrium
BMC Bioinformatics, 11:66, February 2010.

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