StructHDP 1.1 – Inference of number of Clusters and Population Structure from Admixed Genotype data.

StructHDP 1.1

:: DESCRIPTION

StructHDP is a program for automatically inferring the population structure and number of clusters from a sample of admixed genotype data. It extends the model used by Structure to allow for a potentially infinite number of populations and then chooses the number of populations that best explain the data.

::DEVELOPER

Suyash Shringarpure

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Windows / MacOsX
C++ Compiler

:: DOWNLOAD

StructHDP

:: MORE INFORMATION

Citation

StructHDP: automatic inference of number of clusters and population structure from admixed genotype data Suyash Shringarpure;
Suyash Shringarpure ,Daegun Won; Eric P. Xing
Bioinformatics 2011 27: i324-i332

PSGInfer 1.2.1 – Inference of Alternative Splicing from RNA-Seq data with probabilistic Splice Graphs

PSGInfer 1.2.1

:: DESCRIPTION

PSGInfer (Probabilistic Splice Graph Inference) analyzes RNA-Seq data with probabilistic splice graph models of alternative RNA processing (transcription initiation, splicing, and polyadenylation).

::DEVELOPER

Laura H. LeGault , Colin Dewey

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux

:: DOWNLOAD

PSGInfer

:: MORE INFORMATION

Citation

Laura H. LeGault and Colin N. Dewey. (2013)
Inference of alternative splicing from RNA-Seq data with probabilistic splice graphs.
Bioinformatics. 29(18):2300-2310.

CCLasso – Correlation Inference for Compositional Data through Lasso

CCLasso

:: DESCRIPTION

CCLasso is a novel method based on least squares with ℓ1 penalty to infer the correlation network for latent variables of compositional data from metagenomic data

::DEVELOPER

Fang Huaying (hyfang@pku.edu.cn) , Minghua Deng

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Windows
R

:: DOWNLOAD

CCLasso

:: MORE INFORMATION

Citation

CCLasso: Correlation Inference for Compositional Data through Lasso.
Fang H, Huang C, Zhao H, Deng M.
Bioinformatics. 2015 Jun 4. pii: btv349.

QuasiRecomb 1.2 – Inference of Quasispecies subjected to Recombination

QuasiRecomb 1.2

:: DESCRIPTION

QuasiRecomb is a software of Inference of Quasispecies subjected to Recombination.RNA viruses are present in a single host as a population of different but related strains. This population, shaped by the combination of genetic change and selection, is called quasispecies. Genetic change is due to both point mutations and recombination events. We present a jumping hidden Markov model that describes the generation of the viral quasispecies and a method to infer its parameters by analysing next generation sequencing data. We offer an implementation of the EM algorithm to find maximum a posteriori estimates of the model parameters and a method to estimate the distribution of viral strains in the quasispecies. The model is validated on simulated data, showing the advantage of explicitly taking the recombination process into account, and tested by applying to reads obtained from experimental HIV samples.

::DEVELOPER

the Computational Biology Group (CBG)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows / Mac / Linux
Java

:: DOWNLOAD

QuasiRecomb

:: MORE INFORMATION

Citation

J Comput Biol. 2013 Feb;20(2):113-23. doi: 10.1089/cmb.2012.0232.
Probabilistic inference of viral quasispecies subject to recombination.
Töpfer A, Zagordi O, Prabhakaran S, Roth V, Halperin E, Beerenwinkel N.

REDUCE 1.0 – Optimal Design of Gene Knock-out (KO) for the purpose of Gene Regulatory Network (GRN) Inference

REDUCE 1.0

:: DESCRIPTION

REDUCE (REDuction of UnCertain Edges) is an algorithm for finding the optimal gene KO experiment for inferring directed graphs (digraphs) of gene regulatory network (GRN).

:: DEVELOPER

Chemical and Biological Systems Engineering Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / windows/ MacOsX
MatLab

:: DOWNLOAD

REDUCE

:: MORE INFORMATION

Citation

Optimal design of gene knock-out experiments for gene regulatory network inference.
Ud-Dean SM, Gunawan R.
Bioinformatics. 2015 Nov 14. pii: btv672

Hieranoid 2.0 – Hierarchical Orthology Inference

Hieranoid 2.0

:: DESCRIPTION

Hieranoid is an orthology inference method using a hierarchical approach. Hieranoid performs pairwise orthology analysis using InParanoid at each node in a guide tree as it progresses from its leaves to the root. This concept reduces the total runtime complexity from a quadratic to a linear function of the number of species.

::DEVELOPER

Sonnhammer Bioinformatics Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux

:: DOWNLOAD

Hieranoid

:: MORE INFORMATION

Citation

J Mol Biol. 2013 Jun 12;425(11):2072-2081. doi: 10.1016/j.jmb.2013.02.018.
Hieranoid: hierarchical orthology inference.
Schreiber F, Sonnhammer ELL.

TWIGS – Three-Way module Inference via Gibbs Sampling

TWIGS

:: DESCRIPTION

TWIGS is a tool for advanced analysis of three-way data (e.g., patient-gene-time in gene expression or subject-voxel-time in fMRI). TWIGS identifies both core modules that appear in multiple patients and patient-specific augmentations of these core modules that contain additional genes.

::DEVELOPER

Ron Shamir’s lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows/Linux/ MacOsX
R

:: DOWNLOAD

TWIGS

:: MORE INFORMATION

Citation

A hierarchical Bayesian model for flexible module discovery in three-way time-series data.
Amar D, Yekutieli D, Maron-Katz A, Hendler T, Shamir R.
Bioinformatics. 2015 Jun 15;31(12):i17-i26. doi: 10.1093/bioinformatics/btv228.

lpNet 2.18.0 – Linear Programming Model for Network Inference

lpNet 2.18.0

:: DESCRIPTION

lpNet aims at infering biological networks, in particular signaling and gene networks.

::DEVELOPER

Lars Kaderali

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Windows/ MacOsX
R
BioConductor

:: DOWNLOAD

lpNet

:: MORE INFORMATION

Citation

lpNet: a linear programming approach to reconstruct signal transduction networks.
Matos MR, Knapp B, Kaderali L.
Bioinformatics. 2015 May 29. pii: btv327

SSA 1.0 – Inference of Maximum Likelihood Phylogenetic Trees Using a Stochastic Search Algorithm

SSA 1.0

:: DESCRIPTION

SSA is a program for inferring maximum likelihood phylogenies from DNA sequences. Two versions of the program are available: one which assumes a molecular clock and one which does not make this assumption. The method for searching the space of trees for the ML tree is based on a simulated-annealing type algorithm and is described in the reference above. The program implements Felsenstein’s F84 model of nucleotide substitution and associated sub-models. The program estimates the ML tree and branch lengths, and can optionally estimate the transversion/transversion ratio. Upon termination, the program returns the k trees of highest likelihood found during the search, where k can be set by the user.

::DEVELOPER

Laura S. Kubatko

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux/ Windows

:: DOWNLOAD

SSA

:: MORE INFORMATION

Citation

Salter, L. and D. Pearl. 2001.
Stochastic Search Strategy for Estimation of Maximum Likelihood Phylogenetic Trees,
Systematic Biology 50(1): 7-17.

SSAMK – Inference of Maximum Likelihood Phylogenetic Trees for Morphological Data

SSAMK

:: DESCRIPTION

SSAMK uses a stochastic search algorithm for estimation of maximum likelihood phylogenetic trees for morphological data

::DEVELOPER

Laura S. Kubatko

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux

:: DOWNLOAD

SSAMK

:: MORE INFORMATION

Citation

Syst Biol. 2001 Nov-Dec;50(6):913-25.
A likelihood approach to estimating phylogeny from discrete morphological character data.
Lewis PO.