Hi-Jack – Pathway-based Inference of Host-pathogen Interactions

Hi-Jack

:: DESCRIPTION

Hi-Jack, a novel computational framework, for inferring pathway-based interactions between a host and a pathogen that relies on the idea of metabolite hijacking.

::DEVELOPER

InfoCloud Research Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / MacOsX / Linux
  • C++ Compiler

:: DOWNLOAD

Hi-Jack

:: MORE INFORMATION

Citation:

Hi-Jack: A novel computational framework for pathway-based inference of host-pathogen interactions.
Kleftogiannis D, Wong L, Archer JA, Kalnis P.
Bioinformatics. 2015 Mar 9. pii: btv138

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.

ANAT 20180308 – Inference and Analysis of Functional Networks of Proteins

ANAT 20180308

:: DESCRIPTION

ANAT (Advanced Network Analysis Tool) , is an all-in-one resource that provides access to up-to-date large-scale physical association data in several organisms, advanced algorithms for network reconstruction, and a number of tools for exploring and evaluating the obtained network models

::DEVELOPER

Prof. Roded Sharan

:: SCREENSHOTS

:: REQUIREMENTS

:: DOWNLOAD

 ANAT

:: MORE INFORMATION

Citation

ANAT: A Tool for Constructing and Analyzing Functional Protein Networks.
N. Yosef, E. Zalckvar, A. D. Rubinstein, M. Homilius, N. Atias, L. Vardi, I. Berman, H. Zur, A. Kimchi, E. Ruppin and R. Sharan
Sci. Signal. 4, pl1 (2011).

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.

MrBayes 3.2.7a – Bayesian Inference of Phylogeny

MrBayes 3.2.7a

:: DESCRIPTION

MrBayes is a program for the Bayesian estimation of phylogeny. Bayesian inference of phylogeny is based upon a quantity called the posterior probability distribution of trees, which is the probability of a tree conditioned on the observations. The conditioning is accomplished using Bayes’s theorem. The posterior probability distribution of trees is impossible to calculate analytically; instead, MrBayes uses a simulation technique called Markov chain Monte Carlo (or MCMC) to approximate the posterior probabilities of trees.

::DEVELOPER

MrBayes Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / MacOsX / Linux

:: DOWNLOAD

MrBayes

:: MORE INFORMATION

Citation:

MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space.
Ronquist F, Teslenko M, van der Mark P, Ayres DL, Darling A, Höhna S, Larget B, Liu L, Suchard MA, Huelsenbeck JP.
Syst Biol. 2012 May;61(3):539-42. doi: 10.1093/sysbio/sys029.

Ronquist F, Huelsenbeck JP.
MrBayes 3: Bayesian phylogenetic inference under mixed models.
Bioinformatics. 2003 Aug 12;19(12):1572-4.

GeneRax 1.0.0 – Maximum Likelihood based Gene Tree Inference

GeneRax 1.0.0

:: DESCRIPTION

GeneRax is a tool for species tree-aware maximum likelihood based gene tree inference under gene duplication, transfer, and loss.

::DEVELOPER

the Exelixis Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOs

:: DOWNLOAD

GeneRax

:: MORE INFORMATION

Citation

Benoit Morel, Alexey M. Kozlov, Alexandros Stamatakis, Gergely Szöllősi.
GeneRax: A tool for species tree-aware maximum likelihood based gene tree inference under gene duplication, transfer, and loss.
bioRxiv, 779066, 2019

RAxML 8.2.12 / RAxML-NG 0.9.0- Sequential and Parallel Maximum Likelihood based inference of large phylogenetic trees

RAxML 8.2.12 / RAxML-NG 0.9.0

:: DESCRIPTION

RAxML (Randomized Axelerated Maximum Likelihood) is a program for sequential and parallel Maximum Likelihood based inference of large phylogenetic trees. It has originally been derived from fastDNAml which in turn was derived from Joe Felsentein’s dnaml which is part of the PHYLIP package.

RAxML-NG is a phylogenetic tree inference tool which uses maximum-likelihood (ML) optimality criterion. Its search heuristic is based on iteratively performing a series of Subtree Pruning and Regrafting (SPR) moves, which allows to quickly navigate to the best-known ML tree. RAxML-NG is a successor of RAxML (Stamatakis 2014) and leverages the highly optimized likelihood computation implemented in libpll (Flouri et al. 2014).

::DEVELOPER

the Exelixis Lab

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • Java

:: DOWNLOAD

  RAxML , RAxML-NG

:: MORE INFORMATION

Citation

Bioinformatics. 2019 Nov 1;35(21):4453-4455. doi: 10.1093/bioinformatics/btz305.
RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference.
Kozlov AM, Darriba D, Flouri T, Morel B, Stamatakis A

Bioinformatics. 2014 May 1;30(9):1312-3. doi: 10.1093/bioinformatics/btu033. Epub 2014 Jan 21.
RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies.
Stamatakis A.

S.A. Berger, D. Krompaß, A. Stamatakis:
Performance, Accuracy and Web-Server for Evolutionary Placement of Short Sequence Reads under maximum-likelihood“.
Systematic Biology 60(3):291-302, 2011.