Cytoprophet 1.0 – A Cytoscape plug-in for Protein and Domain Interaction Networks Inference

Cytoprophet 1.0

:: DESCRIPTION

Cytoprophet is a project developed by the Laboratory for Computational Life Sciences at the Computer Science Department of the University of Notre Dame. It is a tool to help researchers to infer new potential protein (PPI) and domain (DDI) interactions. It is implemented as a Cytoscape plugin, where users input a set of proteins and retrieve a network of plausible protein and domain interactions with a score. Three algorithms are used for the estimation of PPI/DDI: Maximum Specificity Set Cover (MSSC) Approach, Maximum Likelihood Estimation (MLE) and the Sum-Product Algorithm (SPA) for protein networks.

::DEVELOPER

Cytoprophet team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 Cytoprophet

:: MORE INFORMATION

Citation

Chengbang Huang , Faruck Morcos, Simon P. Kanaan, Stefan Wuchty, Danny Z. Chen, and Jesús A. Izaguirre
Predicting Protein-Protein Interactions from Protein Domains Using a Set Cover Approach.
IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol 4 pp. 78-87. Jan-March 2007

Tronco v2.26.0 – Inference of Cancer Progression Models

Tronco v2.26.0

:: DESCRIPTION

Tronco (TRONCO TRanslational ONCOlogy)is an R suite for state-of-the-art algorithms for the reconstruction of causal models of cancer progressions from genomic cross-sectional data.

::DEVELOPER

Data and Computational Biology @ University of Milan – Bicocca.

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Linux/windows/MacOsX
  • R
  • BioConductor
  • rgraphviz

:: DOWNLOAD

 Tronco

:: MORE INFORMATION

Citation

TRONCO: an R package for the inference of cancer progression models from heterogeneous genomic data.
De Sano L, Caravagna G, Ramazzotti D, Graudenzi A, Mauri G, Mishra B, Antoniotti M.
Bioinformatics. 2016 Feb 9. pii: btw035.

CAPRI: Efficient Inference of Cancer Progression Models from Cross-sectional Data.
Ramazzotti D, Caravagna G, Olde-Loohuis L, Graudenzi A, Korsunsky I, Mauri G, Antoniotti M, Mishra B.
Bioinformatics. 2015 May 13. pii: btv296.

MixTreEM – Species Tree Inference Using a Mixture Model

MixTreEM

:: DESCRIPTION

MixTreEM (Mixture of Trees using Expectation Maximization) is a species tree reconstruction method.

::DEVELOPER

the Science for Life Laboratory, Uppsala University,

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 MixTreEM

:: MORE INFORMATION

Citation:

Species Tree Inference Using a Mixture Model.
Ullah I, Parviainen P, Lagergren J.
Mol Biol Evol. 2015 Sep;32(9):2469-82. doi: 10.1093/molbev/msv115.

SIRENE 1.1 – Supervised Inference of REgulatory NEtworks

SIRENE 1.1

:: DESCRIPTION

A MATLAB implementation of SIRENE that allows you in reconstruct missing regulations in a regulatory network given expression data.

::DEVELOPER

Centre for Computational Biology

:: SCREENSHOTS

 N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • MatLab

:: DOWNLOAD

 SIRENE

 :: MORE INFORMATION

Citation

SIRENE: supervised inference of regulatory networks.
Mordelet F, Vert JP.
Bioinformatics. 2008 Aug 15;24(16):i76-82. doi: 10.1093/bioinformatics/btn273

BNFinder 2.1.1 – Bayesian Network Topology Inference

BNFinder 2.1.1

:: DESCRIPTION

BNFinder (Bayesian Network Finder) allows for Bayesian network reconstruction from experimental data. It supports dynamic Bayesian networks and, if the variables are partially ordered, also static Bayesian networks.

::DEVELOPER

CompBio@MIMUW

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • Python

:: DOWNLOAD

 BNFinder

:: MORE INFORMATION

Citation

BNFinder2: Faster Bayesian network learning and Bayesian classification.
Dojer N, Bednarz P, Podsiadlo A, Wilczynski B.
Bioinformatics. 2013 Aug 15;29(16):2068-70. doi: 10.1093/bioinformatics/btt323.

Bioinformatics. 2009 Jan 15;25(2):286-7. doi: 10.1093/bioinformatics/btn505. Epub 2008 Sep 30.
BNFinder: exact and efficient method for learning Bayesian networks.
Wilczyński B, Dojer N.

ANAT 3.0 – Inference and Analysis of Functional Networks of Proteins

ANAT 3.0

:: 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

Signorini LF, Almozlino T, Sharan R.
ANAT 3.0: a framework for elucidating functional protein subnetworks using graph-theoretic and machine learning approaches.
BMC Bioinformatics. 2021 Oct 27;22(1):526. doi: 10.1186/s12859-021-04449-1. PMID: 34706638.

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).

EditR 1.0.8 / MultiEditR 1.1.0 – Multiple Edit Deconvolution by Inference of Traces in R

EditR 1.0.8 / MultiEditR 1.1.0

:: DESCRIPTION

EditR is an algorithm for simple and cost effective measurement of base editing by quantifying Sanger trace fluorescence

MultiEditR is an easy validation method for detecting and quantifying RNA editing from Sanger sequencing.

::DEVELOPER

Moriarity Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • R

:: DOWNLOAD

EditR , MultiEditR

:: MORE INFORMATION

Citation

Kluesner MG, Nedveck DA, Lahr WS, Garbe JR, Abrahante JE, Webber BR, Moriarity BS.
EditR: A Method to Quantify Base Editing from Sanger Sequencing.
CRISPR J. 2018 Jun;1(3):239-250. doi: 10.1089/crispr.2018.0014. PMID: 31021262; PMCID: PMC6694769.

Kluesner MG, Tasakis RN, Lerner T, Arnold A, Wüst S, Binder M, Webber BR, Moriarity BS, Pecori R.
MultiEditR: The first tool for the detection and quantification of RNA editing from Sanger sequencing demonstrates comparable fidelity to RNA-seq.
Mol Ther Nucleic Acids. 2021 Jul 21;25:515-523. doi: 10.1016/j.omtn.2021.07.008. PMID: 34589274; PMCID: PMC8463291.

RIPE 1.1 – Regulatory Network Inference

RIPE 1.1

:: DESCRIPTION

RIPE (Regulatory network Inference from joint Perturbation and Expression data) is a novel three-step method that integrates both perturbation data and steady state gene expression data in order to estimate a regulatory network.

::DEVELOPER

Alexandra Jauhiainen

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/Windows/MacOsX
  • R

:: DOWNLOAD

 RIPE

:: MORE INFORMATION

Citation

PLoS One. 2014 Feb 28;9(2):e82393. doi: 10.1371/journal.pone.0082393. eCollection 2014.
Inferring regulatory networks by combining perturbation screens and steady state gene expression profiles.
Shojaie A1, Jauhiainen A2, Kallitsis M3, Michailidis G

GESTE 2.0 – GEnetic STructure inference based on genetic and Environmental data

GESTE 2.0

:: DESCRIPTION

GESTE (GEnetic STructure inference based on genetic and Environmental data) is a Bayesian method to evaluate the effect that biotic and abiotic environmental factors (geographic distance, language, temperature, altitude, local population sizes, etc.) have on the genetic structure of populations. It can also be used to study spatial population processes, such as range expansions, by simply introducing longitude and latitude as the explanatory variables.

::DEVELOPER

Laboratoire d’Ecologie Alpine

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows

:: DOWNLOAD

 GESTE

:: MORE INFORMATION

Citation

Foll, M., and O.E. Gaggiotti, 2006.
Identifying the environmental factors that determine the genetic structure of Populations.
Genetics 174: 875-891.

SCHISM 1.1.3 – Subclonal Hierarchy Inference from Somatic Mutations

SCHISM 1.1.3

:: DESCRIPTION

SCHISM is a computational tool designed to infer subclonal hierarchy and the tumor evolution from somatic mutations. The inference process involves computational assessment of two fundamental properties of tumor evolution: lineage precedence rule and lineage divergence rule.

::DEVELOPER

Karchin Lab

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

SCHISM

:: MORE INFORMATION

Citation

Niknafs N, Beleva-Guthrie V, Naiman DQ, Karchin R.
SubClonal Hierarchy Inference from Somatic Mutations: Automatic Reconstruction of Cancer Evolutionary Trees from Multi-region Next Generation Sequencing.
PLoS Comput Biol. 2015 Oct 5;11(10):e1004416. doi: 10.1371/journal.pcbi.1004416. PMID: 26436540; PMCID: PMC4593588.