HumanNet v2 – Human Gene Networks for Disease Research

HumanNet v2

:: DESCRIPTION

HumanNet is a human functional gene network by integrating diverse types of omics data using Bayesian statistics framework and demonstrated its ability to retrieve disease genes.

::DEVELOPER

Network Biomedicine Laboratory at Yonsei University, Korea

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

HumanNet v2: human gene networks for disease research.
Hwang S, Kim CY, Yang S, Kim E, Hart T, Marcotte EM, Lee I.
Nucleic Acids Res. 2019 Jan 8;47(D1):D573-D580. doi: 10.1093/nar/gky1126.

PEDDY v0.4.3 – Detect Sample Mixups in Family based studies of Disease

PEDDY v0.4.3

:: DESCRIPTION

PEDDY is a software package to identify and facilitate the remediation of such errors via interactive visualizations and reports comparing the stated sex, relatedness, and ancestry to what is inferred from the individual genotypes derived from whole-genome (WGS) or whole-exome (WES) sequencing.

::DEVELOPER

The Quinlan Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

PEDDY

:: MORE INFORMATION

Citation:

Am J Hum Genet. 2017 Mar 2;100(3):406-413. doi: 10.1016/j.ajhg.2017.01.017. Epub 2017 Feb 9.
Who’s Who? Detecting and Resolving Sample Anomalies in Human DNA Sequencing Studies with Peddy.
Pedersen BS, Quinlan AR.

HDMP – Human Disease-related miRNA Prediction

HDMP

:: DESCRIPTION

HDMP can predict the disease-related miRNA candidates for 18 human diseases.

::DEVELOPER

NClab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

PLoS One. 2013 Aug 8;8(8):e70204. doi: 10.1371/journal.pone.0070204. eCollection 2013.
Prediction of microRNAs associated with human diseases based on weighted k most similar neighbors.
Xuan P, Han K, Guo M, Guo Y, Li J, Ding J, Liu Y, Dai Q, Li J, Teng Z, Huang Y.

APSampler 3.6.1 – Use Monte Carlo Markov Chain for Identifying of Genetic Background of Complex Diseases

APSampler 3.6.1

:: DESCRIPTION

APSampler is a tool that allows multi-locus and multi-level association analysis of genotypic and phenotypic data. The goal is to find the allelic sets (patterns) that are associated with phenotype. The main difficulty of such a task is, given the multiple loci and multiple alleles, the number of all possible classifiers tends to be extremely large. Therefore, Monte Carlo Markov Chain method is applied to reduce the space of solutions and sample only from regions where it is likely to find a good classifier. Once a set of classifiers is found, there is a problem to validate the results, and this is done using a number of well known methods. In case of single disease level, the resulting classifier divides the space of healthy and ill individuals, and the result is represented in a classic Fisher table. Odds ratio and Fisher’s p-value are calculated if applicable. Also, Kruskal’s gamma and the corresponding p-value can be calculated. After each pattern in the output is described by a p-values set of different multiple-hypothesis corrections, including permutation tests.

::DEVELOPER

Alexander Favorov.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • WIndows / Linux

:: DOWNLOAD

 APSampler

:: MORE INFORMATION

Citation:

Favorov, A.V. et al.
A Markov chain Monte Carlo technique for identification of combinations of allelic variants underlying complex diseases in humans.
Genetics 171, 2113-2121 (2005).

WAFFECT 1.2 – A package to Simulate Constrained Phenotypes under Disease model H1

WAFFECT 1.2

:: DESCRIPTION

WAFFECT (pronounced ‘double-u affect’ for ‘weighted affectation’) is a package to simulate phenotypic (case or control) datasets under a disease model H1 such that the total number of cases is constant across all the simulations (the constrain in the title).

::DEVELOPER

Gregory Nuel

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows
  • R

:: DOWNLOAD

 WAFFECT

:: MORE INFORMATION

Citation

Hum Hered. 2012;73(2):95-104. doi: 10.1159/000336194. Epub 2012 Mar 28.
Alternative methods for H1 simulations in genome-wide association studies.
Perduca V, Sinoquet C, Mourad R, Nuel G.

LncDisease 1.41 – Predicting lncRNA-disease Associations

LncDisease 1.41

:: DESCRIPTION

LncDisease is a novel computational method and tool to predict the associations between lncRNAs and diseases

::DEVELOPER

the Cui Lab

:: SCREENSHOTS

LncDisease

:: REQUIREMENTS

  • Windows

:: DOWNLOAD

 LncDisease

:: MORE INFORMATION

Citation

LncDisease: a sequence based bioinformatics tool for predicting lncRNA-disease associations.
Wang J, Ma R, Ma W, Chen J, Yang J, Xi Y, Cui Q.
Nucleic Acids Res. 2016 Feb 16. pii: gkw093

BiRW – Reconstruct Disease Phenome-genome Association

BiRW

:: DESCRIPTION

BiRW (Bi-Random Walk)is an algorithm to capture the CBG patterns in the networks for unveiling the associations between the complete collection of disease phenotypes (phenome) and genes.

::DEVELOPER

Rui Kuang 

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / WIndows / MacOsX
  • Matlab

:: DOWNLOAD

 BiRW

:: MORE INFORMATION

Citation

MaoQiang Xie, TaeHyun Hwang and Rui Kuang
Reconstructing Disease Phenome-genome Association by Bi-Random Walk
Bioinformatics (2012)doi: 10.1093/bioinformatics/bts06

multiMiR 1.0.1 – Integration of microRNA-target Interactions along with their Disease and Drug Associations

multiMiR 1.0.1

:: DESCRIPTION

The R package multiMiR is a comprehensive collection of predicted and validated miRNA-target interactions and their associations with diseases and drugs.

::DEVELOPER

Yuanbin Ru at Windber Research Institute & Katerina Kechris at the University of Colorado Denver.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux
  • R

:: DOWNLOAD

 multiMiR

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2014;42(17):e133. doi: 10.1093/nar/gku631. Epub 2014 Jul 24.
The multiMiR R package and database: integration of microRNA-target interactions along with their disease and drug associations.
Ru Y, Kechris KJ, Tabakoff B, Hoffman P, Radcliffe RA, Bowler R, Mahaffey S, Rossi S, Calin GA, Bemis L, Theodorescu D.

Folding@home 7.4.4 – Understand Protein Folding, Misfolding & Related Diseases

Folding@home 7.4.4

:: DESCRIPTION

Folding@home is a distributed computing project — people from throughout the world download and run software to band together to make one of the largest supercomputers in the world. Folding@home’goal is to understand protein folding, misfolding, and related diseases

::DEVELOPER

Folding@home Team

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows / Linux / Mac OsX / PlayStation3 /Android

:: DOWNLOAD

Folding@home

:: MORE INFORMATION

miRPD – miRNA Protein Disease Associations

miRPD

:: DESCRIPTION

miRPD is a web server in which miRNA-Protein-Disease associations are explicitly inferred.

::DEVELOPER

Center for non-coding RNA in Technology and Health (RTH), JensenLab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

 miRPD

:: MORE INFORMATION

Citation

Bioinformatics. 2014 Feb 1;30(3):392-7. doi: 10.1093/bioinformatics/btt677. Epub 2013 Nov 21.
Protein-driven inference of miRNA-disease associations.
Mørk S1, Pletscher-Frankild S, Palleja Caro A, Gorodkin J, Jensen LJ.