SpectralTDF – Spectral Representation of Transition Density Functions

SpectralTDF

:: DESCRIPTION

SpectralTDF is a program for computing the transition density function (TDF) of the diffusion approximation of the Wright-Fisher process with general diploid selection and recurrent mutation.

::DEVELOPER

Yun S. Song

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux /MacOsX / WIndows
Java

:: DOWNLOAD

SpectralTDF

:: MORE INFORMATION

Citation

SpectralTDF: transition densities of diffusion processes with time-varying selection parameters, mutation rates, and effective population sizes.
Steinrücken M, Jewett EM, Song YS.
Bioinformatics. 2015 Nov 9. pii: btv627.

miRUPnet – A System Biology Approach to Infer the Function of miRNAs

miRUPnet

:: DESCRIPTION

miRUPnet is a tool to predict the function of a miRNA based on functional enrichment analysis of its upstream interacting context.

::DEVELOPER

the Cui Lab

:: SCREENSHOTS

n/a

:: REQUIREMENTS

Web browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

Qiu C, Wang D, Wang E, Cui Q.
An upstream interacting context based framework for the computational inference of microRNA functions.
Mol Biosyst. 2012 Apr;8(5):1492-8. doi: 10.1039/c2mb05469h. Epub 2012 Feb 23. PMID: 22362105.

PANDA – Protein Function Prediction Using Domain Architecture and Affinity Propagation on the GO Graph

PANDA

:: DESCRIPTION

PANDA (Propagation of Affinity and Domain Architecture) is a web server to predict protein functions in the format of Gene Ontology (GO) terms.

::DEVELOPER

Z. WANG LAB

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

Wang Z, Zhao C, Wang Y, Sun Z, Wang N.
PANDA: Protein function prediction using domain architecture and affinity propagation.
Sci Rep. 2018 Feb 22;8(1):3484. doi: 10.1038/s41598-018-21849-1. PMID: 29472600; PMCID: PMC5823857.

DanQ – A Hybrid Convolutional and Recurrent Neural Network for predicting the function of DNA Sequences

DanQ

:: DESCRIPTION

DanQ is a hybrid convolutional and recurrent neural network model for predicting the function of DNA de novo from sequence.

::DEVELOPER

CBCL Lab (Computational Biology and Computational Learning) @ UCI

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
Python

:: DOWNLOAD

DanQ

:: MORE INFORMATION

Citation

DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences.
Quang D, Xie X.
Nucleic Acids Res. 2016 Apr 15. pii: gkw226.

kinship2 1.8.5 – Pedigree Functions

kinship2 1.8.5

:: DESCRIPTION

kinship2 offers routines to handle family data with a pedigree object.

::DEVELOPER

Statistical Genetics and Genetic Epidemiology Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Windows / MacOsX
R

:: DOWNLOAD

kinship2

:: MORE INFORMATION

Citation

Hum Hered. 2014;78(2):91-3. doi: 10.1159/000363105. Epub 2014 Jul 29.
The kinship2 R package for pedigree data.
Sinnwell JP1, Therneau TM, Schaid DJ.

spkTools 1.43.0 – Collection of Functions to Analyze Microarray Spike-in data

spkTools 1.43.0

:: DESCRIPTION

spkTools contains functions that can be used to compare expression measures on different array platforms.

::DEVELOPER

Matthew N. McCall

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows / MacOsX / Linux
R package
Bioconductor

:: DOWNLOAD

spkTools

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2008 Oct;36(17):e108. doi: 10.1093/nar/gkn430. Epub 2008 Aug 1.
Consolidated strategy for the analysis of microarray spike-in data.
McCall MN, Irizarry RA.

HoloVir 1.0 – Investigating the Diversity and Function of Viruses in Invertebrate Holobionts

HoloVir 1.0

:: DESCRIPTION

HoloVir is a robust and flexible data analysis pipeline that provides an optimised and validated workflow for taxonomic and functional characterisation of viral metagenomes.

::DEVELOPER

CUBE – Bioinformatics and Computational Systems Biology, University of Vienna

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
Python
Perl

:: DOWNLOAD

HoloVir

:: MORE INFORMATION

Citation:

Front Microbiol, 7, 822 2016 Jun 9 eCollection 2016
HoloVir: A Workflow for Investigating the Diversity and Function of Viruses in Invertebrate Holobionts
Patrick W Laffy et al.

genio 1.0.12 – Genetics Input/Output Functions

genio 1.0.12

:: DESCRIPTION

The genio (GENetics I/O) package provides easy-to-use and efficient readers and writers for formats in genetics research.

::DEVELOPER

The Ochoa Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux/ MacOsX / Windows
R

:: DOWNLOAD

genio

:: MORE INFORMATION

INGA 2.0-1 – Protein Function Prediction

INGA 2.0-1

:: DESCRIPTION

INGA (Interaction Network GO Annotator) is a web server for the prediction of protein function.

::DEVELOPER

The BioComputing UP lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web browser

:: DOWNLOAD

:: MORE INFORMATION

Citation:

INGA: protein function prediction combining interaction networks, domain assignments and sequence similarity.
Piovesan D, Giollo M, Leonardi E, Ferrari C, Tosatto SC.
Nucleic Acids Res. 2015 May 27. pii: gkv523.

IntFOLD 5.0 – Integrated Protein Structure and Function Prediction Server

IntFOLD 5.0

:: DESCRIPTION

The IntFOLD server is a novel independent server that integrates several cutting edge methods for the prediction of structure and function from sequence.

::DEVELOPER

McGuffin Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web Browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

IntFOLD: an integrated server for modelling protein structures and functions from amino acid sequences.
McGuffin LJ, Atkins JD, Salehe BR, Shuid AN, Roche DB.
Nucleic Acids Res. 2015 Mar 27. pii: gkv236.

Nucleic Acids Res. 2011 Jul;39(Web Server issue):W171-6. doi: 10.1093/nar/gkr184. Epub 2011 Mar 31.
The IntFOLD server: an integrated web resource for protein fold recognition, 3D model quality assessment, intrinsic disorder prediction, domain prediction and ligand binding site prediction.
Roche DB1, Buenavista MT, Tetchner SJ, McGuffin LJ.

Buenavista, M. T., Roche, D. B., & McGuffin, L. J. (2012)
Improvement of 3D protein models using multiple templates guided by single-template model quality assessment.
Bioinformatics, 28, 1851-1857