Ancestor v1.1 – Computational Inference of Ancestral Genomes

Ancestor v1.1

:: DESCRIPTION

Ancestors is a web server allowing one to easily and quickly perform the last three steps of the ancestral genome reconstruction procedure.

::DEVELOPER

Abdoulaye Banire Diallo

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Bioinformatics. 2010 Jan 1;26(1):130-1. doi: 10.1093/bioinformatics/btp600. Epub 2009 Oct 22.
Ancestors 1.0: a web server for ancestral sequence reconstruction.
Diallo AB1, Makarenkov V, Blanchette M.

ACG – Inference of Population History from Genetic data

ACG

:: DESCRIPTION

ACG (Ancestral Recombination Graph) is a graphical desktop application that allows genetics researchers to infer properties of a population based on genetic sequences sampled from it.

::DEVELOPER

Brendan O’Fallon (brendan.d.ofallon@aruplab.com).

:: SCREENSHOTS

ACG

:: REQUIREMENTS

  • Linux /Windows / MacOsX
  • Java

:: DOWNLOAD

 ACG

:: MORE INFORMATION

Citation:

ACG: rapid inference of population history from recombining nucleotide sequences.
O’Fallon BD.
BMC Bioinformatics. 2013 Feb 5;14:40. doi: 10.1186/1471-2105-14-40.

MixTreEM – Species Tree Inference Using a Mixture Model

MixTreEM

:: DESCRIPTION

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

::DEVELOPER

Ikram Ullah

:: 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.

D-GEX 1.01 – Deep Learning for Gene Expression Inference

D-GEX 1.01

:: DESCRIPTION

DGEX is a deep learning method to infer the expression of target genes from the expression of landmark genes.

::DEVELOPER

CBCL Lab (Computational Biology and Computational Learning) @ UCI

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows/MacOsX
  • Python

:: DOWNLOAD

 D-GEX

:: MORE INFORMATION

Citation

Gene expression inference with deep learning.
Chen Y, Li Y, Narayan R, Subramanian A, Xie X.
Bioinformatics. 2016 Feb 11. pii: btw074.

Tronco v2.0.0 – Inference of Cancer Progression Models

Tronco v2.0.0

:: 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

the Bimib Lab  – University of Milano – 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.

Infernal 1.1.1 – Inference of RNA Alignment

Infernal 1.1.1

:: DESCRIPTION

Infernal (INFERence of RNA ALignment) is for searching DNA sequence databases for RNA structure and sequence similarities. It is an implementation of a special case of profile stochastic context-free grammars called covariance models (CMs). A CM is like a sequence profile, but it scores a combination of sequence consensus and RNA secondary structure consensus, so in many cases, it is more capable of identifying RNA homologs that conserve their secondary structure more than their primary sequence.

::DEVELOPER

Eddy lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

Infernal

:: MORE INFORMATION

Citation

E. P. Nawrocki, D. L. Kolbe, and S. R. Eddy
Infernal 1.0: Inference of RNA alignments
Bioinformatics 25:1335-1337 (2009), .

hapassoc 1.2-8 – Inference of Trait Associations with SNP Haplotypes and other attributes using the EM Algorithm

hapassoc 1.2-8

:: DESCRIPTION

The hapassoc R package implementing methods described in Burkett et al. (2004) for likelihood inference of trait associations with SNP haplotypes and other attributes using the EM Algorithm.

::DEVELOPER

SFU Statistical Genetics working group

:: REQUIREMENTS

:: DOWNLOAD

 hapassoc

:: MORE INFORMATION

Citation

Burkett et al. (2004)
A note on inference of trait associations with SNP haplotypes and other attributes in generalized linear models.
Hum Hered. 2004;57(4):200-6.

ClonalFrame 1.2 / ClonalFrameML 1.0 – Inference of Bacterial Microevolution using Multilocus Sequence data

ClonalFrame 1.2 / ClonalFrameML 1.0

:: DESCRIPTION

ClonalFrame is a computer package for the inference of bacterial microevolution using multilocus sequence data.In a nutshell, ClonalFrame identifies the clonal relationships between the members of a sample, while also estimating the chromosomal position of homologous recombination events that have disrupted the clonal inheritance.ClonalFrame can be applied to any kind of sequence data, from a single fragment of DNA to whole genomes. It is well suited for the analysis of MLST data, where 7 gene fragments have been sequenced, but becomes progressively more powerful as the sequenced regions increase in length and number up to whole genomes. However, it requires the sequences to be aligned. If you have genomic data that is not aligned, we recommend using Mauve which produces alignment of whole bacterial genomes in exactly the format required for analysis with ClonalFrame.

ClonalFrameML is a software package that performs efficient inference of recombination in bacterial genomes.

::DEVELOPER

Xavier Didelot and Daniel Wilson

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux / Windows / MacOsX

:: DOWNLOAD

 ClonalFrame , ClonalFrameML

:: MORE INFORMATION

Citation

ClonalFrameML: efficient inference of recombination in whole bacterial genomes.
Didelot X, Wilson DJ.
PLoS Comput Biol. 2015 Feb 12;11(2):e1004041. doi: 10.1371/journal.pcbi.1004041.

Didelot and Falush (2007)
Inference of Bacterial Microevolution Using Multilocus Sequence Data
Genetics March 2007 vol. 175 no. 3 1251-1266

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

PIA 1.0.1 – Protein Inference Algorithms

PIA 1.0.1

:: DESCRIPTION

PIA is a toolbox for MS based protein inference and identification analysis.

::DEVELOPER

Medizinisches Proteom-Center, Medical Bioinformatics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 PIA

:: MORE INFORMATION

Citation

PIA: An Intuitive Protein Inference Engine with a Web-Based User Interface.
Uszkoreit J, Maerkens A, Perez-Riverol Y, Meyer HE, Marcus K, Stephan C, Kohlbacher O, Eisenacher M.
J Proteome Res. 2015 Jul 2;14(7):2988-97. doi: 10.1021/acs.jproteome.5b00121