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

Graham & McNeney Labs

:: 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.

Entropy – Uses an EM algorithm for Haplotype Frequency Estimation

Entropy

:: DESCRIPTION

Entropy is a PERL program that uses an EM algorithm for haplotype frequency estimation. It reads standard linkage format files.

::DEVELOPER

Institute for Clinical Molecular Biology

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Linux/Windows
  • Perl

:: DOWNLOAD

 Entropy

:: MORE INFORMATION

Citation

Hampe J, Schreiber S, Krawczak M (2003).
Entropy-based SNP selection for genetic association studies.
Hum Genet 114(1): 36-43

EXTREME 2.0 – EM algorithm for Motif Discovery

EXTREME 2.0

:: DESCRIPTION

EXTREME is an online EM implementation of the MEME model for fast motif discovery in large ChIP-Seq and DNase-Seq Footprinting data

::DEVELOPER

CBCL Lab (Computational Biology and Computational Learning) @ UCI

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows/MacOsX
  • Java
  • Python

:: DOWNLOAD

   EXTREME

:: MORE INFORMATION

Citation

EXTREME: an online EM algorithm for motif discovery.
Quang D, Xie X.
Bioinformatics. 2014 Mar 10

fdrMotif – Identify Cis-elements by an EM Algorithm Coupled with False Discovery Rate Control

fdrMotif

:: DESCRIPTION

fdrMotif is iterative and alternates between updating the position weight matrix (PWM) and significance testing. It starts with an initial PWM and a set of sequences (e.g., from ChIP experiments). It generates many sets of background (null) sequences under the input sequence probability model. At each model estimation step, fdrMotif determines the number of binding sites in each sequence by performing statistical tests. The FDR in the original dataset is controlled by monitoring the proportion of background subsequences that are declared as binding sites. The PWM is updated using an EM algorithm with two iterative steps (the E and M steps) until convergence. In the E-step, fdrMotif normalizes the sum of the probabilities over all position s in a sequence to the number of binding sites found in the sequence.

::DEVELOPER

Leping Li, Ph.D.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 fdrMotif

:: MORE INFORMATION

Citation:

Bioinformatics. 2008 Mar 1;24(5):629-36.
fdrMotif: identifying cis-elements by an EM algorithm coupled with false discovery rate control.
Li L, Bass RL, Liang Y.

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