BEESEM – Binding Energy Estimation on SELEX with Expectation Maximization

BEESEM

:: DESCRIPTION

The BEESEM program is designed for transcription factor binding motif discovery using HT-SELEX data.

::DEVELOPER

Stormo Lab in Department of Genetics, Washington University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

BEESEM

:: MORE INFORMATION

Citation

Bioinformatics. 2017 Aug 1;33(15):2288-2295. doi: 10.1093/bioinformatics/btx191.
BEESEM: estimation of binding energy models using HT-SELEX data.
Ruan S, Swamidass SJ, Stormo GD

RSEM 1.2.22 – RNA-Seq Expression Estimation by Expectation-Maximization

RSEM 1.2.22

:: DESCRIPTION

RSEM (RNA-Seq expression estimation by Expectation-Maximization) estimates gene and isoform expression levels from RNA-Seq data with a statistical model that takes into account reads that map to multiple positions.

::DEVELOPER

Colin DeweyBo Li

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 RSEM

:: MORE INFORMATION

Citation

Li, B. and Dewey, C. N.
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome.
BMC Bioinformatics 2011, 12:323

AREM 1.0.1 – Aligning Short Reads from ChIP-sequencing by Expectation Maximization

AREM 1.0.1

:: DESCRIPTION

AREM (Aligning reads by Expectation-Maximization) is a peak caller for ChIP-Seq experiments that robustly handles short reads with *multiple* possible mappings.

::DEVELOPER

CBCL Lab (Computational Biology and Computational Learning) @ UCI

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • C Compiler

:: DOWNLOAD

  AREM

:: MORE INFORMATION

Citation:

J Comput Biol. 2011 Nov;18(11):1495-505. Epub 2011 Oct 28.
AREM: aligning short reads from ChIP-sequencing by expectation maximization.
Newkirk D, Biesinger J, Chon A, Yokomori K, Xie X.

G-to-A – Hypermutation Filter Generation using Expectation Maximization

G-to-A

:: DESCRIPTION

G-to-A hypermutation filter generation using expectation maximization. Given a table of aggregated values and a set of preliminary filter constants, this program will construct a set of frequency distributions characterizing the hypermutated and non-hypermutated sequence subsets.

::DEVELOPER

Stanford HIVDB Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • Perl

:: DOWNLOAD

 G-to-A

:: MORE INFORMATION

Citation

A classification model for G-to-A hypermutation in hepatitis B virus ultra-deep pyrosequencing reads.
Reuman EC, Margeridon-Thermet S, Caudill HB, Liu T, Borroto-Esoda K, Svarovskaia ES, Holmes SP, Shafer RW.
Bioinformatics. 2010 Dec 1;26(23):2929-32. doi: 10.1093/bioinformatics/btq570.