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= MotifAdjuster =
= MotifAdjuster =


by Jens Keilwagen, Jan Baumbach, Thomas Kohl and Ivo Grosse.
by Jens Keilwagen, Jan Baumbach, Thomas A. Kohl and Ivo Grosse.


== Description ==
== Description ==
Line 9: Line 9:


== Paper ==
== Paper ==
The paper '''''MotifAdjuster: A tool for computational reassessment of transcription factor binding site annotations''''' has been submitted to [http://genomebiology.com/software/ Genome Biology].
The paper [http://genomebiology.com/2009/10/5/R46/abstract '''''MotifAdjuster: A tool for computational reassessment of transcription factor binding site annotations'''''] has been published in [http://genomebiology.com/ Genome Biology].


== Download ==
== Download ==

Revision as of 07:05, 4 May 2009

This site contains projects that use Jstacs.

MotifAdjuster

by Jens Keilwagen, Jan Baumbach, Thomas A. Kohl and Ivo Grosse.

Description

Valuable binding site annotation data are stored in databases. However, several types of errors can, and do, occur in the process of manually incorporating annotation data from scientific literature into these databases. Here, we introduce MotifAdjuster, a software that helps to detect these errors, and we demonstrate its efficacy on public data sets.

Paper

The paper MotifAdjuster: A tool for computational reassessment of transcription factor binding site annotations has been published in Genome Biology.

Download

MotifAdjuster can be downloaded here.

Start instructions

If you have unzipped the archive, you can start the MotifAdjuster by invoking

java -cp ./:./jstacs-1.2.2.jar:./numericalMethods.jar MotifAdjuster <file> <ignoreChar> <length> <fgOrder> <fgEss> <bothStrands> <output> <sigma> <p(no motif)>

In Windows, you have to use ";" instead of ":" in the class path.

The arguments have the following meaning

name comment type

file the location of the data set String
ignoreChar char for comment lines (e.g. for a FastA-file '>') char
length the motif length int
fgOrder the order of the inhomogeneous Markov model that is uses for the motif; 0 yields in a PWM byte
ess the equivalent sample size that is used for the mixture model double >= 0
bothStrands use both strands boolean
output output of the EM boolean
sigma the sigma of the truncated discrete Gaussian distribution double>0
p(no motif) the probability for finding no motif 0<=double<1

DiPoMM

by Jens Keilwagen, Jan Grau, Stefan Posch, Marc Strickert and Ivo Grosse.

Description

Transcription factors are one main component of gene regulation, as they activate or repress gene expression by binding to their binding sites. The de-novo discovery of transcription factor binding sites in the promoters of target genes is a challenging problem in bioinformatics, which has not yet been solved satisfactorily. We present DiPoMM, a discriminative de-novo motif discovery tool that models existing positional preferences of binding sites and adjusts the length of the motif in the learning process.

Paper

The paper DiPoMM: Discriminative de-novo motif discovery utilizing positional preference has been submitted to ISMB 2009.

Download

DiPoMM can be downloaded here.

Start instructions

Once you have unzipped the archive, you can start DiPoMM e.g. by invoking

java -cp .:jstacs-1.2.2.jar:lib/numericalMethods.jar:lib/bytecode.jar:lib/biojava-live.jar projects.DiPoMM home=path/to/data/directory/ fg=fgfile.txt bg=bgfile.txt init=best-random=100 p-val=1E-4

to search for motifs that are over-represented in path/to/data/directory/fgfile.txt but not in path/to/data/directory/bgfile.txt, initialize DiPoMM with the best from 100 randomly drawn starting values, and search for motif occurrences with a p-value less than 1E-4.

Under Windows, you must use ";" instead of ":" in the class path.

The arguments have the following meaning

name comment type

home the path to the data directory, default = ./ String
ignore the char that is used to mask comment lines in data files, e.g., '>' in a FASTA-file, default = > Character
fg the file name of the foreground data file (the file containing sequences which are expected to contain binding sites of a common motif) String
bg the file name of the background data file String
length the motif length that is used at the beginning, valid range = [1, 50], default = 15 Integer
flankOrder The Markov order of the model for the flanking sequence and the background sequence, valid range = [0, 5], default = 0 Integer
motifOrder The Markov order of the motif model, valid range = [0, 3], default = 0 Integer
bothStrands a switch whether to use both strands or not, default = true Boolean
init the method that is used for initialization, one of 'best-random=<number>', 'enum=<length>', and 'specific=<sequence or file of sequence>' String=[Integer | String]
xml the file name of the xml file the classifier is written to, default = ./classifier.xml String
adjust a switch whether to adjust the motif length, i.e., either to shrink or expand, default = true Boolean
p-val a p-value for predicting binding sites, valid range = [0.0, 1.0], OPTIONAL Double