1.-Abstract

For understand the transcriptional regulatory mechanism that operate in genes, it's important to realize a mapping of transcription factor binding sites in genes. Now, there are many of these site experimentally identified that can be used to perform a computational pattern-based searches. Motifs are a short and variable regions that will produce over-predictions problems.
We have developed a new program (in Perl language) for deteccion of promoter motif in a DNA sequences. Motifs,are descripted in a Position Weight Matrix (PWM) from TRANSFAC database. Position Weigth Matrix is a motif descriptor. It attempts to capture the intrinsic variability characteristic of sequence patterns. It is usually derived from a set of aligned sequences functionally related.

2.-Biological Problem

The regulation of gene expresion in eukariotes is a complex process that is difficult to understand due to variability of the mechanism involved in and the great number of different actors playing some minor o major role.
How is gene expression regulated?There are several methods used by eukaryotes.

• Altering the rate of transcription of the gene. This is the most important and widely-used strategy and the one we shall examine here.
• However, eukaryotes supplement transcriptional regulation with several other methods:
  • Altering the rate at which RNA transcripts are processed while still within the nucleus.
  • Altering the stability of mRNA molecules; that is, the rate at which they are degraded.
  • Altering the efficiency at which the ribosomes translate the mRNA into a polypeptide.

• exons whose sequence encodes the polypeptide.
• introns that will be removed from the mRNA before it is translated.
• a transcription start site : The start site is where transcription of the gene into mRNA begins. This is where a molecule of RNA polymerase II (pol II) binds. Pol II is a complex of some 10 different proteins.
• a promoter : regulatory region of DNA near the transcription start site(TSS).
  • the basal or core promoter located within about 40 bp of the start site.
  • an "upstream" promoter, which may extend over as many as 200 bp farther upstream.
• enhancers.
• silencers.

3.- The Program

Download it!

Introduction
Promotif will detect promoter motif in a DNA sequence using a PWM
Inputs: Tranfac matrix and fasta sequence
ProMotif compares input PWM and input sequence. It compares every subsequence (with a position numbers like matrix- positions number)of input sequence, whit PWM, and calculate a score.
We can choose the type of PWM that ProMotif use to compute the scores:

• Relative Matrix
• Log-likelihood matrix(whit a priory frequency-0'25-)
• Log-likelihood matrix(frequency in the aligned region)

Requests
matrix_file.mat: should be in Transfac format.
sequence_file.txt: should be in Fasta format.

Basic program ejecution
$./perl ProMotif.pl matrix_file.mat sequence_file.txt

Options

	-c_x.x	:Cutoff value
	-v	:Show information of the processing
	-n_x	:Chose type of matrix-Relative Matrix
			Default matrix: relative	
			x=1 -Log-likelihood matrix(whit a priory frequency-0'25-)
			x=2 -Log-likelihood matrix(frequency in the aligned region)
	-m	:Show information about the matrices
	-s	:Show information about DNA sequences (C+G content, lenght)
	-o	:Makes a HTML Output
	-h	:help

Execution example:
$./perl ProMotif.pl -svc 0.55 matrix_file.mat sequence_file.txt

4.- Test

With the finality of test our program we have designed an exercise. We searched a HNF1alpha motif in a problem sequences. We obtained he HNF1alpha PWM in TRANSFAC database. To compare the results ofproblem sequences, we have maked a negative control using a aleatory sequences obtained whith a program(AleatorySequences) We run ProMotif with these input files:

• HNF1a and TATA box matrix
• problem sequences
• Aleatory sequences

Output for problem sequences		Output for aleatory sequences
treshold	hits	treshold	hits
0.95	1	0.95	0
0.85	10	0.85	0
0.80	33	0.80	5

We compare the problem sequences with aleatory sequences in order to show that the reults of ProMotif aren't due to fate.
If we compare the outputs for the two cases, and each treshold, we can observe a major occurrence number in outputs of problem sequences. For example, in study of aleatory sequences with a tresold of 0.95 and 0'85, we obtained no occurrences, whereas that in problem sequences we have obtained some occurrences.

In conclusion, we can say that ProMotif search specífic motif, and his reults aren't due to fate.

5.- Bibliography
-MatInd and MatInspector: new fast and versatile tools for detection of consensus matches in nucleotide sequence data Quandt K, Frech K, Karas H, Wingender E, Werner T. Nucleic Acids research, 1995, Vol.23, No.23
-TRANSFAC