File: Readme.ELPH

package info (click to toggle)
elph 1.0.1-2
  • links: PTS, VCS
  • area: main
  • in suites: buster
  • size: 296 kB
  • sloc: cpp: 4,142; makefile: 39
file content (115 lines) | stat: -rw-r--r-- 6,175 bytes parent folder | download | duplicates (2)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
// Copyright (c) 2005 Center for Bioinformatics and Computational Biology, University of Maryland

Motif finder program ELPH (Estimated Locations of Pattern Hits)

ELPH is a general-purpose Gibbs sampler for finding motifs in a 
set of DNA or protein sequences.  The program takes as input a set 
containing anywhere from a few dozen to thousands of sequences, 
and searches through them for the most common motif, assuming that 
each sequence contains one copy of the motif. We have used ELPH 
to find patterns such as ribosome binding sites (RBSs) and exon 
splicing enhancers (ESEs).  

There are two ways to run the program:
1. Input is a file in multi FASTA format containing the sequences:
	elph <multi-fasta_file> [options]

   Common usage:
	elph DNAseqs.fasta LEN=5

   The common usage of the program is to use as motif finder a Gibbs 
   site sampler. The program begins by randomly selecting one motif
   element in each sequence. After this initially setup the program
   iteratively runs through the following two steps:
   - predictive update step: one sequence from the input file is selected, 
   beginning with the first sequence and proceeding to the last 
   sequence. The current motif element from the sequence is added too 
   the background and the motif matrix is updated accordingly
   - sampling step: each possible starting position for a motif in the 
   given sequence is assigned a probability of being a motif starting at 
   that position; after that, a motif element is assigned to the sequence
   by performing a weighted sample from all the possible positions.
   These steps are repeated until a local maximum is reached or a fixed 
   maximum number of iterations are made. The Gibbs sampler is restarted
   several times with a different seed in order to avoid trappings into a
   local maximum. Once the motif alignment is found, the posteriori value 
   of the alignment is computed. An optimizing procedure is run to 
   maximize this posteriori value returning the MAP (maximum a posteriori 
   probability) of the motif.
   	
   Several options can be specified:
   LEN=n : n is the length of the searched motif; if the length of the
	motif is not given, the program will ask for it from stdin. 
   ITERNO=n : n represents the maximum number of times that the Gibbs 
      	sampler is restarted in order to avoid trapping into the local
	maximum; default=10.
   MAXLOOP=n : n represents the maximum number of iterations used by the 
	program to compute the local maximum; default = 500.
   SGFNO=n : n is the number of iterations to compute significance of 
	motif (see also the -g option); default = 1000.
   -h : prints a help with the options the program.
   -o <out_file> : write output in <out_file> instead of stdout
   -a : by default the multiFASTA file is considered to contain DNA 
	sequences; if this option is specified the input file would be 
	considered to contain amino acid sequences (this option has not 
	been tested yet!).
   -s <seed> : sets the seed for the random generation.
   -p n : n represents the number of iterations before deciding that 
	the local maximum has been reached; default=20.
   -b : if this options is specified then only matrix frequencies for 
	the background and the motif are printed; i.e. the positions of 
	each motif element within the sequence are not shown.
   -x : normally the output of the program shows the motif elements that
	contributed to the computation of the motif matrix; if the -x
	option is used the output will also show for each sequence those
	positions which give the maximal score by using the computed
	motif matrix (can be different from the motif elements' 
	positions).
   -m <motif> : use the given pattern <motif> to compute its best fit 
	matrix to the data.
   -g : if this option is specified then a significance of the motif
	found is computed by comparing the appearances of the motif 
	elements within the input file to the appearances of the motif
	within a randomly generated file containing sequences of the 
	same lengths as in the input file and with the same residue 
	distribution. The randomly generated file is paired to the 
	input file. Given the motif matrix, motif sampling is performed 
	a number of times (specified by SGFNO), and a probability of 
	occurrence of each motif element is computed in the two paired 
	samples. Two significance tests are used: the Wilcoxon pair test 
	(most reliable) and the student test.
   -d : this option regards the way the significance of the motif is 
	computed; when -v is specified, the probability of occurrence
	of each motif element is estimated from the motif matrix, so
	no there isn't necessary to run the Gibbs sampler SGFNO times;
	this option should accompany the -g option.
   -v : if this option is given then the Gibbs sampler is not used
	anymore, and the motif is computed in a deterministic way which 
	maximizes the MAP (faster).
   -l : if the -m option is specified too, computes the Least Likely
	Consensus (LLC) score for the given motif; this score measure 
	the information content of the motif combined in respect to its
	background rareness.
   -n [0..5] : degree of Markov chain used to generate the random file
        used to test the significance of the motif (default = 2)

2. Input are two files in multi FASTA format: 	
  	elph <multi-fasta_file-1> <multi-fasta_file-2> [options]

   In this case the program computes a motif in <multi-fasta_file-1> 
   and then estimates if the motif is significantly more represented
   in <multi-fasta_file-1> compared to <multi-fasta_file-2>. All the 
   above options for computing the motif can be specified. There is an
   additional option which can only be given to this way of running 
   the program:

  -t <matrix>    : test if there is significant difference between the two 
                   input files for a given motif matrix; <matrix> is the file
                   containing the motif matrix

   -e            : only when an additional file is used to test the significance
                   of the motif: find only the motifs that exactly match the
                   input pattern (-m or -t options)