Command line Training Set First Motif Summary of Motifs Termination Explanation


Search sequence databases with these motifs using MAST.
Submit these motifs to BLOCKS multiple alignment processor.
Build and use a motif-based hidden Markov model (HMM) using Meta-MEME.


MEME - Motif discovery tool

MEME version 3.0 (Release date: 2004/07/16 05:53:30)

For further information on how to interpret these results or to get a copy of the MEME software please access http://meme.sdsc.edu.

This file may be used as input to the MAST algorithm for searching sequence databases for matches to groups of motifs. MAST is available for interactive use and downloading at http://meme.sdsc.edu.


REFERENCE

If you use this program in your research, please cite:

Timothy L. Bailey and Charles Elkan, "Fitting a mixture model by expectation maximization to discover motifs in biopolymers", Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology, pp. 28-36, AAAI Press, Menlo Park, California, 1994.


TRAINING SET

DATAFILE= /home/meme/meme.3.0.6/tests/lipocalin.s
ALPHABET= ACDEFGHIKLMNPQRSTVWY
Sequence name            Weight Length  Sequence name            Weight Length  
-------------            ------ ------  -------------            ------ ------  
ICYA_MANSE               1.0000    189  LACB_BOVIN               1.0000    178  
BBP_PIEBR                1.0000    173  RETB_BOVIN               1.0000    183  
MUP2_MOUSE               1.0000    180  

COMMAND LINE SUMMARY

This information can also be useful in the event you wish to report a
problem with the MEME software.

command: meme /home/meme/meme.3.0.6/tests/lipocalin.s -mod tcm -protein -nmotifs 2 

model:  mod=           tcm    nmotifs=         2    evt=           inf
object function=  E-value of product of p-values
width:  minw=            8    maxw=           50    minic=        0.00
width:  wg=             11    ws=              1    endgaps=       yes
nsites: minsites=        2    maxsites=       25    wnsites=       0.8
theta:  prob=            1    spmap=         pam    spfuzz=        120
em:     prior=       megap    b=            4515    maxiter=        50
        distance=    1e-05
data:   n=             903    N=               5

sample: seed=            0    seqfrac=         1
Dirichlet mixture priors file: prior30.plib
Letter frequencies in dataset:
A 0.072 C 0.029 D 0.069 E 0.078 F 0.043 G 0.058 H 0.025 I 0.048 K 0.086 
L 0.087 M 0.018 N 0.053 P 0.032 Q 0.029 R 0.031 S 0.059 T 0.048 V 0.070 
W 0.017 Y 0.050 
Background letter frequencies (from dataset with add-one prior applied):
A 0.072 C 0.029 D 0.068 E 0.077 F 0.043 G 0.057 H 0.026 I 0.048 K 0.086 
L 0.087 M 0.018 N 0.053 P 0.033 Q 0.029 R 0.031 S 0.059 T 0.048 V 0.069 
W 0.017 Y 0.050 

P N
MOTIF 1     width = 28     sites = 3     llr = 196     E-value = 1.5e-004

SimplifiedA:::3::::::::::::33:7:3::3:a:
pos.-specificC::3:7:::::::::::::::::::::::
probabilityD:7::::3::::33:a:::::::::::::
matrixE::::::3::::3::::::::::::7:::
F:::::::3:::::a::::7:::::::::
G::7:::::::::::::::::a:::::::
H3::::::::::::::::::3:::3::::
I:::::::::::::::::::::::::3::
K::::::::7:3::::3:::::3:::::a
L:::::::::::::::3::::::::::::
M:::::::::::::::::::::::::3::
N:::::::::::37::::3::::::::::
P:3:::7:::7::::::::::::::::::
Q::::::::::::::::::::::::::::
R3::3::::3::::::::3::::::::::
S:::::33:::::::::7:::::::::::
T:::::::::::::::::::::3::::::
V::::3::7:37::::::::::::::3::
W:::::::::::::::3::::::a3::::
Y3::3::::::::::::::3::::3::::
bits 5.9 
5.3 
4.7  
4.1     
Information 3.5               
content 2.9                         
(94.0 bits)2.3                            
1.8                            
1.2                            
0.6                            
0.0
Multilevel HDGACPDVKPVDNFDKSAFAGAWHEIAK
consensus RPCRVSEFRVKEDLANYHKWAM
sequence YYSNWRTYV
NAME   START P-VALUE    SITES 
BBP_PIEBR41.01e-31 NVYHDGACPEVKPVDNFDWSNYHGKWWEVAKYPNSVEKYGK
ICYA_MANSE51.13e-28 GDIFYPGYCPDVKPVNDFDLSAFAGAWHEIAKLPLENENQGK
RETB_BOVIN22.52e-21 ERDCRVSSFRVKENFDKARFAGTWYAMAKKDPEGLFLQD

Motif 1 block diagrams

NameLowest
p-value
   Motifs
BBP_PIEBR 1e-31

1
ICYA_MANSE 1.1e-28

1
RETB_BOVIN 2.5e-21

1
SCALE
| | | | | | | |
1 25 50 75 100 125 150 175

Motif 1 in BLOCKS format


to BLOCKS multiple alignment processor.
Motif 1 position-specific scoring matrix


Motif 1 position-specific probability matrix






Time  0.67 secs.


P N
MOTIF 2     width = 42     sites = 2     llr = 218     E-value = 9.9e-004

SimplifiedA:::::5::::::5::::::::::::5::::5:::::::::::
pos.-specificC:::::::::::::::::a::::::::::::::::::::::::
probabilityD:::::::a::::::::::5:5:a:::::5:::::::::::::
matrixE:::::::::::::::::::::5::::::::::::::::::5:
F:5:::::::::::::::::::::::::::5::::::::::::
G::::::::::::::5::::::::::5:::::::::::::::a
H::::::::::::::::::::5:::::a::5::::::::::::
I::::::::::::5a::::::::::::::5:::5:::::::::
K:::::::::a::::::::5::::aa::::::::::5:a::::
L::::a::::::::::::::::::::::::::::a:::::a::
M::::::::::::::::::::::::::::::::::::::::::
N::5:::::5:a:::5:5:::::::::::::::::::::::::
P:5:::::::::::::::::::5::::::::::::::::::::
Q:::::::::::::::::::::::::::5::::::::::::::
R:::::::::::::::::::::::::::::::::::5::::::
S:::::5:::::::::::::::::::::5::::::a:a:::::
T::::::a:::::::::::::::::::::::::::::::::5:
Va::a::::::::::::::::::::::::::5:5:::::a:::
W::5::::::::::::::::::::::::::::a::::::::::
Y::::::::5::a:::a5::a::::::::::::::::::::::
bits 5.9 
5.3   
4.7     
4.1                   
Information 3.5                                 
content 2.9                                          
(157.6 bits)2.3                                          
1.8                                          
1.2                                          
0.6                                          
0.0
Multilevel VFNVLATDNKNYAIGYNCDYDEDKKAHQDFAWILSKSKVLEG
consensus PWSYINYKHPGSIHVVRT
sequence
NAME   START P-VALUE    SITES 
ICYA_MANSE1028.32e-48 FKFGQRVVNLVPWVLATDYKNYAINYNCDYHPDKKAHSIHAWILSKSKVLEGNTKEVVDNVL
BBP_PIEBR983.83e-47 LTYGGVTKENVFNVLSTDNKNYIIGYYCKYDEDKKGHQDFVWVLSRSKVLTGEAKTAVENYL

Motif 2 block diagrams

NameLowest
p-value
   Motifs
ICYA_MANSE 8.3e-48

2
BBP_PIEBR 3.8e-47

2
SCALE
| | | | | | | |
1 25 50 75 100 125 150 175

Motif 2 in BLOCKS format


to BLOCKS multiple alignment processor.
Motif 2 position-specific scoring matrix


Motif 2 position-specific probability matrix






Time  1.32 secs.


P N
SUMMARY OF MOTIFS


Combined block diagrams: non-overlapping sites with p-value < 0.0001

NameCombined
p-value
   Motifs
ICYA_MANSE 3.69e-69

1
2
LACB_BOVIN 5.96e-03

1
BBP_PIEBR 1.26e-71

1
2
RETB_BOVIN 1.82e-18

1
MUP2_MOUSE 5.20e-02

SCALE
| | | | | | | |
1 25 50 75 100 125 150 175

Motif summary in machine readable format.
Stopped because nmotifs = 2 reached.


CPU: chromo


EXPLANATION OF MEME RESULTS

The MEME results consist of:

MOTIFS

For each motif that it discovers in the training set, MEME prints the following information:


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