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/farntrans5.s
ALPHABET= ACDEFGHIKLMNPQRSTVWY
Sequence name            Weight Length  Sequence name            Weight Length  
-------------            ------ ------  -------------            ------ ------  
RAM1_YEAST               1.0000    431  PFTB_RAT                 1.0000    437  
BET2_YEAST               1.0000    325  RATRABGERB               1.0000    331  
CAL1_YEAST               1.0000    376  

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/farntrans5.s -mod zoops -protein -nmotifs 2 

model:  mod=         zoops    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=        5    wnsites=       0.8
theta:  prob=            1    spmap=         pam    spfuzz=        120
em:     prior=       megap    b=            9500    maxiter=        50
        distance=    1e-05
data:   n=            1900    N=               5

sample: seed=            0    seqfrac=         1
Dirichlet mixture priors file: prior30.plib
Letter frequencies in dataset:
A 0.061 C 0.037 D 0.062 E 0.061 F 0.044 G 0.075 H 0.030 I 0.053 K 0.051 
L 0.114 M 0.021 N 0.034 P 0.041 Q 0.038 R 0.041 S 0.078 T 0.046 V 0.057 
W 0.018 Y 0.041 
Background letter frequencies (from dataset with add-one prior applied):
A 0.061 C 0.037 D 0.061 E 0.060 F 0.044 G 0.075 H 0.030 I 0.053 K 0.051 
L 0.113 M 0.021 N 0.034 P 0.041 Q 0.039 R 0.041 S 0.078 T 0.046 V 0.057 
W 0.018 Y 0.041 

P N
MOTIF 1     width = 32     sites = 5     llr = 352     E-value = 2.1e-031

SimplifiedA::::::::::::::2::::2:::22:24:::2
pos.-specificC:::::::2::2::::::a::::2:::::::::
probabilityD::22:::::::::::a::::::::::::::::
matrixE::26::::::22::::::::::::::::::2:
F::2:::6::::::2::::::6:::::::::::
G::::8a::a:::::::4::::::24:::::4:
H:::::::::::::::::::::::::::2::2:
I::::::::::::::::::::::::::::64::
K::::::::::::a::::::::::::::2:::2
L24::::4::::::8:::::::::6:28:46::
M2:::::::::::::::::::::::::::::::
N:::::::4:::6::::::::::::2:::::::
P::4:::::::4:::4::::::::::::2::::
Q62:::::4::::::::::::::2:::::::::
R:2::2::::a:::::::::::::::::::::6
S:2:2::::::22:::::::8::::28::::::
T::::::::::::::::2:::::::::::::2:
V::::::::::::::4:4:::::6:::::::::
W::::::::::::::::::::4a::::::::::
Y::::::::::::::::::a:::::::::::::
bits 5.8 
5.2 
4.6    
4.0       
Information 3.5            
content 2.9                        
(101.7 bits)2.3                               
1.7                                
1.2                                
0.6                                
0.0
Multilevel QLPEGGFNGRPNKLPDGCYSFWVLGSLAILGR
consensus LQDDRLQCEFVVAWCAALAHLIEA
sequence MRESCESATQGNKHK
SFSSPT
NAME   START P-VALUE    SITES 
BET2_YEAST2196.77e-34 EEIGWWLCERQLPEGGLNGRPSKLPDVCYSWWVLSSLAIIGRLDWINYEKLT
RATRABGERB2231.47e-32 DLLGWWLCERQLPSGGLNGRPEKLPDVCYSWWVLASLKIIGRLHWIDREKLR
PFTB_RAT2823.39e-29 SLLQWVTSRQMRFEGGFQGRCNKLVDGCYSFWQAGLLPLLHRALHAQGDPAL
CAL1_YEAST2715.02e-27 FESELNASYDQSDDGGFQGRENKFADTCYAFWCLNSLHLLTKDWKMLCQTEL
RAM1_YEAST2925.02e-27 KLLEWSSARQLQEERGFCGRSNKLVDGCYSFWVGGSAAILEAFGYGQCFNKH

Motif 1 block diagrams

NameLowest
p-value
   Motifs
BET2_YEAST 6.8e-34

1
RATRABGERB 1.5e-32

1
PFTB_RAT 3.4e-29

1
CAL1_YEAST 5e-27

1
RAM1_YEAST 5e-27

1
SCALE
| | | | | | | | | | | | | | | | |
1 25 50 75 100 125 150 175 200 225 250 275 300 325 350 375 400

Motif 1 in BLOCKS format


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


Motif 1 position-specific probability matrix






Time  0.51 secs.


P N
MOTIF 2     width = 50     sites = 4     llr = 429     E-value = 3.0e-023

SimplifiedA:::::::3::a33353::::::3:::::53:::3::::::::::::::3:
pos.-specificC:::::::::8:::::::::::::::::::::::::a::::::::35::::
probabilityD:::a:::::::::::::::::3:3::3:::3::::::::5:::::::::3
matrixE:a:::::::::::::::::3::35::8:::5::::::::5:::::::::8
F::::::5:3::::::::::::::::3:::::5::::::5:::8:::::::
Ga:::::3:::::::::::5::::::::5::::::::::::aa:a3::5::
H:::::::::::::::::::::::::::::::::::::::::::::::3::
I::3::::3:::::3::3::53::3:::::3::3:::::::::3:::::::
K:::::::::::::::::::3:::::::3:::::33:::::::::::::::
L:::::::::::5::5:88::8:::5::::3::35::::::::::3:::::
M::::::::::::::::::::::::::::::::::::3:::::::::::3:
N::::::::::::::::::5:::::3:::::3:::3::a:::::::::3::
P::::::::::::::::::::::3::::3::::::::::::::::::a:::
Q::::::::::::::::::::::::::::::::::::5:::::::::::::
R:::::a::::::::::::::::::::::::::::3::::::::::3::::
S::::::33::::8::5::::::3:::::::::::3:::::::::3:::3:
T::::8::::3:::3:3:3:::8:::3::3:::::::::::::::::::::
V::8:3::3:::3:3::::::::::35::33::5::::::::::::3::3:
W:::::::::::::::::::::::::::::::3::::::3:::::::::::
Y::::::::8::::::::::::::::::::::3::::3:3:::::::::::
bits 5.8
5.2
4.6    
4.0         
Information 3.5                       
content 2.9                                   
(154.9 bits)2.3                                                
1.7                                                  
1.2                                                  
0.6                                                  
0.0
Multilevel GEVDTRFAYCALSAASLLGILTAELVEGAAEFVLKCQNFDGGFGCCPGAE
consensus IVGIFTAAILAITNEIDEDNFDKTIDWIANMWEIGRHMD
sequence SSVTTKPIVTPVLNYLKRYYLVNS
VVSVSSV
NAME   START P-VALUE    SITES 
RAM1_YEAST2067.91e-45 NGGFKTCLEVGEVDTRGIYCALSIATLLNILTEELTEGVLNYLKNCQNYEGGFGSCPHVDEAHGGYTFCA
BET2_YEAST1322.06e-44 EDGSFQGDRFGEVDTRFVYTALSALSILGELTSEVVDPAVDFVLKCYNFDGGFGLCPNAESHAAQAFTCL
PFTB_RAT1979.82e-44 PDGSFLMHVGGEVDVRSAYCAASVASLTNIITPDLFEGTAEWIARCQNWEGGIGGVPGMEAHGGYTFCGL
RATRABGERB1397.47e-43 EDGSFAGDIWGEIDTRFSFCAVATLALLGKLDAINVEKAIEFVLSCMNFDGGFGCRPGSESHAGQIYCCT

Motif 2 block diagrams

NameLowest
p-value
   Motifs
RAM1_YEAST 7.9e-45

2
BET2_YEAST 2.1e-44

2
PFTB_RAT 9.8e-44

2
RATRABGERB 7.5e-43

2
SCALE
| | | | | | | | | | | | | | | | |
1 25 50 75 100 125 150 175 200 225 250 275 300 325 350 375 400

Motif 2 in BLOCKS format


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


Motif 2 position-specific probability matrix






Time  1.03 secs.


P N
SUMMARY OF MOTIFS


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

NameCombined
p-value
   Motifs
RAM1_YEAST 9.17e-64

2
1
PFTB_RAT 8.05e-65

1
2
1
1
BET2_YEAST 1.88e-70

2
2
1
1
RATRABGERB 1.49e-67

2
2
1
1
CAL1_YEAST 1.62e-25

2
1
1
SCALE
| | | | | | | | | | | | | | | | | |
1 25 50 75 100 125 150 175 200 225 250 275 300 325 350 375 400 425

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:


Go to top