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 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=            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 = 30     sites = 24     llr = 854     E-value = 2.2e-094

SimplifiedA:::2::::1:2:11:1:1413314:1:1::
pos.-specificC::::1::::::::2::132::::1::::::
probabilityD::::21::11:6::::::::::::::2:13
matrixE::::::1114::::::::::::::::1111
F:16:::::::::111:31::::::::::1:
G861241:42:::221:::2:22::::3::1
H::::::::11:4::2::::::::::::::1
I::1:::1::::::::1:::1:1::42:11:
K:::::1::3::::::::::::::::::2::
L::21:::::2::31::1::5::9:55::4:
M::::::::::::::::::::::::::::::
N:1:1:::21:::::::::::1:::::1:::
P::::::5:::2:::::::::::::::::::
Q:::1::::1:::::1:::::1:::::::::
R:::::3:::::::2:::::::::::::1::
S:1:2::::::2:::13:1::32:3::12::
T::::::::::::2::5:1:1:1:1:1:::1
V:::::::1::3:2:::::12::::1:::::
W::::::::::::::::13::::::::::::
Y:::::::::::::14:31:::::::1::::
bits 5.8
5.2
4.6
4.0
Information 3.5 
content 2.9   
(51.4 bits)2.3         
1.7                  
1.2                              
0.6                              
0.0
Multilevel GGFGGRPGKEVDLCYTFCALAALALLGSLD
consensus LAHHSYWCVSSSI
sequence PG
NAME   START P-VALUE    SITES 
BET2_YEAST2237.28e-22 WWLCERQLPEGGLNGRPSKLPDVCYSWWVLSSLAIIGRLDWINYEKLTEF
RATRABGERB2276.18e-21 WWLCERQLPSGGLNGRPEKLPDVCYSWWVLASLKIIGRLHWIDREKLRSF
CAL1_YEAST2759.17e-20 LNASYDQSDDGGFQGRENKFADTCYAFWCLNSLHLLTKDWKMLCQTELVT
PFTB_RAT2371.15e-19 EWIARCQNWEGGIGGVPGMEAHGGYTFCGLAALVILKKERSLNLKSLLQW
PFTB_RAT1384.30e-19 QFLELCQSPDGGFGGGPGQYPHLAPTYAAVNALCIIGTEEAYNVINREKL
RATRABGERB1797.36e-19 EFVLSCMNFDGGFGCRPGSESHAGQIYCCTGFLAITSQLHQVNSDLLGWW
RATRABGERB1318.19e-19 AYVQSLQKEDGSFAGDIWGEIDTRFSFCAVATLALLGKLDAINVEKAIEF
BET2_YEAST1722.10e-18 DFVLKCYNFDGGFGLCPNAESHAAQAFTCLGALAIANKLDMLSDDQLEEI
RATRABGERB2761.43e-17 FILACQDEETGGFADRPGDMVDPFHTLFGIAGLSLLGEEQIKPVSPVFCM
BET2_YEAST1243.41e-17 SFIRGNQLEDGSFQGDRFGEVDTRFVYTALSALSILGELTSEVVDPAVDF
RAM1_YEAST2475.00e-17 YLKNCQNYEGGFGSCPHVDEAHGGYTFCATASLAILRSMDQINVEKLLEW
BET2_YEAST2726.64e-17 FILKCQDEKKGGISDRPENEVDVFHTVFGVAGLSLMGYDNLVPIDPIYCM
RAM1_YEAST1451.27e-16 VKLFTISPSGGPFGGGPGQLSHLASTYAAINALSLCDNIDGCWDRIDRKG
PFTB_RAT2863.17e-16 WVTSRQMRFEGGFQGRCNKLVDGCYSFWQAGLLPLLHRALHAQGDPALSM
RAM1_YEAST2963.47e-16 WSSARQLQEERGFCGRSNKLVDGCYSFWVGGSAAILEAFGYGQCFNKHAL
PFTB_RAT3484.30e-15 YILMCCQCPAGGLLDKPGKSRDFYHTCYCLSGLSIAQHFGSGAMLHDVVM
RATRABGERB832.40e-14 VFIKSCQHECGGVSASIGHDPHLLYTLSAVQILTLYDSIHVINVDKVVAY
PFTB_RAT1892.81e-14 QYLYSLKQPDGSFLMHVGGEVDVRSAYCAASVASLTNIITPDLFEGTAEW
BET2_YEAST737.78e-14 FVLSCWDDKYGAFAPFPRHDAHLLTTLSAVQILATYDALDVLGKDRKVRL
CAL1_YEAST2051.14e-13 LLGYIMSQQCYNGAFGAHNEPHSGYTSCALSTLALLSSLEKLSDKFKEDT
RAM1_YEAST1981.33e-13 WLISLKEPNGGFKTCLEVGEVDTRGIYCALSIATLLNILTEELTEGVLNY
RAM1_YEAST3493.52e-13 ILYCCQEKEQPGLRDKPGAHSDFYHTNYCLLGLAVAESSYSCTPNDSPHN
CAL1_YEAST3275.47e-13 LLDRTQKTLTGGFSKNDEEDADLYHSCLGSAALALIEGKFNGELCIPQEI
BET2_YEAST243.11e-10 RYIESLDTNKHNFEYWLTEHLRLNGIYWGLTALCVLDSPETFVKEEVISF

Motif 1 block diagrams

NameLowest
p-value
   Motifs
BET2_YEAST 3.1e-10

1
1
1
1
1
1
RATRABGERB 2.4e-14

1
1
1
1
1
CAL1_YEAST 1.1e-13

1
1
1
PFTB_RAT 4.3e-15

1
1
1
1
1
RAM1_YEAST 1.3e-13

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

Motif 1 in BLOCKS format


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


Motif 1 position-specific probability matrix






Time  1.81 secs.


P N
MOTIF 2     width = 14     sites = 21     llr = 376     E-value = 3.1e-019

SimplifiedA::::111::::1::
pos.-specificC::::::::::::61
probabilityD12:11::1::::::
matrixE1::61::2::::::
F1:::::::5:::::
G::::1:::::::::
H::::::::::::::
I5::::12::4::::
K::313:::::11:1
L11:::53::24:1:
M::::::::::::::
N:4::::::::::::
P::::::::::::::
Q::1::::2:::::6
R::1:::1::::11:
S::::1::1:114::
T::1::1::::::::
V:12::21::3::::
W::::::::2:::::
Y::::::::3:::::
bits 5.8
5.2
4.6
4.0
Information 3.5 
content 2.9   
(25.8 bits)2.3     
1.7         
1.2              
0.6              
0.0
Multilevel INKEKLLEFILSCQ
consensus YV
sequence WL
NAME   START P-VALUE    SITES 
BET2_YEAST2542.24e-13 SLAIIGRLDWINYEKLTEFILKCQDEKKGGISDR
RATRABGERB2581.30e-12 SLKIIGRLHWIDREKLRSFILACQDEETGGFADR
RATRABGERB1624.20e-12 TLALLGKLDAINVEKAIEFVLSCMNFDGGFGCRP
RATRABGERB669.60e-12 VMDLMGQLHRMNKEEILVFIKSCQHECGGVSASI
RAM1_YEAST2785.08e-11 SLAILRSMDQINVEKLLEWSSARQLQEERGFCGR
CAL1_YEAST1905.01e-10 CRSKEDFDEYIDTEKLLGYIMSQQCYNGAFGAHN
BET2_YEAST556.90e-10 ALCVLDSPETFVKEEVISFVLSCWDDKYGAFAPF
RATRABGERB1141.57e-09 ILTLYDSIHVINVDKVVAYVQSLQKEDGSFAGDI
PFTB_RAT1722.34e-09 IIGTEEAYNVINREKLLQYLYSLKQPDGSFLMHV
RAM1_YEAST3304.59e-09 ILEAFGYGQCFNKHALRDYILYCCQEKEQPGLRD
CAL1_YEAST1261.65e-08 LRDYEYFETILDKRSLARFVSKCQRPDRGSFVSC
PFTB_RAT2681.65e-08 ALVILKKERSLNLKSLLQWVTSRQMRFEGGFQGR
PFTB_RAT2201.65e-08 VASLTNIITPDLFEGTAEWIARCQNWEGGIGGVP
RAM1_YEAST2292.54e-08 IATLLNILTEELTEGVLNYLKNCQNYEGGFGSCP
PFTB_RAT3304.58e-08 DPALSMSHWMFHQQALQEYILMCCQCPAGGLLDK
CAL1_YEAST2395.86e-08 LLSSLEKLSDKFKEDTITWLLHRQVSSHGCMKFE
PFTB_RAT1211.52e-07 LELLDEPIPQIVATDVCQFLELCQSPDGGFGGGP
CAL1_YEAST3621.91e-07 IEGKFNGELCIPQEIFNDFSKRCCF
BET2_YEAST1074.34e-07 TYDALDVLGKDRKVRLISFIRGNQLEDGSFQGDR
BET2_YEAST1555.01e-07 ALSILGELTSEVVDPAVDFVLKCYNFDGGFGLCP
RAM1_YEAST1805.78e-07 CDNIDGCWDRIDRKGIYQWLISLKEPNGGFKTCL

Motif 2 block diagrams

NameLowest
p-value
   Motifs
BET2_YEAST 4.3e-07

2
2
2
2
RATRABGERB 1.6e-09

2
2
2
2
RAM1_YEAST 5.8e-07

2
2
2
2
CAL1_YEAST 5.9e-08

2
2
2
2
PFTB_RAT 2.3e-09

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

Motif 2 in BLOCKS format


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


Motif 2 position-specific probability matrix






Time  2.92 secs.


P N
SUMMARY OF MOTIFS


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

NameCombined
p-value
   Motifs
RAM1_YEAST 2.14e-20

1
2
1
2
1
2
1
2
1
2
PFTB_RAT 2.44e-21

2
1
2
1
2
1
2
1
2
1
BET2_YEAST 1.02e-27

2
1
2
1
2
1
2
1
1
2
1
RATRABGERB 4.90e-26

2
1
2
1
2
1
1
2
1
CAL1_YEAST 3.16e-22

2
2
1
2
1
1
2
SCALE
| | | | | | | | | | | | | | | | | | |
1 25 50 75 100 125 150 175 200 225 250 275 300 325 350 375 400 425 450

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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