MEME

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: 2004/07/26 08:17:15)

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

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-suite.org.

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= lipo.fasta
    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 lipo.fasta -mod oops -protein -nmotifs 3

    model:  mod=          oops    nmotifs=         3    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=        5    maxsites=        5    wnsites=       0.8
    theta:  prob=            1    spmap=         pam    spfuzz=        120
    em:     prior=        dmix    b=               0    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 = 19 sites = 5 llr = 187 E-value = 8.9e-006

| Simplified | A |  | : | : | : | : | : | : | : | 2 | 2 | : | 6 | : | 2 | : | : | 2 | : | 8 | :
| pos.-specific | C |  | : | : | : | : | : | : | : | : | : | : | : | : | : | : | : | : | : | : | :
| probability | D |  | : | : | 2 | 2 | : | 8 | : | : | : | : | : | : | : | : | : | : | : | : | :
| matrix | E |  | : | : | 2 | : | : | : | : | 2 | : | : | : | : | 2 | : | : | 4 | : | : | :
|  | F |  | : | : | : | : | 8 | : | : | : | : | 4 | : | : | : | : | : | : | : | : | :
|  | G |  | : | 2 | : | 2 | : | : | : | : | : | : | : | a | : | : | : | : | : | : | :
|  | H |  | : | : | : | : | : | : | : | : | : | : | 2 | : | : | : | 4 | : | : | : | :
|  | I |  | : | : | : | : | : | : | 2 | : | : | 2 | : | : | : | : | : | : | 4 | 2 | :
|  | K |  | : | 2 | 2 | : | : | : | 2 | : | 4 | : | : | : | 2 | : | : | : | : | : | 6
|  | L |  | : | : | : | : | 2 | : | 2 | : | : | : | : | : | : | : | : | : | 2 | : | 2
|  | M |  | : | 2 | : | : | : | : | : | : | : | : | : | : | : | : | : | : | 2 | : | 2
|  | N |  | : | : | 2 | 6 | : | 2 | : | : | 2 | : | 2 | : | : | : | : | : | : | : | :
|  | P |  | 4 | : | : | : | : | : | : | : | : | : | : | : | : | : | : | : | : | : | :
|  | Q |  | : | : | : | : | : | : | : | 2 | : | : | : | : | : | : | : | : | : | : | :
|  | R |  | : | : | 2 | : | : | : | : | : | 2 | : | : | : | : | : | : | : | : | : | :
|  | S |  | : | : | : | : | : | : | : | 4 | : | : | : | : | : | : | : | 2 | : | : | :
|  | T |  | 4 | : | : | : | : | : | : | : | : | : | : | : | 4 | : | : | 2 | : | : | :
|  | V |  | 2 | 4 | : | : | : | : | 2 | : | : | 2 | : | : | : | : | : | : | 2 | : | :
|  | W |  | : | : | : | : | : | : | 2 | : | : | : | : | : | : | a | 2 | : | : | : | :
|  | Y |  | : | : | : | : | : | : | : | : | : | 2 | : | : | : | : | 4 | : | : | : | :
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| Information | 3.5 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| content | 2.9 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| (53.9 bits) | 2.3 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
|  | 1.8 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
|  | 1.2 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
|  | 0.6 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
|  | 0.0 |  |
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| Multilevel |  |  | `P` | `V` | `D` | `N` | `F` | `D` | `I` | `S` | `K` | `F` | `A` | `G` | `T` | `W` | `H` | `E` | `I` | `A` | `K`
| consensus |  |  | `T` | `G` | `E` | `D` | `L` | `N` | `K` | `A` | `A` | `I` | `H` |  | `A` |  | `Y` | `A` | `L` | `I` | `L`
| sequence |  |  | `V` | `K` | `K` | `G` |  |  | `L` | `E` | `N` | `V` | `N` |  | `E` |  | `W` | `S` | `M` |  | `M`
|  |  |  |  | `M` | `N` |  |  |  | `V` | `Q` | `R` | `Y` |  |  | `K` |  |  | `T` | `V` |  |
|  |  |  |  |  | `R` |  |  |  | `W` |  |  |  |  |  |  |  |  |  |  |  |
.
NAME |  |  |  | START |  | P-VALUE |  |  |  |  |  | SITES |
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
ICYA_MANSE |  |  |  | 14 |  | 1.43e-18 |  | `FYPGYCPDVK` |  | `P` | `V` | `N` | `D` | `F` | `D` | `L` | `S` | `A` | `F` | `A` | `G` | `A` | `W` | `H` | `E` | `I` | `A` | `K` |  |  | `LPLENENQGK`
BBP_PIEBR |  |  |  | 13 |  | 9.75e-18 |  | `YHDGACPEVK` |  | `P` | `V` | `D` | `N` | `F` | `D` | `W` | `S` | `N` | `Y` | `H` | `G` | `K` | `W` | `W` | `E` | `V` | `A` | `K` |  |  | `YPNSVEKYGK`
RETB_BOVIN |  |  |  | 11 |  | 1.99e-16 |  | `ERDCRVSSFR` |  | `V` | `K` | `E` | `N` | `F` | `D` | `K` | `A` | `R` | `F` | `A` | `G` | `T` | `W` | `Y` | `A` | `M` | `A` | `K` |  |  | `KDPEGLFLQD`
LACB_BOVIN |  |  |  | 22 |  | 9.62e-15 |  | `CGAQALIVTQ` |  | `T` | `M` | `K` | `G` | `L` | `D` | `I` | `Q` | `K` | `V` | `A` | `G` | `T` | `W` | `Y` | `S` | `L` | `A` | `M` |  |  | `AASDISLLDA`
MUP2_MOUSE |  |  |  | 24 |  | 4.53e-14 |  | `VCVHAEEASS` |  | `T` | `G` | `R` | `N` | `F` | `N` | `V` | `E` | `K` | `I` | `N` | `G` | `E` | `W` | `H` | `T` | `I` | `I` | `L` |  |  | `ASDKREKIED`

Motif 1 block diagrams

Name | Lowest p-value | Motifs
--- | --- | ---
ICYA_MANSE | 1.4e-18 |
| 1 |  |
BBP_PIEBR | 9.7e-18 |
| 1 |  |
RETB_BOVIN | 2e-16 |
| 1 |  |
LACB_BOVIN | 9.6e-15 |
| 1 |  |
MUP2_MOUSE | 4.5e-14 |
| 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  0.47 secs.

P | N

MOTIF 2 width = 20 sites = 5 llr = 195 E-value = 1.3e-004

| Simplified | A |  | : | : | : | : | : | : | : | 2 | : | : | : | : | : | : | 4 | : | 2 | : | : | :
| pos.-specific | C |  | : | : | : | : | : | : | : | : | : | : | : | : | : | : | : | : | : | 2 | : | 6
| probability | D |  | : | 2 | 2 | : | : | : | : | 4 | : | a | : | 2 | : | : | : | : | : | : | : | :
| matrix | E |  | 4 | : | : | : | : | : | : | : | : | : | : | 2 | : | : | : | : | : | : | : | 2
|  | F |  | 2 | : | : | 4 | : | : | : | : | : | : | : | : | : | 4 | : | : | 2 | : | : | :
|  | G |  | : | : | : | : | : | : | : | : | : | : | : | : | : | : | : | : | 2 | : | : | :
|  | H |  | : | : | : | 2 | : | : | : | : | : | : | : | : | : | : | : | : | : | 2 | : | :
|  | I |  | : | : | : | : | : | 4 | 2 | : | : | : | : | : | : | : | 2 | 4 | : | : | : | 2
|  | K |  | : | : | 2 | : | : | : | : | 2 | : | : | : | 6 | 2 | : | : | : | : | : | : | :
|  | L |  | : | 2 | : | : | 2 | : | 6 | : | : | : | : | : | : | : | 4 | 2 | : | : | 2 | :
|  | M |  | : | : | : | : | : | : | : | : | : | : | : | : | : | : | : | 2 | : | : | 2 | :
|  | N |  | 4 | 6 | : | : | 2 | : | : | : | : | : | 2 | : | 6 | : | : | : | 2 | : | 2 | :
|  | P |  | : | : | : | 2 | : | : | 2 | : | : | : | : | : | : | : | : | : | : | : | : | :
|  | Q |  | : | : | : | : | : | : | : | : | : | : | : | : | : | : | : | : | 2 | : | : | :
|  | R |  | : | : | : | : | : | : | : | : | : | : | : | : | : | : | : | : | : | : | : | :
|  | S |  | : | : | : | : | : | : | : | 2 | : | : | : | : | : | : | : | : | : | : | 2 | :
|  | T |  | : | : | 2 | : | 2 | : | : | : | a | : | : | : | 2 | : | : | : | : | : | : | :
|  | V |  | : | : | 4 | 2 | : | 6 | : | : | : | : | : | : | : | : | : | 2 | : | : | : | :
|  | W |  | : | : | : | : | 4 | : | : | : | : | : | : | : | : | : | : | : | : | : | : | :
|  | Y |  | : | : | : | : | : | : | : | : | : | : | 8 | : | : | 6 | : | : | : | 6 | 2 | :
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| bits | 5.9 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
|  | 5.3 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
|  | 4.7 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
|  | 4.1 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| Information | 3.5 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| content | 2.9 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| (56.1 bits) | 2.3 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
|  | 1.8 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
|  | 1.2 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
|  | 0.6 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
|  | 0.0 |  |
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| Multilevel |  |  | `E` | `N` | `V` | `F` | `W` | `V` | `L` | `D` | `T` | `D` | `Y` | `K` | `N` | `Y` | `A` | `I` | `A` | `Y` | `L` | `C`
| consensus |  |  | `N` | `D` | `D` | `H` | `L` | `I` | `I` | `A` |  |  | `N` | `D` | `K` | `F` | `L` | `L` | `F` | `C` | `M` | `E`
| sequence |  |  | `F` | `L` | `K` | `P` | `N` |  | `P` | `K` |  |  |  | `E` | `T` |  | `I` | `M` | `G` | `H` | `N` | `I`
|  |  |  |  |  | `T` | `V` | `T` |  |  | `S` |  |  |  |  |  |  |  | `V` | `N` |  | `S` |
|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | `Q` |  | `Y` |
.
NAME |  |  |  | START |  | P-VALUE |  |  |  |  |  | SITES |
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
ICYA_MANSE |  |  |  | 100 |  | 4.03e-19 |  | `MTFKFGQRVV` |  | `N` | `L` | `V` | `P` | `W` | `V` | `L` | `A` | `T` | `D` | `Y` | `K` | `N` | `Y` | `A` | `I` | `N` | `Y` | `N` | `C` |  |  | `DYHPDKKAHS`
BBP_PIEBR |  |  |  | 96 |  | 1.04e-18 |  | `HKLTYGGVTK` |  | `E` | `N` | `V` | `F` | `N` | `V` | `L` | `S` | `T` | `D` | `N` | `K` | `N` | `Y` | `I` | `I` | `G` | `Y` | `Y` | `C` |  |  | `KYDEDKKGHQ`
RETB_BOVIN |  |  |  | 101 |  | 2.40e-16 |  | `WGVASFLQKG` |  | `N` | `D` | `D` | `H` | `W` | `I` | `I` | `D` | `T` | `D` | `Y` | `E` | `T` | `F` | `A` | `V` | `Q` | `Y` | `S` | `C` |  |  | `RLLNLDGTCA`
MUP2_MOUSE |  |  |  | 105 |  | 6.24e-14 |  | `AGEYSVTYDG` |  | `F` | `N` | `T` | `F` | `T` | `I` | `P` | `K` | `T` | `D` | `Y` | `D` | `N` | `F` | `L` | `M` | `A` | `H` | `L` | `I` |  |  | `NEKDGETFQL`
LACB_BOVIN |  |  |  | 105 |  | 6.57e-13 |  | `PAVFKIDALN` |  | `E` | `N` | `K` | `V` | `L` | `V` | `L` | `D` | `T` | `D` | `Y` | `K` | `K` | `Y` | `L` | `L` | `F` | `C` | `M` | `E` |  |  | `NSAEPEQSLA`

Motif 2 block diagrams

Name | Lowest p-value | Motifs
--- | --- | ---
ICYA_MANSE | 4e-19 |
| 2 |  |
BBP_PIEBR | 1e-18 |
| 2 |  |
RETB_BOVIN | 2.4e-16 |
| 2 |  |
MUP2_MOUSE | 6.2e-14 |
| 2 |  |
LACB_BOVIN | 6.6e-13 |
| 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  0.74 secs.

P | N

MOTIF 3 width = 18 sites = 5 llr = 163 E-value = 1.2e+002

| Simplified | A |  | : | : | : | 2 | 2 | : | 2 | 2 | : | : | : | : | : | : | : | : | : | 2
| pos.-specific | C |  | : | : | : | : | : | : | : | : | : | : | : | : | : | : | : | : | : | :
| probability | D |  | : | 2 | 2 | 2 | : | : | : | : | : | : | : | : | 2 | : | : | : | : | :
| matrix | E |  | : | : | 2 | : | : | : | : | : | 4 | : | : | : | : | 2 | 2 | : | 2 | :
|  | F |  | 6 | : | : | 2 | : | : | : | : | : | 2 | : | : | : | : | : | 4 | : | :
|  | G |  | : | : | : | : | : | : | : | : | : | : | : | : | : | : | : | 6 | : | :
|  | H |  | 4 | : | : | 2 | : | 2 | : | : | : | : | 2 | : | : | : | : | : | 4 | :
|  | I |  | : | : | 2 | : | 2 | 2 | 2 | : | : | : | : | 2 | : | : | : | : | : | :
|  | K |  | : | : | 2 | : | : | 2 | : | : | : | 4 | : | 4 | : | : | 2 | : | : | :
|  | L |  | : | 4 | : | : | 2 | : | : | 8 | : | : | : | 2 | : | 8 | : | : | : | :
|  | M |  | : | : | : | : | : | : | : | : | : | 2 | : | : | : | : | : | : | : | 2
|  | N |  | : | : | : | : | 2 | : | : | : | : | : | : | : | : | : | 2 | : | 4 | :
|  | P |  | : | : | : | : | : | : | : | : | 2 | : | : | : | : | : | : | : | : | 2
|  | Q |  | : | 2 | 2 | 2 | : | : | : | : | : | : | : | : | : | : | : | : | : | :
|  | R |  | : | : | : | : | : | : | : | : | : | 2 | : | : | 2 | : | : | : | : | :
|  | S |  | : | 2 | : | : | : | : | : | : | 4 | : | 8 | : | : | : | 2 | : | : | :
|  | T |  | : | : | : | : | : | : | : | : | : | : | : | : | : | : | 2 | : | : | 4
|  | V |  | : | : | : | : | 2 | : | 6 | : | : | : | : | 2 | 6 | : | : | : | : | :
|  | W |  | : | : | : | : | : | 4 | : | : | : | : | : | : | : | : | : | : | : | :
|  | Y |  | : | : | : | : | : | : | : | : | : | : | : | : | : | : | : | : | : | :
.
| bits | 5.9 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
|  | 5.3 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
|  | 4.7 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
|  | 4.1 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| Information | 3.5 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| content | 2.9 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
| (47.0 bits) | 2.3 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
|  | 1.8 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
|  | 1.2 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
|  | 0.6 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
|  | 0.0 |  |
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| Multilevel |  |  | `F` | `L` | `D` | `A` | `A` | `W` | `V` | `L` | `E` | `K` | `S` | `K` | `V` | `L` | `E` | `G` | `H` | `T`
| consensus |  |  | `H` | `D` | `E` | `D` | `I` | `H` | `A` | `A` | `S` | `F` | `H` | `I` | `D` | `E` | `K` | `F` | `N` | `A`
| sequence |  |  |  | `Q` | `I` | `F` | `L` | `I` | `I` |  | `P` | `M` |  | `L` | `R` |  | `N` |  | `E` | `M`
|  |  |  |  | `S` | `K` | `H` | `N` | `K` |  |  |  | `R` |  | `V` |  |  | `S` |  |  | `P`
|  |  |  |  |  | `Q` | `Q` | `V` |  |  |  |  |  |  |  |  |  | `T` |  |  |
.
NAME |  |  |  | START |  | P-VALUE |  |  |  |  |  | SITES |
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
MUP2_MOUSE |  |  |  | 59 |  | 1.43e-16 |  | `KIEDNGNFRL` |  | `F` | `L` | `E` | `Q` | `I` | `H` | `V` | `L` | `E` | `K` | `S` | `L` | `V` | `L` | `K` | `F` | `H` | `T` |  |  | `VRDEECSELS`
ICYA_MANSE |  |  |  | 128 |  | 4.33e-16 |  | `NCDYHPDKKA` |  | `H` | `S` | `I` | `H` | `A` | `W` | `I` | `L` | `S` | `K` | `S` | `K` | `V` | `L` | `E` | `G` | `N` | `T` |  |  | `KEVVDNVLKT`
BBP_PIEBR |  |  |  | 124 |  | 6.65e-16 |  | `YCKYDEDKKG` |  | `H` | `Q` | `D` | `F` | `V` | `W` | `V` | `L` | `S` | `R` | `S` | `K` | `V` | `L` | `T` | `G` | `E` | `A` |  |  | `KTAVENYLIG`
RETB_BOVIN |  |  |  | 36 |  | 2.92e-11 |  | `AMAKKDPEGL` |  | `F` | `L` | `Q` | `D` | `N` | `I` | `V` | `A` | `E` | `F` | `S` | `V` | `D` | `E` | `N` | `G` | `H` | `M` |  |  | `SATAKGRVRL`
LACB_BOVIN |  |  |  | 152 |  | 3.37e-11 |  | `PEVDDEALEK` |  | `F` | `D` | `K` | `A` | `L` | `K` | `A` | `L` | `P` | `M` | `H` | `I` | `R` | `L` | `S` | `F` | `N` | `P` |  |  | `TQLEEQCHI`

Motif 3 block diagrams

Name | Lowest p-value | Motifs
--- | --- | ---
MUP2_MOUSE | 1.4e-16 |
| 3 |  |
ICYA_MANSE | 4.3e-16 |
| 3 |  |
BBP_PIEBR | 6.7e-16 |
| 3 |  |
RETB_BOVIN | 2.9e-11 |
| 3 |  |
LACB_BOVIN | 3.4e-11 |
| 3 |  |
SCALE |
1 | 25 | 50 | 75 | 100 | 125 | 150 | 175

Motif 3 in BLOCKS format

to BLOCKS multiple alignment processor.

Motif 3 position-specific scoring matrix

Motif 3 position-specific probability matrix

    Time  0.99 secs.

P | N

SUMMARY OF MOTIFS

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

Name | Combined p-value | Motifs
--- | --- | ---
ICYA_MANSE | 6.82e-42 |
| 1 |  | 2 |  | 3 |  |
LACB_BOVIN | 2.15e-27 |
| 1 |  | 2 |  | 3 |  |
BBP_PIEBR | 1.29e-40 |
| 1 |  | 2 |  | 3 |  |
RETB_BOVIN | 2.11e-32 |
| 1 |  | 3 |  | 2 |  |
MUP2_MOUSE | 5.96e-33 |
| 1 |  | 3 |  | 2 |  |
SCALE |
1 | 25 | 50 | 75 | 100 | 125 | 150 | 175

Motif summary in machine readable format.

Stopped because nmotifs = 3 reached.

    CPU: uracil.gs.washington.edu

###

EXPLANATION OF MEME RESULTS

#### The MEME results consist of:

- The version of MEME and the date it was released.
- The reference to cite if you use MEME in your research.
- A description of the sequences you submitted (the "training set") showing the name, "weight" and length of each sequence.
- The command line summary detailing the parameters with which you ran MEME.
- Information on each of the motifs MEME discovered, including:
  1. A summary line showing the width, number of occurrences, log likelihood ratio and statistical significance of the motif.
  2. A simplified position-specific probability matrix.
  3. A diagram showing the degree of conservation at each motif position.
  4. A multilevel consensus sequence showing the most conserved letter(s) at each motif position.
  5. The occurrences of the motif sorted by p-value and aligned with each other.
  6. Block diagrams of the occurrences of the motif within each sequence in the training set.
  7. The motif in BLOCKS or FASTA format.
  8. A position-specific scoring matrix (PSSM) for use by the MAST database search program.
  9. The position specific probability matrix (PSPM) describing the motif.
- A summary of motifs showing an optimized (non-overlapping) tiling of all of the motifs onto each of the sequences in the training set.
- The reason why MEME stopped and the name of the CPU on which it ran.
- This explanation of how to interpret MEME results.

MOTIFS

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

- #### Summary Line

  This line gives the width (`width'), number of occurrences in the training set (`sites'), log likelihood ratio (`llr') and E-value of the motif. Each motif describes a pattern of a fixed width--no gaps are allowed in MEME motifs. MEME numbers the motifs consecutively from one as it finds them. MEME usually finds the most statistically significant (low E-value) motifs first. The statistical significance of a motif is based on its log likelihood ratio, its width and number of occurrences, the background letter frequencies (given in the command line summary), and the size of the training set. The E-value is an estimate of the expected number of motifs with the given log likelihood ratio (or higher), and with the same width and number of occurrences, that one would find in a similarly sized set of random sequences. (In random sequences each position is independent with letters chosen according to the background letter frequencies.) The log likelihood ratio is the logarithm of the ratio of the probability of the occurrences of the motif given the motif model (likelihood given the motif) versus their probability given the background model (likelihood given the null model). (Normally the background model is a 0-order Markov model using the background letter frequencies, but higher order Markov models may be specified via the -bfile option to MEME.) Clicking on the buttons to the left of the motif summary line takes you to the previous motif (P) or next motif (N).

- #### Simplified Position-Specific Probability Matrix

  MEME motifs are represented by position-specific probability matrices that specify the probability of each possible letter appearing at each possible position in an occurrence of the motif. In order to make it easier to see which letters are most likely in each of the columns of the motif, the simplified motif shows the letter probabilities multiplied by 10 rounded to the nearest integer ("a" means 10). Zeros are replaced by ":" (the colon) for readability.

- #### Information Content Diagram

  The information content diagram provides an idea of which positions in the motif are most highly conserved. Each column (position) in a motif can be characterized by the amount of information it contains (measured in bits). Highly conserved positions in the motif have high information; positions where all letters are equally likely have low information. (The information content is relative to the background letter frequencies which are given in the command line summary section.) The diagram is printed so that each column lines up with the same column in the simplified position-specific probability matrix above it. Columns in the information content diagram are colored according to the majority category of the letters occurring in that column of the alignment. If no letter category has frequency above 0.5, the column in the diagram is colored black. For DNA sequences, the letter categories contain one letter each. For proteins, the categories are based on the biochemical properties of the various amino acids. The categories and their colors are:

  NUCLEIC ACIDS | COLOR
  --- | ---
  A | RED
  C | BLUE
  G | ORANGE
  T | GREEN

  AMINO ACIDS | COLOR | PROPERTIES
  --- | --- | ---
  | A, C, F, I, L, V, W and M | BLUE | Most hydrophobic[Kyte and Doolittle, 1982]
  NQST | GREEN | Polar, non-charged, non-aliphatic residues
  DE | MAGENTA | Acidic
  KR | RED | Positively charged
  H | PINK
  G | ORANGE
  P | YELLOW
  Y | TURQUOISE

  J. Kyte and R. Doolittle, 1982. "A Simple Method for Displaying the Hydropathic Character of a Protein", J. Mol Biol. 157, 105-132.

  Summing the information content for each position in the motif gives the total information content of the motif (shown in parentheses to the left of the diagram). The total information content is approximately equal to the log likelihood ratio divided by the number of occurrences times ln(2). The total information content gives a measure of the usefulness of the motif for database searches. For a motif to be useful for database searches, it must as a rule contain at least log_2(N) bits of information where N is the number of sequences in the database being searched. For example, to effectively search a database containing 100,000 sequences for occurrences of a single motif, the motif should have an IC of at least 16.6 bits. Motifs with lower information content are still useful when a family of sequences shares more than one motif since they can be combined in multiple motif searches (using MAST).

- #### Multilevel Consensus Sequence

  The multilevel consensus sequence corresponding to the motif is an aid in remembering and understanding the motif. It is calculated from the motif position-specific probability matrix as follows. Separately for each column of the motif, the letters in the alphabet are sorted in decreasing order by the probability with which they are expected to occur in that position of motif occurrences. The sorted letters are then printed vertically with the most probable letter on top. Only letters with probabilities of 0.2 or higher at that position in the motif are printed. As an example, the multilevel consensus sequence of motif 1 in the sample output is:

      Multilevel TTATGTGAACGACGTCACACT
      consensus  AA  T A G A GA     AA
      sequence           T C TT     T

  This multilevel consensus sequence says several things about the motif. First, the most likely form of the motif can be read from the top line as `TTATGTGAACGACGTCACACT`. Second, that only letter `A` has probability more than 0.2 in position 3 of the motif, both `T` and `A` have probability greater than 0.2 in position 1, etc. Third, a rough approximation of the motif can be made by converting the multilevel consensus sequence into the Prosite signature
  `[TA]-[TA]-A-T-[GT]-[T]-[GA]-A-[AGT]-C-[GAC]-A-[CGT]-[GAT]-T-C-A-C-A-[CAT]-[TA]`.

- #### Occurrences of the Motif

  MEME displays the occurrences (sites) of the motif in the training set. The sites are shown aligned with each other, and the ten sequence positions preceding and following each site are also shown. Each site is identified by the name of the sequence where it occurs, the strand (if both strands of DNA sequences are being used), and the position in the sequence where the site begins. When the DNA strand is specified, `+' means the sequence in the training set, and `-' means the reverse complement of the training set sequence. (For `-' strands, the `start' position is actually the position on the positive strand where the site ends.) The sites are listed in order of increasing statistical significance (p-value). The p-value of a site is computed from the the match score of the site with the position specific scoring matrix for the motif. The p-value gives the probability of a random string (generated from the background letter frequencies) having the same match score or higher. (This is referred to as the position p-value by the MAST algorithm.)

- #### Block Diagrams of Motif Occurrences

  The occurrences of the motif in the training set sequences are shown with MAST-style block diagrams. One diagram is printed for each sequence showing all the occurrences of the motif in that sequence. The sequences are sorted by the lowest p-value among all occurrences of the motif in a given sequence. (The p-value of an occurrence is the probability of a single random subsequence the length of the motif, generated according to the 0-order background model, having a score at least as high as the score of the occurrence.) When the DNA strand is specified, `+' means the motif appears from left to right on the sequence, and `-' means the motif appears from right to left on the complementary strand. A sequence position scale is shown at the end of each table of block diagrams. Very long sequences are shown with thick lines connecting the motifs and are not drawn to scale.

- #### Motif in BLOCKS format or FASTA format

  For use with BLOCKS tools, MEME prints the occurrences of the motif in BLOCKS format.

  You can convert these blocks to PSSMs (position-specific scoring matrices), LOGOS (color representations of the motifs), phylogeny trees and search them against a database of other blocks by pasting everything from the "BL" line to the "//" line (inclusive) into the Multiple Alignment Processor. If you include the -print_fasta switch on the command line, MEME prints the motif sites in FASTA format instead of BLOCKS format.

- #### Position-Specific Scoring Matrix

  The position-specific scoring matrix corresponding to the motif is printed for use by database search programs such as MAST. This matrix is a log-odds matrix calculated by taking the log (base 2) of the ratio `p/f` at each position in the motif where `p` is the probability of a particular letter at that position in the motif, and `f` is the background frequency of the letter (given in the command line summary section.) Each entry in the matrix is multiplied by 100 and rounded to the nearest integer before printing. This is the same matrix that is used above in computing the p-values of the occurrences of the motif in the Occurrences of the Motif and Block Diagrams of Motif Occurrences sections. The scoring matrix is printed "sideways"--columns correspond to the letters in the alphabet (in the same order as shown in the simplified motif) and rows corresponding to the positions of the motif, position one first. The scoring matrix is preceded by a line starting with "log-odds matrix:" and containing the length of the alphabet, width of the motif, number of characters in the training set and a scoring threshold.

- #### Position-Specific Probability Matrix

  The motif itself is a position-specific probability matrix giving, for each position in the pattern, the observed frequency ("probability") of each possible letter. The probability matrix is printed "sideways"--columns correspond to the letters in the alphabet (in the same order as shown in the simplified motif) and rows corresponding to the positions of the motif, position one first. The motif is preceded by a line starting with "letter-probability matrix:" and containing the length of the alphabet, width of the motif, number of occurrences of the motif, and the E-value of the motif.

  Note: Earlier versions of MEME gave the posterior probabilities--the probability after applying a prior on letter frequencies--rather than the observed frequencies. These versions of MEME also gave the number of possible positions for the motif rather than the actual number of occurrences. The output from these earlier versions of MEME can be distinguished by "n=" rather than "nsites=" in the line preceding the matrix.

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