Sequence motif analysis using Bio.motifs
This chapter gives an overview of the functionality of the
Bio.motifs package included in Biopython. It is intended for people
who are involved in the analysis of sequence motifs, so I’ll assume that
you are familiar with basic notions of motif analysis. In case something
is unclear, please look at Section Useful links for some
relevant links.
Most of this chapter describes the new Bio.motifs package included
in Biopython 1.61 onwards, which is replacing the older Bio.Motif
package introduced with Biopython 1.50, which was in turn based on two
older former Biopython modules, Bio.AlignAce and Bio.MEME. It
provides most of their functionality with a unified motif object
implementation.
Speaking of other libraries, if you are reading this you might be interested in TAMO, another python library designed to deal with sequence motifs. It supports more de-novo motif finders, but it is not a part of Biopython and has some restrictions on commercial use.
Motif objects
Since we are interested in motif analysis, we need to take a look at
Motif objects in the first place. For that we need to import the
Bio.motifs library:
>>> from Bio import motifs
and we can start creating our first motif objects. We can either create
a Motif object from a list of instances of the motif, or we can
obtain a Motif object by parsing a file from a motif database or
motif finding software.
Creating a motif from instances
Suppose we have these instances of a DNA motif:
>>> from Bio.Seq import Seq
>>> instances = [
... Seq("TACAA"),
... Seq("TACGC"),
... Seq("TACAC"),
... Seq("TACCC"),
... Seq("AACCC"),
... Seq("AATGC"),
... Seq("AATGC"),
... ]
then we can create a Motif object as follows:
>>> m = motifs.create(instances)
The instances from which this motif was created is stored in the
.alignment property:
>>> print(m.alignment.sequences)
[Seq('TACAA'), Seq('TACGC'), Seq('TACAC'), Seq('TACCC'), Seq('AACCC'), Seq('AATGC'), Seq('AATGC')]
Printing the Motif object shows the instances from which it was constructed:
>>> print(m)
TACAA
TACGC
TACAC
TACCC
AACCC
AATGC
AATGC
The length of the motif is defined as the sequence length, which should be the same for all instances:
>>> len(m)
5
The Motif object has an attribute .counts containing the counts of
each nucleotide at each position. Printing this counts matrix shows it
in an easily readable format:
>>> print(m.counts)
0 1 2 3 4
A: 3.00 7.00 0.00 2.00 1.00
C: 0.00 0.00 5.00 2.00 6.00
G: 0.00 0.00 0.00 3.00 0.00
T: 4.00 0.00 2.00 0.00 0.00
You can access these counts as a dictionary:
>>> m.counts["A"]
[3.0, 7.0, 0.0, 2.0, 1.0]
but you can also think of it as a 2D array with the nucleotide as the first dimension and the position as the second dimension:
>>> m.counts["T", 0]
4.0
>>> m.counts["T", 2]
2.0
>>> m.counts["T", 3]
0.0
You can also directly access columns of the counts matrix
>>> m.counts[:, 3]
{'A': 2.0, 'C': 2.0, 'T': 0.0, 'G': 3.0}
Instead of the nucleotide itself, you can also use the index of the nucleotide in the alphabet of the motif:
>>> m.alphabet
'ACGT'
>>> m.counts["A", :]
(3.0, 7.0, 0.0, 2.0, 1.0)
>>> m.counts[0, :]
(3.0, 7.0, 0.0, 2.0, 1.0)
Obtaining a consensus sequence
The consensus sequence of a motif is defined as the sequence of letters
along the positions of the motif for which the largest value in the
corresponding columns of the .counts matrix is obtained:
>>> m.consensus
Seq('TACGC')
Conversely, the anticonsensus sequence corresponds to the smallest
values in the columns of the .counts matrix:
>>> m.anticonsensus
Seq('CCATG')
Note that there is some ambiguity in the definition of the consensus and anticonsensus sequence if in some columns multiple nucleotides have the maximum or minimum count.
For DNA sequences, you can also ask for a degenerate consensus sequence, in which ambiguous nucleotides are used for positions where there are multiple nucleotides with high counts:
>>> m.degenerate_consensus
Seq('WACVC')
Here, W and R follow the IUPAC nucleotide ambiguity codes: W is either A or T, and V is A, C, or G [Cornish1985]. The degenerate consensus sequence is constructed following the rules specified by Cavener [Cavener1987].
The motif.counts.calculate_consensus method lets you specify in
detail how the consensus sequence should be calculated. This method
largely follows the conventions of the EMBOSS program cons, and
takes the following arguments:
- substitution_matrix
The scoring matrix used when comparing sequences. By default, it is
None, in which case we simply count the frequency of each letter. Instead of the default value, you can use the substitution matrices available inBio.Align.substitution\_matrices. Common choices are BLOSUM62 (also known as EBLOSUM62) for protein, and NUC.4.4 (also known as EDNAFULL) for nucleotides. NOTE: Currently, this method has not yet been implemented for values other than the default valueNone.- plurality
Threshold value for the number of positive matches, divided by the total count in a column, required to reach consensus. If
substitution_matrixisNone, then this argument must also beNone, and is ignored; aValueErroris raised otherwise. Ifsubstitution_matrixis notNone, then the default value of the plurality is 0.5.- identity
Number of identities, divided by the total count in a column, required to define a consensus value. If the number of identities is less than identity multiplied by the total count in a column, then the undefined character (
Nfor nucleotides andXfor amino acid sequences) is used in the consensus sequence. Ifidentityis 1.0, then only columns of identical letters contribute to the consensus. Default value is zero.- setcase
threshold for the positive matches, divided by the total count in a column, above which the consensus is is upper-case and below which the consensus is in lower-case. By default, this is equal to 0.5.
This is an example:
>>> m.counts.calculate_consensus(identity=0.5, setcase=0.7)
'tACNC'
Reverse-complementing a motif
We can get the reverse complement of a motif by calling the
reverse_complement method on it:
>>> r = m.reverse_complement()
>>> r.consensus
Seq('GCGTA')
>>> r.degenerate_consensus
Seq('GBGTW')
>>> print(r)
TTGTA
GCGTA
GTGTA
GGGTA
GGGTT
GCATT
GCATT
The reverse complement is only defined for DNA motifs.
Slicing a motif
You can slice the motif to obtain a new Motif object for the
selected positions:
>>> m_sub = m[2:-1]
>>> print(m_sub)
CA
CG
CA
CC
CC
TG
TG
>>> m_sub.consensus
Seq('CG')
>>> m_sub.degenerate_consensus
Seq('CV')
Relative entropy
The relative entropy (or Kullback-Leibler distance) \(H_j\) of column \(j\) of the motif is defined as in [Schneider1986] [Durbin1998]:
where:
\(M\) – The number of letters in the alphabet (given by
len(m.alphabet));\(p_{ij}\) – The observed frequency of letter \(i\), normalized, in the \(j\)-th column (see below);
\(b_{i}\) – The background probability of letter \(i\) (given by
m.background[i]).
The observed frequency \(p_{ij}\) is computed as follows:
where:
\(c_{ij}\) – the number of times letter \(i\) appears in column \(j\) of the alignment (given by
m.counts[i, j]);\(C_{j}\) – The total number of letters in column \(j\): \(C_{j} = \sum_{i=1}^{M} c_{ij}\) (given by
sum(m.counts[:, j])).\(k_i\) – the pseudocount of letter \(i\) (given by
m.pseudocounts[i]).\(k\) – the total pseudocount: \(k = \sum_{i=1}^{M} k_i\) (given by
sum(m.pseudocounts.values())).
With these definitions, both \(p_{ij}\) and \(b_{i}\) are normalized to 1:
The relative entropy is the same as the information content if the background distribution is uniform.
The relative entropy for each column of motif m can be obtained
using the relative_entropy property:
>>> m.relative_entropy
array([1.01477186, 2. , 1.13687943, 0.44334329, 1.40832722])
These values are calculated using the base-2 logarithm, and are
therefore in units of bits. The second column (which consists of A
nucleotides only) has the highest relative entropy; the fourth column
(which consists of A, C, or G nucleotides) has the lowest
relative entropy). The relative entropy of the motif can be calculated
by summing over the columns:
>>> print(f"Relative entropy is {sum(m.relative_entropy):0.5f}")
Relative entropy is 6.00332
Creating a sequence logo
If we have internet access, we can create a weblogo:
>>> m.weblogo("mymotif.png")
We should get our logo saved as a PNG in the specified file.
Reading motifs
Creating motifs from instances by hand is a bit boring, so it’s useful to have some I/O functions for reading and writing motifs. There are not any really well established standards for storing motifs, but there are a couple of formats that are more used than others.
JASPAR
One of the most popular motif databases is
JASPAR. In addition to the motif
sequence information, the JASPAR database stores a lot of
meta-information for each motif. The module Bio.motifs contains a
specialized class jaspar.Motif in which this meta-information is
represented as attributes:
matrix_id- the unique JASPAR motif ID, e.g. ’MA0004.1’name- the name of the TF, e.g. ’Arnt’collection- the JASPAR collection to which the motif belongs, e.g. ’CORE’tf_class- the structural class of this TF, e.g. ’Zipper-Type’tf_family- the family to which this TF belongs, e.g. ’Helix-Loop-Helix’species- the species to which this TF belongs, may have multiple values, these are specified as taxonomy IDs, e.g. 10090tax_group- the taxonomic supergroup to which this motif belongs, e.g. ’vertebrates’acc- the accession number of the TF protein, e.g. ’P53762’data_type- the type of data used to construct this motif, e.g. ’SELEX’medline- the Pubmed ID of literature supporting this motif, may be multiple values, e.g. 7592839pazar_id- external reference to the TF in the PAZAR database, e.g. ’TF0000003’comment- free form text containing notes about the construction of the motif
The jaspar.Motif class inherits from the generic Motif class and
therefore provides all the facilities of any of the motif formats —
reading motifs, writing motifs, scanning sequences for motif instances
etc.
JASPAR stores motifs in several different ways including three different flat file formats and as an SQL database. All of these formats facilitate the construction of a counts matrix. However, the amount of meta information described above that is available varies with the format.
The JASPAR sites format
The first of the three flat file formats contains a list of instances.
As an example, these are the beginning and ending lines of the JASPAR
Arnt.sites file showing known binding sites of the mouse
helix-loop-helix transcription factor Arnt.
>MA0004 ARNT 1
CACGTGatgtcctc
>MA0004 ARNT 2
CACGTGggaggtac
>MA0004 ARNT 3
CACGTGccgcgcgc
...
>MA0004 ARNT 18
AACGTGacagccctcc
>MA0004 ARNT 19
AACGTGcacatcgtcc
>MA0004 ARNT 20
aggaatCGCGTGc
The parts of the sequence in capital letters are the motif instances that were found to align to each other.
We can create a Motif object from these instances as follows:
>>> from Bio import motifs
>>> with open("Arnt.sites") as handle:
... arnt = motifs.read(handle, "sites")
...
The instances from which this motif was created is stored in the
.alignment property:
>>> print(arnt.alignment.sequences[:3])
[Seq('CACGTG'), Seq('CACGTG'), Seq('CACGTG')]
>>> for sequence in arnt.alignment.sequences:
... print(sequence)
...
CACGTG
CACGTG
CACGTG
CACGTG
CACGTG
CACGTG
CACGTG
CACGTG
CACGTG
CACGTG
CACGTG
CACGTG
CACGTG
CACGTG
CACGTG
AACGTG
AACGTG
AACGTG
AACGTG
CGCGTG
The counts matrix of this motif is automatically calculated from the instances:
>>> print(arnt.counts)
0 1 2 3 4 5
A: 4.00 19.00 0.00 0.00 0.00 0.00
C: 16.00 0.00 20.00 0.00 0.00 0.00
G: 0.00 1.00 0.00 20.00 0.00 20.00
T: 0.00 0.00 0.00 0.00 20.00 0.00
This format does not store any meta information.
The JASPAR pfm format
JASPAR also makes motifs available directly as a count matrix, without
the instances from which it was created. This pfm format only stores
the counts matrix for a single motif. For example, this is the JASPAR
file SRF.pfm containing the counts matrix for the human SRF
transcription factor:
2 9 0 1 32 3 46 1 43 15 2 2
1 33 45 45 1 1 0 0 0 1 0 1
39 2 1 0 0 0 0 0 0 0 44 43
4 2 0 0 13 42 0 45 3 30 0 0
We can create a motif for this count matrix as follows:
>>> with open("SRF.pfm") as handle:
... srf = motifs.read(handle, "pfm")
...
>>> print(srf.counts)
0 1 2 3 4 5 6 7 8 9 10 11
A: 2.00 9.00 0.00 1.00 32.00 3.00 46.00 1.00 43.00 15.00 2.00 2.00
C: 1.00 33.00 45.00 45.00 1.00 1.00 0.00 0.00 0.00 1.00 0.00 1.00
G: 39.00 2.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 44.00 43.00
T: 4.00 2.00 0.00 0.00 13.00 42.00 0.00 45.00 3.00 30.00 0.00 0.00
As this motif was created from the counts matrix directly, it has no instances associated with it:
>>> print(srf.alignment)
None
We can now ask for the consensus sequence of these two motifs:
>>> print(arnt.counts.consensus)
CACGTG
>>> print(srf.counts.consensus)
GCCCATATATGG
As with the instances file, no meta information is stored in this format.
The JASPAR format jaspar
The jaspar file format allows multiple motifs to be specified in a
single file. In this format each of the motif records consist of a
header line followed by four lines defining the counts matrix. The
header line begins with a > character (similar to the Fasta file
format) and is followed by the unique JASPAR matrix ID and the TF name.
The following example shows a jaspar formatted file containing the
three motifs Arnt, RUNX1 and MEF2A:
>MA0004.1 Arnt
A [ 4 19 0 0 0 0 ]
C [16 0 20 0 0 0 ]
G [ 0 1 0 20 0 20 ]
T [ 0 0 0 0 20 0 ]
>MA0002.1 RUNX1
A [10 12 4 1 2 2 0 0 0 8 13 ]
C [ 2 2 7 1 0 8 0 0 1 2 2 ]
G [ 3 1 1 0 23 0 26 26 0 0 4 ]
T [11 11 14 24 1 16 0 0 25 16 7 ]
>MA0052.1 MEF2A
A [ 1 0 57 2 9 6 37 2 56 6 ]
C [50 0 1 1 0 0 0 0 0 0 ]
G [ 0 0 0 0 0 0 0 0 2 50 ]
T [ 7 58 0 55 49 52 21 56 0 2 ]
The motifs are read as follows:
>>> fh = open("jaspar_motifs.txt")
>>> for m in motifs.parse(fh, "jaspar"):
... print(m)
...
TF name Arnt
Matrix ID MA0004.1
Matrix:
0 1 2 3 4 5
A: 4.00 19.00 0.00 0.00 0.00 0.00
C: 16.00 0.00 20.00 0.00 0.00 0.00
G: 0.00 1.00 0.00 20.00 0.00 20.00
T: 0.00 0.00 0.00 0.00 20.00 0.00
TF name RUNX1
Matrix ID MA0002.1
Matrix:
0 1 2 3 4 5 6 7 8 9 10
A: 10.00 12.00 4.00 1.00 2.00 2.00 0.00 0.00 0.00 8.00 13.00
C: 2.00 2.00 7.00 1.00 0.00 8.00 0.00 0.00 1.00 2.00 2.00
G: 3.00 1.00 1.00 0.00 23.00 0.00 26.00 26.00 0.00 0.00 4.00
T: 11.00 11.00 14.00 24.00 1.00 16.00 0.00 0.00 25.00 16.00 7.00
TF name MEF2A
Matrix ID MA0052.1
Matrix:
0 1 2 3 4 5 6 7 8 9
A: 1.00 0.00 57.00 2.00 9.00 6.00 37.00 2.00 56.00 6.00
C: 50.00 0.00 1.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00
G: 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.00 50.00
T: 7.00 58.00 0.00 55.00 49.00 52.00 21.00 56.00 0.00 2.00
Note that printing a JASPAR motif yields both the counts data and the available meta-information.
Accessing the JASPAR database
In addition to parsing these flat file formats, we can also retrieve
motifs from a JASPAR SQL database. Unlike the flat file formats, a
JASPAR database allows storing of all possible meta information defined
in the JASPAR Motif class. It is beyond the scope of this document
to describe how to set up a JASPAR database (please see the main
JASPAR website). Motifs are read from a
JASPAR database using the Bio.motifs.jaspar.db module. First connect
to the JASPAR database using the JASPAR5 class which models the the
latest JASPAR schema:
>>> from Bio.motifs.jaspar.db import JASPAR5
>>>
>>> JASPAR_DB_HOST = "yourhostname" # fill in these values
>>> JASPAR_DB_NAME = "yourdatabase"
>>> JASPAR_DB_USER = "yourusername"
>>> JASPAR_DB_PASS = "yourpassword"
>>>
>>> jdb = JASPAR5(
... host=JASPAR_DB_HOST,
... name=JASPAR_DB_NAME,
... user=JASPAR_DB_USER,
... password=JASPAR_DB_PASS,
... )
Now we can fetch a single motif by its unique JASPAR ID with the
fetch_motif_by_id method. Note that a JASPAR ID consists of a base
ID and a version number separated by a decimal point, e.g. ’MA0004.1’.
The fetch_motif_by_id method allows you to use either the fully
specified ID or just the base ID. If only the base ID is provided, the
latest version of the motif is returned.
>>> arnt = jdb.fetch_motif_by_id("MA0004")
Printing the motif reveals that the JASPAR SQL database stores much more meta-information than the flat files:
>>> print(arnt)
TF name Arnt
Matrix ID MA0004.1
Collection CORE
TF class Zipper-Type
TF family Helix-Loop-Helix
Species 10090
Taxonomic group vertebrates
Accession ['P53762']
Data type used SELEX
Medline 7592839
PAZAR ID TF0000003
Comments -
Matrix:
0 1 2 3 4 5
A: 4.00 19.00 0.00 0.00 0.00 0.00
C: 16.00 0.00 20.00 0.00 0.00 0.00
G: 0.00 1.00 0.00 20.00 0.00 20.00
T: 0.00 0.00 0.00 0.00 20.00 0.00
We can also fetch motifs by name. The name must be an exact match
(partial matches or database wildcards are not currently supported).
Note that as the name is not guaranteed to be unique, the
fetch_motifs_by_name method actually returns a list.
>>> motifs = jdb.fetch_motifs_by_name("Arnt")
>>> print(motifs[0])
TF name Arnt
Matrix ID MA0004.1
Collection CORE
TF class Zipper-Type
TF family Helix-Loop-Helix
Species 10090
Taxonomic group vertebrates
Accession ['P53762']
Data type used SELEX
Medline 7592839
PAZAR ID TF0000003
Comments -
Matrix:
0 1 2 3 4 5
A: 4.00 19.00 0.00 0.00 0.00 0.00
C: 16.00 0.00 20.00 0.00 0.00 0.00
G: 0.00 1.00 0.00 20.00 0.00 20.00
T: 0.00 0.00 0.00 0.00 20.00 0.00
The fetch_motifs method allows you to fetch motifs which match a
specified set of criteria. These criteria include any of the above
described meta information as well as certain matrix properties such as
the minimum information content (min_ic in the example below), the
minimum length of the matrix or the minimum number of sites used to
construct the matrix. Only motifs which pass ALL the specified criteria
are returned. Note that selection criteria which correspond to meta
information which allow for multiple values may be specified as either a
single value or a list of values, e.g. tax_group and tf_family
in the example below.
>>> motifs = jdb.fetch_motifs(
... collection="CORE",
... tax_group=["vertebrates", "insects"],
... tf_class="Winged Helix-Turn-Helix",
... tf_family=["Forkhead", "Ets"],
... min_ic=12,
... )
>>> for motif in motifs:
... pass # do something with the motif
...
Compatibility with Perl TFBS modules
An important thing to note is that the JASPAR Motif class was
designed to be compatible with the popular Perl TFBS
modules. Therefore some specifics about
the choice of defaults for background and pseudocounts as well as how
information content is computed and sequences searched for instances is
based on this compatibility criteria. These choices are noted in the
specific subsections below.
- Choice of background:The Perl
TFBSmodules appear to allow a choice of custom background probabilities (although the documentation states that uniform background is assumed). However the default is to use a uniform background. Therefore it is recommended that you use a uniform background for computing the position-specific scoring matrix (PSSM). This is the default when using the Biopythonmotifsmodule. - Choice of pseudocounts:By default, the Perl
TFBSmodules use a pseudocount equal to \(\sqrt{N} * \textrm{bg}[\textrm{nucleotide}]\), where \(N\) represents the total number of sequences used to construct the matrix. To apply this same pseudocount formula, set the motifpseudocountsattribute using thejaspar.calculate\_pseudcounts()function:>>> motif.pseudocounts = motifs.jaspar.calculate_pseudocounts(motif)
Note that it is possible for the counts matrix to have an unequal number of sequences making up the columns. The pseudocount computation uses the average number of sequences making up the matrix. However, when
normalizeis called on the counts matrix, each count value in a column is divided by the total number of sequences making up that specific column, not by the average number of sequences. This differs from the PerlTFBSmodules because the normalization is not done as a separate step and so the average number of sequences is used throughout the computation of the pssm. Therefore, for matrices with unequal column counts, the PSSM computed by themotifsmodule will differ somewhat from the pssm computed by the PerlTFBSmodules. - Computation of matrix information content:The information content (IC) or specificity of a matrix is computed using the
meanmethod of thePositionSpecificScoringMatrixclass. However of note, in the PerlTFBSmodules the default behavior is to compute the IC without first applying pseudocounts, even though by default the PSSMs are computed using pseudocounts as described above. - Searching for instances:Searching for instances with the Perl
TFBSmotifs was usually performed using a relative score threshold, i.e. a score in the range 0 to 1. In order to compute the absolute PSSM score corresponding to a relative score one can use the equation:>>> abs_score = (pssm.max - pssm.min) * rel_score + pssm.min
To convert the absolute score of an instance back to a relative score, one can use the equation:
>>> rel_score = (abs_score - pssm.min) / (pssm.max - pssm.min)
For example, using the Arnt motif before, let’s search a sequence with a relative score threshold of 0.8.
>>> test_seq = Seq("TAAGCGTGCACGCGCAACACGTGCATTA") >>> arnt.pseudocounts = motifs.jaspar.calculate_pseudocounts(arnt) >>> pssm = arnt.pssm >>> max_score = pssm.max >>> min_score = pssm.min >>> abs_score_threshold = (max_score - min_score) * 0.8 + min_score >>> for pos, score in pssm.search(test_seq, threshold=abs_score_threshold): ... rel_score = (score - min_score) / (max_score - min_score) ... print(f"Position {pos}: score = {score:5.3f}, rel. score = {rel_score:5.3f}") ... Position 2: score = 5.362, rel. score = 0.801 Position 8: score = 6.112, rel. score = 0.831 Position -20: score = 7.103, rel. score = 0.870 Position 17: score = 10.351, rel. score = 1.000 Position -11: score = 10.351, rel. score = 1.000
MEME
MEME [Bailey1994] is a tool for discovering motifs in a group of related DNA or protein sequences. It takes as input a group of DNA or protein sequences and outputs as many motifs as requested. Therefore, in contrast to JASPAR files, MEME output files typically contain multiple motifs. This is an example.
At the top of an output file generated by MEME shows some background information about the MEME and the version of MEME used:
********************************************************************************
MEME - Motif discovery tool
********************************************************************************
MEME version 3.0 (Release date: 2004/08/18 09:07:01)
...
Further down, the input set of training sequences is recapitulated:
********************************************************************************
TRAINING SET
********************************************************************************
DATAFILE= INO_up800.s
ALPHABET= ACGT
Sequence name Weight Length Sequence name Weight Length
------------- ------ ------ ------------- ------ ------
CHO1 1.0000 800 CHO2 1.0000 800
FAS1 1.0000 800 FAS2 1.0000 800
ACC1 1.0000 800 INO1 1.0000 800
OPI3 1.0000 800
********************************************************************************
and the exact command line that was used:
********************************************************************************
COMMAND LINE SUMMARY
********************************************************************************
This information can also be useful in the event you wish to report a
problem with the MEME software.
command: meme -mod oops -dna -revcomp -nmotifs 2 -bfile yeast.nc.6.freq INO_up800.s
...
Next is detailed information on each motif that was found:
********************************************************************************
MOTIF 1 width = 12 sites = 7 llr = 95 E-value = 2.0e-001
********************************************************************************
--------------------------------------------------------------------------------
Motif 1 Description
--------------------------------------------------------------------------------
Simplified A :::9:a::::3:
pos.-specific C ::a:9:11691a
probability G ::::1::94:4:
matrix T aa:1::9::11:
To parse this file (stored as meme.dna.oops.txt), use
>>> with open("meme.INO_up800.classic.oops.xml") as handle:
... record = motifs.parse(handle, "meme")
...
The motifs.parse command reads the complete file directly, so you
can close the file after calling motifs.parse. The header
information is stored in attributes:
>>> record.version
'5.0.1'
>>> record.datafile
'common/INO_up800.s'
>>> record.command
'meme common/INO_up800.s -oc results/meme10 -mod oops -dna -revcomp -bfile common/yeast.nc.6.freq -nmotifs 2 -objfun classic -minw 8 -nostatus '
>>> record.alphabet
'ACGT'
>>> record.sequences
['sequence_0', 'sequence_1', 'sequence_2', 'sequence_3', 'sequence_4', 'sequence_5', 'sequence_6']
The record is an object of the Bio.motifs.meme.Record class. The
class inherits from list, and you can think of record as a list of
Motif objects:
>>> len(record)
2
>>> motif = record[0]
>>> print(motif.consensus)
GCGGCATGTGAAA
>>> print(motif.degenerate_consensus)
GSKGCATGTGAAA
In addition to these generic motif attributes, each motif also stores its specific information as calculated by MEME. For example,
>>> motif.num_occurrences
7
>>> motif.length
13
>>> evalue = motif.evalue
>>> print("%3.1g" % evalue)
0.2
>>> motif.name
'GSKGCATGTGAAA'
>>> motif.id
'motif_1'
In addition to using an index into the record, as we did above, you can also find it by its name:
>>> motif = record["GSKGCATGTGAAA"]
Each motif has an attribute .alignment with the sequence alignment
in which the motif was found, providing some information on each of the
sequences:
>>> len(motif.alignment)
7
>>> motif.alignment.sequences[0]
Instance('GCGGCATGTGAAA')
>>> motif.alignment.sequences[0].motif_name
'GSKGCATGTGAAA'
>>> motif.alignment.sequences[0].sequence_name
'INO1'
>>> motif.alignment.sequences[0].sequence_id
'sequence_5'
>>> motif.alignment.sequences[0].start
620
>>> motif.alignment.sequences[0].strand
'+'
>>> motif.alignment.sequences[0].length
13
>>> pvalue = motif.alignment.sequences[0].pvalue
>>> print("%5.3g" % pvalue)
1.21e-08
MAST
TRANSFAC
TRANSFAC is a manually curated database of transcription factors, together with their genomic binding sites and DNA binding profiles [Matys2003]. While the file format used in the TRANSFAC database is nowadays also used by others, we will refer to it as the TRANSFAC file format.
A minimal file in the TRANSFAC format looks as follows:
ID motif1
P0 A C G T
01 1 2 2 0 S
02 2 1 2 0 R
03 3 0 1 1 A
04 0 5 0 0 C
05 5 0 0 0 A
06 0 0 4 1 G
07 0 1 4 0 G
08 0 0 0 5 T
09 0 0 5 0 G
10 0 1 2 2 K
11 0 2 0 3 Y
12 1 0 3 1 G
//
This file shows the frequency matrix of motif motif1 of 12
nucleotides. In general, one file in the TRANSFAC format can contain
multiple motifs. For example, this is the contents of the example
TRANSFAC file transfac.dat:
VV EXAMPLE January 15, 2013
XX
//
ID motif1
P0 A C G T
01 1 2 2 0 S
02 2 1 2 0 R
03 3 0 1 1 A
...
11 0 2 0 3 Y
12 1 0 3 1 G
//
ID motif2
P0 A C G T
01 2 1 2 0 R
02 1 2 2 0 S
...
09 0 0 0 5 T
10 0 2 0 3 Y
//
To parse a TRANSFAC file, use
>>> with open("transfac.dat") as handle:
... record = motifs.parse(handle, "TRANSFAC")
...
If any discrepancies between the file contents and the TRANSFAC file
format are detected, a ValueError is raised. Note that you may
encounter files that do not follow the TRANSFAC format strictly. For
example, the number of spaces between columns may be different, or a tab
may be used instead of spaces. Use strict=False to enable parsing
such files without raising a ValueError:
>>> record = motifs.parse(handle, "TRANSFAC", strict=False)
When parsing a non-compliant file, we recommend to check the record
returned by motif.parse to ensure that it is consistent with the
file contents.
The overall version number, if available, is stored as
record.version:
>>> record.version
'EXAMPLE January 15, 2013'
Each motif in record is in instance of the
Bio.motifs.transfac.Motif class, which inherits both from the
Bio.motifs.Motif class and from a Python dictionary. The dictionary
uses the two-letter keys to store any additional information about the
motif:
>>> motif = record[0]
>>> motif.degenerate_consensus # Using the Bio.motifs.Motif property
Seq('SRACAGGTGKYG')
>>> motif["ID"] # Using motif as a dictionary
'motif1'
TRANSFAC files are typically much more elaborate than this example, containing lots of additional information about the motif. Table Fields commonly found in TRANSFAC files lists the two-letter field codes that are commonly found in TRANSFAC files:
|
Accession number |
|
Accession numbers, secondary |
|
Statistical basis |
|
Binding factors |
|
Factor binding sites underlying the matrix |
|
Comments |
|
Copyright notice |
|
Short factor description |
|
External databases |
|
Date created/updated |
|
Subfamilies |
|
Superfamilies |
|
Identifier |
|
Name of the binding factor |
|
Taxonomic classification |
|
Species/Taxon |
|
Older version |
|
Preferred version |
|
Type |
|
Empty line; these are not stored in the Record. |
Each motif also has an attribute .references containing the
references associated with the motif, using these two-letter keys:
|
Reference number |
|
Reference authors |
|
Reference data |
|
Reference title |
|
PubMed ID |
Printing the motifs writes them out in their native TRANSFAC format:
>>> print(record)
VV EXAMPLE January 15, 2013
XX
//
ID motif1
XX
P0 A C G T
01 1 2 2 0 S
02 2 1 2 0 R
03 3 0 1 1 A
04 0 5 0 0 C
05 5 0 0 0 A
06 0 0 4 1 G
07 0 1 4 0 G
08 0 0 0 5 T
09 0 0 5 0 G
10 0 1 2 2 K
11 0 2 0 3 Y
12 1 0 3 1 G
XX
//
ID motif2
XX
P0 A C G T
01 2 1 2 0 R
02 1 2 2 0 S
03 0 5 0 0 C
04 3 0 1 1 A
05 0 0 4 1 G
06 5 0 0 0 A
07 0 1 4 0 G
08 0 0 5 0 G
09 0 0 0 5 T
10 0 2 0 3 Y
XX
//
You can export the motifs in the TRANSFAC format by capturing this output in a string and saving it in a file:
>>> text = str(record)
>>> with open("mytransfacfile.dat", "w") as out_handle:
... out_handle.write(text)
...
Writing motifs
Speaking of exporting, let’s look at export functions in general. We can
use the format built-in function to write the motif in the simple
JASPAR pfm format:
>>> print(format(arnt, "pfm"))
4.00 19.00 0.00 0.00 0.00 0.00
16.00 0.00 20.00 0.00 0.00 0.00
0.00 1.00 0.00 20.00 0.00 20.00
0.00 0.00 0.00 0.00 20.00 0.00
Similarly, we can use format to write the motif in the JASPAR
jaspar format:
>>> print(format(arnt, "jaspar"))
>MA0004.1 Arnt
A [ 4.00 19.00 0.00 0.00 0.00 0.00]
C [ 16.00 0.00 20.00 0.00 0.00 0.00]
G [ 0.00 1.00 0.00 20.00 0.00 20.00]
T [ 0.00 0.00 0.00 0.00 20.00 0.00]
To write the motif in a TRANSFAC-like matrix format, use
>>> print(format(m, "transfac"))
P0 A C G T
01 3 0 0 4 W
02 7 0 0 0 A
03 0 5 0 2 C
04 2 2 3 0 V
05 1 6 0 0 C
XX
//
To write out multiple motifs, you can use motifs.write. This
function can be used regardless of whether the motifs originated from a
TRANSFAC file. For example,
>>> two_motifs = [arnt, srf]
>>> print(motifs.write(two_motifs, "transfac"))
P0 A C G T
01 4 16 0 0 C
02 19 0 1 0 A
03 0 20 0 0 C
04 0 0 20 0 G
05 0 0 0 20 T
06 0 0 20 0 G
XX
//
P0 A C G T
01 2 1 39 4 G
02 9 33 2 2 C
03 0 45 1 0 C
04 1 45 0 0 C
05 32 1 0 13 A
06 3 1 0 42 T
07 46 0 0 0 A
08 1 0 0 45 T
09 43 0 0 3 A
10 15 1 0 30 W
11 2 0 44 0 G
12 2 1 43 0 G
XX
//
Or, to write multiple motifs in the jaspar format:
>>> two_motifs = [arnt, mef2a]
>>> print(motifs.write(two_motifs, "jaspar"))
>MA0004.1 Arnt
A [ 4.00 19.00 0.00 0.00 0.00 0.00]
C [ 16.00 0.00 20.00 0.00 0.00 0.00]
G [ 0.00 1.00 0.00 20.00 0.00 20.00]
T [ 0.00 0.00 0.00 0.00 20.00 0.00]
>MA0052.1 MEF2A
A [ 1.00 0.00 57.00 2.00 9.00 6.00 37.00 2.00 56.00 6.00]
C [ 50.00 0.00 1.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00]
G [ 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.00 50.00]
T [ 7.00 58.00 0.00 55.00 49.00 52.00 21.00 56.00 0.00 2.00]
Position-Weight Matrices
The .counts attribute of a Motif object shows how often each
nucleotide appeared at each position along the alignment. We can
normalize this matrix by dividing by the number of instances in the
alignment, resulting in the probability of each nucleotide at each
position along the alignment. We refer to these probabilities as the
position-weight matrix. However, beware that in the literature this term
may also be used to refer to the position-specific scoring matrix, which
we discuss below.
Usually, pseudocounts are added to each position before normalizing.
This avoids overfitting of the position-weight matrix to the limited
number of motif instances in the alignment, and can also prevent
probabilities from becoming zero. To add a fixed pseudocount to all
nucleotides at all positions, specify a number for the pseudocounts
argument:
>>> pwm = m.counts.normalize(pseudocounts=0.5)
>>> print(pwm)
0 1 2 3 4
A: 0.39 0.83 0.06 0.28 0.17
C: 0.06 0.06 0.61 0.28 0.72
G: 0.06 0.06 0.06 0.39 0.06
T: 0.50 0.06 0.28 0.06 0.06
Alternatively, pseudocounts can be a dictionary specifying the
pseudocounts for each nucleotide. For example, as the GC content of the
human genome is about 40%, you may want to choose the pseudocounts
accordingly:
>>> pwm = m.counts.normalize(pseudocounts={"A": 0.6, "C": 0.4, "G": 0.4, "T": 0.6})
>>> print(pwm)
0 1 2 3 4
A: 0.40 0.84 0.07 0.29 0.18
C: 0.04 0.04 0.60 0.27 0.71
G: 0.04 0.04 0.04 0.38 0.04
T: 0.51 0.07 0.29 0.07 0.07
The position-weight matrix has its own methods to calculate the consensus, anticonsensus, and degenerate consensus sequences:
>>> pwm.consensus
Seq('TACGC')
>>> pwm.anticonsensus
Seq('CCGTG')
>>> pwm.degenerate_consensus
Seq('WACNC')
Note that due to the pseudocounts, the degenerate consensus sequence calculated from the position-weight matrix is slightly different from the degenerate consensus sequence calculated from the instances in the motif:
>>> m.degenerate_consensus
Seq('WACVC')
The reverse complement of the position-weight matrix can be calculated
directly from the pwm:
>>> rpwm = pwm.reverse_complement()
>>> print(rpwm)
0 1 2 3 4
A: 0.07 0.07 0.29 0.07 0.51
C: 0.04 0.38 0.04 0.04 0.04
G: 0.71 0.27 0.60 0.04 0.04
T: 0.18 0.29 0.07 0.84 0.40
Position-Specific Scoring Matrices
Using the background distribution and PWM with pseudo-counts added, it’s
easy to compute the log-odds ratios, telling us what are the log odds of
a particular symbol to be coming from a motif against the background. We
can use the .log_odds() method on the position-weight matrix:
>>> pssm = pwm.log_odds()
>>> print(pssm)
0 1 2 3 4
A: 0.68 1.76 -1.91 0.21 -0.49
C: -2.49 -2.49 1.26 0.09 1.51
G: -2.49 -2.49 -2.49 0.60 -2.49
T: 1.03 -1.91 0.21 -1.91 -1.91
Here we can see positive values for symbols more frequent in the motif than in the background and negative for symbols more frequent in the background. \(0.0\) means that it’s equally likely to see a symbol in the background and in the motif.
This assumes that A, C, G, and T are equally likely in the background.
To calculate the position-specific scoring matrix against a background
with unequal probabilities for A, C, G, T, use the background
argument. For example, against a background with a 40% GC content, use
>>> background = {"A": 0.3, "C": 0.2, "G": 0.2, "T": 0.3}
>>> pssm = pwm.log_odds(background)
>>> print(pssm)
0 1 2 3 4
A: 0.42 1.49 -2.17 -0.05 -0.75
C: -2.17 -2.17 1.58 0.42 1.83
G: -2.17 -2.17 -2.17 0.92 -2.17
T: 0.77 -2.17 -0.05 -2.17 -2.17
The maximum and minimum score obtainable from the PSSM are stored in the
.max and .min properties:
>>> print("%4.2f" % pssm.max)
6.59
>>> print("%4.2f" % pssm.min)
-10.85
The mean and standard deviation of the PSSM scores with respect to a
specific background are calculated by the .mean and .std
methods.
>>> mean = pssm.mean(background)
>>> std = pssm.std(background)
>>> print("mean = %0.2f, standard deviation = %0.2f" % (mean, std))
mean = 3.21, standard deviation = 2.59
A uniform background is used if background is not specified. The
mean is equal to the Kullback-Leibler divergence or relative entropy
described in Section Relative entropy.
The .reverse_complement, .consensus, .anticonsensus, and
.degenerate_consensus methods can be applied directly to PSSM
objects.
Searching for instances
The most frequent use for a motif is to find its instances in some sequence. For the sake of this section, we will use an artificial sequence like this:
>>> test_seq = Seq("TACACTGCATTACAACCCAAGCATTA")
>>> len(test_seq)
26
Searching for exact matches
The simplest way to find instances, is to look for exact matches of the true instances of the motif:
>>> for pos, seq in test_seq.search(m.alignment):
... print("%i %s" % (pos, seq))
...
0 TACAC
10 TACAA
13 AACCC
We can do the same with the reverse complement (to find instances on the complementary strand):
>>> for pos, seq in test_seq.search(r.alignment):
... print("%i %s" % (pos, seq))
...
6 GCATT
20 GCATT
Searching for matches using the PSSM score
It’s just as easy to look for positions, giving rise to high log-odds scores against our motif:
>>> for position, score in pssm.search(test_seq, threshold=3.0):
... print("Position %d: score = %5.3f" % (position, score))
...
Position 0: score = 5.622
Position -20: score = 4.601
Position 10: score = 3.037
Position 13: score = 5.738
Position -6: score = 4.601
The negative positions refer to instances of the motif found on the
reverse strand of the test sequence, and follow the Python convention on
negative indices. Therefore, the instance of the motif at pos is
located at test_seq[pos:pos+len(m)] both for positive and for
negative values of pos.
You may notice the threshold parameter, here set arbitrarily to \(3.0\). This is in \(log_2\), so we are now looking only for words, which are eight times more likely to occur under the motif model than in the background. The default threshold is \(0.0\), which selects everything that looks more like the motif than the background.
You can also calculate the scores at all positions along the sequence:
>>> pssm.calculate(test_seq)
array([ 5.62230396, -5.6796999 , -3.43177247, 0.93827754,
-6.84962511, -2.04066086, -10.84962463, -3.65614533,
-0.03370807, -3.91102552, 3.03734159, -2.14918518,
-0.6016975 , 5.7381525 , -0.50977498, -3.56422281,
-8.73414803, -0.09919716, -0.6016975 , -2.39429784,
-10.84962463, -3.65614533], dtype=float32)
In general, this is the fastest way to calculate PSSM scores. The scores
returned by pssm.calculate are for the forward strand only. To
obtain the scores on the reverse strand, you can take the reverse
complement of the PSSM:
>>> rpssm = pssm.reverse_complement()
>>> rpssm.calculate(test_seq)
array([ -9.43458748, -3.06172252, -7.18665981, -7.76216221,
-2.04066086, -4.26466274, 4.60124254, -4.2480607 ,
-8.73414803, -2.26503372, -6.49598789, -5.64668512,
-8.73414803, -10.84962463, -4.82356262, -4.82356262,
-5.64668512, -8.73414803, -4.15613794, -5.6796999 ,
4.60124254, -4.2480607 ], dtype=float32)
Selecting a score threshold
If you want to use a less arbitrary way of selecting thresholds, you can explore the distribution of PSSM scores. Since the space for a score distribution grows exponentially with motif length, we are using an approximation with a given precision to keep computation cost manageable:
>>> distribution = pssm.distribution(background=background, precision=10**4)
The distribution object can be used to determine a number of
different thresholds. We can specify the requested false-positive rate
(probability of “finding” a motif instance in background generated
sequence):
>>> threshold = distribution.threshold_fpr(0.01)
>>> print("%5.3f" % threshold)
4.009
or the false-negative rate (probability of “not finding” an instance generated from the motif):
>>> threshold = distribution.threshold_fnr(0.1)
>>> print("%5.3f" % threshold)
-0.510
or a threshold (approximately) satisfying some relation between the false-positive rate and the false-negative rate (\(\frac{\textrm{fnr}}{\textrm{fpr}}\simeq t\)):
>>> threshold = distribution.threshold_balanced(1000)
>>> print("%5.3f" % threshold)
6.241
or a threshold satisfying (roughly) the equality between the \(-log\) of the false-positive rate and the information content (as used in patser software by Hertz and Stormo):
>>> threshold = distribution.threshold_patser()
>>> print("%5.3f" % threshold)
0.346
For example, in case of our motif, you can get the threshold giving you exactly the same results (for this sequence) as searching for instances with balanced threshold with rate of \(1000\).
>>> threshold = distribution.threshold_fpr(0.01)
>>> print("%5.3f" % threshold)
4.009
>>> for position, score in pssm.search(test_seq, threshold=threshold):
... print("Position %d: score = %5.3f" % (position, score))
...
Position 0: score = 5.622
Position -20: score = 4.601
Position 13: score = 5.738
Position -6: score = 4.601
Each motif object has an associated Position-Specific Scoring Matrix
To facilitate searching for potential TFBSs using PSSMs, both the position-weight matrix and the position-specific scoring matrix are associated with each motif. Using the Arnt motif as an example:
>>> from Bio import motifs
>>> with open("Arnt.sites") as handle:
... motif = motifs.read(handle, "sites")
...
>>> print(motif.counts)
0 1 2 3 4 5
A: 4.00 19.00 0.00 0.00 0.00 0.00
C: 16.00 0.00 20.00 0.00 0.00 0.00
G: 0.00 1.00 0.00 20.00 0.00 20.00
T: 0.00 0.00 0.00 0.00 20.00 0.00
>>> print(motif.pwm)
0 1 2 3 4 5
A: 0.20 0.95 0.00 0.00 0.00 0.00
C: 0.80 0.00 1.00 0.00 0.00 0.00
G: 0.00 0.05 0.00 1.00 0.00 1.00
T: 0.00 0.00 0.00 0.00 1.00 0.00
>>> print(motif.pssm)
0 1 2 3 4 5
A: -0.32 1.93 -inf -inf -inf -inf
C: 1.68 -inf 2.00 -inf -inf -inf
G: -inf -2.32 -inf 2.00 -inf 2.00
T: -inf -inf -inf -inf 2.00 -inf
The negative infinities appear here because the corresponding entry in the frequency matrix is 0, and we are using zero pseudocounts by default:
>>> for letter in "ACGT":
... print("%s: %4.2f" % (letter, motif.pseudocounts[letter]))
...
A: 0.00
C: 0.00
G: 0.00
T: 0.00
If you change the .pseudocounts attribute, the position-frequency
matrix and the position-specific scoring matrix are recalculated
automatically:
>>> motif.pseudocounts = 3.0
>>> for letter in "ACGT":
... print("%s: %4.2f" % (letter, motif.pseudocounts[letter]))
...
A: 3.00
C: 3.00
G: 3.00
T: 3.00
>>> print(motif.pwm)
0 1 2 3 4 5
A: 0.22 0.69 0.09 0.09 0.09 0.09
C: 0.59 0.09 0.72 0.09 0.09 0.09
G: 0.09 0.12 0.09 0.72 0.09 0.72
T: 0.09 0.09 0.09 0.09 0.72 0.09
>>> print(motif.pssm)
0 1 2 3 4 5
A: -0.19 1.46 -1.42 -1.42 -1.42 -1.42
C: 1.25 -1.42 1.52 -1.42 -1.42 -1.42
G: -1.42 -1.00 -1.42 1.52 -1.42 1.52
T: -1.42 -1.42 -1.42 -1.42 1.52 -1.42
You can also set the .pseudocounts to a dictionary over the four
nucleotides if you want to use different pseudocounts for them. Setting
motif.pseudocounts to None resets it to its default value of
zero.
The position-specific scoring matrix depends on the background distribution, which is uniform by default:
>>> for letter in "ACGT":
... print("%s: %4.2f" % (letter, motif.background[letter]))
...
A: 0.25
C: 0.25
G: 0.25
T: 0.25
Again, if you modify the background distribution, the position-specific scoring matrix is recalculated:
>>> motif.background = {"A": 0.2, "C": 0.3, "G": 0.3, "T": 0.2}
>>> print(motif.pssm)
0 1 2 3 4 5
A: 0.13 1.78 -1.09 -1.09 -1.09 -1.09
C: 0.98 -1.68 1.26 -1.68 -1.68 -1.68
G: -1.68 -1.26 -1.68 1.26 -1.68 1.26
T: -1.09 -1.09 -1.09 -1.09 1.85 -1.09
Setting motif.background to None resets it to a uniform
distribution:
>>> motif.background = None
>>> for letter in "ACGT":
... print("%s: %4.2f" % (letter, motif.background[letter]))
...
A: 0.25
C: 0.25
G: 0.25
T: 0.25
If you set motif.background equal to a single value, it will be
interpreted as the GC content:
>>> motif.background = 0.8
>>> for letter in "ACGT":
... print("%s: %4.2f" % (letter, motif.background[letter]))
...
A: 0.10
C: 0.40
G: 0.40
T: 0.10
Note that you can now calculate the mean of the PSSM scores over the background against which it was computed:
>>> print("%f" % motif.pssm.mean(motif.background))
4.703928
as well as its standard deviation:
>>> print("%f" % motif.pssm.std(motif.background))
3.290900
and its distribution:
>>> distribution = motif.pssm.distribution(background=motif.background)
>>> threshold = distribution.threshold_fpr(0.01)
>>> print("%f" % threshold)
3.854375
Note that the position-weight matrix and the position-specific scoring
matrix are recalculated each time you call motif.pwm or
motif.pssm, respectively. If speed is an issue and you want to use
the PWM or PSSM repeatedly, you can save them as a variable, as in
>>> pssm = motif.pssm
Comparing motifs
Once we have more than one motif, we might want to compare them.
Before we start comparing motifs, I should point out that motif boundaries are usually quite arbitrary. This means we often need to compare motifs of different lengths, so comparison needs to involve some kind of alignment. This means we have to take into account two things:
alignment of motifs
some function to compare aligned motifs
To align the motifs, we use ungapped alignment of PSSMs and substitute zeros for any missing columns at the beginning and end of the matrices. This means that effectively we are using the background distribution for columns missing from the PSSM. The distance function then returns the minimal distance between motifs, as well as the corresponding offset in their alignment.
To give an example, let us first load another motif, which is similar to
our test motif m:
>>> with open("REB1.pfm") as handle:
... m_reb1 = motifs.read(handle, "pfm")
...
>>> m_reb1.consensus
Seq('GTTACCCGG')
>>> print(m_reb1.counts)
0 1 2 3 4 5 6 7 8
A: 30.00 0.00 0.00 100.00 0.00 0.00 0.00 0.00 15.00
C: 10.00 0.00 0.00 0.00 100.00 100.00 100.00 0.00 15.00
G: 50.00 0.00 0.00 0.00 0.00 0.00 0.00 60.00 55.00
T: 10.00 100.00 100.00 0.00 0.00 0.00 0.00 40.00 15.00
To make the motifs comparable, we choose the same values for the
pseudocounts and the background distribution as our motif m:
>>> m_reb1.pseudocounts = {"A": 0.6, "C": 0.4, "G": 0.4, "T": 0.6}
>>> m_reb1.background = {"A": 0.3, "C": 0.2, "G": 0.2, "T": 0.3}
>>> pssm_reb1 = m_reb1.pssm
>>> print(pssm_reb1)
0 1 2 3 4 5 6 7 8
A: 0.00 -5.67 -5.67 1.72 -5.67 -5.67 -5.67 -5.67 -0.97
C: -0.97 -5.67 -5.67 -5.67 2.30 2.30 2.30 -5.67 -0.41
G: 1.30 -5.67 -5.67 -5.67 -5.67 -5.67 -5.67 1.57 1.44
T: -1.53 1.72 1.72 -5.67 -5.67 -5.67 -5.67 0.41 -0.97
We’ll compare these motifs using the Pearson correlation. Since we want it to resemble a distance measure, we actually take \(1-r\), where \(r\) is the Pearson correlation coefficient (PCC):
>>> distance, offset = pssm.dist_pearson(pssm_reb1)
>>> print("distance = %5.3g" % distance)
distance = 0.239
>>> print(offset)
-2
This means that the best PCC between motif m and m_reb1 is
obtained with the following alignment:
m: bbTACGCbb
m_reb1: GTTACCCGG
where b stands for background distribution. The PCC itself is
roughly \(1-0.239=0.761\).
De novo motif finding
Currently, Biopython has only limited support for de novo motif
finding. Namely, we support running xxmotif and also parsing of
MEME. Since the number of motif finding tools is growing rapidly,
contributions of new parsers are welcome.
MEME
Let’s assume, you have run MEME on sequences of your choice with your
favorite parameters and saved the output in the file meme.out. You
can retrieve the motifs reported by MEME by running the following piece
of code:
>>> from Bio import motifs
>>> with open("meme.psp_test.classic.zoops.xml") as handle:
... motifsM = motifs.parse(handle, "meme")
...
>>> motifsM
[<Bio.motifs.meme.Motif object at 0xc356b0>]
Besides the most wanted list of motifs, the result object contains more useful information, accessible through properties with self-explanatory names:
.alphabet.datafile.sequences.version.command
The motifs returned by the MEME Parser can be treated exactly like regular Motif objects (with instances), they also provide some extra functionality, by adding additional information about the instances.
>>> motifsM[0].consensus
Seq('GCTTATGTAA')
>>> motifsM[0].alignment.sequences[0].sequence_name
'iYFL005W'
>>> motifsM[0].alignment.sequences[0].sequence_id
'sequence_15'
>>> motifsM[0].alignment.sequences[0].start
480
>>> motifsM[0].alignment.sequences[0].strand
'+'
>>> motifsM[0].alignment.sequences[0].pvalue
1.97e-06
Useful links
Sequence motif in wikipedia
PWM in wikipedia
Consensus sequence in wikipedia