bioplus.motif

class bioplus.motif.CharDict(dict={})

CharDict is a generalized dictionary that inherits defaultdict. default values are 0. Accepts any keys.

uncertainty()

calculates the uncertainty H in this position (specified by CharDict). reats 0*log(0) as 0

bioplus.motif.KL(p, q)

returns a list of the KL divergence (relative entropy) at each position from positionmatrix p to positionmatrix q. use sum() for the sum

class bioplus.motif.PositionWeightMatrix(n=None)

Stores counts of nucleotide bases at each position. objects are immutable. sequences may be added to the counts, but the object may not be modified in situ

Rs()

returns the Schneider Rs value, which is the expectation of Ri over all possible sequences, calculated as the sum of 2-uncertainty.

count_file(seqsFile, n=0)

uses a tabFile with a list of sequences, in column n (by default n=0, the first column) and extracts counts

count_seqs(L, debug=False)

adds a list of sequences to the counts

import_from_MEME(filename, n=1, mode='biotools')

imports a motif from the output of MEME (meme.txt)

if there are multiple motifs in the output, we will use motif n (the first is n=1, which is also the default)

make_Ri()

changes from counts or probabilities to Ri, information content

make_probs(trueProbs=False)

normalizes everything to 1

rc()

returns the reverse complement of this object

seq_Ri(s)

seqRi returns the information content Ri in bits of a sequences, as measured with the given positionmatrix

uncertainty()

returns the uncertainty H(l) of the matrix as a list. Use sum() for the total uncertainty.

Note: this function calls uncertainty() from the baseDict instance, and as such it can be overwritten implicitly. baseDict.uncertainty() treats 0*log(0) as 0

bioplus.motif.center_region(f, max_dist=75, motif_length=17)

returns a function that specifies whether a given motif is in +/- x bp from the peak_center

requires the tabFile object f to determine the indices properly

bioplus.motif.count_letters(L)

bioplus.motif.count_spacers_from_info(foo, cutoff=None, region_rule=None, region_width=None, spacer_offset=8, spacer_length=3, output_file=None)

count spacers from a .sites.info or .peaks.info file

optionally you may supply cutoff, a minimum cutoff (float or int) region_rule, a function that selects the column

bioplus.motif.joint_matrix(sites)

takes as input a filename and returns the joint Rate matrix for the list of sequences contained in that file

Joint rates R(X;Y_ are defined as R(X;Y) = - sum over X,Y p(x,y) * I(X;Y) I(X;y) = - sum over X,Y p(x,y) * log2[p(x,y)/(p(x)p(y))]

bioplus.motif.spacerGC(L, spacerOffset=6, spacerLength=3)

spacerGC takes as input a list of [15 bp GBSs (strings)] and returns the number of sequences that have 0,1,2,3 G/Cs in the 3 bp spacer as an array in that order