An object-oriented Python library for simulating biological sequences.
Description
This module is interested in modeling noisy biological sequence data. This holds broad applicability. For example, by simulating biological sequences, we can experiment with different model configurations (models that are meant to learn and output patterns from biological sequences) and test their performance on data where we know the ground truth labels. While some repos such as simDNA attempt to do this, they are rather inflexible, and can only be used to simulate a limited number of patterns
Additionally, SeqSpawner can be used for any sequence alphabet, allowing it to be used for proteins, rna, or even text. It will allow researchers to simulate regulatory sequences from a known ground truth, thereby allowing careful investigation of complex methods such as sequence-based deep learning models.
Future Work
- add functionality to library that allows users to specify grammars for simulating more complex patterns.
- create methods for generating negative examples for a given grammar
- develop algorithms to learn the grammar tree from the sequence using unsupervised dependency parsing methods from natural language processing.
git clone https://github.com/tybens/seqspawner.git
cd seqspawner
python setup.py develop
This class will allow for method crossover between different models. That is, it allows for more than just dna
simulation. It also allows for the input of multiple (or no) motifs distributed over the length of
a simulated sequence.
Parameters
----------
name : str
A str that describes the title of the model.
list_motifs : list(object)
THe motifs as a list of motif objects (i.e. PWM, VariableLengthPWMMotif, VariableLengthMotifList,
FixedLengthMotifList)
background_weights : list(float)
A list of weights in float between 0 and 1 representing the background nucleotide probabilities.
dist_fn : list(function)
A list of functions for each motif that when called outputs a random int position for the motif.
length : int
The desired total length of each sequence
motif_names : list(str), optional
Default is None. A list of length == len(list_motifs) of the names of each motif.
resolve_overlap : {'reject', 'merge'}, optional
Default is 'reject'. The method for which to resolve overlapping motifs. 'reject' re-samples until dist_fn
generates positions which don't involve overlapping motifs, 'merge' combines the positional weights of
overlapping positions
total_tries_per_sample : int, optional
Default is 10. The number of attempts at generating a non-overlapping motifs or ordered positions.
Preventative infinite loop measure. To be used if ordered == True and/or if resolve_overlap == 'reject'.
ordered : bool, optional
Default is `False`. Boolean for rejection sampling motif position generation until the motifs are placed in
the order of the user's motif.
Parameters
----------
gc_frac : float
fraction of G's and C's in background dna sequencing
Returns
-------
list(float)
frequency of nucleotides in the background sequence. ['A', 'C', 'G', 'T']
from seqspawner import motifs
from seqspawner.model import Model
# load position weight matrix from .meme file
ctcf_pwm = motifs.PWM.from_meme_file("/home/tylerbenson/CTCF.meme")
# function that returns motif location
loc_fn_10 = lambda: 10
# background weights
background = [0.25, 0.25, 0.25, 0.25]
model = Model("test1",
ctcf_pwm.alphabet,
ctcf_pwm,
background,
loc_fn_10,
length=50)
model.sample(num_samples=10)
OUTPUT:
['GACAACACGTCTCCAGCAGGGGGAGCTATAGCAAATGATATTCCTAAGTT',
'CGCGTACCATGGACTCCAGGTGGCGCAAAGACAGTCCTAGGACCACCTTG',
'GTAATATCGTGGCCTCAAGGTGGCGGTGGCAGAGGAAGCTGTACGTTTAT',
'TGGCGTCATTGGCCACTAGGTGGCGCTTTTTAATCTATGGTCGATAACGC',
'CCTTGGTTGCGCACAGTAGGGGATACTACAATGAGCGCAGAGTACTGTCA',
'GCATGGGGATCGCCACTAGGTGGCACTTTTATATCAGAGAAGTTGACTCC',
'GGGTTTCATTGAACACTAGGTGGCGGTTAAAAAGCATTAGTTGCACCCCC',
'GAGCCCAATTACCCAGCAGGTGGCGCCCCCCGTCTCATACTGTCCTCACC',
'ATATATATGTCGCCTCCAGGTGGCAGTGAATCGGTACAAGCGTAGGAAGT',
'GCTTAGGCATTACCAGGGGAGGGCGCCGTCGCCCGAACTCCCAGTATACA']from seqspawner import motifs
from seqspawner.model import Model
import numpy
# initialize PWM motif objects from meme file
tbp_pwm = motifs.PWM.from_meme_file("/home/tylerbenson/TBP.meme")
ctcf_pwm = motifs.PWM.from_meme_file("/home/tylerbenson/CTCF.meme")
# all adenines separating the variable separation
background_pwm = numpy.array([[1], [0], [0], [0]])
background = [1, 0, 0, 0] # all adenines
# function that returns motif location
loc_fn_10 = lambda: 10
# function that returns 1-4 variable separation nt's
sep_fn = lambda: int(numpy.random.choice(4))+1
repeated_bg = motifs.VariableRepeatedMotif(PWM(background_pwm, tbp_pwm.alphabet), sep_fn)
list_test = motifs.create_motif_list([ctcf_pwm, repeated_bg, tbp_pwm])
model = Model("test2",
list_test.alphabet,
list_test,
background,
loc_fn_10,
length=50)
model.sample(num_samples=10)
OUTPUT:
['AAAAAAAAATTAACGCCAGAGGGCGCTCAAAACTATAAATTCCGGGTAAA',
'AAAAAAAAACGACCACTAGAAGGCACCAAAAACTATATAAAGAGTCGAAA',
'AAAAAAAAACTGCCACTAGGGGTCGGAGAATATAAATTGGTTGTAAAAAA',
'AAAAAAAAATTACCACAAGGGGGCGCTAAAAAGCTTAAAAAGCCTGGAAA',
'AAAAAAAAACCGCCAGTAGGGGGCGGTTAAAACTATTTCTACTCGTGAAA',
'AAAAAAAAACTGCCTCAAGGTGGCTGTCAAAATACAAAGGTCGGGAAAAA',
'AAAAAAAAATCACCAGCAGGGGGCGCTAAACTATAAAAATGAAGTAAAAA',
'AAAAAAAAACCACTTCAAGATGGCGGCCAATCATATAATTGAGCCAAAAA',
'AAAAAAAAATAGCCGCCAGGGGACACAAAAAGTATATAAAGACTCCAAAA',
'AAAAAAAAATGTCCACCTGAGGGAGACAAAAATATAAATACGCAAAAAAA']from seqspawner import motifs
from seqspawner.model import Model
# Load motifs
tbp_pwm = motifs.PWM.from_meme_file("/~/TBP.meme")
ctcf_pwm = motifs.PWM.from_meme_file("/~/CTCF.meme")
# create a motif list with lists embedded (list_test and repeated_bg is initialized in above example)
motif_list_of_lists = motifs.create_motif_list([tbp_pwm, repeated_bg, list_test])
# independent spacing list of motif_list_of_lists and tbp_pwm
motifs_all = [motif_list_of_lists, tbp_pwm]
# two location functions for the two motifs parsed into the Model
loc_fn_10, loc_fn_20 = lambda: 10, lambda: 20
dist_fns = [loc_fn_10, loc_fn_20] # parsed as a list
background = [1, 0, 0, 0]
model = Model("test3",
motif_list_of_lists.alphabet,
motifs_all,
background,
dist_fns,
length=70,
resolve_overlap='merge') # resolve overlap by merging the motifs
model.sample(1)
OUTPUT:
['AAAAAAAAACTATAAATGGGTAGAAAAGGGCAAACTAGAGGGCGCTAAGCATAAAAGCAATTCAAAAAAA']