4
\$\begingroup\$

I came across this BMC Genomics paper: Analysis of intra-genomic GC content homogeneity within prokaryotes

And I implemented some Python functions to make this available as part of a personal project. The functions are these:

import numpy as np
from collections import defaultdict

def get_sequence_chunks(sequence, window, step, as_overlap=False):
    """GC Content in a DNA/RNA sub-sequence length k. In
    overlap windows of length k, other wise non overlapping.
    
    Inputs:
        sequence - a string representing a DNA sequence.    
        as_overlap - boolean that represents if overlap is needed.
        k - a integer representing the lengths of overlapping bases.
            Default is 20.
    
    Outputs:
        subseq - a string representing a slice of the sequence with or without
                 overlapping characters.
    """
    # make sequence upper case and getting the length of it
    sequence, seq_len = sequence.upper(), len(sequence)
    # non overlap sequence length
    non_overlap = range(0, seq_len - window + 1, step)
    # overlap sequence length
    overlap = range(0, seq_len - window + 1)
    # overlap is needed
    if as_overlap:
        # iterates to the overlap region
        for i in overlap:
            # creates the substring 
            subseq = sequence[i:i + window]
            yield subseq
    # if non overlap is choosed
    else:
        # iterates to the non overlap region
        for j in non_overlap:
            # creates the sub-string to count the gc_content
            subseq = sequence[j:j + window]
            yield subseq

def gc_content(seq, percent=True):
    """
    Calculate G+C content, return percentage (as float between 0 and 100).
    Copes mixed case sequences, and with the ambiguous nucleotide S (G or C)
    when counting the G and C content.  The percentage is calculated against
    the full length.
    
    Inputs:
        sequence - a string representing a sequence (DNA/RNA/Protein)
    
    Outputs:
        gc - a float number or a percent value representing the gc content of the sequence.     
    
    """
    seq_len = len(seq) 
    gc = sum(seq.count(x) for x in ["G", "C", "g", "c", "S", "s"])
    if percent:
        try:
            return gc * 100.0 / seq_len
        except ZeroDivisionError:
            return 0.0 
    else:
        return gc / seq_len      


def get_gc_content_slide_window(sequence, window, step):
    """
    Calculate teh GC (G+C) content along a sequence. Returns a dictionary -like
    object mapping the sequence window to the gc value (floats).
    Returns 0 for windows without any G/C by handling zero division errors, and 
    does NOT look at any ambiguous nucleotides.
    
    Inputs:
        sequence - a string representing a sequence (DNA/RNA/Protein)
        window_size - a integer representing the length of sequence to be analyzed
                   at each step in the full sequence.
        step - a integer representing the length of oerlapping sequence allowed.
    
    Outputs:
        gc- a dictionary-like object mapping the window size sequence to it gc values 
            as floating numbers
    """
    gc = defaultdict(float)
    for s in get_sequence_chunks(sequence, window, step, as_overlap=False):
        gc[s] = gc.get(s, 0.0) + gc_content(s)
    return gc   

def difference_gc(mean_gc, gc_dict):
    """
    Calculates the difference between the mean GC content of window i, and the
    mean chromosomal GC content, as Di = GC i − GC
    """
    # get the keys and default values in the dictionary
    d_i = defaultdict(float, [(k, 0.0) for k in gc_dict.keys()])
    # iterate through all keys, gc calculated values for the
    # genome chunk
    for chunk, gc_cnt in gc_dict.items():
        # get the difference between the chromosomal mean and the chunks
        dif = gc_cnt - mean_gc
        # add the difference to the appropriate chunk
        d_i[chunk] = dif
    return d_i

def get_chromosomal_gc_variantion(difference_dic):
    """
    Calculates the chromosomal GC variation defined as the log-transformed average 
    of the absolute value of the difference between the mean GC content of each 
    non-overlapping sliding window i and mean chromosomal GC content.    
    chromosomal_gc_variantion = log(1/N*sum(|Di|), where N is the maximum number of 
    non-overlapping window size in bp in a sliding windows.
    """
    # get the number of chunks
    n = len(difference_dic.keys())
    arr = np.array(list(difference_dic.values()))
    var = np.log((1/n) * sum(abs(arr)))
    return var

I worked with e. coli genome: Escherichia coli strain RM13322 chromosome, complete genome

gc = gc_content(ec, percent=True) = 50.76819424506625

It is similar to found here: Genome Assembly and Annotation report (26286) ~ 50.73.

The slide window gave it non overlapping results like, where ec = escherichia coli genome, 100 size window, and step = 100:

get_gc_content_slide_window(ec, 100, 100)

defaultdict(float,
            {'GTTGGCATGCCATGGTGATGTTTGTTAGGAAAGCAAAGATGGCAAAACTGCTGGGGGTTTTGTGGTTGAGTATGCCAATATAATTAATAGATTAAAGAGT': 39.0,
             'TAGTTGTGAAGAAAATATGGATAAACAGGACGACGAATGCTTTCACCGATAAGGACAACTTTCCATAACTCAGTAAATATAGTGCAGAGTTCACCCTGTC': 39.0,
             'AAACGGTTTCTTTTGCAGGAAAGGAATATGAGTTAAAGGTCATTGATGAAAAAACGCCTATTCTTTTTCAGTGGTTTGAACCTAATCCTGAACGATATAA': 34.0,
             'GAAAGATGAGGTTCCAATAGTTAATACTAAGCAGCATCCCTATTTAGATAATGTCACAAATGCGGCAAGGATAGAGAGTGATCGTATGATAGGTATTTTT': 36.0,
             'GTTGATGGCGATTTTTCAGTCAACCAAAAGACTGCTTTTTCAAAATTGGAACGAGATTTTGAAAATGTAATGATAATCTATCGGGAAGATGTTGACTTCA': 34.0,
             'GTATGTATGACAGAAAACTATCAGATATTTATCATGATATTATATGTGAACAAAGGTTACGAACTGAAGACAAAAGAGATGAATACTTGTTGAATCTGTT': 29.0,...}

The difference function (slide window gc content - gc content all sequence), where ec_gc = gc content of e.coli genome and ec_non = gc content in each subsequence of size 100 non overlapped:

difference_gc(ec_gc, ec_non)

defaultdict(float,
            {'GTTGGCATGCCATGGTGATGTTTGTTAGGAAAGCAAAGATGGCAAAACTGCTGGGGGTTTTGTGGTTGAGTATGCCAATATAATTAATAGATTAAAGAGT': -11.768194245066248,
             'TAGTTGTGAAGAAAATATGGATAAACAGGACGACGAATGCTTTCACCGATAAGGACAACTTTCCATAACTCAGTAAATATAGTGCAGAGTTCACCCTGTC': -11.768194245066248,
             'AAACGGTTTCTTTTGCAGGAAAGGAATATGAGTTAAAGGTCATTGATGAAAAAACGCCTATTCTTTTTCAGTGGTTTGAACCTAATCCTGAACGATATAA': -16.768194245066248,
             'GAAAGATGAGGTTCCAATAGTTAATACTAAGCAGCATCCCTATTTAGATAATGTCACAAATGCGGCAAGGATAGAGAGTGATCGTATGATAGGTATTTTT': -14.768194245066248,
             'GTTGATGGCGATTTTTCAGTCAACCAAAAGACTGCTTTTTCAAAATTGGAACGAGATTTTGAAAATGTAATGATAATCTATCGGGAAGATGTTGACTTCA': -16.768194245066248,...}

And the (ec_dif = difference on gc content from total sequence and from subsequences):

get_chromosomal_gc_variantion(ec_dif)
1.7809591767493207

My principal concern It is to know if this function get_chromosomal_gc_variantion(ec_dif) was implemented as in the paper above (GCVAR):

journal excerpt

As suggested, a toy example:

s="TAGTTGTGAAGAAAATATGGATAAACAGGACGACGAATGCTTTCACCGATAAGGACAACTTTCCATAACTCAGTAAATATAGTGCAGAGTTCACCCTGTC"

seq_gc_non_overllap = get_gc_content_slide_window(s, 20, 20)
seq_gc_non_overllap
defaultdict(float,
            {'TAGTTGTGAAGAAAATATGG': 30.0,
             'ATAAACAGGACGACGAATGC': 45.0,
             'TTTCACCGATAAGGACAACT': 40.0,
             'TTCCATAACTCAGTAAATAT': 25.0,
             'AGTGCAGAGTTCACCCTGTC': 55.0})

seq_gc_total = gc_content(s, percent=True)
seq_gc_total
39.0

seq_dif_gctot_gc_slidewindow = difference_gc(seq_gc_total, seq_gc_non_overllap)
defaultdict(float,
            {'TAGTTGTGAAGAAAATATGG': -9.0,
             'ATAAACAGGACGACGAATGC': 6.0,
             'TTTCACCGATAAGGACAACT': 1.0,
             'TTCCATAACTCAGTAAATAT': -14.0,
             'AGTGCAGAGTTCACCCTGTC': 16.0})

chromosome_gc_variation = get_chromosomal_gc_variantion(seq_dif_gctot, gc_slidewindow)
chromosome_gc_variation
2.2192034840549946

Any tip to improve the code would be very welcome, once I only have been working with python for a couple of years and I am not very skilled, and much less with numpy.

\$\endgroup\$
4
  • 1
    \$\begingroup\$ What are ec, ec_gc, ec_non, ec_dif? \$\endgroup\$
    – Reinderien
    Nov 6, 2021 at 18:02
  • 1
    \$\begingroup\$ It's good for you to have described them, but it would be more helpful to show the code that loads them, and perhaps a reduced-size sample for your reviewers to be able to run your code. \$\endgroup\$
    – Reinderien
    Nov 6, 2021 at 19:52
  • \$\begingroup\$ @Reinderien thank you! \$\endgroup\$ Nov 6, 2021 at 20:10
  • 1
    \$\begingroup\$ @PauloSergioSchlogl Could you also link your project once you've completed/published it? \$\endgroup\$
    – kubatucka
    Nov 6, 2021 at 20:22

1 Answer 1

2
\$\begingroup\$

Quite interesting!

You will benefit from introducing PEP484 type hints.

Your docstring for get_sequence_chunks is way out of whack; you'll need to revisit your parameter names.

There isn't a whole lot of benefit from tuple-combination assignment for your sequence and seq_len so I suggest just doing them separately.

Your overlap logic can be greatly simplified: notice that the only difference between the two code paths is the step size.

Minor spelling issues such as other wise that should be one word, overllap -> overlap, and variantion -> variation.

Comments such as

# make sequence upper case and getting the length of it

are less helpful than not having a comment at all. Note the difference between that and

# get the difference between the chromosomal mean and the chunks

which has content that can't necessarily be inferred from the code alone (though even this will be redundant with a sufficiently descriptive variable name).

This comprehension:

gc = sum(seq.count(x) for x in ["G", "C", "g", "c", "S", "s"])

will iterate through the entire seq string for every potential character. It's likely that refactoring this to use set membership checks and one single iteration through the string will be more efficient.

This code to prevent division-by-zero:

seq_len = len(seq) 
gc = sum(seq.count(x) for x in ["G", "C", "g", "c", "S", "s"])
if percent:
    try:
        return gc * 100.0 / seq_len
    except ZeroDivisionError:
        return 0.0 
else:
    return gc / seq_len      

first of all can trivially avoid logic-by-exception by checking if the sequence length is zero; and also should extend that check to the case where a non-percent figure is returned.

In difference_gc the initial defaultdict initialization is not needed, since you overwrite every single key. You can replace the whole function with one dictionary comprehension.

Try to avoid abbreviating words like count to cnt.

The len of a dict's keys is equal to the len of a dict itself, so you can drop the .keys() in len(difference_dic.keys()).

You can avoid the construction of a list here:

arr = np.array(list(difference_dic.values()))

by using fromiter instead.

Your example is kind of broken. get_chromosomal_gc_variation only accepts one parameter.

Consider reworking your example into a basic unit test.

Suggested

import math
from typing import Iterator, DefaultDict, Dict

import numpy as np
from collections import defaultdict


def get_sequence_chunks(
    sequence: str,
    window: int,
    step: int,
    as_overlap: bool = False,
) -> Iterator[str]:
    """GC Content in a DNA/RNA sub-sequence length k. In
    overlap windows of length k, other wise non overlapping.

    Inputs:
        sequence - a string representing a DNA sequence.
        as_overlap - boolean that represents if overlap is needed.
        k - a integer representing the lengths of overlapping bases.
            Default is 20.

    Outputs:
        subseq - a string representing a slice of the sequence with or without
                 overlapping characters.
    """
    sequence = sequence.upper()

    if as_overlap:
        # overlap sequence length
        step = 1

    # iterates to the overlap region
    for i in range(0, len(sequence) - window + 1, step):
        # creates the substring
        yield sequence[i:i + window]


def gc_content(seq: str, percent: bool = True) -> float:
    """
    Calculate G+C content, return percentage (as float between 0 and 100).
    Copes mixed case sequences, and with the ambiguous nucleotide S (G or C)
    when counting the G and C content.  The percentage is calculated against
    the full length.

    Inputs:
        sequence - a string representing a sequence (DNA/RNA/Protein)

    Outputs:
        gc - a float number or a percent value representing the gc content of the sequence.

    """
    seq_len = len(seq)
    gc = sum(1 for x in seq.upper() if x in {"G", "C", "S"})
    if seq_len == 0:
        return 0
    gc /= seq_len
    if percent:
        return gc * 100
    return gc


def get_gc_content_slide_window(sequence: str, window: int, step: int) -> DefaultDict[str, float]:
    """
    Calculate the GC (G+C) content along a sequence. Returns a dictionary-like
    object mapping the sequence window to the gc value (floats).
    Returns 0 for windows without any G/C by handling zero division errors, and
    does NOT look at any ambiguous nucleotides.

    Inputs:
        sequence - a string representing a sequence (DNA/RNA/Protein)
        window_size - a integer representing the length of sequence to be analyzed
                   at each step in the full sequence.
        step - a integer representing the length of oerlapping sequence allowed.

    Outputs:
        gc- a dictionary-like object mapping the window size sequence to it gc values
            as floating numbers
    """
    gc = defaultdict(float)
    for s in get_sequence_chunks(sequence, window, step, as_overlap=False):
        gc[s] += gc_content(s)
    return gc


def difference_gc(mean_gc: float, gc_dict: Dict[str, float]) -> Dict[str, float]:
    """
    Calculates the difference between the mean GC content of window i, and the
    mean chromosomal GC content, as Di = GC i − GC
    """
    # iterate through all keys, gc calculated values for the
    # genome chunk
    # get the difference between the chromosomal mean and the chunks
    # add the difference to the appropriate chunk
    d_i = {
        chunk: gc_cnt - mean_gc
        for chunk, gc_cnt in gc_dict.items()
    }
    return d_i


def get_chromosomal_gc_variation(difference_dic: Dict[str, float]) -> float:
    """
    Calculates the chromosomal GC variation defined as the log-transformed average
    of the absolute value of the difference between the mean GC content of each
    non-overlapping sliding window i and mean chromosomal GC content.
    chromosomal_gc_variantion = log(1/N*sum(|Di|), where N is the maximum number of
    non-overlapping window size in bp in a sliding windows.
    """
    # get the number of chunks
    n = len(difference_dic)
    arr = np.fromiter(
        difference_dic.values(),
        dtype=np.float32,
        count=len(difference_dic),
    )
    var = np.log(np.sum(np.abs(arr)) / n)
    return var


def test() -> None:
    s = (
        "TAGTTGTGAAGAAAATATGGATAAACAGGACGACGAATGCTTTCACCGAT"
        "AAGGACAACTTTCCATAACTCAGTAAATATAGTGCAGAGTTCACCCTGTC"
    )

    seq_gc_non_overlap = get_gc_content_slide_window(s, 20, 20)
    expected = {
        'TAGTTGTGAAGAAAATATGG': 30,
        'ATAAACAGGACGACGAATGC': 45,
        'TTTCACCGATAAGGACAACT': 40,
        'TTCCATAACTCAGTAAATAT': 25,
        'AGTGCAGAGTTCACCCTGTC': 55,
    }
    assert expected.keys() == seq_gc_non_overlap.keys()
    for k, v in expected.items():
        assert math.isclose(seq_gc_non_overlap[k], v)

    seq_gc_total = gc_content(s, percent=True)
    assert math.isclose(seq_gc_total, 39)

    seq_dif_gctot_gc_slidewindow = difference_gc(seq_gc_total, seq_gc_non_overlap)
    expected = {
        'TAGTTGTGAAGAAAATATGG':  -9,
        'ATAAACAGGACGACGAATGC':   6,
        'TTTCACCGATAAGGACAACT':   1,
        'TTCCATAACTCAGTAAATAT': -14,
        'AGTGCAGAGTTCACCCTGTC':  16,
    }
    assert expected.keys() == seq_dif_gctot_gc_slidewindow.keys()
    for k, v in expected.items():
        assert math.isclose(seq_dif_gctot_gc_slidewindow[k], v)

    chromosome_gc_variation = get_chromosomal_gc_variation(seq_dif_gctot_gc_slidewindow)
    assert math.isclose(chromosome_gc_variation, 2.2192034840549946)


if __name__ == '__main__':
    test()
\$\endgroup\$
1
  • 2
    \$\begingroup\$ thank you for your suggestions! I will check it out and sorry because English it is not my native language...some times I do a lot of mistakes. But for me it is much important to check if the statistics function chromosome_gc_variation is doing what it is need to do. But, Is always excellent to learn to write a good/efficient and beautiful code. Thank you for your time and tips. They are awesome. Paulo \$\endgroup\$ Nov 7, 2021 at 13:12

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.