Conversation
michtrofimov
left a comment
michtrofimov
left a comment
There was a problem hiding this comment.
Хорошая работа, все задания сделаны, даже чересчур
Comment on lines
+1
to
+40
| from Bio import SeqIO | ||
| from abc import ABC, abstractclassmethod | ||
| from Bio.SeqUtils import GC123 | ||
|
|
||
|
|
||
| RETRANSLATION_DICT = { | ||
| 'F': 'TTC', 'f': 'ttc', | ||
| 'L': 'TTA', 'l': 'tta', | ||
| 'S': 'TCG', 's': 'tcg', | ||
| 'Y': 'TAC', 'y': 'tac', | ||
| 'C': 'TGC', 'c': 'tgc', | ||
| 'W': 'TGG', 'w': 'tgg', | ||
| 'P': 'CCC', 'p': 'ccc', | ||
| 'H': 'CAT', 'h': 'cat', | ||
| 'Q': 'GAA', 'q': 'gaa', | ||
| 'R': 'CGA', 'r': 'cga', | ||
| 'I': 'ATT', 'i': 'att', | ||
| 'M': 'ATG', 'm': 'atg', | ||
| 'T': 'ACC', 't': 'acc', | ||
| 'N': 'AAT', 'n': 'aat', | ||
| 'K': 'AAA', 'k': 'aaa', | ||
| 'V': 'GTT', 'v': 'gtt', | ||
| 'A': 'GCA', 'a': 'gca', | ||
| 'D': 'GAT', 'd': 'gca', | ||
| 'E': 'GAG', 'e': 'gag', | ||
| 'G': 'GGG', 'g': 'ggg' | ||
| } | ||
|
|
||
|
|
||
| THREE_LETTER_ALPHABET = { | ||
| 'A': 'ALA', 'R': 'ARG', 'N': 'ASN', 'D': "ASP", 'V': 'VAL', | ||
| 'H': 'HIS', 'G': "GLY", 'Q': "GLN", 'E': 'GLU', 'I': 'ILE', | ||
| 'L': 'LEU', 'K': 'LYS', 'M': 'MET', 'P': 'PRO', 'S': 'SER', | ||
| 'Y': 'TYR', 'T': 'THR', 'W': 'TRP', 'F': 'PHE', 'C': 'CYS', | ||
| 'a': 'ala', 'r': 'arg', 'n': 'asn', 'd': "asp", 'v': 'val', | ||
| 'h': 'his', 'g': "gly", 'q': "gln", 'e': 'glu', 'i': 'ile', | ||
| 'l': 'leu', 'k': 'lys', 'm': 'met', 'p': 'pro', 's': 'ser', | ||
| 'y': 'tyr', 't': 'thr', 'w': 'trp', 'f': 'phe', 'c': 'cys' | ||
| } | ||
|
|
Comment on lines
+42
to
+57
| class BiologicalSequence(ABC): | ||
| @abstractclassmethod | ||
| def __len__(): | ||
| pass | ||
|
|
||
| @abstractclassmethod | ||
| def __getitem__(): | ||
| pass | ||
|
|
||
| @abstractclassmethod | ||
| def __str__(): | ||
| pass | ||
|
|
||
| @abstractclassmethod | ||
| def __is_correct_alphabet__(): | ||
| pass |
Comment on lines
+60
to
+61
| class InvalidInputError(ValueError): | ||
| pass |
There was a problem hiding this comment.
Кастомная ошибка! Про неё я совсем забыл)
Comment on lines
+77
to
+90
| def complement(self): | ||
| if not self.is_correct_alphabet(): | ||
| raise InvalidInputError('Cannot complement: incorrect input sequence. Only nucleotides (in both cases) are supported!') | ||
| sequence_complement_list = [] | ||
| for nucleotide in self: | ||
| sequence_complement_list.append(self.complement_dictionary[nucleotide]) | ||
| return type(self)(''.join(sequence_complement_list)) | ||
|
|
||
| def gc_content(self): | ||
| gc_count = 0 | ||
| for nucleotide in self: | ||
| if nucleotide == 'G' or nucleotide == 'C' or nucleotide == 'g' or nucleotide == 'c': | ||
| gc_count += 1 | ||
| return gc_count / len(self) |
There was a problem hiding this comment.
отлично! в complement возвращается экземпляр класса который пришел на вход, в gc_content учтён нижний регистр. благодать!
| @abstractclassmethod | ||
| def __len__(): | ||
| pass | ||
|
|
There was a problem hiding this comment.
я бы еще добавил __repr__
Suggested change
| def __repr__(self): | |
| return f'NucleicAcid("{self.sequence}")' |
Comment on lines
+93
to
+136
| class RNASequence(NucleicAcidSequence): | ||
| def __init__(self, sequence: str): | ||
| self.alphabet = 'AUGCaugc' | ||
| self.complement_dictionary = { | ||
| 'A': 'U', | ||
| 'U': 'A', | ||
| 'G': 'C', | ||
| 'C': 'G', | ||
| 'a': 'u', | ||
| 'u': 'a', | ||
| 'g': 'c', | ||
| 'c': 'g' | ||
| } | ||
|
|
||
|
|
||
| class DNASequence(NucleicAcidSequence): | ||
| def __init__(self, sequence: str): | ||
| self.alphabet = 'ATGCatgc' | ||
| self.complement_dictionary = { | ||
| 'A': 'T', | ||
| 'T': 'A', | ||
| 'G': 'C', | ||
| 'C': 'G', | ||
| 'a': 't', | ||
| 't': 'a', | ||
| 'g': 'c', | ||
| 'c': 'g' | ||
| } | ||
|
|
||
| def transcribe(self) -> RNASequence: | ||
| transcription_dictionary = { | ||
| 'A': 'U', | ||
| 'T': 'A', | ||
| 'G': 'C', | ||
| 'C': 'G', | ||
| 'a': 'u', | ||
| 't': 'a', | ||
| 'g': 'c', | ||
| 'c': 'g' | ||
| } | ||
| dna_transcript_list = [] | ||
| for nucleotide in self: | ||
| dna_transcript_list.append(transcription_dictionary[nucleotide]) | ||
| return RNASequence(''.join(dna_transcript_list)) |
Comment on lines
+139
to
+166
| class AminoAcidSequence(str, BiologicalSequence): | ||
| def __init__(self, sequence: str): | ||
| self.alphabet = 'ARNDCQEGHILKMFPSTWYVarndcqeghilkmfpstwyv' | ||
| if not self.is_correct_alphabet(): | ||
| raise InvalidInputError('Only amino acid names (one-letter) are supported. Please, check your input!') | ||
|
|
||
| def __str__(self) -> str: | ||
| sequence_to_str_list = [] | ||
| for amino_acid in self: | ||
| sequence_to_str_list.append(amino_acid) | ||
| return ''.join(sequence_to_str_list) | ||
|
|
||
| def is_correct_alphabet(self) -> bool: | ||
| for amino_acid in self: | ||
| if amino_acid not in self.alphabet: | ||
| return False | ||
| return True | ||
|
|
||
| def info_amino_acid_percentage(self) -> dict: | ||
| info_dict = {} | ||
| for amino_acid in self: | ||
| if amino_acid not in info_dict: | ||
| info_dict[amino_acid] = 1 | ||
| else: | ||
| info_dict[amino_acid] += 1 | ||
| info_dict.update((key, round(value / len(self) * 100, 2)) for key, value in info_dict.items()) | ||
| info_dict = {key: value for key, value in sorted(info_dict.items(), key=lambda item: item[1], reverse=True)} | ||
| return info_dict |
Comment on lines
+169
to
+422
| def reverse(seqs: list) -> list: | ||
| """ | ||
| Produce a list of reverse sequences | ||
| arguments: | ||
| - seqs (list): a list of sequences | ||
| return | ||
| - reverse_seqs (list): a list of reverse sequences | ||
| """ | ||
| reverse_seqs = [] | ||
| for seq in seqs: | ||
| reverse_seqs.append(seq[::-1]) | ||
| return reverse_seqs | ||
|
|
||
|
|
||
| def reverse_complement(seqs: list) -> list: | ||
| """ | ||
| Produce a list of reverse complementary sequences | ||
| arguments: | ||
| - seqs (list): a list of sequences | ||
| return | ||
| - (list): a list of reverse complementary sequences | ||
| """ | ||
| reverse_complement_seqs = [] | ||
| for seq in seqs: | ||
| if 'U' in seq or 'u' in seq: | ||
| reverse_complement_seqs.append(str(RNASequence(seq).complement())[::-1]) | ||
| else: | ||
| reverse_complement_seqs.append(str(DNASequence(seq).complement())[::-1]) | ||
| return reverse_complement_seqs | ||
|
|
||
|
|
||
| def calculate_similarity(sequences: list, precision: int = 3, percentages: bool = False) -> dict: | ||
| """ | ||
| Calculate similarity in AMINOACIDS between reference sequence and other sequences | ||
| arguments: | ||
| - sequences (list): reference sequence and other sequences for comparison | ||
| - precision (int): a number of decimals to round the number to | ||
| - percentages (bool): whether percentages are returned instead of fractions | ||
| return: | ||
| - similarities (dict): dictionary with compared sequences as keys and percentages/fractions as their values | ||
| """ | ||
| similarities = {} | ||
| for i in range(1, len(sequences)): | ||
| similarity = [] | ||
| for j in range(0, len(sequences[i])): | ||
| similarity.append(sequences[0][j] == sequences[i][j]) | ||
| if percentages: | ||
| similarities[sequences[i]] = round(sum(similarity) * 100 / len(sequences[i]), precision) | ||
| else: | ||
| similarities[sequences[i]] = round(sum(similarity) / len(sequences[i]), precision) | ||
| return similarities | ||
|
|
||
|
|
||
| def find_pattern(sequences: list, pattern: str) -> dict: | ||
| """ | ||
| Find all non-overlaping instances of a given pattern in sequences | ||
| arguments: | ||
| - sequences (list): sequences to find the pattern in | ||
| - pattern (str): pattern in question | ||
| return | ||
| - finds(dict): dictionary with sequences as keys and lists of indexes of patterns and the number of patterns as values | ||
| """ | ||
| finds = {} | ||
| for j in range(0, len(sequences)): | ||
| find = [] | ||
| i = 0 | ||
| while i < len(sequences[j]): | ||
| pattern_index = sequences[j].find(pattern, i) | ||
| if pattern_index != -1: | ||
| find.append(pattern_index) | ||
| i = pattern_index + len(pattern) | ||
| else: | ||
| break | ||
| finds[sequences[j]] = [len(find)] + find | ||
| return finds | ||
|
|
||
|
|
||
| def convert_to_gene(protein: str) -> str: | ||
| """ | ||
| Transforming of an amino acid sequence/protein to DNA sequence | ||
| :param protein: amino acid sequence of protein | ||
| :return: sequence of protein in the DNA sequence form | ||
| """ | ||
| return ''.join([RETRANSLATION_DICT[aa] for aa in protein]) | ||
|
|
||
|
|
||
| def recode_3letter_to_1letter(seqs: list, sep='') -> list: | ||
| """ | ||
| Transform into a three-letter amino acids entry. | ||
| arguments: | ||
| - seqs (list): list of sequences for transforming to three-letter entire | ||
| - sep (str): separator between AMINOACIDS, default = '' | ||
| return: | ||
| - three_letter_result (list): transformed sequences with separators | ||
| """ | ||
| three_letter_result = [] | ||
| for seq in seqs: | ||
| threel_form = '' | ||
| for aa in seq: | ||
| threel_form += THREE_LETTER_ALPHABET[aa] + sep | ||
| if sep: | ||
| threel_form = threel_form[:-1] | ||
| three_letter_result.append(threel_form) | ||
| return three_letter_result | ||
|
|
||
|
|
||
| def check_fastq_file(path: str): | ||
| """ | ||
| Check whether the path and file provided are valid for further processing | ||
| arguments: | ||
| - path (str): path to .fastq file | ||
| return: | ||
| - no return | ||
| """ | ||
| import os | ||
| if '.fastq' in os.path.basename(path): | ||
| if os.path.isfile(path): | ||
| with open(path) as fastq: | ||
| line_count = 0 | ||
| total_line_count = 0 | ||
| for line in fastq: | ||
| line_count += 1 | ||
| total_line_count += 1 | ||
| if line_count == 1: | ||
| if not line.startswith('@'): | ||
| raise ValueError('Incorrect file content! Suggestion: check the lines near line number ' + str(total_line_count) + ' in the provided file.') | ||
| if line_count != 1 and line_count != 4 and line.startswith('@'): | ||
| raise ValueError('Incorrect file content! Suggestion: check the lines near line number ' + str(total_line_count) + ' in the provided file.') | ||
| if line_count == 4: | ||
| line_count = 0 | ||
| if line_count != 0: | ||
| raise ValueError('Incorrect file content! Suggestion: check the end of the file') | ||
| else: | ||
| raise ValueError(path + ' is not a correct path to a file!') | ||
| else: | ||
| raise ValueError('Invalid input: path to a .fastq file was expected!') | ||
|
|
||
|
|
||
| def run_dna_rna_tools(inputs: tuple) -> list | str: | ||
| """ | ||
| Produce a list of either transcripts, reverse sequences, complementary sequences or reverse complementary sequences | ||
| arguments: | ||
| - inputs (tuple): an arbitrary amount of strings where the last one is the name of desired operation, and other strings are sequences | ||
| return | ||
| - complement_seqs (list): a list of complementary sequences | ||
| """ | ||
| if len(inputs) < 2: | ||
| raise ValueError('Invalid input: the function requires at least one sequence and an operation name!') | ||
| *seqs, operation = inputs | ||
| if operation == 'transcribe': | ||
| transcribed_seqs = [] | ||
| for seq in seqs: | ||
| transcribed_seqs.append(str(DNASequence(seq).transcribe())) | ||
| return transcribed_seqs | ||
| elif operation == 'reverse': | ||
| result = reverse(seqs) | ||
| elif operation == 'complement': | ||
| complement_seqs = [] | ||
| for seq in seqs: | ||
| if 'U' in seq or 'u' in seq: | ||
| complement_seqs.append(str(RNASequence(seq).complement())) | ||
| else: | ||
| complement_seqs.append(str(DNASequence(seq).complement())) | ||
| return complement_seqs | ||
| elif operation == 'reverse_complement': | ||
| result = reverse_complement(seqs) | ||
| else: | ||
| raise ValueError('Invalid input: unknown operation! Check the last argument.') | ||
| if len(result) == 1: | ||
| result = ''.join(result) | ||
| return result | ||
|
|
||
|
|
||
| def run_protein_tools(inputs: tuple, options: str = None) -> list | dict: | ||
| """ | ||
| Produce a list or dictionary according to the option specified | ||
| arguments: | ||
| - inputs (tuple): a tuple of inputs | ||
| - options (str): option name | ||
| """ | ||
| operations = { | ||
| 'similarity': calculate_similarity, | ||
| 'pattern': find_pattern, | ||
| '3letter_name': recode_3letter_to_1letter, | ||
| 'dna_code': convert_to_gene | ||
| } | ||
|
|
||
| if options == 'similarity': | ||
| result = operations[options](inputs[:-2], inputs[-2], inputs[-1]) | ||
| return result | ||
| elif options == 'pattern': | ||
| result = operations[options](inputs[1:len(inputs)], inputs[0]) | ||
| return result | ||
| elif options == '3letter_name': | ||
| result = operations[options](inputs[:-1], inputs[-1]) | ||
| return result | ||
| elif options == 'dna_code': | ||
| result = [] | ||
| for inpt in inputs: | ||
| res = operations[options](inpt) | ||
| result.append(res) | ||
| return result | ||
| elif options == 'length': | ||
| lengths = [] | ||
| for inpt in inputs: | ||
| lengths.append(len(AminoAcidSequence(inpt))) | ||
| return lengths | ||
| elif options == 'percentage': | ||
| percentages = [] | ||
| for inpt in inputs: | ||
| percentages.append(AminoAcidSequence(inpt).info_amino_acid_percentage()) | ||
| return percentages | ||
| else: | ||
| raise ValueError('Incorrect options input, please try again') | ||
|
|
||
|
|
||
| def create_results_dir_if_doesnt_exist(): | ||
| import os | ||
| if not os.path.exists('fastq_filtrator_results'): | ||
| os.makedirs('fastq_filtrator_results') | ||
|
|
||
|
|
||
| def run_beginner_bioinf_tools(*input_data: str, toolbox: str = None, **kwargs: str | tuple | int) -> str | list | dict: | ||
| """ | ||
| Performs various operations on nucleic acid, protein and fastq sequences | ||
| arguments: | ||
| - input_data (str or dict): data to be processed | ||
| - toolbox (str): determines which of the three toolkits is used | ||
| return: | ||
| - (str or list or dict): processed data | ||
| """ | ||
| if toolbox == 'dna_rna': | ||
| return run_dna_rna_tools(input_data) | ||
| elif toolbox == 'proteins': | ||
| return run_protein_tools(input_data, kwargs['options']) | ||
| else: | ||
| raise ValueError('Invalid input: there is no toolbox corresponding to value ' + str(toolbox) + '!') | ||
|
|
||
|
|
||
| def filter_fastq(path_to_fastq: str, gc_bounds: tuple = (0, 100), length_bounds: tuple = (0, 2**32), quality_threshold: int = 0): | ||
| check_fastq_file(path_to_fastq) | ||
| seqs_filtered = [] | ||
| if isinstance(gc_bounds, int): | ||
| gc_bounds = tuple([0, gc_bounds]) | ||
| if isinstance(length_bounds, int): | ||
| length_bounds = tuple([0, length_bounds]) | ||
| for i, record in enumerate(SeqIO.parse(path_to_fastq, "fastq")): | ||
| gc_check = gc_bounds[0] < GC123(record.seq)[0] < gc_bounds[1] | ||
| length_check = length_bounds[0] < len(record.seq) < length_bounds[1] | ||
| quality_per_letter = record.letter_annotations["phred_quality"] | ||
| quality_check = sum(quality_per_letter) / len(quality_per_letter) >= quality_threshold | ||
| if gc_check and length_check and quality_check: | ||
| seqs_filtered.append(record) | ||
| return seqs_filtered |
There was a problem hiding this comment.
фильтр фасткю написан, за это респект! остальное можно было убрать
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
2 participants
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Review HMGB1