{fun.__name__} завершилась с исключением {
+ type(result).__name__}:{str(result)}'
+ request = requests.post(
+ f'https://api.telegram.org/bot{TG_API_TOKEN}/sendMessage?chat_id={chat_id}&text={message}&parse_mode={parse_mode}')
+ request2 = requests.post(
+ f'https://api.telegram.org/bot{TG_API_TOKEN}/sendPhoto?chat_id={chat_id}&photo={not_happy}')
+
+ else:
+ message = f'Функция {
+ fun.__name__} выполнена за {time}'
+ request = requests.post(f'https://api.telegram.org/bot{TG_API_TOKEN}/sendMessage?chat_id={
+ chat_id}&text={message}&parse_mode={parse_mode}')
+ request2 = requests.post(
+ f'https://api.telegram.org/bot{TG_API_TOKEN}/sendDocument?chat_id={chat_id}&document={happy}')
+ return result
+ return wrapper
+ return decorator
+
+
+@dataclass
+class GenscanOutput:
+ status: int
+ cds_list: list
+ intron_list: list = "Empty"
+ exon_list: list = "Empty"
+
+
+def genscan(sequence=None, sequence_file=None, organism="Vertebrate ", exon_cutoff=1.00, sequence_name=""):
+ '''
+ Python API for Genscan service (http://hollywood.mit.edu/GENSCAN.html)
+ Args:
+ sequence: nucleotide sequence in str format
+ sequence_file: path to the FASTA file with the nucleotide sequence
+ organism: the type of organism whose nucleotide sequence was uploaded. Сould be either Vertebrate, Arabidopsis or Maize.
+ exon_cutoff: confidence threshold for intron excision in float format. Can take on the following values: 1.00, 0.50, 0.25, 0.10, 0.05, 0.02, 0.01.
+ sequence_name: optionally, sequence name in str format
+
+ Returns:
+ GenscanOutput class object, with the following attributes:
+ status: server request status
+ cds_list: list of predicted protein sequences
+ intron_list: list of predicted introns
+ exon_list: list of predicted exons
+ '''
+ url = 'http://hollywood.mit.edu/cgi-bin/genscanw_py.cgi'
+ rec_param = {"-o": "organism", "-e": "exon_cutoff",
+ "-p": "Predicted peptides only", "-s": "sequence", "-u": ""}
+
+ if sequence_file is not None:
+ sequence_file = open(sequence_file, 'rb').read()
+
+ rec_param["-o"] = organism
+ rec_param["-e"] = exon_cutoff
+ rec_param["-p"] = "Predicted peptides only"
+ rec_param["-s"] = sequence
+ rec_param["-u"] = sequence_file
+
+ response = requests.post(url, data=rec_param)
+ status = response.status_code
+ soup = BeautifulSoup(response.content, "lxml")
+ str_soup = str(soup)
+
+ peptide_pattern = r"GENSCAN_predicted_peptide_\d\|\d+_aa\n\n[A-Z\\n]+"
+ peptides = re.findall(peptide_pattern, str_soup)
+
+ intron_pattern = r"\d.\d+ Intr [+-] +\d+ +\d+"
+ introns = re.findall(intron_pattern, str_soup)
+
+ str_intr = str(introns)
+ sub_pattern = r"'\d.\d\d "
+ replacement = ""
+ str_intr_clear = re.sub(sub_pattern, replacement, str_intr)
+ exon_pattern = r"\d+"
+ exons = re.findall(exon_pattern, str_intr_clear)
+ exons = list(map(int, exons))
+
+ exons_borders = []
+ counter = 0
+
+ for i in exons[1:]:
+ if counter == len(exons)-2:
+ break
+ counter += 1
+ if counter % 2 == 0:
+ border = i - 1
+ else:
+ border = i + 1
+ exons_borders.append(border)
+
+ exons_borders_array = np.asarray(exons_borders)
+ exons_borders_array = exons_borders_array.reshape(
+ int(len(exons_borders_array)/2), 2)
+
+ counter_2 = 0
+ for obj in exons_borders:
+ counter_2 += 1
+ new_str = str(counter_2) + " " + str(obj)
+ exons_borders[counter_2 - 1] = new_str
+
+ genscan_output = GenscanOutput(
+ status=status, cds_list=peptides, intron_list=introns, exon_list=exons_borders)
+
+ return genscan_output
+
+
+class BiologicalSequence(ABC):
+ def __init__(self, seq):
+ self.seq = seq
+
+ def __len__(self):
+ return len(self.seq)
+
+ def seq_slices(self, start, stop):
+ slice = self.seq[start:stop]
+ return slice
+
+ def seq_indexes(self, index):
+ return self.seq[index]
+
+ def __str__(self):
+ return str(self.seq)
+
+ def check_alphabet():
+ pass
+
+
+class NucleicAcidSequence(BiologicalSequence):
+ COMPLEMENTARITY_DICT = {"A": "T", "T": "A", "C": "G", "G": "C"}
+ alphabet = ["A", "T", "U", "G", "C"]
+
+ def __init__(self, seq):
+ super().__init__(seq)
+ self.seq = seq
+
+ def check_alphabet(self):
+ if set(self.seq) <= set(self.alphabet):
+ return True
+ else:
+ return False
+
+ def complement(self):
+ complement = ""
+ for nucl in self.seq:
+ complement += self.COMPLEMENTARITY_DICT.get(nucl)
+ return complement
+
+ def gc_content(self):
+ return (self.seq.count('C') + self.seq.count('G'))/len(self.seq)
+
+
+class DNASequence(NucleicAcidSequence):
+ COMPLEMENTARITY_DICT = {"A": "T", "T": "A", "C": "G", "G": "C"}
+ TRANSCRIBE_DICT = {"A": "U", "T": "A", "C": "G", "G": "C"}
+ alphabet = ["A", "T", "G", "C"]
+
+ def __init__(self, seq):
+ super().__init__(seq)
+ self.seq = seq
+
+ def transcribe(self):
+ transcribed = ""
+ for nucl in self.seq:
+ transcribed += self.TRANSCRIBE_DICT.get(nucl)
+ return transcribed
+
+
+class RNASequence(NucleicAcidSequence):
+ COMPLEMENTARITY_DICT = {"A": "U", "U": "A", "C": "G", "G": "C"}
+ alphabet = ["A", "U", "G", "C"]
+
+ def __init__(self, seq):
+ super().__init__(seq)
+ self.seq = seq
+
+
+class AminoAcidSequence(BiologicalSequence):
+ alphabet = frozenset('ARNDCEQGHILKMFPSTWYV')
+
+ AA_CHARGES = {"A": 0, "R": 1, "N": 0, "D": -1, "C": 0,
+ "Q": 0, "E": -1, "G": 0, "H": 1, "I": 0,
+ "L": 0, "K": 1, "M": 0, "F": 0, "P": 0,
+ "S": 0, "T": 0, "W": 0, "Y": 0, "V": 0}
+
+ def __init__(self, seq):
+ super().__init__(seq)
+ self.seq = seq
+
+ def check_alphabet(self):
+ if set(self.seq) <= self.alphabet:
+ return True
+ else:
+ return False
+
+ def aa_chain_charge(self):
+ aa_charge = 0
+ for amino in self.seq:
+ aa_charge += self.AA_CHARGES.get(amino)
+ return aa_charge
From 3a2353c17ff50948937ea0fa4381da6811eb71aa Mon Sep 17 00:00:00 2001
From: LinaWhite15 <129277151+LinaWhite15@users.noreply.github.com>
Date: Wed, 1 May 2024 13:01:37 +0300
Subject: [PATCH 19/29] Add test_bioinf_utils.py
---
test_bioinf_utils.py | 73 ++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 73 insertions(+)
create mode 100644 test_bioinf_utils.py
diff --git a/test_bioinf_utils.py b/test_bioinf_utils.py
new file mode 100644
index 0000000..89c4f55
--- /dev/null
+++ b/test_bioinf_utils.py
@@ -0,0 +1,73 @@
+import pytest
+import os
+
+from bioinfUtils import genscan, GenscanOutput
+from bio_files_processor import convert_multiline_fasta_to_oneline, select_genes_from_gbk_to_fasta
+
+
+def test_genscan_status():
+ inp = 'sequence_file="./data/SOWAHA.fasta"'
+ result = genscan(inp)
+ result = result.status
+ assert result == 200
+
+
+def test_genscan_content(inp):
+ result = genscan(inp)
+ result = len(result.cds_list)
+ assert result != 0
+
+
+def test_write_fasta_to_oneline(input_data, output_data):
+ convert_multiline_fasta_to_oneline(input_data, output_data)
+ assert os.path.exists(output_data)
+
+
+def test_empty_fasta_to_oneline(input_data, output_data):
+ convert_multiline_fasta_to_oneline(input_data, output_data)
+ target = os.stat(output_data).st_size == 0
+ assert target is False
+
+
+def test_fasta_to_oneline_is_oneline(input_data, output_data):
+ convert_multiline_fasta_to_oneline(input_data, output_data)
+ result = 0
+ target = 0
+ with open(output_data) as file:
+ for line in file:
+ target += 1
+ if line.startswith('>'):
+ result += 1
+ result = result*2
+ assert target == result
+
+
+def test_fasta_to_oneline_no_output(input_data, output_data=None):
+ with pytest.raises(ValueError):
+ convert_multiline_fasta_to_oneline(input_data, output_data)
+
+
+def test_gbk_fasta_len(input_gbk, genes, n_before, n_after, output_fasta):
+ select_genes_from_gbk_to_fasta(
+ input_gbk, genes, n_before, n_after, output_fasta)
+ target = len(genes)*(n_before+n_after)
+
+ result = 0
+ with open(output_fasta) as file:
+ for line in file:
+ if line.startswith('>'):
+ result += 1
+ assert target == result
+
+
+def test_gbk_empty(input_gbk, genes, n_before, n_after, output_fasta):
+ select_genes_from_gbk_to_fasta(
+ input_gbk, genes, n_before, n_after, output_fasta)
+ target = len(genes)*(n_before+n_after)
+
+ result = 0
+ with open(output_fasta) as file:
+ for line in file:
+ if line.startswith('>'):
+ result += 1
+ assert target == result
From 1ff2082bd0a1c24f6ae9925999f4a41872ae1696 Mon Sep 17 00:00:00 2001
From: LinaWhite15 <129277151+LinaWhite15@users.noreply.github.com>
Date: Wed, 1 May 2024 13:02:40 +0300
Subject: [PATCH 20/29] Add Showcases.ipynb
---
Showcases.ipynb | 1146 +++++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 1146 insertions(+)
create mode 100644 Showcases.ipynb
diff --git a/Showcases.ipynb b/Showcases.ipynb
new file mode 100644
index 0000000..ce2c2db
--- /dev/null
+++ b/Showcases.ipynb
@@ -0,0 +1,1146 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from bioinfUtils import genscan, GenscanOutput,BiologicalSequence, DNASequence, AminoAcidSequence\n",
+ "from custom_random_forest import RandomForestClassifierCustom\n",
+ "from bio_files_processor import OpenFasta\n",
+ "\n",
+ "from sklearn.datasets import make_classification"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### RandomForestClassifierCastom"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X, y = make_classification(n_samples=100000)\n",
+ "random_forest = RandomForestClassifierCustom(max_depth=30, n_estimators=10, \n",
+ " max_features=2, random_state=42)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: total: 2.38 s\n",
+ "Wall time: 5.63 s\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "RandomForestClassifierCustom(max_depth=30, max_features=2, random_state=42)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
RandomForestClassifierCustom(max_depth=30, max_features=2, random_state=42)
RandomForestClassifierCustom(max_depth=30, max_features=2, random_state=42)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
RandomForestClassifierCustom(max_depth=30, max_features=2, random_state=42)