diff --git a/.gitignore b/.gitignore
deleted file mode 100644
index 2e906cc871537e0816ae7fd4437ade59afa2986c..0000000000000000000000000000000000000000
--- a/.gitignore
+++ /dev/null
@@ -1,5 +0,0 @@
-# ignore ALL .log files
-*.log
-
-# ignore ALL files in ANY directory named temp
-temp/
diff --git a/Inputs_files/mini_file.txt b/Inputs_files/mini_file.txt
deleted file mode 100644
index ba6539a78d4752c68a2408395dd471a0f42f501e..0000000000000000000000000000000000000000
--- a/Inputs_files/mini_file.txt
+++ /dev/null
@@ -1,19 +0,0 @@
->ATAD3B
-1 ENST00000673477 NA 6
-2 ENST00000472194 1 43
-3 ENST00000378736 5 58
-4 ENST00000485748 2 63
-5 ENST00000474481 2 74
-6 ENST00000308647 1 80
-7 ENST00000442483 3 113
->PRDM16
-1 ENST00000511072 5 138
-2 ENST00000607632 2 175
-3 ENST00000378391 1 178
-4 ENST00000514189 5 217
-5 ENST00000270722 1 254
-6 ENST00000512462 1 293
-7 ENST00000463591 5 310
-8 ENST00000509860 5 319
-9 ENST00000378389 5 348
-10 ENST00000606170 4 354
\ No newline at end of file
diff --git a/LICENSE b/LICENSE
deleted file mode 100644
index edb874900e3f120cb97930894f9a0c54cbb340b1..0000000000000000000000000000000000000000
--- a/LICENSE
+++ /dev/null
@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2022 zavolan_group / tools
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
diff --git a/README.md b/README.md
deleted file mode 100644
index 64a092fbd9ad6482a7c3cac4b6b0570aa7743a20..0000000000000000000000000000000000000000
--- a/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
-# Transcript Sampler
-
-This workflow takes as input:
- - genome annotation gtf file
- - expression levels of each gene
- - csv file with transcript IDs and expression levels
-
- The output is a trancript sample gtf file and csv file containing transcript IDs and counts.
-
-
diff --git a/images/.gitkeep b/images/.gitkeep
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/images/screenshot_git_tutorial_1_hGillet.png b/images/screenshot_git_tutorial_1_hGillet.png
deleted file mode 100644
index 68151e8b0b837c03b7fcac317aa6a989333244f5..0000000000000000000000000000000000000000
Binary files a/images/screenshot_git_tutorial_1_hGillet.png and /dev/null differ
diff --git a/images/screenshot_git_tutorial_2_hGillet.png b/images/screenshot_git_tutorial_2_hGillet.png
deleted file mode 100644
index ec1d38848ce1a364475038fb0a74b49e4f6cce07..0000000000000000000000000000000000000000
Binary files a/images/screenshot_git_tutorial_2_hGillet.png and /dev/null differ
diff --git a/images/screenshot_markdown_tutorial_hGillet.png b/images/screenshot_markdown_tutorial_hGillet.png
deleted file mode 100644
index a3ea90d1c9fa47190015f028d4b7fb09a0e0031b..0000000000000000000000000000000000000000
Binary files a/images/screenshot_markdown_tutorial_hGillet.png and /dev/null differ
diff --git a/scripts/Excecution_file.py b/scripts/Excecution_file.py
deleted file mode 100644
index 788525eb4438f485ed1e2a554be3f61ccc102170..0000000000000000000000000000000000000000
--- a/scripts/Excecution_file.py
+++ /dev/null
@@ -1,20 +0,0 @@
-### Imports ###
-import os
-
-import transkript_extractor as te
-import Exon_length_filter as elf
-import representative_v4 as rtcl
-
-### Scipt ###
-def exe(file_name = "test",source_pathway_name = os.getcwd(),deposit_pathway_name = os.getcwd(),Input_free = True):
- file_name,source_pathway_name_2,deposit_pathway_name_2 = te.extract_transkript(file_name,source_pathway_name,deposit_pathway_name,Input_free = Input_free)
- inter_mediate_file_directory = os.path.join(deposit_pathway_name,file_name+"_intermediate_file.txt")
- print("Transcripts are filterd based on transcipt score please wait...")
- pre_filter_representative_transcripts_dict = rtcl.find_repr_by_SupportLevel(inter_mediate_file_directory)
- print("Transcripts filtered\n")
- elf.exon_length_filter(file_name,source_pathway_name,deposit_pathway_name,gen_dict= pre_filter_representative_transcripts_dict,Input_free = Input_free)
- return(file_name,source_pathway_name,deposit_pathway_name)
-### from consol ####
-##D:\\Uni\\Sem 9\\Programing in the Life sciences\\Projekt\\Intermediat Files
-if __name__ == "__main__":
- exe()
\ No newline at end of file
diff --git a/scripts/Exon_length_filter.py b/scripts/Exon_length_filter.py
deleted file mode 100644
index 162ff1f302b4ef144d9c34bde47b5b69c4ba72c0..0000000000000000000000000000000000000000
--- a/scripts/Exon_length_filter.py
+++ /dev/null
@@ -1,188 +0,0 @@
-#### Exon length filter #####
-"""Exon length filter
-Version 1.1.0"""
-### Called Packages ###
-import re
-import os
-
-import transkript_extractor as te
-### Functions ###
-
-def exon_length_calculator(entry):
- """This funtion finds the start and end cordinates of the exon and uses them to calculate its lenght"""
- try:
- find_exon_coordinates = re.compile("\t\d{1,15}\t")
- #this difines the pattern of the coordinates
- try_find_start_coordinates = find_exon_coordinates.search(entry)
- #this line findes the start coordinares based on the pattern
- start_coordinates = int(try_find_start_coordinates[0].replace("\t",""))
- #this line removes the \t at the end and the start of the pattern and
- #turn the string of the coordinates into intergers
- final_index_start_coordinates = entry.find(try_find_start_coordinates[0])+len(try_find_start_coordinates[0])-1
- #this line determines the indes of the final digit of the start coordinates
- sub_entry = entry[final_index_start_coordinates:]
- #this lineused the index determin above a starting point for a new sub entry
- try_find_end_coordinates = find_exon_coordinates.search(sub_entry)
- end_coordinates = int(try_find_end_coordinates[0].replace("\t",""))
- #these two lines find the end coordinates and turn tham int an int
- exon_lenght = end_coordinates-start_coordinates
- #this line claculates the transcript length
- except:
- print("\n\nIn the following enty only one or no valid coordinates could be found:\n",entry,"the value will be set to NA")
- exon_lenght = "NA"
- return(exon_lenght)
-
-def exon_fider(entry):
- """This funtion determines if a given entry belongs to an exon
- Expected inputs:
- entry: str #any enty of a gtf file"""
- exon_test = entry.find("\texon\t")
- #This line look for the entry exon in the file
- if exon_test == -1:
- try_exon_test = False
- else:
- try_exon_test = True
- #The block above evaluates the results of the search for the wort exon
- return(try_exon_test)
-
-def __longest_transcript_finder(current_exon_length,longest_transcript,longest_transcript_ID,old_transcript_ID):
- """This funtion encapsulates an opperation that has to be carried out at several point ind the exon_length_filter funktion and servers to make that funktion more modular"""
- if current_exon_length > longest_transcript:
- #This condition updates the most promesing for
- #beeing the representative transcript
- longest_transcript = current_exon_length
- longest_transcript_ID = old_transcript_ID
- current_exon_length = 0
- return(current_exon_length,longest_transcript,longest_transcript_ID)
-
-def _representative_transcript_csv (representative_transcript,file_name = "test",deposit_pathway_name =os.getcwd()):
- with open(os.path.join(deposit_pathway_name,file_name+"_"+"representative_transcripts"+".csv"),"w") as rt:
- for i in representative_transcript:
- transcript = representative_transcript[i]
- new_entry = str(i)+","+transcript+"\n"
- rt.write(new_entry)
-
-
-
-def _exon_length_filter(file_name = "test",source_pathway_name = os.getcwd(),deposit_pathway_name =os.getcwd(),gen_dict = {"ENSG00000160072":["ENST00000673477","ENST00000472194","ENST00000378736","ENST00000308647","ENST00000442483"],"ENSG00000225972":["ENST00000416931"],"ENSG00000279928":["ENST00000624431","ENST00000424215"],"ENSG00000142611":["ENST00000378391","ENST00000607632","ENST00000511072"]}):
- """This funtion selects only the transcripts for a dictionar that have the longest total mRNA"""
- bar,start_time = te.bar_builder(length_multiplyer = 3)
- total_genes = len(gen_dict)
- gens_done = 0
-
- with open(os.path.join(source_pathway_name,file_name+".gtf"), 'r') as f:
-
- old_gen = str()
- old_transcript_ID = str()
- representative_transcript = dict()
- representative_trasnscript_not_found = True
- longest_transcript_ID = str()
- current_exon_length = 0
- longest_transcript = 0
- percentage_done = 0
-
- for entry in f:
-
- try:
- corrent_gen = te.gene_ID_finder(entry)
- except:
- corrent_gen = old_gen
- #The block above test if there is a gen name in the entry
- if corrent_gen != old_gen:
- representative_trasnscript_not_found = True
-
- #The block above determines if the Gen name is new and set the test
- #representative_trasnscript_not_found back to true which is used to
- #make the program faster if there is just one transcript for a given
- #gen in the dict
- if representative_trasnscript_not_found and corrent_gen != str():
- #print(corrent_gen)
- #The conditon prvents serges if a representative transcript has
- #all ready been chosen
- if corrent_gen != old_gen:
- current_exon_length,longest_transcript,longest_transcript_ID = __longest_transcript_finder(current_exon_length,longest_transcript,longest_transcript_ID,old_transcript_ID)
- representative_transcript[old_gen] = longest_transcript_ID
- try:
- del gen_dict[old_gen]
- old_gen = corrent_gen
- gens_done += 1
- corrent_percentage_done = (gens_done/total_genes)*100
- if corrent_percentage_done > percentage_done+10:
- bar,start_time = te.bar_builder(percentage=percentage_done+10,length_multiplyer = 3,start_time=start_time,bar =bar)
- percentage_done = int(corrent_percentage_done)
-
-
- except:
- old_gen = corrent_gen
- longest_transcript = 0
- #The block above adds the transcript of the last gen that
- #had the longest exons into the representative transcripts dict
- try:
- #This try / except block test if the gen is in the input dictionary
- transcript_IDs = gen_dict[corrent_gen]
- if len(gen_dict[corrent_gen]) == 1:
- #This conditions is a short cut for Genes that
- #allready have a representative transcript
- representative_transcript=gen_dict[corrent_gen[0]]
- representative_trasnscript_not_found = False
- continue
- except:
- continue
-
- try:
- current_transcript_ID = te.transcript_ID_finder(entry)
- except:
- continue
- #The block above searches for a trnascript ID in the current enty
-
- if current_transcript_ID in transcript_IDs:
- #This condition test if the Transcript is one of the
- #candidates for representative transcripts
- if current_transcript_ID != old_transcript_ID:
- #This condition if the enty still belongs to the
- #previous transcript and is triggers if that is not the case
- current_exon_length,longest_transcript,longest_transcript_ID = __longest_transcript_finder(current_exon_length,longest_transcript,longest_transcript_ID,old_transcript_ID)
- try:
- transcript_IDs.remove(old_transcript_ID)
- old_transcript_ID = current_transcript_ID
- except:
- old_transcript_ID = current_transcript_ID
- if exon_fider(entry):
- exon_length = exon_length_calculator(entry)
- current_exon_length += exon_length
- else:
- continue
- current_exon_length,longest_transcript,longest_transcript_ID = __longest_transcript_finder(current_exon_length,longest_transcript,longest_transcript_ID,old_transcript_ID)
- representative_transcript[old_gen] = longest_transcript_ID
- del representative_transcript[str()]
- te.bar_builder(100,length_multiplyer = 3,start_time=start_time,bar =bar)
- return(representative_transcript)
-
-def exon_length_filter(file_name = "test",source_pathway_name = os.getcwd(),deposit_pathway_name =os.getcwd(),gen_dict = {"ENSG00000160072":["ENST00000673477","ENST00000472194","ENST00000378736","ENST00000308647","ENST00000442483"],"ENSG00000225972":["ENST00000416931"],"ENSG00000279928":["ENST00000624431","ENST00000424215"],"ENSG00000142611":["ENST00000378391","ENST00000607632","ENST00000511072"]},Input_free = False):
- """This function filters a dictionary of genes and there transcripts by the length of there exons an selects the longes transcript for each gene ans saves tham in a "," seperated csv file.
- Expected inputs:
- file_name: str ; default = test #the name of the gft file you want to look at
- source_pathway_name: str ; default = current work directory #path of the gtf file
- deposit_pathway_name: str ; default = current work directory #path for saving the csv file
- gen_dict:dict{key == gene ID:[transcript IDs that belong to that gene]}
- Input_free: tuple ; default = False # this input should be set to True for automation"""
-
- print("Representative trascipts are filterd based on exon length please wait...")
- source_pathway_name,deposit_pathway_name = te.__do_pathways_exist__(source_pathway_name,deposit_pathway_name)
- if Input_free:
- pre_existing_file = False
- else:
- search_profile = file_name+"_"+"representative_transcripts"+".csv"
- pre_existing_file = te.__searche_for_preexisting_files(search_profile,deposit_pathway_name)
- if pre_existing_file == False:
- representative_transcript = _exon_length_filter(file_name,source_pathway_name,deposit_pathway_name,gen_dict)
- _representative_transcript_csv(representative_transcript,file_name,deposit_pathway_name)
- print("\nRepresentative transcripts collected")
-
-
-if __name__ == "__main__":
- help(exon_length_filter)
- exon_length_filter()
-
-
-#This line allows the file to be executed on its own also from
\ No newline at end of file
diff --git a/scripts/match_reprTranscript_expressionLevel.py b/scripts/match_reprTranscript_expressionLevel.py
deleted file mode 100644
index 2dfca50a3c3ee1458b7358fbd1e1d801751d727c..0000000000000000000000000000000000000000
--- a/scripts/match_reprTranscript_expressionLevel.py
+++ /dev/null
@@ -1,200 +0,0 @@
-
-import pandas as pd
-import json
-import re
-import rerpresentative_v4 as repr
-import os
-
-
-def dict_reprTrans_to_df(dict_reprTrans: dict):
-
- """Convert a dictionary of genes and their representative transcript into a dataframe
-
- Args:
- dict_reprTrans (dict) : {'Gene':['transcriptA', 'transcriptB'], ...}
-
- Returns:
- Pandas dataframe having Gene and transcript as columns
-
- Raises:
- /!\ None, I wasn't able to make a TypeError with dict
- : Only dict made of key string and value string is allowed
-
- """
- pass
-
- df_reprTrans = pd.DataFrame.from_dict(dict_reprTrans, orient="index", columns=["reprTranscript"])
- df_reprTrans = df_reprTrans.reset_index(level=0)
- df_reprTrans.columns = ["Gene", 'reprTrans']
- df_reprTrans["reprTrans"] = df_reprTrans["reprTrans"].str.replace(r'\.[1-9]', '', regex=True)
- return df_reprTrans
-
-
-def txt_to_dict(dict_txt: str):
- """Convert a txt file into a dictionary
-
- Args:
- dict_txt (str) : pathe to a txt file of a dict
- structured as {'Gene':['transcriptA', 'transcriptB'], ...}
-
- Returns:
- dict (dict) : dictionary stuctured as {'Gene':['transcriptA', 'transcriptB'], ...}
-
- Raises:
- None
- """
- pass
-
- input : str = open(dict_txt, "r").read()
- input : str = input.replace("\'", "\"")
- dict = json.loads(input)
- return dict
-
-
-
-def transcripts_by_gene_inDf(df_gtfSelection: str) -> pd.DataFrame:
- """Convert multiindex dataframe from function into a simple dataframe
-
- Args:
- df_gtfSelection (str): Pandas multiindex dataframe having Gene,
- transcript as indexs and support level as columns.
- Come from the function import_gtfSelection_to_df()
-
- Returns:
- df_gene (str): Pandas dataframe having Gene and
- transcript as columns
-
- Raises:
- None
- """
- pass
- df_gene = df_gtfSelection.set_index(["Gene"])
- df_gene = df_gene.drop(columns=["Support_level"])
- df_gene['Transcript']=df_gene['Transcript'].str.replace(r"\.[0-9]","", regex=True)
- df_gene = df_gene.reset_index(level=0)
- return df_gene
-
-
-def tsv_or_csv_to_df(input_txt:str) :
- """Convert tsv or csv file into a pandas dataframe
-
- Args:
- input_txt (str): csv or tsv file containing transcript expression level
-
- Returns:
- df_gene (str): Pandas dataframe having transcript and expression level
- as columns
-
- Raises:
- None
- """
- pass
- df_input =pd.read_csv(input_txt, sep=r"[\t,]", lineterminator='\n',
- names=["Transcript", "Expression_level"],
- engine = "python")
- return df_input
-
-
-def exprLevel_byGene(df_exprTrasncript:str, df_output_gtf_selection:str) -> pd.DataFrame :
- """Find matching transcripts bewteen the 2 args
-
- Args:
- df_exprTranscript (str): pandas Dataframe containing transcript and their expression level
- df_output_gtf_selection (str) : pandas Dataframe containing genes and transcripts
-
- Returns:
- Pandas dataframe having gene and sum of its transcript expression level
-
- Raises:
- None
- """
- pass
- df_merged = pd.merge(df_output_gtf_selection, df_exprTrasncript , how="inner", on="Transcript")
- df_sum = df_merged.groupby("Gene").sum("Expression_level") # sum transcripts comming from the same gene
- return df_sum
-
-def match_byGene(df_reprTranscript:str, df_expressionLevel_byGene:str) -> pd.DataFrame:
- """Find matching genes bewteen the 2 args
-
- Args:
- df_reprTranscript (str): pandas Dataframe containing genes
- and their representative transcript
- df_expressionLevel_byGene (str) : pandas Dataframe containing
- genes and their expression level
-
- Returns:
- Pandas dataframe having representative trasncripts
- and their expression level
-
- Raises:
- None
- """
- pass
- df_merged = pd.merge(df_reprTranscript, df_expressionLevel_byGene , how="outer", on="Gene")
- df_clean = df_merged.dropna(axis=0)
- df_clean = df_clean.loc[:, ["reprTrans","Expression_level"]]
- return df_clean
-
-def output_tsv(dataframe:str)-> pd.DataFrame :
- """Convert pandas dataframe into a tsv file
-
- Args:
- dataframe (str): Pandas dataframe containing
- representative transcripts and their expression level
-
- Returns:
- Tsv file containing representative transcripts
- and their expression level in the same directory
-
- Raises:
- None
- """
- pass
-
- csv_file = dataframe.to_csv(os.getcwd()+"\ReprTrans_ExpressionLevel.tsv", sep="\t",
- index=False, header=False)
- return csv_file
-
-### functions to run this part of the programm
-
-def match_reprTranscript_expressionLevel(exprTrans:str, dict_reprTrans:dict, intermediate_file:str):
- """Combine functions to replace transcripts from an expression level csv/tsv file
- with representative transcripts
-
- Args:
- exprTrans (str): csv or tsv file containing transcripts
- and their expression level
- dict_reprTrans (dict) : dict of genes and their
- representative transcipt
- intemediate_file (str) : txt file containing genes, transcript
- and their expression level from the transkript_extractor function
-
- Returns:
- tsv file of representative trasncripts and their expression level
-
- Raises:
- None
- """
- df_intermediate = repr.import_gtfSelection_to_df(intermediate_file)
- df_geneTrans = transcripts_by_gene_inDf(df_intermediate)
- df_exprTrans = tsv_or_csv_to_df(exprTrans)
- df_reprTrans = dict_reprTrans_to_df(dict_reprTrans)
- df_exprLevel_byGene = exprLevel_byGene(df_exprTrans, df_geneTrans)
- df_match = match_byGene(df_reprTrans, df_exprLevel_byGene)
- output = output_tsv(df_match)
- return output
-
-
-# run the programm
-
-dict_txt = a #input a dict of {gene:reprTrans} in the form of a txt file
-input_intermediate_file = b #input the intermediate file generated by transckript extractor
-input_expr = c #input a csv or tsv file containing the expr level
-
-dict_reprTrans = txt_to_dict(dict_txt)
-match_final = match_reprTranscript_expressionLevel(input_expr, dict_reprTrans, input_intermediate_file)
-print("this is the function :\n\n {}".format(match_final))
-
-if __name__ == "__main__":
- match_reprTranscript_expressionLevel()
-
\ No newline at end of file
diff --git a/scripts/poisson_sampling.py b/scripts/poisson_sampling.py
deleted file mode 100644
index 60d043db32e0daed7e4cfdf685ed024942af5747..0000000000000000000000000000000000000000
--- a/scripts/poisson_sampling.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import pandas as pd
-import numpy as np
-import argparse
-
-
-'''
-Sample transcript
-
-This part of the code does Poisson sampling proportionally to gene expression levels for each gene.
-
-input: total transcript number (int)
- csv file with gene id and gene expression levels (columns named 'id' and 'level')
-
-output: csv file with gene id and count
- gtf file with transcript samples
-'''
-
-
-def transcript_sampling(total_transcript_number, csv_file, output_csv):
- df = pd.read_csv(csv_file, sep='\t', lineterminator='\n', names=["id", "level"])
- levels = []
- sums = df['level'].tolist()
- total = sum(sums)
- normalized = total_transcript_number/total
- for expression_level in df['level']:
- poisson_sampled = np.random.poisson(expression_level*normalized)
- levels.append(poisson_sampled)
-
- transcript_numbers = pd.DataFrame({'id': df['id'],'count': levels})
- pd.DataFrame.to_csv(transcript_numbers, output_csv)
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser(
- description="Transcript Poisson sampler, csv output",
- formatter_class=argparse.ArgumentDefaultsHelpFormatter
- )
-
- parser.add_argument("--expression_level", required=True, help="csv file with expression level")
- parser.add_argument("--output_csv", required=True, help="output csv file")
- parser.add_argument("--input_csv", required=True, help="input csv file")
- parser.add_argument("--transcript_number", required=True, help="total number of transcripts to sample")
- args = parser.parse_args()
-
-
- transcript_sampling(args.transcript_number, args.input_csv, args.output_csv, args.transcript_number)
-
-
diff --git a/scripts/representative_v4.py b/scripts/representative_v4.py
deleted file mode 100644
index c940686b2126121b33d12a858026338aff737706..0000000000000000000000000000000000000000
--- a/scripts/representative_v4.py
+++ /dev/null
@@ -1,96 +0,0 @@
-
-import pandas as pd
-
-'''
-This part of the code take as input a gtf modified file
-and return a dictionary of transcripts with best
-support level for each gene of the input
-
-'''
-
-
-
-
-def import_gtfSelection_to_df(gtf_modified_file: str):
- """Import intermediate file from gtf and create a df
-
- Args:
- gtf_modified_file (str) : path to the intermediate file
-
- Returns:
- Pandas dataframe having Gene, transcript
- and support level as columns
-
- Raises:
- TypeError : Only str path is allowed
-
- """
- pass
- if not type(gtf_modified_file) is str:
- raise TypeError("Only str path is allowed")
- df_input = pd.read_csv(gtf_modified_file, sep = '\t', lineterminator = '\n',
-names = ["Gene_mixed", "Transcript", "Support_level", "Na1", "Na2"] )
- df_input["Support_level"] = df_input["Support_level"].replace(" ", "")
- df_input["Gene"] = df_input["Gene_mixed"].str.extract('([A-Z]\w{0,})', expand=True)
- df_input["Transcript_number"] = df_input["Gene_mixed"].str.extract('(^\d)', expand=True)
- df_clean = df_input.loc[:, ["Gene", "Transcript","Support_level"]]
- df_clean["Gene"] = df_clean["Gene"].fillna(method = 'ffill')
- df_clean = df_clean.dropna(axis = 0)
- return df_clean
-
-
-
-
-def representative_transcripts_inDict(df_gtfSelection: str) -> pd.DataFrame:
- """Return a dict containing for each gene transcripts
- with highest confidence level
-
- Args:
- df_gtfSelection (str): Pandas dataframe having Gene,
- transcript and support level as columns
-
- Returns:
- Dict {'Gene':['transcriptA', 'transcriptB'], ...}
-
- Raises:
- TypeError : Only pandas DataFrame is allowed
- """
- pass
-
- if not type(df_gtfSelection) is pd.DataFrame:
- raise TypeError("Only pandas DataFrame is allowed")
-
- df_multIndex = df_gtfSelection.set_index(["Gene", "Transcript"])
- #highest support level = 1 , worst = 5, NA = 100
- df_min = df_multIndex.groupby(level=["Gene"])["Support_level"].transform("min")
- df_final = df_min.reset_index(level = "Transcript")
- df_final = df_final.drop(columns = ["Support_level"])
- dict_representative_transcripts = df_final.groupby("Gene")["Transcript"].apply(list).to_dict()
- return dict_representative_transcripts
-
-
-
-def find_repr_by_SupportLevel(intermediate_file:str):
- """Combine functions import_gtfSelection_to_df()
- and representative_transcripts_inDict()
-
- Args:
- intermediate_file : path to the intermediate file
-
- Returns:
- Dict {'Gene':['transcriptA', 'transcriptB'], ...}
-
- Raises:
- None
-
-
- """
- pass
- df_gtf = import_gtfSelection_to_df(intermediate_file)
- dict_reprTrans = representative_transcripts_inDict(df_gtf)
- return dict_reprTrans
-
-
-
-if __name__ == "__main__":
- find_repr_by_SupportLevel()
diff --git a/scripts/transkript_extractor.py b/scripts/transkript_extractor.py
deleted file mode 100644
index 6bcd13bd151ae173cfbacfd57ebb2a247320c669..0000000000000000000000000000000000000000
--- a/scripts/transkript_extractor.py
+++ /dev/null
@@ -1,311 +0,0 @@
-#### Transcript extractor #####
-"""Transcript extractor
-Version 1.1.0"""
-### Called Packages ###
-import re
-import os
-import time
-
-### Functions ###
-
-
-
-def __parameter_editor(file_name,source_pathway_name,deposit_pathway_name):
- """This function allows for chaging the parameters after running the program"""
- while True:
- print("The program will run with the following parameters:\nFile name:\t\t",file_name,"\nSource pathway:\t",source_pathway_name,"\nDeposit pathway:\t",deposit_pathway_name,"\n")
- parameter_conformation = input("To continue with these parameters input [continue or c] to change them input [edit]\n>")
- if parameter_conformation == "continue"or parameter_conformation =="c":
- break
- elif parameter_conformation == "edit":
- #edit the parameters
- while True:
- change_question = input("select the parameter you want to change [nfile/spath/dpath] or input [b] to go back\n>")
- if change_question == "nfile":
- #This condition allows the user to chenge the file name
- file_name = input("Please input the new file name\n>")
- break
- elif change_question == "spath":
- #This condition allows the user to change the source path
- source_pathway_name = input("Please input the new source path\n>")
-
- does_source_pathway_exist = os.path.exists(source_pathway_name)
- if does_source_pathway_exist:
- break
- else:
- print("The new source pathway:",source_pathway_name,"does not exist\nThe source pathway was returned to default:",os.getcwd())
- source_pathway_name = os.getcwd()
- elif change_question == "dpath":
- #This condition allows the user to change output file location
- deposit_pathway_name = input("Please input the new output file path name\n>")
- does_deposit_pathway_exist = os.path.exists(deposit_pathway_name)
- if does_deposit_pathway_exist:
- break
- else:
- print("The new deposit pathway:",deposit_pathway_name,"does not existe\nThe deposit pathway was returnt to default:",source_pathway_name)
- deposit_pathway_name = source_pathway_name
- #The block above test if the new deposit pathway is valid
- elif change_question == "b":
- # This condition allows the user to return to the main loop
- break
- else:
- #This condition covers all non valid inputs into the secund loop
- print("The input",change_question,"is not valid. Please use one of the specified commands")
-
- else:
- #This condition covers all non valid input for the main loop
- print("The input",parameter_conformation,"is not valide please use one of the specified comands\n")
- return(file_name,source_pathway_name,deposit_pathway_name)
-
-
-
-
-
-
-
-def __searche_for_preexisting_files(file_name,deposit_pathway_name = os.getcwd()):
- """This function searches for preexisting files of the same name as the results file of the current program. It allows the user to choose to move on with the pre-existing file """
- File_of_same_name_found = False
- generat_new_file = False
- directory_content = os.listdir(deposit_pathway_name)
- for file in directory_content:
- if file == file_name:
- while True:
- File_found_input = input (file_name+" has allready been generated\nDo you want to generate a new one [y/n] \n>")
- if File_found_input == "n":
- File_of_same_name_found = True
- break
- elif File_found_input == "y":
- generat_new_file = True
- break
- else:
- print("Invalid input\nPlease press [y] if you want to generate a new file or [n] if you want to use the preexisting file")
- break
- else:
- continue
- if File_of_same_name_found:
- print("No new file will be generated, the program can continue")
- elif generat_new_file:
- print("A new file will be generated please wait...\n")
- else:
- print("No pre-existing file of the relevant type has been found.\nA new file will be generated please wait...\n")
- return(File_of_same_name_found)
-
-def bar_builder(percentage = 0,length_multiplyer = 2,start_time = time.time(),bar = str()):
- """This function creates a loading bar that can load in 10% increments starting a 0% and ending at 100%
- Expected inputs:
- percentage: int between 0 and 100 in steps of 10; default = 0 #defines the current loading increment
- length_multiplyer: int > 0 ; default = 2 #determiens the amount of symbols per loading increment
- start_time: any int ; default= time.time() #for determening loading time
- bar: str ; default = str()#input of the current bar status does not need to be defined if for the 0% increment
- """
- if percentage == 100:
- bar = bar.replace("-","#")
- print("\r"+bar+"\t"+"100%\t\t"+str(int(time.time()-start_time)))
- elif percentage > 0:
- bar = bar.replace("-","#",length_multiplyer)
- print("\r"+bar+"\t"+str(percentage)+"%", end='',flush=True)
- elif percentage == 0:
- bar = "["+"-"*length_multiplyer*10+"]"
- print(bar+"\t", end='',flush=True)
- return(bar,start_time)
-
-def __test_file_name(file_name,source_pathway_name = os.getcwd()):
- """This function validates that the source file exists at the source path. It turns the file name input in a standardized format that can be used in the next steps"""
-
- directory_content = os.listdir(source_pathway_name)
-
- index_of_the_dot = file_name.rfind(".")
- valide_source_file = False
- validate_source_file = True
- if index_of_the_dot ==-1:
- file_name += ".gtf"
- else:
- source_file_typ = file_name[index_of_the_dot:]
- not_a_file_type = re.compile(".\d{1,13}")
- try_not_a_file_type = not_a_file_type.search(source_file_typ)
- if source_file_typ == ".gtf":
- file_name = file_name
- elif try_not_a_file_type:
- file_name += ".gtf"
- else:
- print("This program can not handle",source_file_typ,"files. \nplease use a .gtf file" )
- validate_source_file = False
- #The block above tests if the file_name includes the file type and if no
- #file type is found adds ".gtf" und if a non ".gtf" file is found gives an error
-
- if validate_source_file:
- for file in directory_content:
- if file == file_name:
- valide_source_file = True
- break
- #The block above tests if a file on the given name is in the given directora
-
- if valide_source_file:
- print("The file:",file_name,"has been found.\n")
- else:
- print("No .gtf file of the name",file_name,"has been found in this pathway")
- #The bock above gives feed back regarding the results of the file test
-
- file_name = file_name.replace(".gtf","")
- #This line normalizes the file name
- return(valide_source_file,file_name)
-
-def __do_pathways_exist__(source_pathway_name,deposit_pathway_name):
- """This funtion tests that the entered pathways actualy exist"""
- does_source_pathway_exist = os.path.exists(source_pathway_name)
- does_deposit_pathway_exist = os.path.exists(deposit_pathway_name)
- #The Block above does the actual testing
- if does_source_pathway_exist:
- source_pathway_name = source_pathway_name
- else:
- print("The source pathway:",source_pathway_name,"has not been found\nThe source pathway was set to the default")
- source_pathway_name = os.getcwd()
- #The block above detail the possible reactions for the source pathe existing or not existing
- if does_deposit_pathway_exist:
- deposit_pathway_name = deposit_pathway_name
- else:
- print("The deposit pathway:",deposit_pathway_name,"has not been found\nThe deposit pathway was set to the default")
- deposit_pathway_name = source_pathway_name
- #The block above details the possible reactions for the deposit pathway existing or not existing
- return(source_pathway_name,deposit_pathway_name)
-
-def gene_ID_finder(entry):
- """This function is supposed to find the gene ID of a known gene entry
- Expected inputs:
- entry: str #a line from a gtf file that contains a gene ID"""
- index_gene_id = entry.find("gene_id")
- find_gene_id_name = re.compile("\"\S{1,25}\"")
- sub_entry = entry[index_gene_id:]
- try_find_gene_id_name = find_gene_id_name.search(sub_entry)
- gene_ID = try_find_gene_id_name[0].replace("\"","")
- return (gene_ID)
-
-def transcript_ID_finder (entry):
- """This function is supposed to finde the transcript ID in a known transcript entry
- Expected inputs:
- entry: str #a line from a gtf file that contains a transcript ID"""
- index_transcript_id = entry.find("transcript_id")
- find_transcript_id_name = re.compile("\"\S{1,25}\"")
- sub_entry = entry[index_transcript_id:]
- try_find_transcript_id_name = find_transcript_id_name.search(sub_entry)
-
- try:
- transcript_ID = try_find_transcript_id_name[0].replace("\"","")
- except:
- transcript_ID = ""
- return (transcript_ID)
-
-def transcript_support_level_finder(entry):
- """This function is supposed to find the transcript support level in a known transcript entry
- Expected input:
- entry: str #a line from a gtf file that be blongs to a transcript"""
- transcript_support_level_start_ID = entry.find("transcript_support_level")
- sub_entry = entry[transcript_support_level_start_ID:]
-
- try:
- score_finder = re.compile("\W\w{1,16}\W{2}")
- try_score_finder = score_finder.search(sub_entry)
- Pre_score_1 = try_score_finder[0]
- Pre_score_2 = Pre_score_1.replace("\"","")
- Pre_score_2 = Pre_score_2.replace("(","")
- transcript_support_level = Pre_score_2.replace(";","")
- if "NA" in transcript_support_level:
- transcript_support_level = 100
- #I changed This tell laura
-
-
- except:
- transcript_support_level = 100
- return (transcript_support_level)
-
-
-
-
-def _transcript_extractor (file_name,source_pathway_name,deposit_pathway_name):
- """This function extracts the transcript number ,transcript ID, the transcript support level, the transcrip length and the line index from a gtf file of a given name and saves tham as a new file name given_name_intermediat_file.txt.
- Expected input:
- file_name: str #the name of the gft file you want to look at without the .gtf part
- source_pathway_name: str #path of the gtf file
- deposit_pathway_name: str #path for saving the intermediat file"""
-
- with open(os.path.join(source_pathway_name,file_name+".gtf"), 'r') as f:
- total_entrys =len(f.readlines())
- with open(os.path.join(source_pathway_name,file_name+".gtf"), 'r') as f:
- current_entry = 0
- percentage_done = 0
- bar,start_time = bar_builder(length_multiplyer = 3)
-
-
- Old_gen_ID = str()
- #stand-in as the first couple entrys are not genes
- with open(os.path.join(deposit_pathway_name,file_name+"_"+"intermediate_file"+".txt"),"w") as IMF:
- transcript_number = 0
- for entry in f:
-
-
- current_entry += 1
- current_percentage_done = 100* current_entry/total_entrys
- if current_percentage_done > percentage_done +10:
- bar,start_time = bar_builder(percentage=percentage_done+10,length_multiplyer = 3,start_time=start_time,bar =bar)
- percentage_done = int(current_percentage_done)
-
- if "gene_id" in entry:
- Gen_ID = gene_ID_finder(entry)
- else:
- Gen_ID = Old_gen_ID
-
- if Gen_ID != Old_gen_ID:
- Gen_entry = ">"+ Gen_ID +"\n"
- IMF.write(Gen_entry)
- transcript_number = 0
- Old_gen_ID = Gen_ID
-
- if "\ttranscript\t" in entry:
- transcript_number += 1
- Transcript_ID = transcript_ID_finder(entry)
- #the function that determins the transcript ID is called
- transcript_support_level = transcript_support_level_finder(entry)
- #the function that determins the transcript support level is called
- New_entry = str(transcript_number)+"\t"+str(Transcript_ID)+"\t"+str(transcript_support_level)+"\t"+"\t\n"
- IMF.write(New_entry)
- bar_builder(100,length_multiplyer = 3,start_time=start_time,bar =bar)
- print("The transcripts have been collected")
-
-
-def extract_transkript (file_name = "test",source_pathway_name = os.getcwd(),deposit_pathway_name = False,Input_free = False):
- """This it the overall exetutable funtion that will execute the transcript extraction process for a given file with all checks.
- Expected input:
- file_name: str ; default = test #the name of the gft file you want to look at
- source_pathway_name: str ; default = current work directory #path of the gtf file
- deposit_pathway_name: str ; default = source_pathway_name #path for saving the intermediat file
- Outputs:
- file_name: str
- source_pathway_name: str
- deposit_pathway_name: str"""
-
- if deposit_pathway_name == False:
- deposit_pathway_name = source_pathway_name
- if Input_free:
- validated_file_name = __test_file_name(file_name,source_pathway_name)
- file_name = validated_file_name[1]
- _transcript_extractor (file_name,source_pathway_name,deposit_pathway_name)
- else:
- file_name,source_pathway_name,deposit_pathway_name = __parameter_editor(file_name,source_pathway_name,deposit_pathway_name)
- source_pathway_name,deposit_pathway_name =__do_pathways_exist__(source_pathway_name,deposit_pathway_name)
- validated_file_name = __test_file_name(file_name,source_pathway_name)
- file_name = validated_file_name[1]
- if validated_file_name[0]:
- if __searche_for_preexisting_files(file_name+"_intermediate_file.txt",deposit_pathway_name):
- print("The transcripts has been collected\n")
- else:
- _transcript_extractor (file_name,source_pathway_name,deposit_pathway_name)
- return(file_name,source_pathway_name,deposit_pathway_name)
-
-#### Dev part ####
-
-if __name__ == "__main__":
- extract_transkript()
-#This line allows the file to be executed on its own also from
-
-
diff --git a/scripts/writegtf.py b/scripts/writegtf.py
deleted file mode 100644
index 9a8c7a8a7bbafbf7191f679c1a4d654f1ba9e9b9..0000000000000000000000000000000000000000
--- a/scripts/writegtf.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import pandas as pd
-import numpy as np
-import argparse
-import re
-
-def transcript_ID_finder (entry):
- index_transcript_id = entry.find("transcript_id")
- find_transcript_id_name = re.compile("\"\S{1,25}\"")
- sub_entry = entry[index_transcript_id:]
- try_find_transcript_id_name = find_transcript_id_name.search(sub_entry)
-
- try:
- transcript_ID = try_find_transcript_id_name[0].replace("\"","")
- except:
- transcript_ID = ""
- return (transcript_ID)
-
-
-'''gtf_file_writer takes as input the original gtf file and the csv file containing relevant transcripts.
-
- It produces a gtf file containing only the transcript entries of those contained in the csv file
-
- based on id'''
-
-
-def gtf_file_writer (original_file, csv_file, output_file):
- output = []
-
- df = pd.read_csv(csv_file)
- listoftranscripts = df['id'].tolist()
- if df['id'] == False:
- print('Error. \'id\' column needed in input csv file.')
-
- with open(original_file, 'r') as f:
- for entry in f:
- if "\ttranscript\t" in entry:
- transcript_id = transcript_ID_finder(entry)
- if transcript_id in listoftranscripts:
- output.append(entry)
- with open(output_file, 'w') as last_file:
- last_file.write(output)
-
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser(
- description="gtf output file writer",
- formatter_class=argparse.ArgumentDefaultsHelpFormatter
- )
- parser.add_argument("--annotation", required=True, help="gtf file with genome annotation")
- parser.add_argument("--output_gtf", required=True, help="output gtf file")
- parser.add_argument("--input_csv", required=True, help="input csv file")
- args = parser.parse_args()
-
- gtf_file_writer(args.annotation, args.input_csv, args.output_gtf)