diff --git a/read_gff.py b/read_gff.py
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+++ b/read_gff.py
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+import pandas as pd
+import re
+def add_gene_annotation(
+ gene_data_path #initTable_path=None, df_gene_csv_path=None, df_exon_csv_path=None
+):
+ """
+ add_gene_annotation
+ for the first 22 chromosomes, retrieves the label of the genes
+ and their position as well as those of the exons associated with the genes.
+ Then store this information in a hdf5 file
+
+ :param gene_data_path: path to the GFF file containing gene and exon data (for example, GRCh37_latest_genomic.gff)
+ :type gene_data_path: str
+ :param initTable_path: path to the file initTable.hdf5
+ :type initTable_path: str
+ :param df_gene_csv_path: path to the file df_gene.csv
+ :type df_gene_csv_path: str
+ :param df_exon_csv_path: path to the file df_exon.csv
+ :type df_exon_csv_path: str
+
+ :return: the dataframes df_gene and df_exon
+ :rtype: 2 PANDAS dataframes
+ """
+ # lists containing gene data
+ gene_id_label = []
+ gene_GeneID = []
+ gene_chr = []
+ gene_start = []
+ gene_end = []
+ gene_direction = []
+ gene_biotype = []
+
+ # lists containing exon data
+ exon_id_label = []
+ exon_GeneID = []
+ exon_chr = []
+ exon_start = []
+ exon_end = []
+ exon_direction = []
+
+ # temporary list containing the data of all exons
+ TMP__exon_id_label = []
+ TMP__exon_GeneID = []
+ TMP__exon_chr = []
+ TMP__exon_start = []
+ TMP__exon_end = []
+ TMP__exon_direction = []
+
+ fichier = open(gene_data_path, "r")
+ lignes = fichier.readlines()
+ fichier.close()
+
+ for ligne in lignes:
+ elements = ligne.split("\t")
+ if elements[0].startswith("NC_"):
+ decode_chr = elements[0].strip("NC_").split(".")
+ chr = int(decode_chr[0])
+ if chr <= 22:
+ if elements[2] == "gene":
+ gene_chr.append(chr)
+ gene_start.append(int(elements[3]))
+ gene_end.append(int(elements[4]))
+ gene_direction.append(elements[6])
+ decode_id_1 = elements[8].split(";")
+ decode_id_2 = decode_id_1[0].split("=")
+ gene_id_label.append(decode_id_2[1].strip("gene-"))
+ decode_id_3 = decode_id_1[1].split(",")
+ decode_id_4 = decode_id_3[0].split("=")
+ decode_id_5 = decode_id_4[1].split(":")
+ gene_GeneID.append(decode_id_5[1])
+ decode_id_6 = decode_id_1[5].split("=")
+ if decode_id_6[0] == "gene_biotype":
+ The_biotype = decode_id_6[1].rstrip('\r\n')
+ gene_biotype.append(The_biotype)
+ else:
+ decode_id_7 = decode_id_1[6].split("=")
+ if decode_id_7[0] == "gene_biotype":
+ The_biotype = decode_id_7[1].rstrip('\r\n')
+ gene_biotype.append(The_biotype)
+ else:
+ decode_id_8 = decode_id_1[7].split("=")
+ if decode_id_8[0] == "gene_biotype":
+ The_biotype = decode_id_8[1].rstrip('\r\n')
+ gene_biotype.append(The_biotype)
+ else:
+ The_biotype = "unknown"
+ gene_biotype.append(The_biotype)
+
+ elif elements[2] == "exon":
+ TMP__exon_chr.append(chr)
+ TMP__exon_start.append(int(elements[3]))
+ TMP__exon_end.append(int(elements[4]))
+ TMP__exon_direction.append(elements[6])
+ decode_id_1 = elements[8].split(";")
+ decode_id_2 = decode_id_1[0].split("=")
+ TMP__exon_id_label.append(decode_id_2[1].strip("exon-"))
+ decode_id_3 = decode_id_1[2].split(",")
+ decode_id_4 = decode_id_3[0].split("=")
+ decode_id_5 = decode_id_4[1].split(":")
+ TMP__exon_GeneID.append(decode_id_5[1])
+
+ # We only keep the exons that correspond to a gene
+ for i in range(len(TMP__exon_id_label)):
+ if TMP__exon_GeneID[i] in gene_GeneID:
+ exon_id_label.append(TMP__exon_id_label[i])
+ exon_GeneID.append(TMP__exon_GeneID[i])
+ exon_chr.append(TMP__exon_chr[i])
+ exon_start.append(TMP__exon_start[i])
+ exon_end.append(TMP__exon_end[i])
+ exon_direction.append(TMP__exon_direction[i])
+
+ # We insert genes and exons into dataframes
+ df_gene = pd.DataFrame(
+ {
+ "Chr": gene_chr,
+ "GeneID": gene_GeneID,
+ "gene_label": gene_id_label,
+ "start": gene_start,
+ "end": gene_end,
+ "direction": gene_direction,
+ "biotype": gene_biotype,
+ }
+ )
+ df_exon = pd.DataFrame(
+ {
+ "Chr": exon_chr,
+ "exon_label": exon_id_label,
+ "GeneID": exon_GeneID,
+ "start": exon_start,
+ "end": exon_end,
+ "direction": exon_direction,
+ }
+ )
+ # The rows of the dataframes are sorted by chromosome number and start position (and end position for exon)
+ df_gene.sort_values(by=["Chr", "start"], inplace=True, ignore_index=True)
+ df_exon.sort_values(by=["Chr", "start", "end"], inplace=True, ignore_index=True)
+
+ return (df_gene, df_exon)
+
+#initTable_path=None, df_gene_csv_path=None, df_exon_csv_path=None
+gene_data_path = "/home/hjulienn/Project/jass/data/Extract_GRCh37.gff"
+dgen, dexon = add_gene_annotation(gene_data_path)
+print(dexon.head())
+print(dgen.head())
+
+
+def get_chr(x):
+ return(int(re.split("NC_|\.", x)[1]))
+
+def get_label(x):
+ return x[0].split("=")[1].strip("gene-")
+
+def get_gene_ID(x):
+ return re.split("=|:", x[1].split(",")[0])[2]
+
+bt = re.compile("gene_biotype=")
+def get_biotype(x):
+ print(list(filter(bt.match, x))[0].split("=")[1])
+ return(list(filter(bt.match, x))[0].split("=")[1])
+
+GRC = pd.read_csv(gene_data_path, sep="\t", skiprows=9, nrows=10000, names=["Loci", "Annot", "biotype", "start", "end", "a", "direction", "b", "Info"])
+#Filter out to keep only genes and exons
+GRC = GRC.loc[(GRC.biotype=='gene') | (GRC.biotype=="exon")]
+GRC["Chr"] = GRC.Loci.apply(get_chr)
+GRC["info_element"]=GRC.Info.str.split(";")
+
+GRC_gene = GRC.loc[(GRC.biotype=='gene')]
+GRC_exon = GRC.loc[(GRC.biotype=='exon')]
+GRC_gene["gene_label"] = GRC_gene.info_element.apply(get_label)
+GRC_gene["GeneID"] = GRC_gene.info_element.apply(get_gene_ID)
+GRC_gene["biotype"] = GRC_gene.info_element.apply(get_biotype)
+
+
+print(GRC.shape)
+print(GRC.head(30))
+print(GRC.iloc[0,8])
+
+print(GRC_exon.head())
+print("cyril func")
+print(dgen.head())
+print(dexon.head())
+print("hanna func")
+print(GRC_gene[["Chr", "GeneID", "gene_label","start","end","direction","biotype"]].head())