def planttfdb(MSUID):
# Find the file
url = 'http://planttfdb.cbi.pku.edu.cn/download.php'
html_page = helper.connectionError(url)
soup = BeautifulSoup(html_page.content, "lxml")
# Find headers
for search in soup.findAll('table', {"id": "oid_tfid"}):
for linkfound in search.findAll('a'):
if (linkfound.contents[0] == "Oryza sativa subsp. japonica"):
link = 'http://planttfdb.cbi.pku.edu.cn/' + linkfound.get(
'href')
break
# Give the entire name of the file with the extension .gz
filename = link.split("/")[-1]
# Give the name of the file without .gz
uncompressName = filename[:-3] + ".txt"
pathToFile = helper.formatPathToFile(uncompressName)
# Test existant file
if (not helper.existFile(pathToFile)):
# Fetch the file by the url and decompress it
r = helper.connectionError(link)
decompressedFile = gzip.decompress(r.content)
# Create the file .txt
with open(pathToFile, "wb") as f:
f.write(decompressedFile)
f.close()
# Use the previous created file (.txt)
with open(pathToFile, "r+b") as file:
# Import file tab-delimited
try:
array = pd.read_csv(file, sep="\t", header=None)
except pd.io.common.EmptyError:
array = pd.DataFrame()
# Named columns
array.columns = ["TF_ID", "Gene_ID", "Family"]
data = array.loc[array['TF_ID'] == MSUID]
if (not data.empty):
return data
else:
data = array.loc[array['Gene_ID'] == MSUID]
if (data.empty):
return False
else:
hashmap = {"Family": data["Family"].values[0]}
return hashmap
def oryzabaseRapId(RAPID):
pathToFile = helper.formatPathToFile("OryzabaseGeneListEn.txt")
if (helper.existFile(pathToFile) == False):
loadFileURL(
pathToFile,
"https://shigen.nig.ac.jp/rice/oryzabase/gene/download?classtag=GENE_EN_LIST"
)
else:
print("File already exist")
print("Find file OK")
# Import file tab-delimited
try:
array = pd.read_csv(pathToFile, sep="\t", encoding='utf-8')
except NameError:
array = pd.DataFrame()
#array.columns = ['Trait Id', 'CGSNL Gene Symbol', 'Gene symbol synonym(s)', ' CGSNL Gene Name', 'Gene name synonym(s)', 'Protein Name', 'Allele', 'Chromosome No.', 'Explanation', 'Trait Class', 'RAP ID', 'GrameneId', 'Arm', 'Locate(cM)', 'Gene Ontology', 'Trait Ontology', 'Plant Ontology']
data = array.loc[array['RAP ID'] == RAPID]
return data
def oryzabaseCGSNL(CGSNL):
pathToFile = helper.formatPathToFile("OryzabaseGeneListEn.txt")
if (helper.existFile(pathToFile) == False):
loadFileURL(
pathToFile,
"https://shigen.nig.ac.jp/rice/oryzabase/gene/download?classtag=GENE_EN_LIST"
)
else:
print("File already exist")
print("Find file OK")
# Import file tab-
try:
array = pd.read_csv(pathToFile, sep="\t", encoding='utf-8')
except NameError:
array = pd.DataFrame()
print(array)
print("Find by CGSNL Gene Name")
data = array.loc[array['CGSNL Gene Name'] == CGSNL]
return data
def main():
pathScript = sys.argv[0]
contig = sys.argv[1]
if len(contig) < 2:
contig = 'chr0' + contig # test if for 10 - 11 - 12
else:
contig = 'chr' + contig
start = sys.argv[2]
end = sys.argv[3]
db = sys.argv[4]
dataSnp = snpSeek.snpSeek(contig, start, end)
id = sys.argv[5]
if (db == "1"):
dataRapdb = rapdb.rapdb(id)
print(dataRapdb)
elif (db == "call_snpSeek"):
for i in range(0, len(dataSnp)):
print(dataSnp[i])
elif (db == "2"):
dataGramene = gramene.gramene(id)
print(dataGramene)
elif (db == "3"):
dataOryzabase = oryzabase.oryzabase(id)
print(dataOryzabase)
elif (db == "4"):
ic4r.ic4r(id)
elif (db == "5"):
dataPlanttfdb = planttfdb.planttfdb(id)
print(dataPlanttfdb)
# LOC_xxxxxxxx
elif (db == "6"):
dataPlntfdb = plntfdb.plntfdb(id)
print(dataPlntfdb)
elif (db == "7"):
dataFunricegenes = funricegenes.funricegenes(id)
print(dataFunricegenes)
elif (db == "8"):
dataFunricegenes2 = funricegenes.funricegenes2(id)
print(dataFunricegenes2)
elif (db == "9"):
dataFunricegenes3 = funricegenes.funricegenes3(id)
print(dataFunricegenes3)
elif (db == "10"):
dataMsu = msu.msu(id)
print(dataMsu)
elif (db == "10.1"):
dataMsu = msu.msu_orf(id)
print(dataMsu)
# Ecriture fichier a revoir !!!!!!!!! pour les id et hashmap[iricname] et hashmpap [raprepname]
elif (db == "13"):
url = "http://rapdb.dna.affrc.go.jp/download/archive/RAP-MSU_2017-04-14.txt.gz"
filename = url.split("/")[-1]
# Give the name of the file without .gz
pathToFile = helper.formatPathToFile(filename[:-3])
if (not os.path.isfile(pathToFile)):
# Fetch the file by the url and decompress it
r = requests.get(url)
decompressedFile = gzip.decompress(r.content)
# Create the file .txt
with open(pathToFile, "w") as f:
f.write(decompressedFile)
f.close()
newFile = helper.formatPathToFile("geneID.txt")
with open(newFile, "a") as f:
# Import file tab-delimited
try:
array = pd.read_csv(pathToFile, sep="\t", header=None)
except:
array = pd.DataFrame()
# Named columns
array.columns = ["RAP", "LOC"]
# Find the line corresponding to the entered RAP ID (Select LOC FROM LOC where RAP = RapID)
data = array.loc[array['RAP'] == id]
#data.loc[:, 'iricname'] = hashmap['iricname']
# Store the corresponding LOC ID and split the string
print(data['iricname'])
data.to_csv(f, sep='\t')
f.close()
# Plage chromosome
# Cree le fichier fileID.txt
elif (db == "11"):
snpSeekAll.snpSeekAll("Os12:1..27,531,856")
snpSeekAll.snpSeekAll("Os02:1..35,937,250")
snpSeekAll.snpSeekAll("Os03:1..36,413,819")
snpSeekAll.snpSeekAll("Os04:1..35,502,694")
snpSeekAll.snpSeekAll("Os05:1..29,958,434")
snpSeekAll.snpSeekAll("Os06:1..31,248,787")
snpSeekAll.snpSeekAll("Os07:1..29,697,621")
snpSeekAll.snpSeekAll("Os08:1..28,443,022")
snpSeekAll.snpSeekAll("Os09:1..23,012,720")
snpSeekAll.snpSeekAll("Os10:1..23,207,287")
snpSeekAll.snpSeekAll("Os11:1..29,021,106")
snpSeekAll.snpSeekAll("Os12:1..27,531,856")
# Return the SnpSeek Call
elif (db == "12"):
print(dataSnp)
"""