Louis BECQUEY / RNANet

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Louis BECQUEY
6cc5142f 2 parents 60bd1aec d645ce5e

Merge branch 'master' of https://github.com/persalteas/RNANet into master

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Showing 1 changed file with 27 additions and 19 deletions
......@@ -132,9 +132,11 @@ class BufferingSummaryInfo(AlignInfo.SummaryInfo):
class Chain:
""" The object which stores all our data and the methods to process it.
"""
The object which stores all our data and the methods to process it.
Chains accumulate information through this scipt, and are saved to files at the end of major steps."""
Chains accumulate information through this scipt, and are saved to files at the end of major steps.
"""
def __init__(self, pdb_id, pdb_model, pdb_chain_id, chain_label, eq_class, rfam="", inferred=False, pdb_start=None, pdb_end=None):
self.pdb_id = pdb_id # PDB ID
......@@ -144,6 +146,7 @@ class Chain:
self.mapping = Mapping(chain_label, rfam, pdb_start, pdb_end, inferred)
else:
self.mapping = None
self.eq_class = eq_class # BGSU NR list class id
self.chain_label = chain_label # chain pretty name
self.file = "" # path to the 3D PDB file
self.seq = "" # sequence with modified nts
......@@ -523,30 +526,33 @@ class Chain:
# Register the chain in table chain
if self.mapping is not None:
sql_execute(conn, f""" INSERT INTO chain
(structure_id, chain_name, pdb_start, pdb_end, rfam_acc, inferred, issue)
(structure_id, chain_name, pdb_start, pdb_end, rfam_acc, eq_class, inferred, issue)
VALUES
(?, ?, ?, ?, ?, ?, ?)
(?, ?, ?, ?, ?, ?, ?, ?)
ON CONFLICT(structure_id, chain_name, rfam_acc) DO
UPDATE SET pdb_start=excluded.pdb_start,
pdb_end=excluded.pdb_end,
eq_class=excluded.eq_class,
inferred=excluded.inferred,
issue=excluded.issue;""",
data=(str(self.pdb_id), str(self.pdb_chain_id),
int(self.mapping.nt_start), int(self.mapping.nt_end),
str(self.mapping.rfam_acc),
str(self.mapping.rfam_acc), str(self.eq_class),
int(self.mapping.inferred), int(self.delete_me)))
# get the chain id
self.db_chain_id = sql_ask_database(conn, f"""SELECT (chain_id) FROM chain
WHERE structure_id='{self.pdb_id}'
AND chain_name='{self.pdb_chain_id}'
AND rfam_acc='{self.mapping.rfam_acc}';""")[0][0]
AND rfam_acc='{self.mapping.rfam_acc}'
AND eq_class='{self.eq_class}';""")[0][0]
else:
sql_execute(conn, """INSERT INTO chain (structure_id, chain_name, rfam_acc, issue) VALUES (?, ?, NULL, ?)
ON CONFLICT(structure_id, chain_name, rfam_acc) DO UPDATE SET issue=excluded.issue;""",
data=(str(self.pdb_id), str(self.pdb_chain_id), int(self.delete_me)))
sql_execute(conn, """INSERT INTO chain (structure_id, chain_name, rfam_acc, eq_class, issue) VALUES (?, ?, NULL, ?, ?)
ON CONFLICT(structure_id, chain_name, rfam_acc) DO UPDATE SET issue=excluded.issue, eq_class=excluded.eq_class;""",
data=(str(self.pdb_id), str(self.pdb_chain_id), str(self.eq_class), int(self.delete_me)))
self.db_chain_id = sql_ask_database(conn, f"""SELECT (chain_id) FROM chain
WHERE structure_id='{self.pdb_id}'
AND chain_name='{self.pdb_chain_id}'
AND eq_class='{self.eq_class}'
AND rfam_acc IS NULL;""")[0][0]
# Add the nucleotides if the chain is not an issue
......@@ -859,14 +865,14 @@ class Downloader:
if path.isfile(path_to_3D_data + f"latest_nr_list_{nr_code}A.csv"):
print("\t> Use of the previous version.\t", end = "", flush=True)
else:
return [], []
return pd.DataFrame([], columns=["class", "class_members"])
nrlist = pd.read_csv(path_to_3D_data + f"latest_nr_list_{nr_code}A.csv")
full_structures_list = nrlist['class_members'].tolist()
full_structures_list = [ tuple(i[1]) for i in nrlist[['class','class_members']].iterrows() ]
print(f"\t{validsymb}", flush=True)
# The beginning of an adventure.
return full_structures_list
return full_structures_list # list of ( str (class), str (class_members) )
def download_from_SILVA(self, unit):
if not path.isfile(path_to_seq_data + f"realigned/{unit}.arb"):
......@@ -1068,8 +1074,8 @@ class Pipeline:
elif opt == "--from-scratch":
warn("Deleting previous database and recomputing from scratch.")
subprocess.run(["rm", "-rf",
path_to_3D_data + "annotations",
# path_to_3D_data + "RNAcifs", # DEBUG : keep the cifs !
# path_to_3D_data + "annotations", # DEBUG : keep the annotations !
# path_to_3D_data + "RNAcifs", # DEBUG : keep the cifs !
path_to_3D_data + "rna_mapped_to_Rfam",
path_to_3D_data + "rnaonly",
path_to_seq_data + "realigned",
......@@ -1103,7 +1109,7 @@ class Pipeline:
If self.HOMOLOGY is set to False, simply returns a list of Chain() objects with available 3D chains."""
# List all 3D RNA chains below given resolution
full_structures_list = self.dl.download_BGSU_NR_list(self.CRYSTAL_RES)
full_structures_list = self.dl.download_BGSU_NR_list(self.CRYSTAL_RES) # list of tuples ( class, class_members )
# Check for a list of known problems:
if path.isfile(runDir + "/known_issues.txt"):
......@@ -1140,8 +1146,8 @@ class Pipeline:
exit(1)
else:
conn = sqlite3.connect(runDir+"/results/RNANet.db", timeout=10.0)
for codelist in tqdm(full_structures_list):
codes = str(codelist).replace('+',',').split(',')
for eq_class, codelist in tqdm(full_structures_list):
codes = codelist.replace('+',',').split(',')
# Simply convert the list of codes to Chain() objects
for c in codes:
......@@ -1408,7 +1414,7 @@ class Pipeline:
with sqlite3.connect(runDir+"/results/RNANet.db") as conn:
pd.read_sql_query("SELECT rfam_acc, description, idty_percent, nb_homologs, nb_3d_chains, nb_total_homol, max_len, comput_time, comput_peak_mem from family ORDER BY nb_3d_chains DESC;",
conn).to_csv(runDir + f"/results/archive/families_{time_str}.csv", float_format="%.2f", index=False)
pd.read_sql_query("""SELECT structure_id, chain_name, pdb_start, pdb_end, rfam_acc, inferred, date, exp_method, resolution, issue FROM structure
pd.read_sql_query("""SELECT eq_class, structure_id, chain_name, pdb_start, pdb_end, rfam_acc, inferred, date, exp_method, resolution, issue FROM structure
JOIN chain ON structure.pdb_id = chain.structure_id
ORDER BY structure_id, chain_name, rfam_acc ASC;""", conn).to_csv(runDir + f"/results/archive/summary_{time_str}.csv", float_format="%.2f", index=False)
......@@ -1522,6 +1528,7 @@ def sql_define_tables(conn):
chain_id INTEGER PRIMARY KEY NOT NULL,
structure_id CHAR(4) NOT NULL,
chain_name VARCHAR(2) NOT NULL,
eq_class VARCHAR(10),
pdb_start SMALLINT,
pdb_end SMALLINT,
issue TINYINT,
......@@ -1785,7 +1792,8 @@ def work_infer_mappings(update_only, allmappings, codelist):
known_mappings = pd.DataFrame()
# Split the comma-separated list of chain codes into chain codes:
codes = str(codelist).replace('+',',').split(',')
eq_class = codelist[0]
codes = codelist[1].replace('+',',').split(',')
# Search for mappings that apply to an element of this PDB chains list:
for c in codes:
......