Louis BECQUEY / RNANet

Go to a project
Toggle navigation Toggle navigation pinning
Louis BECQUEY
3c2f649f 1 parent bd665f4f

Reordered steps in 3D data extraction + removed inverted numbering support

Inline Side-by-side
Showing 3 changed files with 141 additions and 136 deletions
......@@ -52,7 +52,7 @@ class SelectivePortionSelector(object):
def __init__(self, model_id, chain_id, valid_resnums, khetatm):
self.chain_id = chain_id
self.resnums = valid_resnums
self.resnums = valid_resnums # list of strings, that are mostly ints
self.pdb_model_id = model_id
self.hydrogen_regex = re.compile("[123 ]*H.*")
self.keep_hetatm = khetatm
......@@ -70,15 +70,12 @@ class SelectivePortionSelector(object):
if hetatm_flag in ["W", "H_MG"]:
return int(self.keep_hetatm)
# I don't really know what this is but the doc said to warn:
if icode != " ":
pass
# warn(f"icode {icode} at position {resseq}\t\t")
# Accept the residue if it is in the right interval:
if len(self.resnums):
return int(resseq in self.resnums)
else:
if icode == " " and len(self.resnums):
return int(str(resseq) in self.resnums)
elif icode != " " and len(self.resnums):
return int(str(resseq)+icode in self.resnums)
else: # len(resnum) == 0, we don't use mappings (--no-homology option)
return 1
def accept_atom(self, atom):
......@@ -153,7 +150,7 @@ class Chain:
def __hash__(self):
return hash((self.pdb_id, self.pdb_model, self.pdb_chain_id, self.chain_label))
def extract(self, khetatm):
def extract(self, df, khetatm):
""" Extract the part which is mapped to Rfam from the main CIF file and save it to another file.
"""
......@@ -180,12 +177,8 @@ class Chain:
mmcif_parser = MMCIFParser()
s = mmcif_parser.get_structure(self.pdb_id, path_to_3D_data + "RNAcifs/"+self.pdb_id+".cif")
# Extract the desired chain
c = s[model_idx][self.pdb_chain_id]
if self.mapping is not None:
valid_set = set(self.mapping.old_nt_resnums)
valid_set = set(df.old_nt_resnum)
else:
valid_set = set()
......@@ -261,18 +254,33 @@ class Chain:
#############################################
# Select the nucleotides we need
#############################################
# Remove nucleotides of the chain that are outside the Rfam mapping, if any
if self.mapping is not None:
if self.mapping.nt_start > self.mapping.nt_end:
warn(f"Mapping is reversed, this case is not supported (yet). Ignoring chain {self.chain_label}.")
self.delete_me = True
self.error_messages = f"Mapping is reversed, this case is not supported (yet)."
return None
df = self.mapping.filter_df(df)
# Duplicate residue numbers : shift numbering
# Example: 4v9q-DV contains 17 and 17A which are both read 17 by DSSR.
while True in df.duplicated(['nt_resnum']).values:
i = df.duplicated(['nt_resnum']).values.tolist().index(True)
self.mapping.shift_resnum_range(i)
df.iloc[i:, 1] += 1
resnumlist = df.nt_resnum.tolist()
if self.mapping is not None:
self.mapping.log(f"Shifting nt_resnum numbering because of duplicate residue {resnumlist[i]}")
if resnumlist[i] == resnumlist[i-1]:
# Common 4v9q-DV case : e.g. chains contains 17 and 17A which are both read 17 by DSSR.
# Solution : we shift the numbering of 17A (to 18) and the following residues.
df.iloc[i:, 1] += 1
else:
# 4v9k-DA case : the nt_id is not the full nt_resnum: ... 1629 > 1630 > 163B > 1631 > ...
# Here the 163B is read 163 by DSSR, but there already is a residue 163.
# Solution : set nt_resnum[i] to nt_resnum[i-1] + 1, and shift the following by 1.
df.iloc[i, 1] = 1 + df.iloc[i-1, 1]
df.iloc[i+1:, 1] += 1
# Search for ligands at the end of the selection
# Drop ligands detected as residues by DSSR, by detecting several markers
......@@ -280,11 +288,12 @@ class Chain:
(df.iloc[[-1]][["alpha", "beta", "gamma", "delta", "epsilon", "zeta", "v0", "v1", "v2", "v3", "v4"]].isna().values).all()
or (df.iloc[[-1]].puckering=='').any()
)
or ( len(df.index_chain) >= 2 and df.iloc[-1,1] > 50 + df.iloc[-2,1] )
or ( len(df.index_chain) >= 2 and df.iloc[-1,1] > 50 + df.iloc[-2,1] ) # large nt_resnum gap between the two last residues
or ( len(df.index_chain) and df.iloc[-1,2] in ["GNG", "E2C", "OHX", "IRI", "MPD", "8UZ"] )
):
if self.mapping is not None:
self.mapping.drop_ligand(df.tail(1))
self.mapping.log("Droping ligand:")
self.mapping.log(df.tail(1))
df = df.head(-1)
# Duplicates in index_chain : drop, they are ligands
......@@ -295,8 +304,6 @@ class Chain:
warn(f"Found duplicated index_chain {df.iloc[i,0]} in {self.chain_label}. Keeping only the first.")
if self.mapping is not None:
self.mapping.log(f"Found duplicated index_chain {df.iloc[i,0]}. Keeping only the first.")
with open("duplicates.txt", "a") as f:
f.write(f"DEBUG: {self.chain_label} has duplicate index_chains !\n")
df = df.drop_duplicates("index_chain", keep="first") # drop doublons in index_chain
# drop eventual nts with index_chain < the first residue,
......@@ -305,30 +312,31 @@ class Chain:
if len(ligands.index_chain):
if self.mapping is not None:
for line in ligands.iterrows():
self.mapping.drop_ligand(line)
self.mapping.log("Droping ligand:")
self.mapping.log(line)
df = df.drop(ligands.index)
# Find missing index_chain values
# This happens because of resolved nucleotides that have a
# strange nt_resnum value
# strange nt_resnum value. Thanks, biologists ! :@ :(
# e.g. 4v49-AA, position 5'- 1003 -> 2003 -> 1004 - 3'
diff = set(range(df.shape[0])).difference(df['index_chain'] - 1)
if len(diff):
warn(f"Missing residues regarding index_chain: {[1+i for i in sorted(diff)]}")
if len(diff) and self.mapping is not None:
# warn(f"Missing residues in chain numbering: {[1+i for i in sorted(diff)]}")
for i in sorted(diff):
# check if a nucleotide numbered +1000 exists in the nts object
# check if a nucleotide with the correct index_chain exists in the nts object
found = None
for nt in nts: # nts is the object from the loaded JSON and contains all nts
if nt['chain_name'] != self.pdb_chain_id:
continue
if nt['index_chain'] == i + 1 :
if nt['index_chain'] == i + 1 + self.mapping.st:
found = nt
break
if found:
self.mapping.log(f"Residue {i+1+self.mapping.st}-{self.mapping.st} = {i+1} has been saved and renumbered {df.iloc[i,1]} instead of {found['nt_id'].replace(found['chain_name']+ '.' + found['nt_name'], '').replace('^','')}")
df_row = pd.DataFrame([found], index=[i])[df.columns.values]
if self.mapping is not None:
self.mapping.insert_new(i+1, found['nt_resnum'], df.iloc[i,1])
df_row.iloc[0,1] = df.iloc[i,1]
df_row.iloc[0,0] = i+1 # index_chain
df_row.iloc[0,1] = df.iloc[i,1] # nt_resnum
df = pd.concat([ df.iloc[:i], df_row, df.iloc[i:] ])
df.iloc[i+1:, 1] += 1
else:
......@@ -369,7 +377,7 @@ class Chain:
for i in sorted(diff):
# Add a row at position i
df = pd.concat([ df.iloc[:i],
pd.DataFrame({"index_chain": i+1, "nt_resnum": i+resnum_start, "nt_code":'-', "nt_name":'-'}, index=[i]),
pd.DataFrame({"index_chain": i+1, "nt_resnum": i+resnum_start, "nt_id":"not resolved", "nt_code":'-', "nt_name":'-'}, index=[i]),
df.iloc[i:] ])
# Increase the index_chain of all following lines
df.iloc[i+1:, 0] += 1
......@@ -424,11 +432,11 @@ class Chain:
if paired[nt1_idx] == "":
pair_type_LW[nt1_idx] = lw_pair
pair_type_DSSR[nt1_idx] = dssr_pair
paired[nt1_idx] = str(nt2_idx + 1)
paired[nt1_idx] = str(nt2_idx + 1) # index + 1 is actually index_chain.
else:
pair_type_LW[nt1_idx] += ',' + lw_pair
pair_type_DSSR[nt1_idx] += ',' + dssr_pair
paired[nt1_idx] += ',' + str(nt2_idx + 1)
paired[nt1_idx] += ',' + str(nt2_idx + 1) # index + 1 is actually index_chain.
# set nucleotide 2 with the opposite base-pair
if nt2 in res_ids:
......@@ -442,44 +450,15 @@ class Chain:
pair_type_DSSR[nt2_idx] += ',' + dssr_pair[0] + dssr_pair[3] + dssr_pair[2] + dssr_pair[1]
paired[nt2_idx] += ',' + str(nt1_idx + 1)
# transform nt_id to shorter values
df['old_nt_resnum'] = [ n.replace(self.pdb_chain_id+'.'+name, '').replace('^','') for n, name in zip(df.nt_id, df.nt_name) ]
df['paired'] = paired
df['pair_type_LW'] = pair_type_LW
df['pair_type_DSSR'] = pair_type_DSSR
df['nb_interact'] = interacts
df = df.drop(['nt_id'], axis=1) # remove now useless descriptors
if self.mapping.reversed:
# The 3D structure is numbered from 3' to 5' instead of standard 5' to 3'
# or the sequence that matches the Rfam family is 3' to 5' instead of standard 5' to 3'.
# Anyways, you need to invert the angles.
# TODO: angles alpha, beta, etc
warn(f"Has {self.chain_label} been numbered from 3' to 5' ? Inverting pseudotorsions, other angle measures are not corrected.")
df = df.reindex(index=df.index[::-1]).reset_index(drop=True)
df['index_chain'] = 1 + df.index
temp_eta = df['eta']
df['eta'] = [ df['theta'][n] for n in range(l) ] # eta(n) = theta(l-n+1) forall n in ]1, l]
df['theta'] = [ temp_eta[n] for n in range(l) ] # theta(n) = eta(l-n+1) forall n in [1, l[
temp_eta = df['eta_prime']
df['eta_prime'] = [ df['theta_prime'][n] for n in range(l) ] # eta(n) = theta(l-n+1) forall n in ]1, l]
df['theta_prime'] = [ temp_eta[n] for n in range(l) ] # theta(n) = eta(l-n+1) forall n in [1, l[
temp_eta = df['eta_base']
df['eta_base'] = [ df['theta_base'][n] for n in range(l) ] # eta(n) = theta(l-n+1) forall n in ]1, l]
df['theta_base'] = [ temp_eta[n] for n in range(l) ] # theta(n) = eta(l-n+1) forall n in [1, l[
newpairs = []
for v in df['paired']:
if ',' in v:
temp_v = []
vs = v.split(',')
for _ in vs:
temp_v.append(str(l-int(_)+1) if int(_) else _ )
newpairs.append(','.join(temp_v))
else:
if len(v):
newpairs.append(str(l-int(v)+1) if int(v) else v )
else: # means unpaired
newpairs.append(v)
df['paired'] = newpairs
df = df.drop(['nt_id', 'nt_resnum'], axis=1) # remove now useless descriptors
self.seq = "".join(df.nt_code)
self.seq_to_align = "".join(df.nt_align_code)
self.length = len([ x for x in self.seq_to_align if x != "-" ])
......@@ -503,20 +482,19 @@ class Chain:
with sqlite3.connect(runDir+"/results/RNANet.db", timeout=10.0) as conn:
# Register the chain in table chain
if self.mapping.nt_start is not None:
if self.mapping is not None:
sql_execute(conn, f""" INSERT INTO chain
(structure_id, chain_name, pdb_start, pdb_end, reversed, rfam_acc, inferred, issue)
(structure_id, chain_name, pdb_start, pdb_end, rfam_acc, inferred, issue)
VALUES
(?, ?, ?, ?, ?, ?, ?, ?)
(?, ?, ?, ?, ?, ?, ?)
ON CONFLICT(structure_id, chain_name, rfam_acc) DO
UPDATE SET pdb_start=excluded.pdb_start,
pdb_end=excluded.pdb_end,
reversed=excluded.reversed,
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),
int(self.mapping.reversed), str(self.mapping.rfam_acc),
str(self.mapping.rfam_acc),
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
......@@ -536,10 +514,10 @@ class Chain:
if df is not None and not self.delete_me: # double condition is theoretically redundant here, but you never know
sql_execute(conn, f"""
INSERT OR IGNORE INTO nucleotide
(chain_id, index_chain, nt_resnum, nt_name, nt_code, dbn, alpha, beta, gamma, delta, epsilon, zeta,
(chain_id, index_chain, nt_name, nt_code, dbn, alpha, beta, gamma, delta, epsilon, zeta,
epsilon_zeta, bb_type, chi, glyco_bond, form, ssZp, Dp, eta, theta, eta_prime, theta_prime, eta_base, theta_base,
v0, v1, v2, v3, v4, amplitude, phase_angle, puckering, nt_align_code, is_A, is_C, is_G, is_U, is_other, nt_position,
paired, pair_type_LW, pair_type_DSSR, nb_interact)
old_nt_resnum, paired, pair_type_LW, pair_type_DSSR, nb_interact)
VALUES ({self.db_chain_id}, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?,
?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?,
?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?);""",
......@@ -890,42 +868,13 @@ class Mapping:
self.rfam_acc = rfam_acc
self.nt_start = pdb_start # nt_resnum numbering
self.nt_end = pdb_end # nt_resnum numbering
self.reversed = (pdb_start > pdb_end)
self.inferred = inferred
self.interval = range(pdb_start, pdb_end+1)
self.old_nt_resnums = [] # to be computed
self.new_nt_resnums = [] #
self.inferred = inferred
self.logs = [] # Events are logged when modifying the mapping
def shift_resnum_range(self, i):
self.log(f"Shifting nt_resnum numbering because of duplicate residue {self.new_nt_resnums[i]}")
for j in range(i, len(self.new_nt_resnums)):
self.new_nt_resnums[j] += 1
def insert_new(self, index_chain, oldresnum, newresnum):
# Adds a nt that did not passed the mapping filter at first
# because it was numbered with a very high nt_resnum value (outside the bounds of the mapping)
# But, in practice, its index_chain is correct and in the bounds and it belongs to the mapped chain.
# Those nts are only searched if there are missing index_chain values in the mapping bounds.
# insert the nt_resnum values in the lists
self.old_nt_resnums.insert(index_chain-1, oldresnum)
self.new_nt_resnums.insert(index_chain-1, newresnum)
# shift the new_nt_resnum values if needed, to avoid creating a doublon
if self.new_nt_resnums[index_chain-1] == self.new_nt_resnums[index_chain]:
for j in range(index_chain, len(self.new_nt_resnums)):
self.new_nt_resnums[j] += 1
# warn(f"Residue {index_chain} has been saved and renumbered {newresnum} instead of {oldresnum}")
self.log(f"Residue {index_chain} has been saved and renumbered {newresnum} instead of {oldresnum}")
def filter_df(self, df):
if not self.reversed:
newdf = df.drop(df[(df.nt_resnum < self.nt_start) | (df.nt_resnum > self.nt_end)].index)
else:
newdf = df.drop(df[(df.nt_resnum < self.nt_end) | (df.nt_resnum > self.nt_start)].index)
newdf = df.drop(df[(df.nt_resnum < self.nt_start) | (df.nt_resnum > self.nt_end)].index)
if len(newdf.index_chain) > 0:
# everything's okay
......@@ -939,24 +888,24 @@ class Mapping:
weird_mappings.add(self.chain_label + "." + self.rfam_acc)
df = df.drop(df[(df.index_chain < self.nt_start) | (df.index_chain > self.nt_end)].index)
# If, for some reason, index_chain does not start at one (e.g. 6boh, chain GB), make it start at one
self.st = 0
if len(df.index_chain) and df.iloc[0,0] != 1:
st = df.iloc[0,0] -1
df.iloc[:, 0] -= st
self.old_nt_resnums = df.nt_resnum.tolist()
self.new_nt_resnums = df.nt_resnum.tolist()
self.st = df.iloc[0,0] -1
df.iloc[:, 0] -= self.st
self.log(f"Shifting index_chain of {self.st}")
# Check that some residues are not included by mistake:
# e.g. 4v4t-AA.RF00382-20-55 contains 4 residues numbered 30 but actually far beyond the mapped part,
# because the icode are not read by DSSR.
toremove = df[ df.index_chain > self.nt_end ]
if not toremove.empty:
df = df.drop(toremove.index)
self.log(f"Some nt_resnum values are likely to be wrong, not considering residues:")
self.log(str(toremove))
return df
def drop_ligand(self, df_row):
self.log("Droping ligand:")
self.log(df_row)
i = self.new_nt_resnums.index(df_row.iloc[0,1])
self.old_nt_resnums.pop(i)
self.new_nt_resnums.pop(i)
def log(self, message):
if isinstance(message, str):
self.logs.append(message+'\n')
......@@ -964,13 +913,15 @@ class Mapping:
self.logs.append(str(message))
def to_file(self, filename):
if self.logs == []:
return # Do not create a log file if there is nothing to log
if not path.exists("logs"):
os.makedirs("logs", exist_ok=True)
with open("logs/"+filename, "w") as f:
f.writelines(self.logs)
class Pipeline:
def __init__(self):
self.dl = Downloader()
......@@ -991,6 +942,7 @@ class Pipeline:
self.EXTRACT_CHAINS = False
self.REUSE_ALL = False
self.SELECT_ONLY = None
self.ARCHIVE = False
def process_options(self):
"""Sets the paths and options of the pipeline"""
......@@ -1002,7 +954,7 @@ class Pipeline:
[ "help", "resolution=", "keep-hetatm=", "from-scratch",
"fill-gaps=", "3d-folder=", "seq-folder=",
"no-homology", "ignore-issues", "extract", "only=", "all",
"update-homologous" ])
"archive", "update-homologous" ])
except getopt.GetoptError as err:
print(err)
sys.exit(2)
......@@ -1043,9 +995,10 @@ class Pipeline:
print("--ignore-issues\t\t\tDo not ignore already known issues and attempt to compute them")
print("--update-homologous\t\tRe-download Rfam and SILVA databases, realign all families, and recompute all CSV files")
print("--from-scratch\t\t\tDelete database, local 3D and sequence files, and known issues, and recompute.")
print("--archive\t\t\tCreate a tar.gz archive of the datapoints text files, and update the link to the latest archive")
print()
print("Typical usage:")
print(f"nohup bash -c 'time {runDir}/RNAnet.py --3d-folder ~/Data/RNA/3D/ --seq-folder /Data/RNA/sequences' &")
print(f"nohup bash -c 'time {runDir}/RNAnet.py --3d-folder ~/Data/RNA/3D/ --seq-folder /Data/RNA/sequences' -s --archive &")
sys.exit()
elif opt == '--version':
print("RNANet 1.0 alpha ")
......@@ -1102,7 +1055,9 @@ class Pipeline:
self.USE_KNOWN_ISSUES = False
elif opt == "--extract":
self.EXTRACT_CHAINS = True
elif opt == "--archive":
self.ARCHIVE = True
if "tobedefinedbyoptions" in [path_to_3D_data, path_to_seq_data]:
print("usage: RNANet.py --3d-folder path/where/to/store/chains --seq-folder path/where/to/store/alignments")
print("See RNANet.py --help for more information.")
......@@ -1420,14 +1375,15 @@ class Pipeline:
conn = sqlite3.connect(runDir+"/results/RNANet.db")
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, reversed, date, exp_method, resolution, issue FROM structure
pd.read_sql_query("""SELECT 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)
conn.close()
# Archive the results
os.makedirs("results/archive", exist_ok=True)
subprocess.run(["tar","-C", path_to_3D_data + "/datapoints","-czf",f"results/archive/RNANET_datapoints_{time_str}.tar.gz","."])
if self.SELECT_ONLY is None:
os.makedirs("results/archive", exist_ok=True)
subprocess.run(["tar","-C", path_to_3D_data + "/datapoints","-czf",f"results/archive/RNANET_datapoints_{time_str}.tar.gz","."])
# Update shortcuts to latest versions
subprocess.run(["rm", "-f", runDir + "/results/RNANET_datapoints_latest.tar.gz", runDir + "/results/summary_latest.csv", runDir + "/results/families_latest.csv"])
......@@ -1549,7 +1505,6 @@ def sql_define_tables(conn):
chain_name VARCHAR(2) NOT NULL,
pdb_start SMALLINT,
pdb_end SMALLINT,
reversed TINYINT,
issue TINYINT,
rfam_acc CHAR(7),
inferred TINYINT,
......@@ -1578,7 +1533,7 @@ def sql_define_tables(conn):
CREATE TABLE IF NOT EXISTS nucleotide (
chain_id INT,
index_chain SMALLINT,
nt_resnum SMALLINT,
old_nt_resnum VARCHAR(5),
nt_position SMALLINT,
nt_name VARCHAR(5),
nt_code CHAR(1),
......@@ -2004,7 +1959,7 @@ def work_build_chain(c, extract, khetatm, retrying=False):
# extract the portion we want
if extract and not c.delete_me:
c.extract(khetatm)
c.extract(df, khetatm)
return c
......@@ -2331,7 +2286,7 @@ def work_save(c, homology=True):
conn = sqlite3.connect(runDir + "/results/RNANet.db", timeout=15.0)
if homology:
df = pd.read_sql_query(f"""
SELECT index_chain, nt_resnum, nt_position, nt_name, nt_code, nt_align_code,
SELECT index_chain, old_nt_resnum, nt_position, nt_name, nt_code, nt_align_code,
is_A, is_C, is_G, is_U, is_other, freq_A, freq_C, freq_G, freq_U, freq_other, dbn,
paired, nb_interact, pair_type_LW, pair_type_DSSR, alpha, beta, gamma, delta, epsilon, zeta, epsilon_zeta,
chi, bb_type, glyco_bond, form, ssZp, Dp, eta, theta, eta_prime, theta_prime, eta_base, theta_base,
......@@ -2344,7 +2299,7 @@ def work_save(c, homology=True):
filename = path_to_3D_data + "datapoints/" + c.chain_label + '.' + c.mapping.rfam_acc
else:
df = pd.read_sql_query(f"""
SELECT index_chain, nt_resnum, nt_position, nt_name, nt_code, nt_align_code,
SELECT index_chain, old_nt_resnum, nt_position, nt_name, nt_code, nt_align_code,
is_A, is_C, is_G, is_U, is_other, dbn,
paired, nb_interact, pair_type_LW, pair_type_DSSR, alpha, beta, gamma, delta, epsilon, zeta, epsilon_zeta,
chi, bb_type, glyco_bond, form, ssZp, Dp, eta, theta, eta_prime, theta_prime, eta_base, theta_base,
......@@ -2405,7 +2360,8 @@ if __name__ == "__main__":
work_save(c, homology=False)
print("Completed.")
exit(0)
# At this point, structure, chain and nucleotide tables of the database are up to date.
# (Modulo some statistics computed by statistics.py)
......
#!python3
import subprocess, os, sys
# Put a list of problematic chains here, they will be properly deleted and recomputed
problems = [
"4v7f_1_1_4-3396",
"4v7f_1_1_1-3167",
"4v7f_1_1_1-3255",
"6g90_1_1_1-407",
"3q3z_1_A_1-74",
"3q3z_1_V_1-74",
"4v7f_1_3_1-121"
]
path_to_3D_data = sys.argv[1]
path_to_seq_data = sys.argv[2]
for p in problems:
print()
print()
print()
print()
# Remove the datapoints files and 3D files
subprocess.run(["rm", '-f', path_to_3D_data + f"/rna_mapped_to_Rfam/{p}.cif"])
files = [ f for f in os.listdir(path_to_3D_data + "/datapoints") if p in f ]
for f in files:
subprocess.run(["rm", '-f', path_to_3D_data + f"/datapoints/{f}"])
# Find more information
structure = p.split('_')[0]
chain = p.split('_')[2]
families = [ f.split('.')[1] for f in files ] # The RFAM families this chain has been mapped onto
# Delete the chain from the database, and the associated nucleotides and re_mappings, using foreign keys
for fam in families:
command = ["sqlite3", "results/RNANet.db", f"PRAGMA foreign_keys=ON; delete from chain where structure_id=\"{structure}\" and chain_name=\"{chain}\" and rfam_acc=\"{fam}\";"]
print(' '.join(command))
subprocess.run(command)
# Re-run RNANet
command = ["python3.8", "RNAnet.py", "--3d-folder", path_to_3D_data, "--seq-folder", path_to_seq_data, "-r", "20.0", "--extract", "--only", p]
print('\n',' '.join(command),'\n')
subprocess.run(command)
# run statistics
......@@ -139,6 +139,7 @@ def reproduce_wadley_results(show=False, carbon=4, sd_range=(1,4)):
fig.savefig(f"results/figures/wadley_plots/wadley_hist_{angle}_{l}.png")
if show:
fig.show()
fig.close()
# Smoothed joint distribution
fig = plt.figure()
......@@ -149,6 +150,7 @@ def reproduce_wadley_results(show=False, carbon=4, sd_range=(1,4)):
fig.savefig(f"results/figures/wadley_plots/wadley_distrib_{angle}_{l}.png")
if show:
fig.show()
fig.close()
# 2D Wadley plot
fig = plt.figure(figsize=(5,5))
......@@ -161,6 +163,7 @@ def reproduce_wadley_results(show=False, carbon=4, sd_range=(1,4)):
fig.savefig(f"results/figures/wadley_plots/wadley_{angle}_{l}.png")
if show:
fig.show()
fig.close()
# print(f"[{worker_nbr}]\tComputed joint distribution of angles (C{carbon}) and saved the figures.")
def stats_len():
......@@ -440,7 +443,7 @@ def to_dist_matrix(f):
idty = dm.get_distance(al).matrix # list of lists
del al
l = len(idty)
np.save("data/"+f+".npy", np.array([ idty[i] + [0]*(l-1-i) if i<l-1 else idty[i] for i in range(l) ]))
np.save("data/"+f+".npy", np.array([ idty[i] + [0]*(l-1-i) if i<l-1 else idty[i] for i in range(l) ], dtype=object))
del idty
notify(f"Computed {f} distance matrix")
return 0
......