Louis BECQUEY / RNANetLegacy

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Louis BECQUEY
8800ab7b 1 parent 1775df02

removed tests

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Showing 2 changed files with 0 additions and 1075 deletions
#!/usr/bin/python3.8
import numpy as np
import pandas as pd
import Bio.PDB.StructureBuilder, json, os, psutil, subprocess, sys, time
from Bio import AlignIO, SeqIO
from Bio.Alphabet import generic_rna
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from Bio.Align import MultipleSeqAlignment
from os import path, makedirs
from multiprocessing import Pool, Manager
from time import sleep
from tqdm import tqdm
from tqdm.contrib.concurrent import process_map
if path.isdir("/home/ubuntu/"): # this is the IFB-core cloud
path_to_3D_data = "/mnt/Data/RNA/3D/"
path_to_seq_data = "/mnt/Data/RNA/sequences/"
elif path.isdir("/home/persalteas"): # this is my personal workstation
path_to_3D_data = "/home/persalteas/Data/RNA/3D/"
path_to_seq_data = "/home/persalteas/Data/RNA/sequences/"
elif path.isdir("/home/lbecquey"): # this is the IBISC server
path_to_3D_data = "/home/lbecquey/Data/RNA/3D/"
path_to_seq_data = "/home/lbecquey/Data/RNA/sequences/"
elif path.isdir("/nhome/siniac/lbecquey"): # this is the office PC
path_to_3D_data = "/nhome/siniac/lbecquey/Data/RNA/3D/"
path_to_seq_data = "/nhome/siniac/lbecquey/Data/RNA/sequences/"
else:
print("I don't know that machine... I'm shy, maybe you should introduce yourself ?")
exit(1)
validsymb = '\U00002705'
warnsymb = '\U000026A0'
errsymb = '\U0000274C'
class Chain:
""" 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."""
def __init__(self, nrlist_code):
nr = nrlist_code.split('|')
self.pdb_id = nr[0].lower() # PDB ID
self.pdb_model = int(nr[1]) # model ID, starting at 1
self.pdb_chain_id = nr[2].upper() # chain ID (mmCIF), multiple letters
self.reversed = False # wether pdb_end > pdb_start in the Rfam mapping
self.chain_label = "" # chain pretty name
self.full_mmCIFpath = "" # path to the source mmCIF structure
self.file = "" # path to the 3D PDB file
self.rfam_fam = "" # mapping to an RNA family
self.seq = "" # sequence with modified nts
self.aligned_seq = "" # sequence with modified nts replaced, but gaps can exist
self.length = -1 # length of the sequence (missing residues are not counted)
self.full_length = -1 # length of the chain extracted from source structure ([start; stop] interval)
self.delete_me = False # an error occured during production/parsing
self.error_messages = "" # Error message(s) if any
self.frequencies = np.zeros((5,0)) # frequencies of nt at every position: A,C,G,U,Other
self.data3D = None # Pandas DataFrame with all the 3D data extracted by DSSR.
def __str__(self):
return self.pdb_id + '[' + str(self.pdb_model) + "]-" + self.pdb_chain_id
def download_3D(self):
""" Look for the main CIF file (with all chains) from RCSB
"""
status = f"\t> Download {self.pdb_id}.cif\t\t\t"
url = 'http://files.rcsb.org/download/%s.cif' % (self.pdb_id)
final_filepath = path_to_3D_data+"RNAcifs/"+self.pdb_id+".cif"
# Check if file already exists, if yes, abort
if os.path.exists(final_filepath):
print(status + f"\t{validsymb}\t(structure exists)")
self.full_mmCIFpath = final_filepath
return
# Attempt to download it
try:
_urlcleanup()
_urlretrieve(url, final_filepath)
self.full_mmCIFpath = final_filepath
print(status + f"\t{validsymb}")
except IOError:
print(status + f"\tERR \U0000274E\t\033[31mError downloading {url} !\033[0m")
self.delete_me = True
self.error_messages = f"Error downloading {url}"
def extract_portion(self, filename, pdb_start, pdb_end):
""" Extract the part which is mapped to Rfam from the main CIF file and save it to another file.
"""
status = f"\t> Extract {pdb_start}-{pdb_end} atoms from {self.pdb_id}-{self.pdb_chain_id}\t"
self.file = path_to_3D_data+"rna_mapped_to_Rfam/"+filename+".cif"
# Check if file exists, if yes, abort (do not recompute)
if os.path.exists(self.file):
print(status + f"\t{validsymb}\t(already done)", flush=True)
return
model_idx = self.pdb_model - (self.pdb_model > 0) # because arrays start at 0, models start at 1
pdb_start = int(pdb_start)
pdb_end = int(pdb_end)
with warnings.catch_warnings():
# TODO: check if this with and warnings catch is still useful since i moved to CIF files
warnings.simplefilter('ignore', PDBConstructionWarning) # ignore the PDB problems
# Check if the whole mmCIF file exists. If not, abort.
if self.full_mmCIFpath == "":
print(status + f"\t\U0000274E\t\033[31mError with CIF file of {self.pdb_id} !\033[0m", flush=True)
self.delete_me = True
self.error_messages = f"Error with CIF file of {self.pdb_id}"
return
# Load the whole mmCIF into a Biopython structure object:
s = mmcif_parser.get_structure(self.pdb_id, self.full_mmCIFpath)
# Extract the desired chain
c = s[model_idx][self.pdb_chain_id]
# Pay attention to residue numbering
first_number = c.child_list[0].get_id()[1] # the chain's first residue is numbered 'first_number'
if pdb_start < pdb_end:
start = pdb_start + first_number - 1 # shift our start_position by 'first_number'
end = pdb_end + first_number - 1 # same for the end position
else:
self.reversed = True # the 3D chain is numbered backwards compared to the Rfam family
end = pdb_start + first_number - 1
start = pdb_end + first_number - 1
# Define a selection
sel = NtPortionSelector(model_idx, self.pdb_chain_id, start, end)
# Save that selection on the mmCIF object s to file
ioobj = MMCIFIO()
ioobj.set_structure(s)
ioobj.save(self.file, sel)
print(status + f"\t{validsymb}")
def set_rfam(self, rfam):
""" Rember the Rfam mapping for this chain.
"""
self.rfam_fam = rfam
print("\t> Associating it to", rfam, f"...\t\t\t{validsymb}")
def extract_3D_data(self):
""" Runs DSSR to annotate the 3D chain and get various information about it. """
# Check if the file exists. If no, compute it.
if not os.path.exists(path_to_3D_data+f"pseudotorsions/{self.chain_label}.csv"):
# run DSSR (you need to have it in your $PATH, follow x3dna installation instructions)
output = subprocess.run(
["x3dna-dssr", f"-i={self.file}", "--json", "--auxfile=no"], stdout=subprocess.PIPE, stderr=subprocess.PIPE)
stdout = output.stdout.decode('utf-8') # this contains the results in JSON format, or is empty if there are errors
stderr = output.stderr.decode('utf-8') # this contains the evenutal errors
try:
if "exception" in stderr:
# DSSR is unable to parse the chain.
warn(f"Exception while running DSSR: {stderr}\n\tIgnoring {self.chain_label}.\t\t\t", error=True)
self.delete_me = True
self.error_messages = f"Exception while running DSSR for {self.chain_label}:\n {stderr}"
return
# Get the JSON from DSSR output
json_object = json.loads(stdout)
# Print eventual warnings given by DSSR, and abort if there are some
if "warning" in json_object.keys():
warn(f"Ignoring {self.chain_label} ({json_object['warning']})\t", error=True)
self.delete_me = True
self.error_messages = f"DSSR warning for {self.chain_label}: {json_object['warning']}"
return
# Extract the interesting parts
nts = json_object["nts"]
# Prepare a data structure (Pandas DataFrame)
resnum_start = int(nts[0]["nt_resnum"])
df = pd.DataFrame(nts)
# remove low pertinence or undocumented descriptors
df = df.drop(['summary', 'chain_name', 'index',
'v0', 'v1', 'v2', 'v3', 'v4', 'splay_angle',
'splay_distance', 'splay_ratio', 'sugar_class',
'amplitude', 'phase_angle'], axis=1)
df['P_x'] = [ float(i[0]) if i[0] is not None else np.NaN for i in df['P_xyz'] ] #
df['P_y'] = [ float(i[1]) if i[1] is not None else np.NaN for i in df['P_xyz'] ] #
df['P_z'] = [ float(i[2]) if i[2] is not None else np.NaN for i in df['P_xyz'] ] # Flatten the
df['C5prime_x'] = [ float(i[0]) if i[0] is not None else np.NaN for i in df['C5prime_xyz'] ] # Python dictionary
df['C5prime_y'] = [ float(i[1]) if i[1] is not None else np.NaN for i in df['C5prime_xyz'] ] #
df['C5prime_z'] = [ float(i[2]) if i[2] is not None else np.NaN for i in df['C5prime_xyz'] ] #
# Add a sequence column just for the alignments
df['nt_align_code'] = [ str(x).upper()
.replace('NAN', '-') # Unresolved nucleotides are gaps
.replace('?', '-') # Unidentified residues, let's delete them
.replace('T', 'U') # 5MU are modified to t, which gives T
.replace('P', 'U') # Pseudo-uridines, but it is not really right to change them to U, see DSSR paper, Fig 2
for x in df['nt_code'] ]
# Shift numbering when duplicate residue numbers are found.
# 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)
df.iloc[i:, 1] += 1
l = df.iloc[-1,1] - df.iloc[0,1] + 1
# Add eventual missing rows because of unsolved residues in the chain:
if l != len(df['index_chain']):
# We have some rows to add. First, identify them:
diff = set(range(l)).difference(df['nt_resnum'] - resnum_start)
for i in sorted(diff):
df = pd.concat([df.iloc[:i-1], pd.DataFrame({"index_chain": i, "nt_resnum": i+resnum_start-1,
"nt_code":'-', "nt_name":'-', 'nt_align_code':'-'}, index=[i-1]), df.iloc[i-1:]])
df.iloc[i:, 0] += 1
df = df.reset_index(drop=True)
# Iterate over pairs to identify base-base interactions
res_ids = list(df['nt_id'])
paired = [ 0 ] * l
pair_type_LW = [ '' ] * l
pair_type_DSSR = [ '' ] * l
interacts = [ 0 ] * l
if "pairs" in json_object.keys():
pairs = json_object["pairs"]
for p in pairs:
nt1 = p["nt1"]
nt2 = p["nt2"]
if nt1 in res_ids and nt2 in res_ids:
nt1_idx = res_ids.index(nt1)
nt2_idx = res_ids.index(nt2)
paired[nt1_idx] = nt2_idx + 1
paired[nt2_idx] = nt1_idx + 1
interacts[nt1_idx] += 1
interacts[nt2_idx] += 1
pair_type_LW[nt1_idx] = p["LW"]
pair_type_LW[nt2_idx] = p["LW"]
pair_type_DSSR[nt1_idx] = p["DSSR"]
pair_type_DSSR[nt2_idx] = p["DSSR"]
elif nt1 in res_ids:
nt1_idx = res_ids.index(nt1)
interacts[nt1_idx] += 1
elif nt2 in res_ids:
nt2_idx = res_ids.index(nt2)
interacts[nt2_idx] += 1
df['paired'] = paired
df['pair_type_LW'] = pair_type_LW
df['pair_type_DSSR'] = pair_type_DSSR
# Iterate over multiplets to identify base-base interactions
if "multiplets" in json_object.keys():
multiplets = json_object["multiplets"]
for m in multiplets:
nts = m["nts_long"].split(',')
# iterate over the nts of a multiplet
for j, nt in enumerate(nts):
# if the nt is in that chain:
if nt in res_ids:
i = res_ids.index(nt)
# iterate over those other nts
for o in nts[:j]+nts[j+1:]:
if o in res_ids and str(res_ids.index(o)+1) not in str(df['paired'][i]): # and it's not already in 'paired'
df.loc[i,'paired'] = str(df['paired'][i]) + ',' + str(res_ids.index(o)+1)
interacts[i] = len(str(df['paired'][i]).split(','))
df['Ninteract'] = interacts
df = df.drop(['C5prime_xyz', 'P_xyz', 'nt_id'], axis=1) # remove now useless descriptors
if self.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.
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))
newpairs.append(','.join(temp_v))
else:
if int(v):
newpairs.append(str(l-int(v)+1))
df['paired'] = newpairs
except KeyError as e:
# Mostly, there are no part about nucleotides in the DSSR output. Abort.
warn(f"Error while parsing DSSR's json output:\n{e}\n\tignoring {self.chain_label}\t\t\t\t", error=True)
self.delete_me = True
self.error_messages = f"Error while parsing DSSR's json output:\n{e}"
return
# Creating a df for easy saving to CSV
df.to_csv(path_to_3D_data + f"pseudotorsions/{self.chain_label}.csv")
del df
print("\t> Saved", self.chain_label, f"pseudotorsions to CSV.\t\t{validsymb}", flush=True)
else:
print("\t> Computing", self.chain_label, f"pseudotorsions...\t{validsymb}\t(already done)", flush=True)
# Now load data from the CSV file
d = pd.read_csv(path_to_3D_data+f"pseudotorsions/{self.chain_label}.csv", index_col=0)
self.seq = "".join(d.nt_code.values)
self.aligned_seq = "".join(d.nt_align_code.values)
self.length = len([ x for x in self.aligned_seq if x != "-" ])
self.full_length = len(d.nt_code)
self.data3D = d
print(f"\t> Loaded data from CSV\t\t\t\t{validsymb}", flush=True)
# Remove too short chains
if self.length < 5:
warn(f"{self.chain_label} sequence is too short, let's ignore it.\t", error=True)
self.delete_me = True
self.error_messages = "Sequence is too short. (< 5 resolved nts)"
return
def set_freqs_from_aln(self, s_seq, freqs):
"""Maps the object's sequence to its version in a MSA, to compute nucleotide frequencies at every position.
s_seq: the aligned version of self.aligned_seq
freqs: the nucleotide frequencies at every position of s_seq
This also replaces gaps by the most common nucleotide.
"""
alilen = len(s_seq)
# Save colums in the appropriate positions
i = 0
j = 0
while i<self.full_length and j<alilen:
# Here we try to map self.aligned_seq (the sequence of the 3D chain, including gaps when residues are missing),
# with s_seq, the sequence aligned in the MSA, containing any of ACGU and two types of gaps, - and .
if self.aligned_seq[i] == s_seq[j].upper(): # alignment and sequence correspond (incl. gaps)
self.frequencies = np.concatenate((self.frequencies, freqs[:,j].reshape(-1,1)), axis=1)
i += 1
j += 1
elif self.aligned_seq[i] == '-': # gap in the chain, but not in the aligned sequence
# search for a gap to the consensus nearby
k = 0
while j+k<alilen and s_seq[j+k] in ['.','-']:
if s_seq[j+k] == '-':
break
k += 1
# if found, set j to that position
if j+k<alilen and s_seq[j+k] == '-':
j = j + k
continue
# if not, search for a insertion gap nearby
if j<alilen and s_seq[j] == '.':
self.frequencies = np.concatenate((self.frequencies, freqs[:,j].reshape(-1,1)), axis=1)
i += 1
j += 1
continue
# else, just ignore the gap.
self.frequencies = np.concatenate((self.frequencies, np.array([0.0,0.0,0.0,0.0,1.0]).reshape(-1,1)), axis=1)
i += 1
elif s_seq[j] in ['.', '-']: # gap in the alignment, but not in the real chain
j += 1 # ignore the column
else: # sequence mismatch which is not a gap...
print(f"You are never supposed to reach this. Comparing {self.chain_label} in {i} ({self.aligned_seq[i-1:i+2]}) with seq[{j}] ({s_seq[j-3:j+4]}).\n",
self.aligned_seq,
sep='', flush=True)
exit(1)
# Replace gapped positions by the consensus sequence:
c_aligned_seq = list(self.aligned_seq)
c_seq = list(self.seq)
letters = ['A', 'C', 'G', 'U', 'N']
for i in range(self.full_length):
if c_aligned_seq[i] == '-': # (then c_seq[i] also is)
freq = self.frequencies[:,i]
l = letters[freq.tolist().index(max(freq))]
c_aligned_seq[i] = l
c_seq[i] = l
self.data3D.iloc[i,3] = l # self.data3D['nt_code'][i]
self.aligned_seq = ''.join(c_aligned_seq)
self.seq = ''.join(c_seq)
# Temporary np array to store the computations
point = np.zeros((11, self.full_length))
for i in range(self.full_length):
# normalized position in the chain
point[0,i] = float(i+1)/self.full_length
# one-hot encoding of the actual sequence
if self.seq[i] in letters[:4]:
point[ 1 + letters[:4].index(self.seq[i]), i ] = 1
else:
point[5,i] = 1
# PSSMs
point[6,i] = self.frequencies[0, i]
point[7,i] = self.frequencies[1, i]
point[8,i] = self.frequencies[2, i]
point[9,i] = self.frequencies[3, i]
point[10,i] = self.frequencies[4, i]
self.data3D = pd.concat([self.data3D, pd.DataFrame(point.T, columns=["position","is_A","is_C","is_G","is_U","is_other","freq_A","freq_C","freq_G","freq_U","freq_other"])], axis=1)
# save to file
self.data3D.to_csv(path_to_3D_data + "datapoints/" + self.chain_label)
def warn(message, error=False):
"""Pretty-print warnings and error messages.
"""
if error:
print(f"\t> \033[31mERR: {message}\033[0m{errsymb}", flush=True)
else:
print(f"\t> \033[33mWARN: {message}\033[0m{warnsymb}", flush=True)
def build_chain(c, rfam, pdb_start, pdb_end):
""" Additionally adds all the desired information to a Chain object.
"""
# Download the whole mmCIF file containing the chain we are interested in
c.download_3D()
# If no problems, extract the portion we want
if not c.delete_me:
c.extract_portion(c.chain_label, pdb_start, pdb_end)
# If no problems, map it to an Rfam family, and annotate it with DSSR
if not c.delete_me:
c.set_rfam(rfam)
c.extract_3D_data()
# The Chain object is ready
return c
def read_cpu_number():
# As one shall not use os.cpu_count() on LXC containers,
# because it reads info from /sys wich is not the VM resources but the host resources.
# This function reads it from /proc/cpuinfo instead.
p = subprocess.run(['grep', '-Ec', '(Intel|AMD)', '/proc/cpuinfo'], stdout=subprocess.PIPE)
return int(int(p.stdout.decode('utf-8')[:-1])/2)
def cm_realign(rfam_acc, chains, label):
""" Runs multiple sequence alignements by RNA family.
It aligns the Rfam hits from a RNA family with the sequences from the list of chains.
Rfam covariance models are used with Infernal tools, except for rRNAs.
cmalign requires too much RAM for them, so we use SINA, a specifically designed tool for rRNAs.
"""
# If the computation was already done before, do not recompute.
if path.isfile(path_to_seq_data + f"realigned/{rfam_acc}++.afa"):
print(f"\t> {label} completed \t\t{validsymb}\t(already done)", flush=True)
return
if not path.isfile(path_to_seq_data + f"realigned/{rfam_acc}++.fa"):
print("\t> Extracting sequences...", flush=True)
# Prepare a FASTA file containing Rfamseq hits for that family + our chains sequences
f = open(path_to_seq_data + f"realigned/{rfam_acc}++.fa", "w")
# Read the FASTA archive of Rfamseq hits, and add sequences to the file
with gzip.open(path_to_seq_data + f"rfam_sequences/fasta/{rfam_acc}.fa.gz", 'rt') as gz:
ids = []
for record in SeqIO.parse(gz, "fasta"):
if record.id not in ids:
f.write(">"+record.description+'\n'+str(record.seq)+'\n')
ids.append(record.id)
# Add the chains sequences to the file
for c in chains:
f.write(f"> {str(c)}\n"+c.aligned_seq.replace('-', '').replace('U','T')+'\n')
f.close()
if rfam_acc not in ["RF00177", "RF01960", "RF02540", "RF02541", "RF02543"]: # Ribosomal Subunits
# Align using Infernal for most RNA families
# Extracting covariance model for this family
if not path.isfile(path_to_seq_data + f"realigned/{rfam_acc}.cm"):
print("\t> Extracting covariance model (cmfetch)...", flush=True)
if not path.isfile(path_to_seq_data + f"realigned/{rfam_acc}.cm"):
f = open(path_to_seq_data + f"realigned/{rfam_acc}.cm", "w")
subprocess.run(["cmfetch", path_to_seq_data + "Rfam.cm", rfam_acc], stdout=f)
f.close()
# Running alignment
print(f"\t> {label} (cmalign)...", flush=True)
f = open(path_to_seq_data + f"realigned/{rfam_acc}++.stk", "w")
subprocess.run(["cmalign", "--mxsize", "2048", path_to_seq_data + f"realigned/{rfam_acc}.cm", path_to_seq_data + f"realigned/{rfam_acc}++.fa"], stdout=f)
f.close()
# Converting to aligned Fasta
print("\t> Converting to aligned FASTA (esl-reformat)...")
f = open(path_to_seq_data + f"realigned/{rfam_acc}++.afa", "w")
subprocess.run(["esl-reformat", "afa", path_to_seq_data + f"realigned/{rfam_acc}++.stk"], stdout=f)
f.close()
# subprocess.run(["rm", path_to_seq_data + f"realigned/{rfam_acc}.cm", path_to_seq_data + f"realigned/{rfam_acc}++.fa", path_to_seq_data + f"realigned/{rfam_acc}++.stk"])
else:
# Ribosomal subunits deserve a special treatment.
# They require too much RAM to be aligned with Infernal.
# Then we will use SINA instead.
# Get the seed alignment from Rfam
print(f"\t> Download latest LSU/SSU-Ref alignment from SILVA...", end="", flush=True)
if rfam_acc in ["RF02540", "RF02541", "RF02543"] and not path.isfile(path_to_seq_data + "realigned/LSU.arb"):
try:
_urlcleanup()
_urlretrieve('http://www.arb-silva.de/fileadmin/arb_web_db/release_132/ARB_files/SILVA_132_LSURef_07_12_17_opt.arb.gz', path_to_seq_data + "realigned/LSU.arb.gz")
print(f"\t{validsymb}", flush=True)
except:
print('\n')
warn(f"Error downloading and/or extracting {rfam_acc}'s seed alignment !\t", error=True)
print(f"\t\t> Uncompressing LSU.arb...", end='', flush=True)
subprocess.run(["gunzip", path_to_seq_data + "realigned/LSU.arb.gz"], stdout=subprocess.DEVNULL)
print(f"\t{validsymb}", flush=True)
else:
print(f"\t{validsymb}\t(no need)", flush=True)
if rfam_acc in ["RF00177", "RF01960"] and not path.isfile(path_to_seq_data + "realigned/SSU.arb"):
try:
_urlcleanup()
_urlretrieve('http://www.arb-silva.de/fileadmin/silva_databases/release_138/ARB_files/SILVA_138_SSURef_05_01_20_opt.arb.gz', path_to_seq_data + "realigned/SSU.arb.gz")
print(f"\t{validsymb}", flush=True)
except:
print('\n')
warn(f"Error downloading and/or extracting {rfam_acc}'s seed alignment !\t", error=True)
print(f"\t\t> Uncompressing SSU.arb...", end='', flush=True)
subprocess.run(["gunzip", path_to_seq_data + "realigned/SSU.arb.gz"], stdout=subprocess.DEVNULL)
print(f"\t{validsymb}", flush=True)
else:
print(f"\t{validsymb}\t(no need)", flush=True)
if rfam_acc in ["RF00177", "RF01960"]:
arbfile = "realigned/SSU.arb"
else:
arbfile = "realigned/LSU.arb"
# Run alignment
print(f"\t> {label} (SINA)...", flush=True)
subprocess.run(["sina", "-i", path_to_seq_data + f"realigned/{rfam_acc}++.fa",
"-o", path_to_seq_data + f"realigned/{rfam_acc}++.afa",
"-r", path_to_seq_data + arbfile,
"--meta-fmt=csv"])
return 0
def summarize_position(col):
""" Counts the number of nucleotides at a given position, given a "column" from a MSA.
"""
# Count the different chars in the column
counts = { 'A':col.count('A'), 'C':col.count('C'),
'G':col.count('G'), 'U':col.count('U'),
'-':col.count('-'), '.':col.count('.') }
# Count modified nucleotides
known_chars_count = 0
chars = set(col)
for char in chars:
if char in "ACGU":
known_chars_count += counts[char]
# elif char not in "-.":
# counts[char] = col.count(char)
N = len(col) - counts['-'] - counts['.'] # number of ungapped residues
if N: # prevent division by zero if the column is only gaps
return ( counts['A']/N, counts['C']/N, counts['G']/N, counts['U']/N, (N - known_chars_count)/N) # other residues, or consensus (N, K, Y...)
else:
return (0, 0, 0, 0, 0)
def alignment_nt_stats(f):
""" Computes Position-Specific-Scoring-Matrices given the multiple sequence alignment of the RNA family.
Also saves every chain of the family to file.
Uses only 1 core, so this function can be called in parallel.
"""
# Get a worker number to position the progress bar
global idxQueue
thr_idx = idxQueue.get()
# get the chains of this family
list_of_chains = rfam_acc_to_download[f]
chains_ids = [ str(c) for c in list_of_chains ]
# Open the alignment
try:
align = AlignIO.read(path_to_seq_data + f"realigned/{f}++.afa", "fasta")
alilen = align.get_alignment_length()
except:
warn(f"{f}'s alignment is wrong. Recompute it and retry.", error=True)
exit(1)
# Compute statistics per column
run_dir = path.abspath(os.getcwd())
if not path.isfile(run_dir + "/tests/RF00177.npz"):
pbar = tqdm(iterable=range(alilen), position=thr_idx+1, desc=f"Worker {thr_idx+1}: {f}", leave=False)
results = [ summarize_position(align[:,i]) for i in pbar ]
pbar.close()
frequencies = np.array(results).T
np.savez(run_dir + "/tests/RF00177.npz", freqs= frequencies)
else:
frequencies = np.load(run_dir + "/tests/RF00177.npz")['freqs']
# For each sequence, find the right chain and save the PSSMs inside.
pbar = tqdm(total=len(chains_ids), position=thr_idx+1, desc=f"Worker {thr_idx+1}: {f} chains", leave=False)
pbar.update(0)
for s in align:
if not '[' in s.id: # this is a Rfamseq entry, not a 3D chain
continue
if not s.id in chains_ids: # skip other RF00177 chains, keep only our test example (5wnt)
continue
# get the right 3D chain:
idx = chains_ids.index(s.id)
list_of_chains[idx].set_freqs_from_aln(s.seq, frequencies)
pbar.update(1)
pbar.close()
idxQueue.put(thr_idx) # replace the thread index in the queue
return 0
ncores = read_cpu_number()
c = Chain("5WNT|1|A")
c.chain_label = f"{c.pdb_id}_{str(c.pdb_model)}_{c.pdb_chain_id}_2-1520"
c = build_chain(c, "RF00177", 2, 1520)
rfam_acc_to_download = { c.rfam_fam:[c] }
cm_realign("RF00177", rfam_acc_to_download["RF00177"], "Realign RF00177 + 1 chains")
thr_idx_mgr = Manager()
idxQueue = thr_idx_mgr.Queue()
for i in range(ncores):
idxQueue.put(i)
alignment_nt_stats("RF00177")
\ No newline at end of file
#!/usr/bin/python3.8
import numpy as np
import pandas as pd
import concurrent.futures, Bio.PDB.StructureBuilder, gzip, io, json, os, psutil, re, requests, sqlalchemy, subprocess, sys, time, warnings
from Bio import AlignIO, SeqIO
from Bio.PDB import MMCIFParser
from Bio.PDB.mmcifio import MMCIFIO
from Bio.PDB.PDBExceptions import PDBConstructionWarning
from Bio._py3k import urlretrieve as _urlretrieve
from Bio._py3k import urlcleanup as _urlcleanup
from Bio.Alphabet import generic_rna
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from Bio.Align import MultipleSeqAlignment
from collections import OrderedDict
from functools import partial
from os import path, makedirs
from multiprocessing import Pool, Manager
from time import sleep
from tqdm import tqdm
from tqdm.contrib.concurrent import process_map
if path.isdir("/home/ubuntu/"): # this is the IFB-core cloud
path_to_3D_data = "/mnt/Data/RNA/3D/"
path_to_seq_data = "/mnt/Data/RNA/sequences/"
elif path.isdir("/home/persalteas"): # this is my personal workstation
path_to_3D_data = "/home/persalteas/Data/RNA/3D/"
path_to_seq_data = "/home/persalteas/Data/RNA/sequences/"
elif path.isdir("/home/lbecquey"): # this is the IBISC server
path_to_3D_data = "/home/lbecquey/Data/RNA/3D/"
path_to_seq_data = "/home/lbecquey/Data/RNA/sequences/"
elif path.isdir("/nhome/siniac/lbecquey"): # this is the office PC
path_to_3D_data = "/nhome/siniac/lbecquey/Data/RNA/3D/"
path_to_seq_data = "/nhome/siniac/lbecquey/Data/RNA/sequences/"
else:
print("I don't know that machine... I'm shy, maybe you should introduce yourself ?")
exit(1)
validsymb = '\U00002705'
warnsymb = '\U000026A0'
errsymb = '\U0000274C'
def warn(message, error=False):
"""Pretty-print warnings and error messages.
"""
if error:
print(f"\t> \033[31mERR: {message}\033[0m{errsymb}", flush=True)
else:
print(f"\t> \033[33mWARN: {message}\033[0m{warnsymb}", flush=True)
class NtPortionSelector(object):
"""Class passed to MMCIFIO to select some chain portions in an MMCIF file.
Validates every chain, residue, nucleotide, to say if it is in the selection or not.
"""
def __init__(self, model_id, chain_id, start, end):
self.chain_id = chain_id
self.start = start
self.end = end
self.pdb_model_id = model_id
self.hydrogen_regex = re.compile("[123 ]*H.*")
def accept_model(self, model):
return int(model.get_id() == self.pdb_model_id)
def accept_chain(self, chain):
return int(chain.get_id() == self.chain_id)
def accept_residue(self, residue):
hetatm_flag, resseq, icode = residue.get_id()
# Refuse waters and magnesium ions
if hetatm_flag in ["W", "H_MG"]:
return 0
# I don't really know what this is but the doc said:
if icode != " ":
warn(f"icode {icode} at position {resseq}\t\t")
# Accept the residue if it is in the right interval:
return int(self.start <= resseq <= self.end)
def accept_atom(self, atom):
# Refuse hydrogens
if self.hydrogen_regex.match(atom.get_id()):
return 0
# Accept all atoms otherwise.
return 1
class Chain:
def __init__(self, nrlist_code):
nr = nrlist_code.split('|')
self.pdb_id = nr[0].lower() # PDB ID
self.pdb_model = int(nr[1]) # model ID, starting at 1
self.pdb_chain_id = nr[2].upper() # chain ID (mmCIF), multiple letters
self.reversed = False # wether pdb_end > pdb_start in the Rfam mapping
self.chain_label = "" # chain pretty name
self.full_mmCIFpath = "" # path to the source mmCIF structure
self.file = "" # path to the 3D PDB file
self.rfam_fam = "" # mapping to an RNA family
self.seq = "" # sequence with modified nts
self.aligned_seq = "" # sequence with modified nts replaced
self.length = -1 # length of the sequence (missing residues are not counted)
self.full_length = -1 # length of the chain extracted from source structure ([start; stop] interval)
self.delete_me = False # an error occured during production/parsing
self.delete_reason = "" # Error message(s) if any
self.frequencies = np.zeros((5,0)) # frequencies of nt at every position: A,C,G,U,Other
def __str__(self):
return self.pdb_id + '[' + str(self.pdb_model) + "]-" + self.pdb_chain_id
def download_3D(self):
""" Look for the main CIF file (with all chains) from RCSB
"""
status = f"\t> Download {self.pdb_id}.cif\t\t\t"
url = 'http://files.rcsb.org/download/%s.cif' % (self.pdb_id)
final_filepath = path_to_3D_data+"RNAcifs/"+self.pdb_id+".cif"
# Check if file already exists, if yes, abort
if os.path.exists(final_filepath):
print(status + f"\t{validsymb}\t(structure exists)")
self.full_mmCIFpath = final_filepath
return
# Attempt to download it
try:
_urlcleanup()
_urlretrieve(url, final_filepath)
self.full_mmCIFpath = final_filepath
print(status + f"\t{validsymb}")
except IOError:
print(status + f"\tERR \U0000274E\t\033[31mError downloading {url} !\033[0m")
self.delete_me = True
self.delete_reason = f"Error downloading {url}"
def extract_portion(self, filename, pdb_start, pdb_end):
""" Extract the part which is mapped to Rfam from the main CIF file and save it to another file.
"""
status = f"\t> Extract {pdb_start}-{pdb_end} atoms from {self.pdb_id}-{self.pdb_chain_id}\t"
self.file = path_to_3D_data+"rna_mapped_to_Rfam/"+filename+".cif"
# Check if file exists, if yes, abort (do not recompute)
if os.path.exists(self.file):
print(status + f"\t{validsymb}\t(already done)", flush=True)
return
model_idx = self.pdb_model - (self.pdb_model > 0) # because arrays start at 0, models start at 1
pdb_start = int(pdb_start)
pdb_end = int(pdb_end)
with warnings.catch_warnings():
# TODO: check if this with and warnings catch is still useful since i moved to CIF files
warnings.simplefilter('ignore', PDBConstructionWarning) # ignore the PDB problems
# Check if the whole mmCIF file exists. If not, abort.
if self.full_mmCIFpath == "":
print(status + f"\t\U0000274E\t\033[31mError with CIF file of {self.pdb_id} !\033[0m", flush=True)
self.delete_me = True
self.delete_reason = f"Error with CIF file of {self.pdb_id} !"
return
# Load the whole mmCIF into a Biopython structure object:
s = mmcif_parser.get_structure(self.pdb_id, self.full_mmCIFpath)
# Extract the desired chain
c = s[model_idx][self.pdb_chain_id]
# Pay attention to residue numbering
first_number = c.child_list[0].get_id()[1] # the chain's first residue is numbered 'first_number'
if pdb_start < pdb_end:
start = pdb_start + first_number - 1 # shift our start_position by 'first_number'
end = pdb_end + first_number - 1 # same for the end position
else:
self.reversed = True # the 3D chain is numbered backwards compared to the Rfam family
end = pdb_start + first_number - 1
start = pdb_end + first_number - 1
# Define a selection
sel = NtPortionSelector(model_idx, self.pdb_chain_id, start, end)
# Save that selection on the mmCIF object s to file
ioobj = MMCIFIO()
ioobj.set_structure(s)
ioobj.save(self.file, sel)
print(status + f"\t{validsymb}")
def set_rfam(self, rfam):
""" Rember the Rfam mapping for this chain.
"""
self.rfam_fam = rfam
print("\t> Associating it to", rfam, f"...\t\t\t{validsymb}")
def extract_3D_data(self):
""" Runs DSSR to annotate the 3D chain and get various information about it. """
# Check if the file exists. If no, compute it.
if not os.path.exists(path_to_3D_data+f"pseudotorsions/{self.chain_label}.csv"):
# run DSSR (you need to have it in your $PATH, follow x3dna installation instructions)
output = subprocess.run(
["x3dna-dssr", f"-i={self.file}", "--json", "--auxfile=no"], stdout=subprocess.PIPE, stderr=subprocess.PIPE)
stdout = output.stdout.decode('utf-8') # this contains the results in JSON format, or is empty if there are errors
stderr = output.stderr.decode('utf-8') # this contains the evenutal errors
# try:
if "exception" in stderr:
# DSSR is unable to parse the chain.
warn(f"Exception while running DSSR: {stderr}\n\tIgnoring {self.chain_label}.\t\t\t", error=True)
self.delete_me = True
self.error_messages = f"Exception while running DSSR for {self.chain_label}:\n {stderr}"
return
# Get the JSON from DSSR output
json_object = json.loads(stdout)
# Print eventual warnings given by DSSR, and abort if there are some
if "warning" in json_object.keys():
warn(f"Ignoring {self.chain_label} ({json_object['warning']})\t", error=True)
self.delete_me = True
self.error_messages = f"DSSR warning for {self.chain_label}: {json_object['warning']}"
return
# Extract the interesting parts
nts = json_object["nts"]
# Prepare a data structure (Pandas DataFrame)
resnum_start = int(nts[0]["nt_resnum"])
df = pd.DataFrame(nts)
# remove low pertinence or undocumented descriptors
df = df.drop(['summary', 'frame', 'chain_name', 'index',
'v0', 'v1', 'v2', 'v3', 'v4', 'splay_angle',
'splay_distance', 'splay_ratio', 'sugar_class',
'amplitude', 'phase_angle' ], axis=1)
df['P_x'] = [ float(i[0]) if i[0] is not None else np.NaN for i in df['P_xyz'] ] #
df['P_y'] = [ float(i[1]) if i[1] is not None else np.NaN for i in df['P_xyz'] ] #
df['P_z'] = [ float(i[2]) if i[2] is not None else np.NaN for i in df['P_xyz'] ] # Flatten the
df['C5prime_x'] = [ float(i[0]) if i[0] is not None else np.NaN for i in df['C5prime_xyz'] ] # Python dictionary
df['C5prime_y'] = [ float(i[1]) if i[1] is not None else np.NaN for i in df['C5prime_xyz'] ] #
df['C5prime_z'] = [ float(i[2]) if i[2] is not None else np.NaN for i in df['C5prime_xyz'] ] #
# Add a sequence column just for the alignments
df['nt_align_code'] = [ str(x).upper()
.replace('U','T') # We align as DNA
.replace('NAN', '-') # Unresolved nucleotides are gaps
.replace('?','-') # Unidentified residues, let's delete them
.replace('P', 'U') # Pseudo-uridines, but it is not really right to change them to U, see DSSR paper, Fig 2
for x in df['nt_code'] ]
# Shift numbering when duplicate residue numbers are found.
# 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)
df.iloc[i:, 1] += 1
l = df.iloc[-1,1] - df.iloc[0,1] + 1
# Add eventual missing rows because of unsolved residues in the chain:
if l != len(df['index_chain']):
# We have some rows to add. First, identify them:
diff = set(range(l)).difference(df['nt_resnum'] - resnum_start)
# if 1+df.iloc[-1,0] not in df['nt_resnum'] - resnum_start +1 and 1+df.iloc[-1,0] not in diff:
# diff.add(1+df.iloc[-1,0])
for i in sorted(diff):
df = pd.concat([df.iloc[:i-1], pd.DataFrame({"index_chain": i, "nt_resnum": i+resnum_start-1, "nt_code":'-', "nt_name":'-', 'nt_align_code':'-'}, index=[i-1]), df.iloc[i-1:]])
df.iloc[i:, 0] += 1
df = df.reset_index(drop=True)
# Iterate over pairs to identify base-base interactions
res_ids = list(df['nt_id'])
paired = [ 0 ] * l
pair_type_LW = [ '' ] * l
pair_type_DSSR = [ '' ] * l
interacts = [ 0 ] * l
if "pairs" in json_object.keys():
pairs = json_object["pairs"]
for p in pairs:
nt1 = p["nt1"]
nt2 = p["nt2"]
if nt1 in res_ids and nt2 in res_ids:
nt1_idx = res_ids.index(nt1)
nt2_idx = res_ids.index(nt2)
paired[nt1_idx] = nt2_idx + 1
paired[nt2_idx] = nt1_idx + 1
interacts[nt1_idx] += 1
interacts[nt2_idx] += 1
pair_type_LW[nt1_idx] = p["LW"]
pair_type_LW[nt2_idx] = p["LW"]
pair_type_DSSR[nt1_idx] = p["DSSR"]
pair_type_DSSR[nt2_idx] = p["DSSR"]
elif nt1 in res_ids:
nt1_idx = res_ids.index(nt1)
interacts[nt1_idx] += 1
elif nt2 in res_ids:
nt2_idx = res_ids.index(nt2)
interacts[nt2_idx] += 1
df['paired'] = paired
df['pair_type_LW'] = pair_type_LW
df['pair_type_DSSR'] = pair_type_DSSR
# Iterate over multiplets to identify base-base interactions
if "multiplets" in json_object.keys():
multiplets = json_object["multiplets"]
for m in multiplets:
nts = m["nts_long"].split(',')
# iterate over the nts of a multiplet
for j, nt in enumerate(nts):
# if the nt is in that chain:
if nt in res_ids:
i = res_ids.index(nt)
# iterate over those other nts
for o in nts[:j]+nts[j+1:]:
if o in res_ids and str(res_ids.index(o)+1) not in str(df['paired'][i]): # and it's not already in 'paired'
df.loc[i,'paired'] = str(df['paired'][i]) + ',' + str(res_ids.index(o)+1)
interacts[i] = len(str(df['paired'][i]).split(','))
df['Ninteract'] = interacts
df = df.drop(['C5prime_xyz', 'P_xyz', 'nt_id'], axis=1) # remove now useless descriptors
if self.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.
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))
newpairs.append(','.join(temp_v))
else:
if int(v):
newpairs.append(str(l-int(v)+1))
df['paired'] = newpairs
# except KeyError as e:
# # Mostly, there are no part about nucleotides in the DSSR output. Abort.
# warn(f"Error while parsing DSSR's json output:\n{e}\n\tignoring {self.chain_label}\t\t\t\t", error=True)
# self.delete_me = True
# self.error_messages = f"Error while parsing DSSR's json output:\n{e}"
# return
# Creating a df for easy saving to CSV
df.to_csv(path_to_3D_data + f"pseudotorsions/{self.chain_label}.csv")
print("\t> Saved", self.chain_label, f"pseudotorsions to CSV.\t\t{validsymb}", flush=True)
else:
print("\t> Computing", self.chain_label, f"pseudotorsions...\t{validsymb}\t(already done)", flush=True)
# Now load data from the CSV file
d = pd.read_csv(path_to_3D_data+f"pseudotorsions/{self.chain_label}.csv", index_col=0)
self.seq = "".join(d.nt_code.values)
self.aligned_seq = "".join(d.nt_align_code.values)
self.length = len([ x for x in self.aligned_seq if x != "-" ])
self.full_length = len(d.nt_code)
self.data3D = d
print(f"\t> Loaded data from CSV\t\t\t\t{validsymb}", flush=True)
# Remove too short chains
if self.length < 5:
warn(f"{self.chain_label} sequence is too short, let's ignore it.\t", error=True)
self.delete_me = True
self.error_messages = "Sequence is too short. (< 5 resolved nts)"
return
def build_chain(c, rfam, pdb_start, pdb_end):
""" Additionally adds all the desired information to a Chain object.
"""
# Download the whole mmCIF file containing the chain we are interested in
c.download_3D()
# If no problems, extract the portion we want
if not c.delete_me:
c.extract_portion(c.chain_label, pdb_start, pdb_end)
# If no problems, map it to an Rfam family, and annotate it with DSSR
if not c.delete_me:
c.set_rfam(rfam)
c.extract_3D_data()
# The Chain object is ready
return c
subprocess.run(["rm", "-f", path_to_3D_data + f"pseudotorsions/4w2e_1_A_1-2912.csv"])
mmcif_parser = MMCIFParser()
thr_idx_mgr = Manager()
idxQueue = thr_idx_mgr.Queue()
idxQueue.put(0)
c = Chain("4W2E|1|A")
c.chain_label = "4w2e_1_A_1-2912"
build_chain(c, "RF02541", 1, 2912)