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/
RNANetLegacy
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Authored by
Louis BECQUEY
2020-03-11 08:36:39 +0000
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Commit
39010e7637c1a9c7870f731307f3851245f45647
39010e76
1 parent
d5dc9e3a
Include tests in git repo
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tests/test_alignt_stats.py
tests/test_dssr.py
tests/test_alignt_stats.py
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39010e7
#!/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
"
\t
ERR
\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\t
Ignoring {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\t
ignoring {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
tests/test_dssr.py
0 → 100755
View file @
39010e7
#!/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
"
\t
ERR
\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\t
Ignoring {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
)
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