def plot_pb_scheme_alignment():
random.seed(1)
scheme_file = biotite.temp_file("json")
mat_file = biotite.temp_file("mat")
with open(mat_file, "w") as file:
# PB substitution matrix, adapted from PBxplore
file.write("""
a b c d e f g h i j k l m n o p
a 516 -59 113 -105 -411 -177 -27 -361 47 -103 -644 -259 -599 -372 -124 -83
b -59 541 -146 -210 -155 -310 -97 90 182 -128 -30 29 -745 -242 -165 22
c 113 -146 360 -14 -333 -240 49 -438 -269 -282 -688 -682 -608 -455 -147 6
d -105 -210 -14 221 5 -131 -349 -278 -253 -173 -585 -670 -1573 -1048 -691 -497
e -411 -155 -333 5 520 185 186 138 -378 -70 -112 -514 -1136 -469 -617 -632
f -177 -310 -240 -131 185 459 -99 -45 -445 83 -214 -88 -547 -629 -406 -552
g -27 -97 49 -349 186 -99 665 -99 -89 -118 -409 -138 -124 172 128 254
h -361 90 -438 -278 138 -45 -99 632 -205 316 192 -108 -712 -359 95 -399
i 47 182 -269 -253 -378 -445 -89 -205 696 186 8 15 -709 -269 -169 226
j -103 -128 -282 -173 -70 83 -118 316 186 768 196 5 -398 -340 -117 -104
k -644 -30 -688 -585 -112 -214 -409 192 8 196 568 -65 -270 -231 -471 -382
l -259 29 -682 -670 -514 -88 -138 -108 15 5 -65 533 -131 8 -11 -316
m -599 -745 -608 -1573 -1136 -547 -124 -712 -709 -398 -270 -131 241 -4 -190 -155
n -372 -242 -455 -1048 -469 -629 172 -359 -269 -340 -231 8 -4 703 88 146
o -124 -165 -147 -691 -617 -406 128 95 -169 -117 -471 -11 -190 88 716 58
p -83 22 6 -497 -632 -552 254 -399 226 -104 -382 -316 -155 146 58 609
""")
gecli.main(args=[
"--alphabet", "abcdefghijklmnop", "--matrix", mat_file, "--contrast",
"300", "--lmin", "65", "--lmax", "70", "-f", scheme_file
])
colors = graphics.load_color_scheme(scheme_file)["colors"]
fig = plt.figure(figsize=(8.0, 5.0))
ax = fig.gca()
pb_alphabet = seq.LetterAlphabet("abcdefghijklmnop")
fasta_file = fasta.FastaFile()
fasta_file.read(PB_EXAMPLE_FILE_NAME)
seq_strings = list(fasta_file.values())
sequences = [
seq.GeneralSequence(pb_alphabet, seq_str.replace("-", ""))
for seq_str in seq_strings
]
trace = align.Alignment.trace_from_strings(seq_strings)
alignment = align.Alignment(sequences, trace, score=None)
graphics.plot_alignment_type_based(ax,
alignment,
symbols_per_line=60,
spacing=2,
color_scheme=colors)
fig.tight_layout()
return fig
def plot_show_score():
scheme_file = biotite.temp_file("json")
gecli.main(args=[
"--seed", "0", "--show-score", "--smin", "30", "--lmin", "60",
"--lmax", "70", "-f", scheme_file
])
return plt.gcf()
def test_fetch_single_file(as_file_like):
file_name = None if as_file_like else biotite.temp_file("fa")
file = entrez.fetch_single_file(["1L2Y_A", "3O5R_A"], file_name, "protein",
"fasta")
fasta_file = fasta.FastaFile.read(file)
prot_seqs = fasta.get_sequences(fasta_file)
assert len(prot_seqs) == 2
def plot_high_contrast_scheme_alignment():
scheme_file = biotite.temp_file("json")
gecli.main(args=[
"--seed", "0", "--contrast", "2000", "--lmin", "60", "--lmax", "75",
"-f", scheme_file
])
return show_alignment(scheme_file)
def plot_main_example_alignment():
scheme_file = biotite.temp_file("json")
gecli.main(args=[
"--seed", "0", "--matrix", "BLOSUM62", "--lmin", "60", "--lmax", "75",
"-f", scheme_file
])
return show_alignment(scheme_file)
def test_hbond_structure(pdb_id):
file_name = join(data_dir("structure"), pdb_id + ".mmtf")
array = load_structure(file_name)
# Only consider amino acids for consistency
# with bonded hydrogen detection in MDTraj
array = array[..., struc.filter_amino_acids(array)]
if isinstance(array, struc.AtomArrayStack):
# For consistency with MDTraj 'S' cannot be acceptor element
# https://github.com/mdtraj/mdtraj/blob/master/mdtraj/geometry/hbond.py#L365
triplets, mask = struc.hbond(array, acceptor_elements=("O", "N"))
else:
triplets = struc.hbond(array, acceptor_elements=("O", "N"))
# Save to new pdb file for consistent treatment of inscode/altloc
# im MDTraj
file_name = biotite.temp_file("pdb")
save_structure(file_name, array)
# Compare with MDTraj
import mdtraj
traj = mdtraj.load(file_name)
triplets_ref = mdtraj.baker_hubbard(traj, freq=0, periodic=False)
# Both packages may use different order
# -> use set for comparison
triplets_set = set([tuple(tri) for tri in triplets])
triplets_ref_set = set([tuple(tri) for tri in triplets_ref])
assert triplets_set == triplets_ref_set
def test_pdb_to_gro(path, single_model):
# Converting stacks between formats should not change data
model = 1 if single_model else None
# Read in data
pdb_file = pdb.PDBFile()
pdb_file.read(path)
a1 = pdb_file.get_structure(model=model)
# Save stack as gro
tmp_file_name = biotite.temp_file("gro")
gro_file = gro.GROFile()
gro_file.set_structure(a1)
gro_file.write(tmp_file_name)
# Reload stack from gro
gro_file = gro.GROFile()
gro_file.read(tmp_file_name)
a2 = gro_file.get_structure(model=model)
assert a1.array_length() == a2.array_length()
for category in ["res_id", "res_name", "atom_name"]:
assert a1.get_annotation(category).tolist() == \
a2.get_annotation(category).tolist()
# Mind rounding errors when converting pdb to gro (A -> nm)
assert a1.coord.flatten().tolist() \
== approx(a2.coord.flatten().tolist(), abs=1e-2)
def test_dihedral_backbone_result(file_name):
import mdtraj
mmtf_file = mmtf.MMTFFile()
mmtf_file.read(file_name)
array = mmtf.get_structure(mmtf_file, model=1)
array = array[struc.filter_amino_acids(array)]
for chain in struc.chain_iter(array):
print("Chain: ", chain.chain_id[0])
if len(struc.check_id_continuity(chain)) != 0:
# Do not test discontinuous chains
return
test_phi, test_psi, test_ome = struc.dihedral_backbone(chain)
temp_file_name = biotite.temp_file("pdb")
strucio.save_structure(temp_file_name, chain)
traj = mdtraj.load(temp_file_name)
_, ref_phi = mdtraj.compute_phi(traj)
_, ref_psi = mdtraj.compute_psi(traj)
_, ref_ome = mdtraj.compute_omega(traj)
ref_phi, ref_psi, ref_ome = ref_phi[0], ref_psi[0], ref_ome[0]
assert test_phi[1:] == pytest.approx(ref_phi, abs=1e-5, rel=5e-3)
assert test_psi[:-1] == pytest.approx(ref_psi, abs=1e-5, rel=5e-3)
assert test_ome[:-1] == pytest.approx(ref_ome, abs=1e-5, rel=5e-3)
def plot_high_saturation_scheme_alignment():
scheme_file = biotite.temp_file("json")
gecli.main(args=[
"--seed", "0", "--smin", "30", "--lmin", "55", "--lmax", "75", "-f",
scheme_file
])
return show_alignment(scheme_file)
def test_conversion_highlevel(path):
"""
Test whether the high-level GenBank interface can properly read
the locus, annotation and sequence from GenBank file and write
these properties to a file, without data changing.
"""
suffix = path[-2:]
gb_file = gb.GenBankFile()
gb_file.read(path)
ref_locus = gb.get_locus(gb_file)
ref_annot_seq = gb.get_annotated_sequence(gb_file, format=suffix)
gb_file = gb.GenBankFile()
gb.set_locus(gb_file, *ref_locus)
gb.set_annotated_sequence(gb_file, ref_annot_seq)
temp_file_name = biotite.temp_file("gb")
gb_file.write(temp_file_name)
gb_file = gb.GenBankFile()
gb_file.read(temp_file_name)
test_locus = gb.get_locus(gb_file)
test_annot_seq = gb.get_annotated_sequence(gb_file, format=suffix)
assert test_locus == ref_locus
assert test_annot_seq.sequence == ref_annot_seq.sequence
assert test_annot_seq.annotation == ref_annot_seq.annotation
assert test_annot_seq.sequence_start == ref_annot_seq.sequence_start
def plot_no_green_scheme_alignment():
scheme_file = biotite.temp_file("json")
gecli.main(args=[
"--seed", "0", "--amin", "0", "--lmin", "50", "--lmax", "80", "-f",
scheme_file
])
return show_alignment(scheme_file)
def test_conversion_highlevel(path):
"""
Test whether the high-level GFF3 interface can properly read
the features from a GFF3 file and write these properties to a file
without data changing.
The 'phase' is tested additionally, since it is not part of a
`Feature` object.
"""
file = gff.GFFFile.read(join(data_dir("sequence"), path))
ref_annot = gff.get_annotation(file)
ref_phases = []
for _, _, type, _, _, _, _, phase, _ in file:
if type == "CDS":
ref_phases.append(phase)
file = gff.GFFFile()
gff.set_annotation(file, ref_annot)
temp_file_name = biotite.temp_file("gff3")
file.write(temp_file_name)
file = gff.GFFFile.read(temp_file_name)
test_annot = gff.get_annotation(file)
test_phases = []
for _, _, type, _, _, _, _, phase, _ in file:
if type == "CDS":
test_phases.append(phase)
assert ref_annot == test_annot
assert test_phases == ref_phases
def test_array_conversion(format):
template = strucio.load_structure(join(data_dir, "1l2y.mmtf"))[0]
# Add fake box
template.box = np.diag([1, 2, 3])
if format == "trr":
traj_file_cls = trr.TRRFile
if format == "xtc":
traj_file_cls = xtc.XTCFile
if format == "tng":
traj_file_cls = tng.TNGFile
if format == "dcd":
traj_file_cls = dcd.DCDFile
if format == "netcdf":
traj_file_cls = netcdf.NetCDFFile
traj_file = traj_file_cls()
traj_file.read(join(data_dir, f"1l2y.{format}"))
ref_array = traj_file.get_structure(template)
traj_file = traj_file_cls()
traj_file.set_structure(ref_array)
file_name = biotite.temp_file(format)
traj_file.write(file_name)
traj_file = traj_file_cls()
traj_file.read(file_name)
array = traj_file.get_structure(template)
assert ref_array.bonds == array.bonds
assert ref_array.equal_annotation_categories(array)
assert ref_array.box == pytest.approx(array.box)
assert ref_array.coord == pytest.approx(array.coord, abs=1e-2)
def create(pdb_id, directory, include_gro):
# Create *.pdb", *.cif and *.mmtf
for file_format in ["pdb", "cif", "mmtf"]:
rcsb.fetch(pdb_id, file_format, directory, overwrite=True)
try:
array = strucio.load_structure(join(directory, pdb_id + ".pdb"))
except biotite.InvalidFileError:
# Structure probably contains multiple models with different
# number of atoms
# -> Cannot load AtomArrayStack
# -> Skip writing GRO and NPZ file
return
# Create *.gro file
strucio.save_structure(join(directory, pdb_id + ".npz"), array)
# Create *.gro files using GROMACS
# Clean PDB file -> remove inscodes and altlocs
if include_gro:
cleaned_file_name = biotite.temp_file("pdb")
strucio.save_structure(cleaned_file_name, array)
# Run GROMACS for file conversion
subprocess.run([
"editconf", "-f", cleaned_file_name, "-o",
join(directory, pdb_id + ".gro")
],
stdout=subprocess.DEVNULL,
stderr=subprocess.DEVNULL)
def plot_constrained_scheme_alignment():
scheme_file = biotite.temp_file("json")
gecli.main(args=[
"--seed", "0", "-c", "A", "70", "0", "0", "-c", "W", "70", "-10",
"-45", "--lmin", "60", "--lmax", "75", "-f", scheme_file
])
return show_alignment(scheme_file)
def plot_show_example():
random.seed(0)
scheme_file = biotite.temp_file("json")
gecli.main(args=[
"--show-example", "--smin", "30", "--lmin", "60", "--lmax", "70", "-f",
scheme_file
])
return plt.gcf()
def test_fetch_single_file():
file = entrez.fetch_single_file(["1L2Y_A", "3O5R_A"],
biotite.temp_file("fa"), "protein",
"fasta")
fasta_file = fasta.FastaFile()
fasta_file.read(file)
prot_seqs = fasta.get_sequences(fasta_file)
assert len(prot_seqs) == 2
def test_id_overflow():
# Create an atom array >= 100k atoms
length = 100000
a = struc.AtomArray(length)
a.coord = np.zeros(a.coord.shape)
a.chain_id = np.full(length, "A")
# Create residue IDs over 10000
a.res_id = np.arange(1, length + 1)
a.res_name = np.full(length, "GLY")
a.hetero = np.full(length, False)
a.atom_name = np.full(length, "CA")
a.element = np.full(length, "C")
# Write stack to pdb file and make sure a warning is thrown
with pytest.warns(UserWarning):
tmp_file_name = biotite.temp_file(".pdb")
tmp_pdb_file = pdb.PDBFile()
tmp_pdb_file.set_structure(a)
tmp_pdb_file.write(tmp_file_name)
# Assert file can be read properly
a2 = io.load_structure(tmp_file_name)
assert (a2.array_length() == a.array_length())
# Manually check if the written atom id is correct
with open(tmp_file_name) as output:
last_line = output.readlines()[-1]
atom_id = int(last_line.split()[1])
assert (atom_id == 1)
# Write stack as hybrid-36 pdb file: no warning should be thrown
with pytest.warns(None) as record:
tmp_file_name = biotite.temp_file(".pdb")
tmp_pdb_file = pdb.PDBFile()
tmp_pdb_file.set_structure(a, hybrid36=True)
tmp_pdb_file.write(tmp_file_name)
assert len(record) == 0
# Manually check if the output is written as correct hybrid-36
with open(tmp_file_name) as output:
last_line = output.readlines()[-1]
atom_id = last_line.split()[1]
assert (atom_id == "A0000")
res_id = last_line.split()[4][1:]
assert (res_id == "BXG0")
def test_numpy_objects():
"""
Test whether the Msgpack encoder is able to handle NumPy values
(e.g. np.float32) properly.
"""
mmtf_file = mmtf.MMTFFile()
mmtf_file["A float"] = np.float32(42.0)
mmtf_file["A list"] = [np.int64(1), np.int64(2), np.int64(3)]
mmtf_file["A dictionary"] = {"a": np.bool(True), "b": np.bool(False)}
mmtf_file.write(biotite.temp_file("mmtf"))
def test_conversion_lowlevel(path):
"""
Test whether the low-level GFF3 interface can properly read
a GenBank file and write a file, without data changing.
"""
file = gff.GFFFile.read(join(data_dir("sequence"), path))
ref_entries = [entry for entry in file]
file = gff.GFFFile()
for entry in ref_entries:
file.append(*entry)
temp_file_name = biotite.temp_file("gff3")
file.write(temp_file_name)
file = gff.GFFFile.read(temp_file_name)
test_entries = [field for field in file]
assert test_entries == ref_entries
def create(pdb_id, directory, include_gro):
# Create *.pdb", *.cif and *.mmtf
for file_format in ["pdb", "cif", "mmtf"]:
rcsb.fetch(pdb_id, file_format, directory)
if include_gro:
# Create *.gro files using GROMACS
# Clean PDB file -> remove inscodes and altlocs
array = strucio.load_structure(join(directory, pdb_id + ".pdb"))
cleaned_file_name = biotite.temp_file("pdb")
strucio.save_structure(cleaned_file_name, array)
# Run GROMACS for file conversion
subprocess.run([
"gmx", "editconf", "-f", cleaned_file_name, "-o",
join(directory, pdb_id + ".gro")
],
stdout=subprocess.DEVNULL,
stderr=subprocess.DEVNULL)
def test_conversion_lowlevel(path):
"""
Test whether the low-level GenBank interface can properly read
a GenBank file and write a file, without data changing.
"""
gb_file = gb.GenBankFile()
gb_file.read(path)
ref_parsed_fields = [field for field in gb_file]
gb_file = gb.GenBankFile()
for name, content, subfields in ref_parsed_fields:
gb_file.append(name, content, subfields)
temp_file_name = biotite.temp_file("gb")
gb_file.write(temp_file_name)
gb_file = gb.GenBankFile()
gb_file.read(temp_file_name)
test_parsed_fields = [field for field in gb_file]
assert test_parsed_fields == ref_parsed_fields
def test_conversion(chars_per_line):
path = os.path.join(data_dir, "random.fastq")
file1 = fastq.FastqFile(offset=33, chars_per_line=chars_per_line)
file1.read(path)
ref_content = dict(file1.items())
file2 = fastq.FastqFile(offset=33, chars_per_line=chars_per_line)
for identifier, (sequence, scores) in ref_content.items():
file2[identifier] = sequence, scores
file2.write(biotite.temp_file("fastq"))
file3 = fastq.FastqFile(offset=33, chars_per_line=chars_per_line)
file3.read(path)
content = dict(file3.items())
for identifier in ref_content:
ref_sequence, ref_scores = ref_content[identifier]
sequence, scores = content[identifier]
assert ref_sequence == sequence
assert np.array_equal(ref_scores, scores)
def test_guess_elements():
# read valid pdb file
path = join(data_dir("structure"), "1l2y.pdb")
pdb_file = pdb.PDBFile.read(path)
stack = pdb_file.get_structure()
# remove all elements
removed_stack = stack.copy()
removed_stack.element[:] = ''
# save stack without elements to tmp file
tmp_file_name = biotite.temp_file(".pdb")
tmp_pdb_file = pdb.PDBFile()
tmp_pdb_file.set_structure(removed_stack)
tmp_pdb_file.write(tmp_file_name)
# read new stack from file with guessed elements
guessed_pdb_file = pdb.PDBFile.read(tmp_file_name)
guessed_stack = guessed_pdb_file.get_structure()
assert guessed_stack.element.tolist() == stack.element.tolist()
def test_gro_no_box():
"""
.gro file format requires valid box parameters at the end of each
model. However, if we read such a file in, the resulting object should not
have an assigned box.
"""
# Create an AtomArray
atom = Atom([1, 2, 3], atom_name="CA", element="C", res_name="X", res_id=1)
atoms = array([atom])
# Write .gro file
tmp_file_name = biotite.temp_file(".gro")
io.save_structure(tmp_file_name, atoms)
# Read in file
gro_file = gro.GROFile.read(tmp_file_name)
s = gro_file.get_structure()
# Assert no box with 0 dimension
assert s.box is None
def test_gro_id_overflow():
# Create an oversized AtomArray where atom_id > 100000 and res_id > 10000
num_atoms = 100005
atoms = array([
Atom([1, 2, 3],
atom_name="CA",
element="C",
res_name="X",
res_id=i + 1) for i in range(num_atoms)
])
atoms.box = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
# Write .gro file
tmp_file_name = biotite.temp_file(".gro")
io.save_structure(tmp_file_name, atoms)
# Read .gro file
gro_file = gro.GROFile.read(tmp_file_name)
s = gro_file.get_structure()
assert s.array_length() == num_atoms
def test_array_conversion(path, single_model):
model = 1 if single_model else None
mmtf_file = mmtf.MMTFFile()
mmtf_file.read(path)
a1 = mmtf.get_structure(mmtf_file, model=model, include_bonds=True)
mmtf_file = mmtf.MMTFFile()
mmtf.set_structure(mmtf_file, a1)
temp_file_name = biotite.temp_file("mmtf")
mmtf_file.write(temp_file_name)
mmtf_file = mmtf.MMTFFile()
mmtf_file.read(temp_file_name)
a2 = mmtf.get_structure(mmtf_file, model=model, include_bonds=True)
for category in a1.get_annotation_categories():
assert a1.get_annotation(category).tolist() == \
a2.get_annotation(category).tolist()
assert a1.coord.flatten().tolist() == \
approx(a2.coord.flatten().tolist(), abs=1e-3)
assert a1.bonds == a2.bonds
if a1.box is not None:
assert np.allclose(a1.box, a2.box)
r"$\sigma^{38}$": "rpoS",
})
# Find SwissProt entries for these genes in NCBI Entrez protein database
uids = []
for name, gene in genes.items():
query = entrez.SimpleQuery(gene, "Gene Name") \
& entrez.SimpleQuery("srcdb_swiss-prot", "Properties") \
& entrez.SimpleQuery("Escherichia coli K-12", "Organism")
ids = entrez.search(query, "protein")
# Only one entry per gene in E. coli K-12 is expected
assert len(ids) == 1
uids += ids
# Download corresponding GenBank files as single, merged file
file_name = entrez.fetch_single_file(uids,
biotite.temp_file("gb"),
"protein",
ret_type="gb")
# Array that will hold for each of the genes and each of the 4 domains
# the first and last position
# The array is initally filled with -1, as the value -1 will indicate
# that the domain does not exist in the sigma factor
domain_pos = np.full((len(genes), 4, 2), -1, dtype=int)
# Array that will hold the total sequence length of each sigma factor
seq_lengths = np.zeros(len(genes), dtype=int)
# Read the merged file containing multiple GenBank entries
multi_file = gb.MultiFile()
multi_file.read(file_name)
# Iterate over each GenBank entry
for i, gb_file in enumerate(multi_file):
It is basically very similar to using normal functions.
In the following sections you will get an overview over the mentioned
subpackages, so go and grab some tea and cookies und let us begin.
Preliminary note
----------------
The files used in this tutorial will be stored in a temporary directory.
The top level package :mod:`biotite` provides functionality to create
a temporary directory,
called ``.biotitetemp`` in your current working directory.
You can either obtain the path to this directory via
:func:`temp_dir` or directly create an unambiguous file name in this
directory using :func:`temp_file`.
In the end of the session the temporary directory and all its contents
will be automatically deleted, so make sure to put the files, you want
keep, somewhere else.
"""
from os.path import relpath
import biotite
# Create temporary directory
dir_path = biotite.temp_dir()
print(relpath(dir_path))
# Get a path to a temporary FASTA file
# This would also create the temporary directory,
# if it was not created, yet
file_path = biotite.temp_file("fasta")
print(relpath(file_path))
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import biotite
import biotite.sequence as seq
import biotite.sequence.io.fasta as fasta
import biotite.sequence.align as align
import biotite.sequence.graphics as graphics
import biotite.database.entrez as entrez
# Generate example alignment
# (the same as in the bacterial luciferase example)
query = entrez.SimpleQuery("luxA", "Gene Name") \
& entrez.SimpleQuery("srcdb_swiss-prot", "Properties")
uids = entrez.search(query, db_name="protein")
file_name = entrez.fetch_single_file(uids,
biotite.temp_file("fasta"),
db_name="protein",
ret_type="fasta")
fasta_file = fasta.FastaFile.read(file_name)
sequences = [seq.ProteinSequence(seq_str) for seq_str in fasta_file.values()]
matrix = align.SubstitutionMatrix.std_protein_matrix()
alignment, order, _, _ = align.align_multiple(sequences, matrix)
# Order alignment according to the guide tree
alignment = alignment[:, order]
alignment = alignment[220:300]
# Get color scheme names
alphabet = seq.ProteinSequence.alphabet
schemes = [
"rainbow", "clustalx", "flower", "blossom", "spring", "wither", "autumn",
"sunset", "ocean", "zappo", "taylor", "buried", "hydrophobicity",
