import biotite.sequence.io.genbank as gb import biotite.database.entrez as entrez # The names of the sigma factors and the corresponding genes genes = OrderedDict({ r"$\sigma^{70}$": "rpoD", r"$\sigma^{24}$": "rpoE", r"$\sigma^{28}$": "rpoF", r"$\sigma^{32}$": "rpoH", 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
# Code source: Patrick Kunzmann # License: BSD 3 clause import numpy as np 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
show_numbers=show_numbers, number_size=number_size, number_functions=number_functions, labels=labels, label_size=label_size, show_line_position=show_line_position, spacing=spacing) twin = axes.get_shared_x_axes().get_siblings(axes)[0] for ax in (axes, twin): ax.set_yticklabels(ax.get_yticklabels(), fontdict={"color": "white"}) axes.get_figure().patch.set_facecolor("#181818") # Using cyclotide sequences as example query = (entrez.SimpleQuery("Cyclotide") & entrez.SimpleQuery("cter") & entrez.SimpleQuery("srcdb_swiss-prot", field="Properties") ^ entrez.SimpleQuery("Precursor")) uids = entrez.search(query, "protein") fasta_file = fasta.FastaFile.read( entrez.fetch_single_file(uids, None, "protein", "fasta")) sequence_dict = fasta.get_sequences(fasta_file) headers = list(sequence_dict.keys()) sequences = list(sequence_dict.values()) labels = [header[-1] for header in headers] # Perform a multiple sequence alignment matrix = align.SubstitutionMatrix.std_protein_matrix() alignment, order, _, _ = align.align_multiple(sequences, matrix) # Order alignment according to guide tree alignment = alignment[:, order.tolist()]
temp_file = NamedTemporaryFile(suffix=".fasta") file_path = entrez.fetch_single_file(["1L2Y_A", "1AKI_A"], temp_file.name, db_name="protein", ret_type="fasta") print(file_path) temp_file.close() ######################################################################## # Similar to the *RCSB PDB*, you can also search every # `field <https://www.ncbi.nlm.nih.gov/books/NBK49540/>`_ # of the *NCBI Entrez* database. # Search in all fields print(entrez.SimpleQuery("BL21 genome")) # Search in the 'Organism' field print(entrez.SimpleQuery("Escherichia coli", field="Organism")) ######################################################################## # You can also combine multiple :class:`Query` objects in any way you # like using the binary operators ``|``, ``&`` and ``^``, # that represent ``OR``, ``AND`` and ``NOT`` linkage, respectively. composite_query = (entrez.SimpleQuery("50:100", field="Sequence Length") & (entrez.SimpleQuery("Escherichia coli", field="Organism") | entrez.SimpleQuery("Bacillus subtilis", field="Organism"))) print(composite_query) ######################################################################## # Finally, the query is given to the :func:`search()` function to obtain
# License: BSD 3 clause import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import LinearSegmentedColormap import biotite.sequence as seq import biotite.sequence.align as align import biotite.sequence.io.genbank as gb import biotite.sequence.align as align import biotite.sequence.graphics as graphics import biotite.database.entrez as entrez import biotite.application.clustalo as clustalo # Search for DNA sequences that belong to the cited article query = entrez.SimpleQuery("Forensic Sci. Int.", "Journal") \ & entrez.SimpleQuery("159", "Volume") \ & entrez.SimpleQuery("132-140", "Page Number") uids = entrez.search(query, db_name="nuccore") # Download and read file containing the Genbank records for the THCA # synthase genes multi_file = gb.MultiFile.read(entrez.fetch_single_file( uids, file_name=None, db_name="nuccore", ret_type="gb" )) # This dictionary maps the strain ID to the protein sequence sequences = {} for gb_file in multi_file:
"T": -0.7, "S": -0.8, "W": -0.9, "Y": -1.3, "P": -1.6, "H": -3.2, "E": -3.5, "Q": -3.5, "D": -3.5, "N": -3.5, "K": -3.9, "R": -4.5 } # Look for the Swiss-Prot entry contaning the human HCN1 channel query = entrez.SimpleQuery("HCN1", "Gene Name") \ & entrez.SimpleQuery("h**o sapiens", "Organism") \ & entrez.SimpleQuery("srcdb_swiss-prot", "Properties") uids = entrez.search(query, db_name="protein") file_name = entrez.fetch(uids[0], biotite.temp_dir(), "gp", db_name="protein", ret_type="gp") gp_file = gb.GenBankFile.read(file_name) hcn1 = seq.ProteinSequence(gb.get_sequence(gp_file, format="gp")) print(hcn1) ######################################################################## # The positional hydropathy is calculated and smoothened using