def generate_html(region_layer: RegionLayer, results: LanthiResults,
_record_layer: RecordLayer,
_options_layer: OptionsLayer) -> HTMLSections:
""" Generates HTML output for the module """
html = HTMLSections("lanthipeptides")
detail_tooltip = (
"Lists the possible core peptides for each biosynthetic enzyme, including the predicted class. "
"Each core peptide shows the leader and core peptide sequences, separated by a dash."
)
template = FileTemplate(
path.get_full_path(__file__, "templates", "details.html"))
motifs = results.get_motifs_for_region(region_layer.region_feature)
html.add_detail_section(
"Lanthipeptides",
template.render(results=motifs, tooltip=detail_tooltip))
side_tooltip = (
"Lists the possible core peptides in the region. "
"Each core peptide lists the number of lanthionine bridges, possible molecular weights, "
"and the scores for cleavage site prediction and RODEO.")
template = FileTemplate(
path.get_full_path(__file__, "templates", "sidepanel.html"))
motifs = results.get_motifs_for_region(region_layer.region_feature)
html.add_sidepanel_section(
"Lanthipeptides", template.render(results=motifs,
tooltip=side_tooltip))
return html
def generate_html(region_layer: RegionLayer, results: ThioResults,
record_layer: RecordLayer, options_layer: OptionsLayer
) -> HTMLSections:
""" Generates HTML for the module """
html = HTMLSections("thiopeptides")
if not results:
return html
thio_layer = ThiopeptideLayer(record_layer, results, region_layer.region_feature)
detail_tooltip = ("Lists the possible core peptides for each biosynthetic enzyme, including the predicted class. "
"Each core peptide shows the leader and core peptide sequences, separated by a dash. "
"Predicted tail sequences are also shown.")
template = FileTemplate(path.get_full_path(__file__, "templates", "details.html"))
details = template.render(record=record_layer,
cluster=thio_layer,
options=options_layer,
tooltip=detail_tooltip)
html.add_detail_section("Thiopeptides", details)
side_tooltip = ("Lists the possible core peptides in the region. "
"Each core peptide lists its possible molecular weights "
"and the scores for cleavage site prediction and RODEO. "
"If relevant, other features, such as macrocycle and amidation, will also be listed.")
template = FileTemplate(path.get_full_path(__file__, "templates", "sidepanel.html"))
sidepanel = template.render(record=record_layer,
cluster=thio_layer,
options=options_layer,
tooltip=side_tooltip)
html.add_sidepanel_section("Thiopeptides", sidepanel)
return html
def generate_html(region_layer: RegionLayer, results: T2PKSResults,
_record_layer: RecordLayer,
options_layer: OptionsLayer) -> HTMLSections:
""" Generate the sidepanel HTML with results from the type II PKS module """
html = HTMLSections("t2pks")
predictions = []
for cluster in region_layer.get_unique_clusters():
if cluster.product == "T2PKS":
predictions.append(
results.cluster_predictions[cluster.get_cluster_number()])
template = FileTemplate(
path.get_full_path(__file__, "templates", "sidepanel.html"))
docs_url = options_layer.urls.docs_baseurl + "modules/t2pks"
tooltip_content = (
"Predictions of starter units, elongations, product classes, "
"and potential molecular weights for type II PKS clusters.")
tooltip_content += "<br>More detailed information is available <a href='%s' target='_blank'>here</a>." % docs_url
html.add_sidepanel_section(
"Type II PKS",
template.render(predictions=predictions,
tooltip_content=tooltip_content))
return html
def generate_html(region_layer: RegionLayer, results: SactiResults,
record_layer: RecordLayer,
options_layer: OptionsLayer) -> HTMLSections:
""" Generates HTML for the module """
html = HTMLSections("sactipeptides")
motifs_in_region = defaultdict(list) # type: Dict[str, List[CDSMotif]]
for locus, motifs in results.motifs_by_locus.items():
for motif in motifs:
if motif.is_contained_by(region_layer.region_feature):
motifs_in_region[locus].append(motif)
sacti_layer = SactipeptideLayer(record_layer, region_layer.region_feature)
template = FileTemplate(
path.get_full_path(__file__, "templates", "details.html"))
details = template.render(record=record_layer,
region=sacti_layer,
options=options_layer,
results=motifs_in_region)
html.add_detail_section("Sactipeptides", details)
template = FileTemplate(
path.get_full_path(__file__, "templates", "sidepanel.html"))
sidepanel = template.render(record=record_layer,
region=sacti_layer,
options=options_layer,
results=motifs_in_region)
html.add_sidepanel_section("Sactipeptides", sidepanel)
return html
def generate_html(region_layer: RegionLayer, results: SactiResults,
record_layer: RecordLayer, options_layer: OptionsLayer) -> HTMLSections:
""" Generates HTML for the module """
html = HTMLSections("sactipeptides")
motifs_in_region = defaultdict(list) # type: Dict[str, List[CDSMotif]]
for locus, motifs in results.motifs_by_locus.items():
for motif in motifs:
if motif.is_contained_by(region_layer.region_feature):
motifs_in_region[locus].append(motif)
sacti_layer = SactipeptideLayer(record_layer, region_layer.region_feature)
detail_tooltip = ("Lists the possible core peptides for each biosynthetic enzyme, including the predicted class. "
"Each core peptide shows the leader and core peptide sequences, separated by a dash.")
template = FileTemplate(path.get_full_path(__file__, "templates", "details.html"))
details = template.render(record=record_layer,
region=sacti_layer,
options=options_layer,
results=motifs_in_region,
tooltip=detail_tooltip)
html.add_detail_section("Sactipeptides", details)
side_tooltip = ("Lists the possible core peptides in the region. "
"Each core peptide lists its RODEO score and predicted core sequence.")
template = FileTemplate(path.get_full_path(__file__, "templates", "sidepanel.html"))
sidepanel = template.render(record=record_layer,
region=sacti_layer,
options=options_layer,
results=motifs_in_region,
tooltip=side_tooltip)
html.add_sidepanel_section("Sactipeptides", sidepanel)
return html
def generate_html(region_layer: RegionLayer, results: ThioResults,
record_layer: RecordLayer,
options_layer: OptionsLayer) -> HTMLSections:
""" Generates HTML for the module """
html = HTMLSections("thiopeptides")
if not results:
return html
thio_layer = ThiopeptideLayer(record_layer, results,
region_layer.region_feature)
template = FileTemplate(
path.get_full_path(__file__, "templates", "details.html"))
details = template.render(record=record_layer,
cluster=thio_layer,
options=options_layer)
html.add_detail_section("Thiopeptides", details)
template = FileTemplate(
path.get_full_path(__file__, "templates", "sidepanel.html"))
sidepanel = template.render(record=record_layer,
cluster=thio_layer,
options=options_layer)
html.add_sidepanel_section("Thiopeptides", sidepanel)
return html
def generate_html(region_layer: RegionLayer, results: NRPS_PKS_Results,
record_layer: RecordLayer,
options_layer: OptionsLayer) -> HTMLSections:
""" Generate the sidepanel HTML with results from the NRPS/PKS module """
html = HTMLSections("nrps_pks")
nrps_layer = NrpspksLayer(results, region_layer.region_feature,
record_layer)
features_with_domain_predictions: Dict[str, List[str]] = {}
for domain_name, consensus in results.consensus.items():
if not consensus:
continue
domain = record_layer.get_domain_by_name(domain_name)
features_with_domain_predictions[domain.locus_tag] = []
for feature_name, monomers in features_with_domain_predictions.items():
for domain in record_layer.get_cds_by_name(
feature_name).nrps_pks.domains:
monomer = results.consensus.get(domain.feature_name)
if monomer:
monomers.append(monomer)
prod_tt = (
"Shows estimated product structure and polymer for each candidate cluster in the region. "
"To show the product, click on the expander or the candidate cluster feature drawn in the overview. "
)
mon_tt = (
"Shows the predicted monomers for each adynelation domain and acyltransferase within genes. "
"Each gene prediction can be expanded to view detailed predictions of each domain. "
"Each prediction can be expanded to view the predictions by tool "
" (and, for some tools, further expanded for extra details). ")
if not nrps_layer.has_any_polymer():
return html
for filename, name, class_name, tooltip in [
("products.html", "NRPS/PKS products", "nrps_pks_products", prod_tt),
("monomers.html", "NRPS/PKS monomers", "", mon_tt)
]:
template = FileTemplate(
path.get_full_path(__file__, "templates", filename))
section = template.render(
record=record_layer,
region=nrps_layer,
results=results,
relevant_features=features_with_domain_predictions,
options=options_layer,
tooltip=tooltip)
html.add_sidepanel_section(name, section, class_name)
return html
def generate_html(region_layer: RegionLayer, results: LanthiResults,
_record_layer: RecordLayer, _options_layer: OptionsLayer
) -> HTMLSections:
""" Generates HTML output for the module """
html = HTMLSections("lanthipeptides")
template = FileTemplate(path.get_full_path(__file__, "templates", "details.html"))
motifs = results.get_motifs_for_region(region_layer.region_feature)
html.add_detail_section("Lanthipeptides", template.render(results=motifs))
template = FileTemplate(path.get_full_path(__file__, "templates", "sidepanel.html"))
motifs = results.get_motifs_for_region(region_layer.region_feature)
html.add_sidepanel_section("Lanthipeptides", template.render(results=motifs))
return html
def generate_html(region_layer: RegionLayer, results: RREFinderResults,
_record_layer: RecordLayer,
_options_layer: OptionsLayer) -> HTMLSections:
""" Generates HTML output for the module """
html = HTMLSections("rrefinder")
side_tooltip = ("RREfinder results sidepanel.")
template = FileTemplate(
path.get_full_path(__file__, "templates", "sidepanel.html"))
html.add_sidepanel_section(
"RREFinder",
template.render(results=results,
region=region_layer.region_feature,
tooltip=side_tooltip), "RREfinder")
return html
def generate_html(region_layer: RegionLayer, results: T2PKSResults,
_record_layer: RecordLayer,
_options_layer: OptionsLayer) -> HTMLSections:
""" Generate the sidepanel HTML with results from the type II PKS module """
html = HTMLSections("t2pks")
predictions = []
for cluster in region_layer.get_unique_clusters():
if cluster.product == "t2pks":
predictions.append(
results.cluster_predictions[cluster.get_cluster_number()])
template = FileTemplate(
path.get_full_path(__file__, "templates", "sidepanel.html"))
html.add_sidepanel_section("Type II PKS",
template.render(predictions=predictions))
return html
def generate_html(region_layer: RegionLayer, results: NRPS_PKS_Results,
record_layer: RecordLayer,
options_layer: OptionsLayer) -> HTMLSections:
""" Generate the sidepanel HTML with results from the NRPS/PKS module """
html = HTMLSections("nrps_pks")
nrps_layer = NrpspksLayer(results, region_layer.region_feature,
record_layer)
features_with_domain_predictions = {} # type: Dict[str, List[str]]
for domain_name, consensus in results.consensus.items():
if not consensus:
continue
domain = record_layer.get_domain_by_name(domain_name)
features_with_domain_predictions[domain.locus_tag] = []
for feature_name, monomers in features_with_domain_predictions.items():
for domain in record_layer.get_cds_by_name(
feature_name).nrps_pks.domains:
monomer = results.consensus.get(domain.feature_name)
if monomer:
monomers.append(monomer)
for filename, name, class_name in [
("products.html", "NRPS/PKS products", "nrps_pks_products"),
("monomers.html", "NRPS/PKS monomers", "")
]:
template = FileTemplate(
path.get_full_path(__file__, "templates", filename))
section = template.render(
record=record_layer,
region=nrps_layer,
results=results,
relevant_features=features_with_domain_predictions,
options=options_layer)
html.add_sidepanel_section(name, section, class_name)
return html
def generate_html(region_layer: RegionLayer, results: LassoResults,
record_layer: RecordLayer, _options_layer: OptionsLayer) -> HTMLSections:
""" Generates HTML for the module """
html = HTMLSections("lassopeptides")
motifs_in_region = {}
for locus in results.motifs_by_locus:
if record_layer.get_cds_by_name(locus).is_contained_by(region_layer.region_feature):
motifs_in_region[locus] = results.motifs_by_locus[locus]
detail_tooltip = ("Lists the possible core peptides for each biosynthetic enzyme, including the predicted class. "
"Each core peptide shows the leader and core peptide sequences, separated by a dash.")
template = FileTemplate(path.get_full_path(__file__, "templates", "details.html"))
html.add_detail_section("Lasso peptides", template.render(results=motifs_in_region, tooltip=detail_tooltip))
side_tooltip = ("Lists the possible core peptides in the region. "
"Each core peptide lists the number of disulfide bridges, possible molecular weights, "
"and the scores for cleavage site prediction and RODEO.")
template = FileTemplate(path.get_full_path(__file__, "templates", "sidepanel.html"))
html.add_sidepanel_section("Lasso peptides", template.render(results=motifs_in_region, tooltip=side_tooltip))
return html
def generate_html(region_layer: RegionLayer, results: LassoResults,
record_layer: RecordLayer,
_options_layer: OptionsLayer) -> HTMLSections:
""" Generates HTML for the module """
html = HTMLSections("lassopeptides")
motifs_in_region = {}
for locus in results.motifs_by_locus:
if record_layer.get_cds_by_name(locus).is_contained_by(
region_layer.region_feature):
motifs_in_region[locus] = results.motifs_by_locus[locus]
template = FileTemplate(
path.get_full_path(__file__, "templates", "details.html"))
html.add_detail_section("Lasso peptides",
template.render(results=motifs_in_region))
template = FileTemplate(
path.get_full_path(__file__, "templates", "sidepanel.html"))
html.add_sidepanel_section("Lasso peptides",
template.render(results=motifs_in_region))
return html
def generate_html(region_layer: RegionLayer, results: RREFinderResults,
_record_layer: RecordLayer,
_options_layer: OptionsLayer) -> HTMLSections:
""" Generates HTML output for the module """
html = HTMLSections("rrefinder")
side_tooltip = ("RREfinder results sidepanel.")
template = FileTemplate(
path.get_full_path(__file__, "templates", "sidepanel.html"))
protoclusters = []
for proto in region_layer.get_unique_protoclusters():
if proto.get_protocluster_number() in results.hits_by_protocluster:
protoclusters.append(proto)
if protoclusters:
section = template.render(results=results,
protoclusters=protoclusters,
tooltip=side_tooltip)
html.add_sidepanel_section("RREFinder",
section,
class_name="RREfinder")
return html
