def run_gms2_with_component_toggles_and_get_accuracy(env, gi, components_off, **kwargs):
# type: (Environment, GenomeInfo, Set[str], Dict[str, Any]) -> Dict[str, Any]
pf_mod_original = os_join(env["pd-runs"], gi.name, "gms2", "GMS2.mod")
pf_reference = os_join(env["pd-data"], gi.name, "verified.gff")
pf_sequence = os_join(env["pd-data"], gi.name, "sequence.fasta")
pf_prediction = os_join(env["pd-work"], "prediction.gff")
native_coding_off = get_value(kwargs, "native_coding_off", True)
pf_new_mod = os_join(env["pd-work"], "model.mod")
turn_off_components(pf_mod_original, pf_new_mod, components_off, native_coding_off=native_coding_off)
done = False
while not done:
try:
run_gms2_prediction_with_model(pf_sequence, pf_new_mod, pf_prediction)
done = True
except CalledProcessError:
pass
# compare with verified
lcd = LabelsComparisonDetailed(read_labels_from_file(pf_reference), read_labels_from_file(pf_prediction))
return {
"Error": 100 - 100 * len(lcd.match_3p_5p('a')) / len(lcd.match_3p('a'))
}
def compare_gms2_start_predictions_with_motif_from_toolp_verified(env, gi, **kwargs):
# type: (Environment, GenomeInfo) -> [float, float]
group = get_value(kwargs, "group", None)
pf_gms2 = os_join(env["pd-runs"], gi.name, "gms2", "gms2.gff")
pf_gms2_mod = os_join(env["pd-runs"], gi.name, "gms2", "GMS2.mod")
pf_sbsp = os_join(env["pd-runs"], gi.name, "sbsp_submission/accuracy", f"{gi.name}.gff")
pf_sequence = os_join(env["pd-data"], gi.name, "sequence.fasta")
pf_toolp = os_join(env["pd-work"], "toolp.gff")
pf_verified = os_join(env["pd-data"], gi.name, "verified.gff")
# get toolp predictions
get_identital_labels(
pf_gms2, pf_sbsp, pf_toolp
)
# create new motif model with toolp and add it to new model file
pf_new_mod = os_join(env["pd-work"], "toolp.mod")
add_toolp_rbs_to_gms2_model(env, pf_sequence, pf_toolp, pf_gms2_mod, pf_new_mod, group=group)
# run prediction with new model
pf_new_pred = os_join(env["pd-work"], "new_pred.gff")
run_gms2_prediction_with_model(pf_sequence, pf_new_mod, pf_new_pred)
# compare predictions
lcd1 = LabelsComparisonDetailed(read_labels_from_file(pf_gms2), read_labels_from_file(pf_verified))
lcd2 = LabelsComparisonDetailed(read_labels_from_file(pf_new_pred), read_labels_from_file(pf_verified))
return [100 * len(lcd.match_3p_5p('a')) / len(lcd.match_3p('a')) for lcd in [lcd1, lcd2]]
def get_stats_sn_sp(labels_a, labels_b, output):
# type: (Labels, Labels, Dict[str, Any]) -> None
lcd = LabelsComparisonDetailed(labels_a, labels_b)
a_name = labels_a.name if labels_a.name is not None else "a"
b_name = labels_b.name if labels_b.name is not None else "b"
a_total = len(labels_a)
b_total = len(labels_b)
match_3p = len(lcd.match_3p("a"))
match_3p_5p = len(lcd.match_3p_5p("a"))
sp = 0 if a_total == 0 else round(100 * match_3p / float(a_total), 2)
sn = 0 if match_3p == 0 else round(100 * match_3p_5p / float(match_3p), 2)
output.update({
a_name:
a_total,
b_name:
b_total,
"Number 3p match: {} from {}".format(a_name, b_name):
match_3p,
"Percentage 3p match: {} from {}".format(a_name, b_name):
sp,
"Number 5p-3p match: {} from {}".format(a_name, b_name):
match_3p_5p,
"Percentage 5p-3p match: {} from {}".format(a_name, b_name):
sn,
})
def get_stats_a_from_b_3p(labels_a, labels_b, output):
# type: (Labels, Labels, Dict[str, Any]) -> None
lcd = LabelsComparisonDetailed(labels_a, labels_b)
a_name = labels_a.name if labels_a.name is not None else "a"
b_name = labels_b.name if labels_b.name is not None else "b"
output.update({
a_name:
len(labels_a),
b_name:
len(labels_b),
"Number 3p match: {} from {}".format(a_name, b_name):
len(lcd.match_3p("a")),
"Percentage 3p match: {} from {}".format(a_name, b_name):
round(100 * len(lcd.match_3p("a")) / float(len(labels_b)), 2)
})
def relative_entropy_analysis_for_gi_for_percent(env, pf_sequence, pf_labels,
pf_mod, pf_verified, group,
percent, pd_figures):
# type: (Environment, str, str, str, str, str, float, str) -> Dict[str, Any]
# 1) randomly select percent of labels
pf_labels_percent = os_join(env["pd-work"], "labels_percent.lst")
pf_mod_percent = os_join(env["pd-work"], "model_percent.mod")
pf_labels_predict = os_join(env["pd-work"], "labels_predict.lst")
randomly_select_labels(pf_labels, pf_labels_percent, percent)
# train new model
mod_percent = train_and_create_models(env,
pf_sequences=pf_sequence,
pf_labels=pf_labels_percent,
group=group,
clean=False,
pf_mod=pf_mod_percent)
# add RBSB to GMS2 model
add_toolp_rbs_to_gms2_model(env, pf_sequence, pf_labels_percent, pf_mod,
pf_mod_percent)
logo_rbs_from_gms2_mod_file(pd_figures, pf_mod_percent, str(percent))
# run prediction with new model
run_gms2_prediction_with_model(pf_sequence, pf_mod_percent,
pf_labels_predict)
# compare predictions
lcd = LabelsComparisonDetailed(read_labels_from_file(pf_labels_predict),
read_labels_from_file(pf_verified))
mm = MotifModel(mod_percent.items["RBS_MAT"],
mod_percent.items["RBS_POS_DISTR"])
non = GMS2Noncoding(mod_percent.items["NON_MAT"])
return {
"RE": relative_entropy(mm, non),
"RE Motif": relative_entropy(mm, non, component="motif"),
"RE Spacer": relative_entropy(mm, non, component="spacer"),
"Error": 100 - 100 * len(lcd.match_3p_5p('a')) / len(lcd.match_3p('a'))
}
def compare_gms2_sbsp_ncbi(env, pf_gms2, pf_sbsp, pf_ncbi, **kwargs):
# type: (Environment, str, str, str, Dict[str, Any]) -> None
venn_title = get_value(kwargs, "venn_title", None)
pf_venn = get_value(kwargs, "pf_venn",
os.path.join(env["pd-work"], "venn.pdf"))
labels_gms2 = read_labels_from_file(pf_gms2, name="GMS2")
labels_sbsp = read_labels_from_file(pf_sbsp, name="SBSP")
labels_ncbi = read_labels_from_file(pf_ncbi, name="NCBI")
lcd = LabelsComparisonDetailed(labels_gms2,
labels_sbsp,
name_a="gms2",
name_b="sbsp")
labels_gms2_sbsp_3p_5p = lcd.intersection("a")
lcd_2 = LabelsComparisonDetailed(labels_gms2_sbsp_3p_5p,
labels_ncbi,
name_a="gms2_sbsp",
name_b="ncbi")
labels_gms2_sbsp_ncbi_3p_5p = lcd_2.intersection("a")
out = "gms2,sbsp,ncbi,gms2_sbsp,gms2_sbsp_ncbi"
out += "\n{},{},{},{},{}".format(len(labels_gms2), len(labels_sbsp),
len(labels_ncbi),
len(labels_gms2_sbsp_3p_5p),
len(labels_gms2_sbsp_ncbi_3p_5p))
print(out)
venn_diagram_5prime(labels_gms2, labels_sbsp, labels_ncbi,
FigureOptions(title=venn_title, save_fig=pf_venn))
def main(env, args):
# type: (Environment, argparse.Namespace) -> None
labels_a = read_labels_from_file(args.pf_a)
labels_b = read_labels_from_file(args.pf_b)
lcd = LabelsComparisonDetailed(
labels_a,
labels_b,
name_a=args.name_a,
name_b=args.name_b,
tag=args.tag,
split_on_attributes=args.split_on_attributes)
LabelsComparisonDetailedViz(lcd).run(env["pd-work"])
def pipeline_step_compute_accuracy(env, df, pipeline_options):
# type: (Environment, pd.DataFrame, PipelineSBSPOptions) -> pd.DataFrame
from sbsp_io.labels import read_labels_from_file
for genome in set(df["q-genome"]):
pf_q_labels_true = os.path.join(env["pd-data"], genome, pipeline_options["fn-q-labels-compare"])
labels = read_labels_from_file(pf_q_labels_true, shift=0)
df_add_is_true_start(df, labels, "q-", "is-true",
coordinates_suffix="-sbsp")
df_add_distance_between_predicted_and_true(
df, labels, "q-", "distance-to-true",
coordinates_suffix="-sbsp")
# get labels
genome_to_pf_labels = df_print_labels(env, df, "q", suffix_coordinates="sbsp",
suffix_fname="")
# print accuracies
from sbsp_general.labels_comparison import LabelsComparison
genome_to_comparison = dict()
for genome in genome_to_pf_labels:
pf_q_labels_true = os.path.join(env["pd-data"], genome, pipeline_options["fn-q-labels-compare"])
genome_to_comparison[genome] = LabelsComparison(env, pf_q_labels_true, genome_to_pf_labels[genome])
labels_a = read_labels_from_file(pf_q_labels_true)
labels_b = read_labels_from_file(genome_to_pf_labels[genome])
lcd = LabelsComparisonDetailed(labels_a, labels_b,
name_a="Reference",
name_b="SBSP",
tag=genome,
split_on_attributes=["predicted-at-step"])
# LabelsComparisonDetailedViz(lcd).run(env["pd-work"])
accuracy = LabelsComparison.stringify_genome_accuracies(genome_to_comparison, ",")
import sbsp_io.general
pf_accuracy = os.path.join(env["pd-work"], pipeline_options["fn-compare"])
sbsp_io.general.write_string_to_file(accuracy, pf_accuracy)
return df
def get_comparisons_per_tag(self, attribute_info=None):
# type: (Union[Tuple[str, Any], None]) -> Dict[str, Dict[str, Any]]
dict_comparisons = dict()
for tag, lcd in self.tag_to_lcd.items():
if attribute_info is None:
dict_comparisons[tag] = lcd.comparison["all"]
else:
name, value = attribute_info
if name in lcd.comparison["attribute"] and value in lcd.comparison["attribute"][name]:
dict_comparisons[tag] = lcd.comparison["attribute"][name][value]
else:
dict_comparisons[tag] = LabelsComparisonDetailed(Labels(), Labels())
return dict_comparisons
def analyze_predictions_on_verified_genes(env, gi, pd_sbsp, **kwargs):
# type: (Environment, GenomeInfo, str, Dict[str, Any]) -> Dict[str, Any]
pd_gcfid = os_join(env["pd-data"], gi.name)
pf_sbsp = os_join(pd_sbsp, "accuracy", "{}.gff".format(gi.name))
pf_gms2 = os_join(pd_gcfid, "runs", "gms2", "gms2.gff")
pf_verified = os_join(pd_gcfid, "verified.gff")
pf_ncbi = os_join(pd_gcfid, "ncbi.gff")
pf_sbsp_details = os_join(pd_sbsp, "output.csv")
kwargs_labels = {
"ignore_frameshifted": True,
"ignore_partial": True,
"shift": 0
}
labels_sbsp = read_labels_from_file(pf_sbsp, name="SBSP", **kwargs_labels)
labels_verified = read_labels_from_file(pf_verified,
name="Verified",
**kwargs_labels)
labels_gms2 = read_labels_from_file(pf_gms2, name="GMS2", **kwargs_labels)
labels_ncbi = read_labels_from_file(pf_ncbi, name="NCBI", **kwargs_labels)
df_sbsp_details = pd.read_csv(pf_sbsp_details)
add_q_key_3p_to_df(df_sbsp_details, "q-key-3p")
add_support_to_labels(labels_sbsp, df_sbsp_details)
#labels_sbsp = Labels([l for l in labels_sbsp if l.get_attribute_value('predicted-at-step') != "C"], name="SBSP")
labels_sbsp_eq_gms2 = LabelsComparisonDetailed(
labels_sbsp, labels_gms2).match_3p_5p("a")
labels_sbsp_eq_gms2.name = "GMS2=SBSP"
stats = dict()
# Stats: 3prime match
get_stats_a_from_b_3p(labels_verified, labels_ncbi, stats)
get_stats_a_from_b_3p(labels_verified, labels_gms2, stats)
get_stats_a_from_b_3p(labels_verified, labels_sbsp, stats)
get_stats_a_from_b_3p_by_upstream(labels_verified, labels_ncbi, stats)
# SN SP
get_stats_sn_sp(labels_verified, labels_sbsp, stats)
get_stats_sn_sp(labels_verified, labels_ncbi, stats)
get_stats_sn_sp(labels_verified, labels_gms2, stats)
# Stats: GMS2=SBSP Accuracy on verified
get_stats_sn_sp(labels_verified, labels_sbsp_eq_gms2, stats)
# stats by support
get_stats_sn_sp_by_support(labels_verified, labels_sbsp, stats, "SBSP")
# stats by support
get_stats_sn_sp_by_support(labels_verified, labels_sbsp_eq_gms2, stats,
"GMS2=SBSP")
# stats by steps combinations
get_stats_sn_sp_by_step_group(labels_verified, labels_sbsp, stats, "SBSP")
# stats by steps combinations
get_stats_sn_sp_by_step_group(labels_verified, labels_sbsp_eq_gms2, stats,
"GMS2=SBSP")
return stats
def collect_alignments_for_genome(env, gi):
# type: (Environment, GenomeInfo) -> None
pd_genome = os_join(env["pd-work"], gi.name)
mkdir_p(pd_genome)
pd_run = os_join(env["pd-runs"], gi.name)
# load labels and data files
pf_sbsp = os_join(pd_run, "sbsp", "accuracy", f"{gi.name}.gff")
pf_gms2 = os_join(pd_run, "gms2", "gms2.gff")
pf_ncbi = os_join(pd_run, "ncbi", "ncbi.gff")
pf_sbsp_details = os_join(pd_run, "sbsp", "output.csv")
common_options = {
"ignore_frameshifted": True,
"ignore_partial": True,
"shift": 0
}
try:
labels_sbsp = read_labels_from_file(pf_sbsp,
name="SBSP",
**common_options)
labels_gms2 = read_labels_from_file(pf_gms2,
name="GMS2",
**common_options)
labels_ncbi = read_labels_from_file(pf_ncbi,
name="NCBI",
**common_options)
df_details = pd.read_csv(pf_sbsp_details)
add_q_key_3p_to_df(df_details, "q-3prime")
except FileNotFoundError:
return
# get genes where GMS2=SBSP
lcd_full = LabelsComparisonDetailed(labels_gms2,
labels_sbsp,
name_a="gms2",
name_b="sbsp")
labels_gms2_eq_sbsp = lcd_full.match_3p_5p("a")
# get labels where gms2_eq_sbsp doesn't match NCBI
lcd2 = LabelsComparisonDetailed(labels_gms2_eq_sbsp,
labels_ncbi,
name_a="gms2_eq_sbsp",
name_b="ncbi")
labels_gms2_eq_sbsp_not_ncbi = lcd2.match_3p_not_5p("a")
# get msa files for all these labels
set_3prime_keys = {
create_q_key_3p(l.seqname(), l.left(), l.right(), l.strand())
for l in labels_gms2_eq_sbsp_not_ncbi
}
df_gms2_eq_sbsp_not_ncbi = df_details[df_details["q-3prime"].isin(
set_3prime_keys)]
set_pf_msa_out = set(df_gms2_eq_sbsp_not_ncbi["pf-msa-output"])
for pf_msa_out in set_pf_msa_out:
shutil.copy(pf_msa_out, pd_genome)
def compare_gms2_sbsp_ncbi(env, pf_gms2, pf_sbsp, pf_ncbi, **kwargs):
# type: (Environment, str, str, str, Dict[str, Any]) -> Dict[str, Any]
venn_title = get_value(kwargs, "venn_title", None)
pf_venn = get_value(kwargs, "pf_venn",
os.path.join(env["pd-work"], "venn.pdf"))
pf_prodigal = get_value(kwargs, "pf_prodigal", None)
start_candidate_analysis = get_value(kwargs, "start_candidate_analysis",
False)
gcfid = get_value(kwargs, "gcfid", None)
predicted_at_step = get_value(kwargs, "predicted_at_step", None)
labels_gms2 = read_labels_from_file(pf_gms2, name="GMS2")
labels_sbsp = read_labels_from_file(pf_sbsp, name="SBSP")
labels_ncbi = read_labels_from_file(pf_ncbi, name="NCBI")
if start_candidate_analysis:
# add number of start candidates per gene
sequences = read_fasta_into_hash(
os.path.join(env["pd-data"], gcfid, "sequence.fasta"))
add_number_of_start_candidates_to_labels(sequences, labels_gms2)
if predicted_at_step is not None:
labels_sbsp = Labels([
l for l in labels_sbsp
if l.get_attribute_value("predicted-at-step") == predicted_at_step
],
name="SBSP")
lcd = LabelsComparisonDetailed(labels_gms2,
labels_sbsp,
name_a="gms2",
name_b="sbsp")
labels_gms2_sbsp_3p_5p = lcd.intersection("a")
lcd_2 = LabelsComparisonDetailed(labels_gms2_sbsp_3p_5p,
labels_ncbi,
name_a="gms2_sbsp",
name_b="ncbi")
labels_gms2_sbsp_ncbi_3p_5p = lcd_2.intersection("a")
# venn_diagram_5prime(labels_gms2, labels_sbsp, labels_ncbi, FigureOptions(
# title=venn_title,
# save_fig=pf_venn
# ))
labels_prodigal = None
prodigal_info = dict()
if pf_prodigal is not None:
labels_prodigal = read_labels_from_file(pf_prodigal, name="Prodigal")
lcd_prodigal = LabelsComparisonDetailed(labels_gms2_sbsp_3p_5p,
labels_prodigal,
name_a="gms2_sbsp",
name_b="prodigal")
labels_gms2_sbsp_prodigal_3p_5p = lcd_prodigal.intersection("a")
# Goal: check (GMS2=SBSP) != (Prodigal=NCBI)
# step1: Prodigal=NCBI
labels_ncbi_prodigal_3p_5p = LabelsComparisonDetailed(
labels_ncbi, labels_prodigal, name_a="ncbi",
name_b="prodigal").match_3p_5p("a")
# Get same genes in (GMS2=SBSP) and (Prodigal=NCBI)
lcd_full = LabelsComparisonDetailed(labels_gms2_sbsp_3p_5p,
labels_ncbi_prodigal_3p_5p,
name_a="gms2_sbsp",
name_b="ncbi_prodigal")
labels_match_3p = lcd_full.match_3p("a")
labels_match_3p_5p = lcd_full.match_3p_5p("a")
prodigal_info = {
"(GMS2=SBSP)!=Prodigal":
len(labels_gms2_sbsp_3p_5p) - len(labels_gms2_sbsp_prodigal_3p_5p),
"(GMS2=SBSP)!=(NCBI=Prodigal)":
len(labels_match_3p) - len(labels_match_3p_5p),
}
return {
"GMS2": len(labels_gms2),
"SBSP": len(labels_sbsp),
"NCBI": len(labels_ncbi),
"GMS2=SBSP": len(labels_gms2_sbsp_3p_5p),
"GMS2=SBSP=NCBI": len(labels_gms2_sbsp_ncbi_3p_5p),
**prodigal_info
}