def search(ppr, target, reverse=True, gaps=1, show_stats=False):
itarget = get_iseq(target)
bg = get_background(itarget)
if isinstance(ppr, SeqRecord):
pssm = binding_rules.build_PSSM(ppr, coding='yagi') #, background=bg)
else:
#assume we were passed a pssm
pssm = ppr
alignments = PSSM_gapped_search(pssm, itarget, gaps)
ralignments = PSSM_gapped_search(pssm, ireverse_complement(itarget), gaps)
sf = [x.toSeqFeature(pssm) for x in alignments]
sf += [x.toSeqFeature(pssm, reflect=len(itarget)) for x in ralignments]
sf.sort(key=lambda f: -f.qualifiers['odds'])
if show_stats:
display_results(pssm, sf, len(itarget))
return sf
def search(ppr, target, reverse=True, gaps=1, show_stats=False): itarget = get_iseq(target) bg = get_background(itarget) if isinstance(ppr, SeqRecord): pssm = binding_rules.build_PSSM(ppr, coding='yagi')#, background=bg) else: #assume we were passed a pssm pssm = ppr alignments = PSSM_gapped_search(pssm, itarget, gaps) ralignments = PSSM_gapped_search(pssm, ireverse_complement(itarget), gaps) sf = [x.toSeqFeature(pssm) for x in alignments] sf += [x.toSeqFeature(pssm,reflect=len(itarget)) for x in ralignments] sf.sort(key=lambda f: -f.qualifiers['odds']) if show_stats: display_results(pssm, sf, len(itarget)) return sf
def build(sr, coding='barkan'):
return binding_rules.build_PSSM(sr, coding)
def build(sr, coding='barkan'): return binding_rules.build_PSSM(sr, coding)