def recolorTransMap(self, genome, gp):
     """
     Recolors the comparativeAnnotation results based on the scheme assembly > alignment > biology. Transcripts not in
     one of these categories will become black.
     """
     records = seq_lib.get_gene_pred_transcripts(gp)
     # first we recolor everything black
     for x in records:
         x.rgb = "0"
     # now we find all interesting biology and color that the interesting biology color
     detailsFields, classifyFields, classifyValues, classifyOperations = src.queries.interestingBiology()
     aIds = {x[0] for x in sql_lib.selectBetweenDatabases(self.cur, "details", self.primaryKeyColumn, classifyFields, classifyValues, classifyOperations, self.primaryKeyColumn, genome)}
     for x in records:
         if x.name in aIds:
             x.rgb = self.colors["mutation"]
     # now the alignment errors...
     detailsFields, classifyFields, classifyValues, classifyOperations = src.queries.alignmentErrors()
     aIds = {x[0] for x in sql_lib.selectBetweenDatabases(self.cur, "details", self.primaryKeyColumn, classifyFields, classifyValues, classifyOperations, self.primaryKeyColumn, genome)}
     for x in records:
         if x.name in aIds:
             x.rgb = self.colors["alignment"]
     # finally the assembly
     detailsFields, classifyFields, classifyValues, classifyOperations = src.queries.assemblyErrors()
     aIds = {x[0] for x in sql_lib.selectBetweenDatabases(self.cur, "details", self.primaryKeyColumn, classifyFields, classifyValues, classifyOperations, self.primaryKeyColumn, genome)}
     for x in records:
         if x.name in aIds:
             x.rgb = self.colors["assembly"]
     return ["\t".join(map(str, x.get_bed())) for x in records]
def get_transcript_dict(gp, filter_set):
    transcripts = seq_lib.get_gene_pred_transcripts(gp)
    d = seq_lib.transcript_list_to_dict(transcripts, noDuplicates=True)
    r = defaultdict(list)
    for aln_id, rec in d.iteritems():
        tx_id = strip_alignment_numbers(aln_id)
        if tx_id in filter_set:
            r[tx_id].append(rec)
    return r
def get_transcript_dict(gp, filter_set):
    transcripts = seq_lib.get_gene_pred_transcripts(gp)
    d = seq_lib.transcript_list_to_dict(transcripts, noDuplicates=True)
    r = defaultdict(list)
    for aln_id, rec in d.iteritems():
        tx_id = strip_alignment_numbers(aln_id)
        if tx_id in filter_set:
            r[tx_id].append(rec)
    return r
 def recolorAugustus(self, genome, gp):
     """
     Recolors the Augustus tracks based on OK-ness.
     """
     records = seq_lib.get_gene_pred_transcripts(gp)
     # first we recolor everything black
     for x in records:
         x.rgb = "0"
     detailsFields, classifyFields, classifyValues, classifyOperations = src.augustusQueries.augustusNotOk()
     aIds = {x[0] for x in sql_lib.selectBetweenDatabases(self.cur, "details", self.primaryKeyColumn, classifyFields, classifyValues, classifyOperations, self.primaryKeyColumn, genome)}
     for x in records:
         if x.name in aIds:
             x.rgb = "83,179,64"
     return ["\t".join(map(str, x.get_bed())) for x in records]
Beispiel #5
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 def recolorTransMap(self, genome, gp):
     """
     Recolors the comparativeAnnotation results based on the scheme assembly > alignment > biology. Transcripts not in
     one of these categories will become black.
     """
     records = seq_lib.get_gene_pred_transcripts(gp)
     # first we recolor everything black
     for x in records:
         x.rgb = "0"
     # now we find all interesting biology and color that the interesting biology color
     detailsFields, classifyFields, classifyValues, classifyOperations = src.queries.interestingBiology(
     )
     aIds = {
         x[0]
         for x in sql_lib.selectBetweenDatabases(
             self.cur, "details", self.primaryKeyColumn, classifyFields,
             classifyValues, classifyOperations, self.primaryKeyColumn,
             genome)
     }
     for x in records:
         if x.name in aIds:
             x.rgb = self.colors["mutation"]
     # now the alignment errors...
     detailsFields, classifyFields, classifyValues, classifyOperations = src.queries.alignmentErrors(
     )
     aIds = {
         x[0]
         for x in sql_lib.selectBetweenDatabases(
             self.cur, "details", self.primaryKeyColumn, classifyFields,
             classifyValues, classifyOperations, self.primaryKeyColumn,
             genome)
     }
     for x in records:
         if x.name in aIds:
             x.rgb = self.colors["alignment"]
     # finally the assembly
     detailsFields, classifyFields, classifyValues, classifyOperations = src.queries.assemblyErrors(
     )
     aIds = {
         x[0]
         for x in sql_lib.selectBetweenDatabases(
             self.cur, "details", self.primaryKeyColumn, classifyFields,
             classifyValues, classifyOperations, self.primaryKeyColumn,
             genome)
     }
     for x in records:
         if x.name in aIds:
             x.rgb = self.colors["assembly"]
     return ["\t".join(map(str, x.get_bed())) for x in records]
Beispiel #6
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 def recolorAugustus(self, genome, gp):
     """
     Recolors the Augustus tracks based on OK-ness.
     """
     records = seq_lib.get_gene_pred_transcripts(gp)
     # first we recolor everything black
     for x in records:
         x.rgb = "0"
     detailsFields, classifyFields, classifyValues, classifyOperations = src.augustusQueries.augustusNotOk(
     )
     aIds = {
         x[0]
         for x in sql_lib.selectBetweenDatabases(
             self.cur, "details", self.primaryKeyColumn, classifyFields,
             classifyValues, classifyOperations, self.primaryKeyColumn,
             genome)
     }
     for x in records:
         if x.name in aIds:
             x.rgb = "83,179,64"
     return ["\t".join(map(str, x.get_bed())) for x in records]
def load_gp(path):
    tm_recs = seq_lib.get_gene_pred_transcripts(path)
    tm_dict = seq_lib.transcript_list_to_dict(tm_recs, noDuplicates=True)
    return tm_dict
def load_gp(path):
    tm_recs = seq_lib.get_gene_pred_transcripts(path)
    tm_dict = seq_lib.transcript_list_to_dict(tm_recs, noDuplicates=True)
    return tm_dict