def tes(self, slack=0):
pr = self.pr
if not pr.stranded:
raise Exception(
"Cannot compute TSSes or TESes without strand info. Perhaps use slack() instead?"
)
pr = pr[pr.Feature == "transcript"]
pr = pr.apply(lambda df: _tes(df, slack))
pr.Feature = "tes"
return pr
def tes(self, slack=0):
"""Return the transcription end sites.
Returns the 3' for every interval with feature "transcript".
See Also
--------
pyranges.genomicfeatures.GenomicFeaturesMethods.tss : return the transcription start sites
Examples
--------
>>> gr = pr.data.ensembl_gtf()
>>> gr
+--------------+------------+--------------+-----------+-----------+------------+--------------+------------+------------------------------------+-------+
| Chromosome | Source | Feature | Start | End | Score | Strand | Frame | gene_biotype | +19 |
| (category) | (object) | (category) | (int32) | (int32) | (object) | (category) | (object) | (object) | ... |
|--------------+------------+--------------+-----------+-----------+------------+--------------+------------+------------------------------------+-------|
| 1 | havana | gene | 11868 | 14409 | . | + | . | transcribed_unprocessed_pseudogene | ... |
| 1 | havana | transcript | 11868 | 14409 | . | + | . | transcribed_unprocessed_pseudogene | ... |
| 1 | havana | exon | 11868 | 12227 | . | + | . | transcribed_unprocessed_pseudogene | ... |
| 1 | havana | exon | 12612 | 12721 | . | + | . | transcribed_unprocessed_pseudogene | ... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1 | havana | gene | 1173055 | 1179555 | . | - | . | lncRNA | ... |
| 1 | havana | transcript | 1173055 | 1179555 | . | - | . | lncRNA | ... |
| 1 | havana | exon | 1179364 | 1179555 | . | - | . | lncRNA | ... |
| 1 | havana | exon | 1173055 | 1176396 | . | - | . | lncRNA | ... |
+--------------+------------+--------------+-----------+-----------+------------+--------------+------------+------------------------------------+-------+
Stranded PyRanges object has 2,446 rows and 28 columns from 1 chromosomes.
For printing, the PyRanges was sorted on Chromosome and Strand.
19 hidden columns: gene_id, gene_name, gene_source, gene_version, tag, transcript_biotype, transcript_id, transcript_name, transcript_source, transcript_support_level, ... (+ 9 more.)
>>> gr.features.tes()
+--------------+------------+------------+-----------+-----------+------------+--------------+------------+------------------------------------+-------+
| Chromosome | Source | Feature | Start | End | Score | Strand | Frame | gene_biotype | +19 |
| (category) | (object) | (object) | (int32) | (int32) | (object) | (category) | (object) | (object) | ... |
|--------------+------------+------------+-----------+-----------+------------+--------------+------------+------------------------------------+-------|
| 1 | havana | tes | 14409 | 14410 | . | + | . | transcribed_unprocessed_pseudogene | ... |
| 1 | havana | tes | 13670 | 13671 | . | + | . | transcribed_unprocessed_pseudogene | ... |
| 1 | havana | tes | 31097 | 31098 | . | + | . | lncRNA | ... |
| 1 | havana | tes | 31109 | 31110 | . | + | . | lncRNA | ... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1 | havana | tes | 1092813 | 1092814 | . | - | . | protein_coding | ... |
| 1 | havana | tes | 1116087 | 1116088 | . | - | . | protein_coding | ... |
| 1 | havana | tes | 1116089 | 1116090 | . | - | . | protein_coding | ... |
| 1 | havana | tes | 1179555 | 1179556 | . | - | . | lncRNA | ... |
+--------------+------------+------------+-----------+-----------+------------+--------------+------------+------------------------------------+-------+
Stranded PyRanges object has 280 rows and 28 columns from 1 chromosomes.
For printing, the PyRanges was sorted on Chromosome and Strand.
19 hidden columns: gene_id, gene_name, gene_source, gene_version, tag, transcript_biotype, transcript_id, transcript_name, transcript_source, transcript_support_level, ... (+ 9 more.)
"""
pr = self.pr
if not pr.stranded:
raise Exception(
"Cannot compute TSSes or TESes without strand info. Perhaps use slack() instead?"
)
pr = pr[pr.Feature == "transcript"]
pr = pr.apply(lambda df: _tes(df))
pr.Feature = "tes"
return pr
