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Merge branch 'gene_assignment_CW-5416' into 'dev'

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Account for stringtie multigene transcript artefacts

Closes CW-5369

See merge request epi2melabs/workflows/wf-transcriptomes!189
This commit is contained in:
Neil Horner 2024-12-12 14:30:32 +00:00
commit 4c46f88f00
9 changed files with 208 additions and 71 deletions
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10
.gitlab-ci.yml
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@ -12,6 +12,8 @@ variables:
--direct_rna --minimap2_index_opts '-k 15' --sample_sheet ${CI_PROJECT_NAME}/data/differential_expression/sample_sheet.csv \
-c ${CI_PROJECT_NAME}/data/demo.nextflow.config"
CI_FLAVOUR: "new"
PYTEST_CONTAINER_NAME: "wf-common"
PYTEST_CONTAINER_CONFIG_KEY: "common_sha"
macos-run:
# Let's avoid those ARM64 runners for now
@ -146,7 +148,7 @@ docker-run:
--ref_genome ${CI_PROJECT_NAME}/data/differential_expression_ncbi/GCF_000001405.40_GRCh38.p14_genomic.fna.gz \
--ref_annotation ${CI_PROJECT_NAME}/data/differential_expression_ncbi/GCF_000001405.40_GRCh38.p14_genomic.gff.gz \
--direct_rna --ref_transcriptome ${CI_PROJECT_NAME}/data/differential_expression_ncbi/GCF_000001405.40_GRCh38.p14_rna.fna.gz \
--transcriptome_assembly false --sample_sheet test_data/sample_sheet.csv \
--sample_sheet test_data/sample_sheet.csv \
-c ${CI_PROJECT_NAME}/data/demo.nextflow.config"
NF_IGNORE_PROCESSES: >
preprocess_reads,faidx,gz_faidx,merge_transcriptomes,assemble_transcripts,
@ -160,7 +162,7 @@ docker-run:
--ref_genome ${CI_PROJECT_NAME}/data/differential_expression/Homo_sapiens.GRCh38.dna.primary_assembly.fa.gz \
--ref_annotation ${CI_PROJECT_NAME}/data/differential_expression/Homo_sapiens.GRCh38.109.gtf.gz \
--direct_rna --ref_transcriptome ${CI_PROJECT_NAME}/data/differential_expression/Homo_sapiens.GRCh38.cdna.all.fa.gz \
--transcriptome_assembly false --sample_sheet test_data/sample_sheet.csv \
--sample_sheet test_data/sample_sheet.csv \
-c ${CI_PROJECT_NAME}/data/demo.nextflow.config"
NF_IGNORE_PROCESSES: >
preprocess_reads,faidx,gz_faidx,merge_transcriptomes,assemble_transcripts,
@ -174,7 +176,7 @@ docker-run:
--ref_genome ${CI_PROJECT_NAME}/data/differential_expression_mouse/GRCm39.genome.fa.gz \
--ref_annotation ${CI_PROJECT_NAME}/data/differential_expression_mouse/gencode.vM33.annotation.gtf \
--direct_rna --ref_transcriptome ${CI_PROJECT_NAME}/data/differential_expression_mouse/gencode.vM33.transcripts.fa.gz \
--transcriptome_assembly false --sample_sheet ${CI_PROJECT_NAME}/data/differential_expression_mouse/sample_sheet.csv \
--sample_sheet ${CI_PROJECT_NAME}/data/differential_expression_mouse/sample_sheet.csv \
-c ${CI_PROJECT_NAME}/data/demo.nextflow.config"
NF_IGNORE_PROCESSES: >
preprocess_reads,faidx,gz_faidx,merge_transcriptomes,assemble_transcripts,decompress_annotation,
@ -215,7 +217,7 @@ docker-run:
--ref_genome ${CI_PROJECT_NAME}/data/differential_expression_ncbi/GCF_000001405.40_GRCh38.p14_genomic.fna.gz \
--ref_annotation ${CI_PROJECT_NAME}/data/differential_expression_ncbi/GCF_000001405.40_GRCh38.p14_genomic.gff.gz \
--direct_rna --ref_transcriptome ${CI_PROJECT_NAME}/data/differential_expression_ncbi/GCF_000001405.40_GRCh38.p14_rna.fna.gz \
--transcriptome_assembly false --sample_sheet test_data/sample_sheet.csv \
--sample_sheet test_data/sample_sheet.csv \
--igv \
-c ${CI_PROJECT_NAME}/data/demo.nextflow.config"
NF_IGNORE_PROCESSES: >

4
CHANGELOG.md
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@ -4,6 +4,10 @@ All notable changes to this project will be documented in this file.
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
## [Unreleased]
### Fixed
- Bug that led to incorrect gene_id being assigned in the DE plots.
## [v1.5.0]
### Updated
- Workflow report updated to use `ezcharts`.

135
bin/workflow_glue/de_plots.py
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@ -1,6 +1,5 @@
#!/usr/bin/env python
"""Create de report section."""
import os
from dominate.tags import h5, p
@ -52,7 +51,7 @@ def create_summary_table(df):
avg_acc, avg_mapq])
def dexseq_section(dexseq_file, id_dic, pval_thresh):
def dexseq_section(dexseq_file, tr_id_to_gene_name, tr_id_to_gene_id, pval_thresh):
"""Add gene isoforms table and plot."""
h5("Differential Isoform usage")
p("""Table showing gene isoforms, ranked by adjusted
@ -64,9 +63,17 @@ def dexseq_section(dexseq_file, id_dic, pval_thresh):
dexseq_results = pd.read_csv(dexseq_file, sep='\t')
dexseq_results.index.name = "gene_id:transcript_id"
# Replace gene id with more useful gene name where possible
# Replace any occurrences of stringtie-generated MSTRG gene ids with
# reference gene_ids.
dexseq_results.index = dexseq_results.index.map(
lambda x: str(id_dic.get(x.split(':')[0])) + ':' + str(x.split(':')[1]))
lambda ge_tr: str( # lookup gene_id from transcript_id [1]
f"{tr_id_to_gene_id.get(ge_tr.split(':')[1])}: {str(ge_tr.split(':')[1])}")
)
# Add gene name column.
dexseq_results.insert(0, "gene_name", dexseq_results.index.map(
lambda x: tr_id_to_gene_name.get(x.split(':')[1])))
DataTable.from_pandas(
dexseq_results.sort_values(by='pvalue', ascending=True), use_index=True)
@ -100,13 +107,12 @@ def dexseq_section(dexseq_file, id_dic, pval_thresh):
EZChart(plot)
def dtu_section(dtu_file, gt_dic, ge_dic):
def dtu_section(dtu_file, txid_to_gene_name):
"""Plot dtu section."""
dtu_results = pd.read_csv(dtu_file, sep='\t')
dtu_results["gene_name"] = dtu_results["txID"].apply(
lambda x: gt_dic.get(x))
dtu_results["geneID"] = dtu_results["geneID"].apply(
lambda x: ge_dic.get(x))
lambda x: txid_to_gene_name.get(x))
dtu_pvals = dtu_results.sort_values(by='gene', ascending=True)
raw("""Table showing gene and transcript identifiers
and their FDR-corrected (False discovery rate - Benjamini-Hochberg) probabilities
@ -120,11 +126,10 @@ def dtu_section(dtu_file, gt_dic, ge_dic):
raw("""View dtu_plots.pdf file to see plots of differential isoform usage""")
def dge_section(dge_file, ids_dic, pval_thresh):
def dge_section(df, pval_thresh):
"""Create DGE table and MA plot."""
h5("Differential gene expression")
dge_results = pd.read_csv(dge_file, sep='\t')
dge_results[['logFC', 'logCPM', 'F']] = dge_results[
df[['logFC', 'logCPM', 'F']] = df[
['logFC', 'logCPM', 'F']].round(2)
p("""Table showing the genes from the edgeR analysis.
@ -134,10 +139,9 @@ def dge_section(dge_file, ids_dic, pval_thresh):
This table has not been
filtered for genes that satisfy statistical or magnitudinal thresholds""")
dge_results.index = dge_results.index.map(lambda x: ids_dic.get(x))
dge_results = dge_results.sort_values('FDR', ascending=True)
dge_results.index.name = 'Transcript'
DataTable.from_pandas(dge_results, use_index=True)
df = df.sort_values('FDR', ascending=True)
df.index.name = 'gene_id'
DataTable.from_pandas(df, use_index=True)
h5("Results of the edgeR Analysis.")
@ -150,15 +154,13 @@ def dge_section(dge_file, ids_dic, pval_thresh):
(False discovery rate - Benjamini-Hochberg) p-value thresholds
defined are shaded as 'Up-' or 'Down-' regulated.
""")
dge = pd.read_csv(dge_file, sep="\t")
dge['sig'] = None
dge.loc[(dge["logFC"] > 0) & (dge['PValue'] < pval_thresh), 'sig'] = 'up'
dge.loc[(dge["logFC"] <= 0) & (dge['PValue'] < pval_thresh), 'sig'] = 'down'
dge.loc[(dge["PValue"] >= pval_thresh), 'sig'] = 'not_sig'
df['sig'] = None
df.loc[(df["logFC"] > 0) & (df['PValue'] < pval_thresh), 'sig'] = 'up'
df.loc[(df["logFC"] <= 0) & (df['PValue'] < pval_thresh), 'sig'] = 'down'
df.loc[(df["PValue"] >= pval_thresh), 'sig'] = 'not_sig'
plot = scatterplot(
data=dge, x='logCPM', y='logFC', hue='sig',
data=df, x='logCPM', y='logFC', hue='sig',
palette=['#E32636', '#7E8896', '#0A22DE'],
hue_order=['up', 'not_sig', 'down'], marker='circle')
plot._fig.x_range.start = 10
@ -188,53 +190,58 @@ def salmon_table(salmon_counts):
def get_translations(gtf):
"""Create dict with gene_name and gene_references."""
"""Create gene_and transcript id mappings.
Annotation can be stringtie-generated (GTF) or from the input
reference annotation (GTF or GFF3) and the various attributes can differ
"""
with open(gtf) as fh:
gene_txid = {}
gene_geid = {}
geid_gname = {}
txid_to_gene_name = {}
gid_to_gene_name = {}
tx_id_to_gene_id = {}
def get_feature(row, feature):
return row.split(feature)[1].split(
";")[0].replace('=', '').replace("\"", "").strip()
for i in fh:
if i.startswith("#"):
for gff_entry in fh:
# Process transcripts features only
if gff_entry.startswith("#") or gff_entry.split('\t')[2] != 'transcript':
continue
# Different gtf/gff formats contain different attributes
# and different formating (eg. gene_name="xyz" or gene_name "xyz")
gene_name = None
for var_name in ["gene_name", "gene_id", "gene"]:
if var_name in i:
gene_name = get_feature(i, var_name)
break
gene_name = gene_id = transcript_id = 'unknown'
if 'ref_gene_id' in i:
gene_reference = get_feature(i, 'ref_gene_id')
elif 'gene_id' in i:
gene_reference = get_feature(i, 'gene_id')
if 'ref_gene_id' in gff_entry:
# Favour ref_gene_id over gene_id. The latter can be multi-locus merged
# genes from stringtie
gene_id = get_feature(gff_entry, 'ref_gene_id')
elif 'gene_id' in gff_entry:
gene_id = get_feature(gff_entry, 'gene_id')
else:
gene_reference = gene_name
if 'transcript_id' in i:
transcript_id = get_feature(i, 'transcript_id')
gene_id = get_feature(gff_entry, 'gene')
if 'transcript_id' in gff_entry:
transcript_id = get_feature(gff_entry, 'transcript_id')
if 'gene_name' in gff_entry:
gene_name = get_feature(gff_entry, 'gene_name')
else:
transcript_id = "unknown"
if 'gene_id' in i:
gene_id = get_feature(i, 'gene_id')
else:
gene_id = gene_name
gene_txid[transcript_id] = gene_name
gene_geid[gene_id] = gene_reference
geid_gname[gene_reference] = gene_name
return gene_txid, gene_geid, geid_gname
# Fallback to gene_id if gene_name is not present
gene_name = gene_id
txid_to_gene_name[transcript_id] = gene_name
tx_id_to_gene_id[transcript_id] = gene_id
gid_to_gene_name[gene_id] = gene_name
return txid_to_gene_name, tx_id_to_gene_id, gid_to_gene_name
def de_section(
stringtie, dge, dexseq, dtu,
annotation, dge, dexseq, dtu,
tpm, report, filtered, unfiltered,
gene_counts, aln_stats_dir, pval_threshold=0.01):
"""Differential expression sections."""
with report.add_section("Differential expression", "DE"):
with (report.add_section("Differential expression", "DE")):
p("""This section shows differential gene expression
and differential isoform usage. Salmon was used to
@ -256,39 +263,45 @@ def de_section(
DataTable.from_pandas(alignment_summary_df, use_index=True)
salmon_table(tpm)
gene_txid, gene_name, geid_gname = get_translations(stringtie)
# Get translations for adding gene names to tables
(
txid_to_gene_name, txid_to_gene_id, gid_to_gene_name
) = get_translations(annotation)
# Add gene names columns to counts files and write out
# for publishing to user dir.
df_dge = pd.read_csv(dge, sep='\t')
df_dge.insert(0, 'gene_name', df_dge.index.map(lambda x: geid_gname.get(x)))
df_dge.insert(0, 'gene_name', df_dge.index.map(
lambda x: gid_to_gene_name.get(x)))
df_dge.to_csv('results_dge.tsv', index=True, index_label="gene_id", sep="\t")
# write_dge(gene_counts, geid_gname, "all_gene_counts.tsv")
# write_dge(gene_counts, gid_to_gene_name, "all_gene_counts.tsv")
df_gene_counts = pd.read_csv(gene_counts, sep='\t')
df_gene_counts.insert(
0, 'gene_name', df_gene_counts.index.map(lambda x: geid_gname.get(x)))
0, 'gene_name', df_gene_counts.index.map(
lambda x: gid_to_gene_name.get(x)))
df_gene_counts.to_csv(
'results_dge.tsv', index=True, index_label="gene_id", sep="\t")
df_filtered = pd.read_csv(filtered, sep='\t')
df_filtered.insert(1, "gene_name", df_filtered.gene_id.map(
lambda x: geid_gname.get(x)))
lambda x: gid_to_gene_name.get(x)))
df_filtered.to_csv(
'filtered_transcript_counts_with_genes.tsv', index=False, sep='\t')
df_unfiltered = pd.read_csv(unfiltered, sep='\t')
df_unfiltered.insert(1, "gene_name", df_unfiltered.gene_id.map(
lambda x: geid_gname.get(x)))
lambda x: gid_to_gene_name.get(x)))
df_unfiltered.to_csv(
'unfiltered_transcript_counts_with_genes.tsv', index=False, sep='\t')
df_tpm = pd.read_csv(tpm, sep='\t')
df_tpm.insert(1, "gene_name", df_tpm.Reference.map(
lambda x: gene_txid.get(x)))
lambda x: txid_to_gene_name.get(x)))
df_tpm.to_csv("unfiltered_tpm_transcript_counts.tsv", index=False, sep='\t')
# Add tables to report
dge_section(dge, gene_name, pval_threshold)
dexseq_section(dexseq, gene_name, pval_threshold)
dtu_section(dtu, gene_txid, gene_name)
dge_section(df_dge, pval_threshold)
dexseq_section(dexseq, txid_to_gene_name, txid_to_gene_id, pval_threshold)
dtu_section(dtu, txid_to_gene_name)

7
bin/workflow_glue/report.py
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@ -319,15 +319,16 @@ def de_section(report, de_report_dir, de_aln_stats_dir, pval_threshold):
dexseq = de_report_dir / "results_dexseq.tsv"
dge = de_report_dir / "results_dge.tsv"
dtu = de_report_dir / "results_dtu_stageR.tsv"
# GFF file can have gtf or gff extension
stringtie = next(de_report_dir.glob("*.g*f*"))
# GFF file can have gtf or gff extension.
# Will be the original (transcriptome_source=precomputed) or wf-assembled annotation
annotation = next(de_report_dir.glob("*.g*f*"))
tpm = de_report_dir / "unfiltered_tpm_transcript_counts.tsv"
filtered = de_report_dir / "filtered_transcript_counts_with_genes.tsv"
unfiltered = de_report_dir / "unfiltered_transcript_counts_with_genes.tsv"
gene_counts = de_report_dir / "all_gene_counts.tsv"
# This will also add a gene name column to the above counts tsv files
de_plots.de_section(
stringtie=stringtie,
annotation=annotation,
dexseq=dexseq,
dge=dge,
dtu=dtu,

80
bin/workflow_glue/tests/test_de_plots.py Normal file
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@ -0,0 +1,80 @@
"""Test assign_barcodes."""
from pathlib import Path
import pytest
from workflow_glue.de_plots import get_translations
@pytest.fixture
def test_data(request):
"""Define data location fixture."""
return Path(request.config.getoption("--test_data")) / "workflow_glue"
@pytest.mark.parametrize(
'annotation_file,expected',
[
[
'MSTRG.11088.gtf',
dict(gid_to_gene_name={
'ENSG00000236051.7': 'MYCBP2-AS1',
'ENSG00000283208.2': 'ENSG00000283208',
'ENSG00000102805.16': 'CLN5',
'MSTRG.11088': 'MSTRG.11088'
},
txid_to_gene_name={
'ENST00000636183.2': 'CLN5',
'ENST00000636780.2': 'CLN5',
'ENST00000638147.2': 'ENSG00000283208',
'ENST00000637192.1': 'ENSG00000283208',
'ENST00000636737.1': 'MYCBP2-AS1',
'ENST00000450627.6': 'MYCBP2-AS1',
'MSTRG.11088.2': 'MSTRG.11088'
},
txid_to_gene_id={
'ENST00000636183.2': 'ENSG00000102805.16',
'MSTRG.11088.2': 'MSTRG.11088',
'ENST00000636780.2': 'ENSG00000102805.16',
'ENST00000638147.2': 'ENSG00000283208.2',
'ENST00000637192.1': 'ENSG00000283208.2',
'ENST00000636737.1': 'ENSG00000236051.7',
'ENST00000450627.6': 'ENSG00000236051.7'
})
],
# Small test to check that GFF3 works
[
'MSTRG.11088.gff3',
dict(gid_to_gene_name={
"ENSG00000290825.1": "DDX11L2",
"ENSG00000236397.3": "DDX11L2"
},
txid_to_gene_name={
"ENST00000456328.2": "DDX11L2",
"ENST00000437401.1": "DDX11L2"
},
txid_to_gene_id={
'ENST00000437401.1': 'ENSG00000236397.3',
'ENST00000456328.2': 'ENSG00000290825.1'
})
]
]
)
def test_get_translations(test_data, annotation_file, expected):
"""Test that correct feature identifiers are extracted from the annotation.
`stringtie --merge` can sometimes generate gene models that may span multiple
reference genes. Possibly related issue:
https://github.com/gpertea/stringtie/issues/217
This can lead to the original genes and transcripts being assigned to that
incorrectly-merged gene model. The test data contains such a gene model generated
from `stringtie --merge` but actually consists of multiple different genes.
"""
input_gtf = test_data / annotation_file
txid_to_gene_name, txid_to_gene_id, gid_to_gene_name = get_translations(input_gtf)
assert expected['gid_to_gene_name'] == gid_to_gene_name
assert expected['txid_to_gene_name'] == txid_to_gene_name
assert expected['txid_to_gene_id'] == txid_to_gene_id

2
nextflow.config
View File

@ -95,7 +95,7 @@ params {
]
agent = null
container_sha = "shad8671ea3a8ed52f2c0f40355e8eb5c6f00d2cbda"
common_sha="shaf15f9d80aba72c20e3e71f84869619873a56b8af"
common_sha="shabadd33adae761be6f2d59c6ecfb44b19cf472cfc"
}
}

3
subworkflows/differential_expression.nf
View File

@ -162,7 +162,8 @@ workflow differential_expression {
analysis = deAnalysis(sample_sheet, merged, ref_annotation)
plotResults(analysis.flt_counts, analysis.stageR, sample_sheet)
// Concat files required for making the report
de_report = analysis.flt_counts.concat(analysis.gene_counts, analysis.dge, analysis.dexseq,
de_report = analysis.flt_counts.concat(
analysis.gene_counts, analysis.dge, analysis.dexseq,
analysis.stageR, sample_sheet, merged, ref_annotation, merged_TPM, analysis.unflt_counts).collect()
// Concat files required to be output to user without any changes
de_outputs_concat = analysis.cpm.concat(plotResults.out.dtu_plots, analysis.dge_pdf, analysis.dge_tsv, analysis.dexseq,

2
test_data/workflow_glue/MSTRG.11088.gff3 Normal file
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@ -0,0 +1,2 @@
chr1 HAVANA transcript 11869 14409 . + . ID=ENST00000456328.2;Parent=ENSG00000290825.1;gene_id=ENSG00000290825.1;transcript_id=ENST00000456328.2;gene_type=lncRNA;gene_name=DDX11L2;transcript_type=lncRNA;transcript_name=DDX11L2-202;level=2;transcript_support_level=1;tag=basic,Ensembl_canonical;havana_transcript=OTTHUMT00000362751.1
chr2 HAVANA transcript 113599036 113601261 . - . ID=ENST00000437401.1;Parent=ENSG00000236397.3;gene_id=ENSG00000236397.3;transcript_id=ENST00000437401.1;gene_type=unprocessed_pseudogene;gene_name=DDX11L2;transcript_type=unprocessed_pseudogene;transcript_name=DDX11L2-201;level=2;transcript_support_level=NA;hgnc_id=HGNC:37103;ont=PGO:0000005;tag=basic,Ensembl_canonical;havana_gene=OTTHUMG00000047823.1;havana_transcript=OTTHUMT00000109036.1

34
test_data/workflow_glue/MSTRG.11088.gtf Normal file
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@ -0,0 +1,34 @@
chr13 StringTie transcript 76990660 77005117 1000 + . gene_id "MSTRG.11088"; transcript_id "ENST00000636183.2"; gene_name "CLN5"; ref_gene_id "ENSG00000102805.16";
chr13 StringTie exon 76990660 76992271 1000 + . gene_id "MSTRG.11088"; transcript_id "ENST00000636183.2"; exon_number "1"; gene_name "CLN5"; ref_gene_id "ENSG00000102805.16";
chr13 StringTie exon 76995063 76995228 1000 + . gene_id "MSTRG.11088"; transcript_id "ENST00000636183.2"; exon_number "2"; gene_name "CLN5"; ref_gene_id "ENSG00000102805.16";
chr13 StringTie exon 76995902 76996127 1000 + . gene_id "MSTRG.11088"; transcript_id "ENST00000636183.2"; exon_number "3"; gene_name "CLN5"; ref_gene_id "ENSG00000102805.16";
chr13 StringTie exon 77000458 77005117 1000 + . gene_id "MSTRG.11088"; transcript_id "ENST00000636183.2"; exon_number "4"; gene_name "CLN5"; ref_gene_id "ENSG00000102805.16";
chr13 StringTie transcript 76991729 77005117 1000 + . gene_id "MSTRG.11088"; transcript_id "MSTRG.11088.2";
chr13 StringTie exon 76991729 76991832 1000 + . gene_id "MSTRG.11088"; transcript_id "MSTRG.11088.2"; exon_number "1";
chr13 StringTie exon 76995063 76995228 1000 + . gene_id "MSTRG.11088"; transcript_id "MSTRG.11088.2"; exon_number "2";
chr13 StringTie exon 76995902 76996127 1000 + . gene_id "MSTRG.11088"; transcript_id "MSTRG.11088.2"; exon_number "3";
chr13 StringTie exon 77000458 77005117 1000 + . gene_id "MSTRG.11088"; transcript_id "MSTRG.11088.2"; exon_number "4";
chr13 StringTie transcript 76992044 77005117 1000 + . gene_id "MSTRG.11088"; transcript_id "ENST00000636780.2"; gene_name "CLN5"; ref_gene_id "ENSG00000102805.16";
chr13 StringTie exon 76992044 76992271 1000 + . gene_id "MSTRG.11088"; transcript_id "ENST00000636780.2"; exon_number "1"; gene_name "CLN5"; ref_gene_id "ENSG00000102805.16";
chr13 StringTie exon 76995063 76995228 1000 + . gene_id "MSTRG.11088"; transcript_id "ENST00000636780.2"; exon_number "2"; gene_name "CLN5"; ref_gene_id "ENSG00000102805.16";
chr13 StringTie exon 76995902 76996127 1000 + . gene_id "MSTRG.11088"; transcript_id "ENST00000636780.2"; exon_number "3"; gene_name "CLN5"; ref_gene_id "ENSG00000102805.16";
chr13 StringTie exon 76998043 76998085 1000 + . gene_id "MSTRG.11088"; transcript_id "ENST00000636780.2"; exon_number "4"; gene_name "CLN5"; ref_gene_id "ENSG00000102805.16";
chr13 StringTie exon 77000458 77005117 1000 + . gene_id "MSTRG.11088"; transcript_id "ENST00000636780.2"; exon_number "5"; gene_name "CLN5"; ref_gene_id "ENSG00000102805.16";
chr13 StringTie transcript 76992078 77078025 1000 + . gene_id "MSTRG.11088"; transcript_id "ENST00000638147.2"; gene_name "ENSG00000283208"; ref_gene_id "ENSG00000283208.2";
chr13 StringTie exon 76992078 76992271 1000 + . gene_id "MSTRG.11088"; transcript_id "ENST00000638147.2"; exon_number "1"; gene_name "ENSG00000283208"; ref_gene_id "ENSG00000283208.2";
chr13 StringTie exon 76995063 76995228 1000 + . gene_id "MSTRG.11088"; transcript_id "ENST00000638147.2"; exon_number "2"; gene_name "ENSG00000283208"; ref_gene_id "ENSG00000283208.2";
chr13 StringTie exon 76995902 76996127 1000 + . gene_id "MSTRG.11088"; transcript_id "ENST00000638147.2"; exon_number "3"; gene_name "ENSG00000283208"; ref_gene_id "ENSG00000283208.2";
chr13 StringTie exon 77075518 77075584 1000 + . gene_id "MSTRG.11088"; transcript_id "ENST00000638147.2"; exon_number "4"; gene_name "ENSG00000283208"; ref_gene_id "ENSG00000283208.2";
chr13 StringTie exon 77076816 77078025 1000 + . gene_id "MSTRG.11088"; transcript_id "ENST00000638147.2"; exon_number "5"; gene_name "ENSG00000283208"; ref_gene_id "ENSG00000283208.2";
chr13 StringTie transcript 76995915 77129717 1000 + . gene_id "MSTRG.11088"; transcript_id "ENST00000637192.1"; gene_name "ENSG00000283208"; ref_gene_id "ENSG00000283208.2";
chr13 StringTie exon 76995915 76996127 1000 + . gene_id "MSTRG.11088"; transcript_id "ENST00000637192.1"; exon_number "1"; gene_name "ENSG00000283208"; ref_gene_id "ENSG00000283208.2";
chr13 StringTie exon 77109648 77110102 1000 + . gene_id "MSTRG.11088"; transcript_id "ENST00000637192.1"; exon_number "2"; gene_name "ENSG00000283208"; ref_gene_id "ENSG00000283208.2";
chr13 StringTie exon 77129147 77129717 1000 + . gene_id "MSTRG.11088"; transcript_id "ENST00000637192.1"; exon_number "3"; gene_name "ENSG00000283208"; ref_gene_id "ENSG00000283208.2";
chr13 StringTie transcript 77026767 77078025 1000 + . gene_id "MSTRG.11088"; transcript_id "ENST00000636737.1"; gene_name "MYCBP2-AS1"; ref_gene_id "ENSG00000236051.7";
chr13 StringTie exon 77026767 77027122 1000 + . gene_id "MSTRG.11088"; transcript_id "ENST00000636737.1"; exon_number "1"; gene_name "MYCBP2-AS1"; ref_gene_id "ENSG00000236051.7";
chr13 StringTie exon 77075518 77075584 1000 + . gene_id "MSTRG.11088"; transcript_id "ENST00000636737.1"; exon_number "2"; gene_name "MYCBP2-AS1"; ref_gene_id "ENSG00000236051.7";
chr13 StringTie exon 77076816 77078025 1000 + . gene_id "MSTRG.11088"; transcript_id "ENST00000636737.1"; exon_number "3"; gene_name "MYCBP2-AS1"; ref_gene_id "ENSG00000236051.7";
chr13 StringTie transcript 77075514 77087778 1000 + . gene_id "MSTRG.11088"; transcript_id "ENST00000450627.6"; gene_name "MYCBP2-AS1"; ref_gene_id "ENSG00000236051.7";
chr13 StringTie exon 77075514 77075584 1000 + . gene_id "MSTRG.11088"; transcript_id "ENST00000450627.6"; exon_number "1"; gene_name "MYCBP2-AS1"; ref_gene_id "ENSG00000236051.7";
chr13 StringTie exon 77076816 77076866 1000 + . gene_id "MSTRG.11088"; transcript_id "ENST00000450627.6"; exon_number "2"; gene_name "MYCBP2-AS1"; ref_gene_id "ENSG00000236051.7";
chr13 StringTie exon 77087552 77087778 1000 + . gene_id "MSTRG.11088"; transcript_id "ENST00000450627.6"; exon_number "3"; gene_name "MYCBP2-AS1"; ref_gene_id "ENSG00000236051.7";