QSplice

Quantifying splice junctions coverage from SJ.out.tab released by STAR mapping it to genome positions.

The Snakemake workflow stored in github.com/fpozoc/appris_rnaseq offers a simple pipeline to generate the splice junctions samples used by QSplice to quantify the transcript expression.

Table of Contents

• QSplice
  • Table of Contents
  • Installation Instructions
  • Usage
  • Example: Chromosome 1 open reading frame 112 (C1orf112)
  • Reference files
  • Author information and license
  • Release History
  • Contributing
  • License

Installation Instructions

Run the silent installation of Miniconda in case you don't have this software in your Linux Environment

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh -b -p $HOME/miniconda3

Once you have installed Miniconda/Anaconda, create a Python environment. Clone this repository and install inside your recently created conda environment.

conda create --name qsplice python=3.7
conda activate qsplice

git clone https://github.com/fpozoc/qsplice.git
cd qsplice
pip install .

genometools must be installed in the OS.

apt-get install genometools

Usage

This package has been implemented for GENCODE and RefSeq genome annotation versions. User has to specify the desired version typing g or r + version number, e.g. g27.

First, user has to generate introns the genome annotation with intronator.py from the reference genome annotation file in gff format.

python -m qsplice.intronator --version g27 --file gencode.v27.annotation.gff3.gz

To map splice junctions previously generated (see STAR) and calculate QSplice scores per transcript run map_junctions.py.

For instance, the output generated by the pipeline github.com/fpozoc/appris_rnaseq is stored in this way: out/E-E-MTAB-XXXX/GRCh38_GENCODE/STAR/g27 + experiment_number + SJ.out.tab.

User has to add as arguments the version id (again) and the directory with the experiments to be globbed.

python -m qsplice.map_junctions --version g27 --globdir out/E-MTAB-XXXX/GRCh38_GENCODE/STAR/g27

However, if user wants to add a previously customized (and annotated) file:

python -m qsplice.map_junctions --version g27 --custom --file out/E-MTAB-XXXX/GRCh38_GENCODE/STAR/g27/SJ.out.tab.concat.gz

Example: Chromosome 1 open reading frame 112 (C1orf112)

ENSG00000000460 (Ensembl) - Q9NSG2 (CA112_HUMAN) (UniProt)

qsplice.emtab2836.g27.tsv.gz sample output for some isoforms of ENSG00000000460.

gene_name	transcript_id	intron_number	start	end	nexons	ncds	unique_reads	tissue	gene_mean	gene_mean_cds	RNA2sj	RNA2sj_cds
C1orf112	ENST00000286031	6	169804241	169806003	24	22	53	testis	69.6	73.8	0.8	0.7
C1orf112	ENST00000359326	7	169804241	169806003	25	22	53	testis	69.6	73.8	0.8	0.7
C1orf112	ENST00000413811	20	169849605	169850264	23	14	62	testis	69.6	73.8	0.9	0.8
C1orf112	ENST00000459772	2	169798959	169802620	23	3	7	fallopiantube	69.6	73.8	0.1	0.1
C1orf112	ENST00000466580	2	169798959	169802620	8	3	7	fallopiantube	69.6	73.8	0.1	0.1
C1orf112	ENST00000472795	5	169803310	169804074	6	4	57	testis	69.6	73.8	0.8	0.8

sj_maxp.emtab2836.g27.mapped.tsv.gz sample output for the isoform ENST00000472795.

In this case, the method selects the splice junction number 5, located between 169803310 and 169804074. This splice junction has the maximum coverage value in testis with 57 unique reads spanning the junction. It is the lowest coverage per isoform, if we only take into account introns that has been spanned by coding exons. The final score RNA2sj and RNA2sj_cds are obtained dividing this score by its respective gene mean.

gene_name	transcript_id	intron_number	start	end	nexons	ncds	unique_reads	tissue	gene_mean	gene_mean_cds	RNA2sj	RNA2sj_cds
C1orf112	ENST00000472795	1	169794906	169798856	6	4	2	testis	69.6	73.8	0.0	0.0
C1orf112	ENST00000472795	2	169798959	169800882	6	4	69	testis	69.6	73.8	1.0	0.0
C1orf112	ENST00000472795	3	169800972	169802620	6	4	74	testis	69.6	73.8	1.1	1.0
C1orf112	ENST00000472795	4	169802726	169803168	6	4	77	testis	69.6	73.8	1.1	1.0
C1orf112	ENST00000472795	5	169803310	169804074	6	4	57	testis	69.6	73.8	0.8	0.8

Figure: ENST00000472795.5 (C1orf112-206) exon distribution scheme to represent how QSplice scores are generated.

Reference files

$ head -n 3 SJ.out.tab

chr1    14830   14969   2       2       0       0       3       44
chr1    15039   15795   2       2       1       1       9       40
chr1    15948   16606   2       2       1       0       11      47

From STAR manual documentation:

SJ.out.tab contains high confidence collapsed splice junctions in tab-delimited format. The columns
have the following meaning:
column 1: chromosome
column 2: first base of the intron (1-based)
column 3: last base of the intron (1-based)
column 4: strand (0: undefined, 1: +, 2: -)
column 5: intron motif: 0: non-canonical; 1: GT/AG, 2: CT/AC, 3: GC/AG, 4: CT/GC, 5:
AT/AC, 6: GT/AT
column 6: 0: unannotated, 1: annotated (only if splice junctions database is used)
column 7: number of uniquely mapping reads crossing the junction
column 8: number of multi-mapping reads crossing the junction
column 9: maximum spliced alignment overhang

Author information and license

• Fernando Pozo (@fpozoca – Google Scholar - fpozoc@cnio.es).
• Michael Tress.

Distributed under the GNU General Public License. See LICENSE for more information.

Release History

• 1.0.0.

Contributing

1. Fork it (https://github.com/fpozoc/qsplice)
2. Create your feature branch (git checkout -b feature/fooBar)
3. Commit your changes (git commit -am 'Add some fooBar')
4. Push to the branch (git push origin feature/fooBar)
5. Create a new Pull Request

License

See LICENSE file.