Quantifing Splicing Efficiency

Quantifying Splicing Efficiency (SE)

SPLICE-q uses splicing reads—split and unsplit—junction reads to quantify splicing efficiency (SE) for each intron individually.

• Split reads are junction reads spanning from one exon to another, thus indicating processed transcripts from which the individual intron has already been excised.
• Unsplit reads are those spanning the intron-exon boundaries (covering both sides of the splice junction), hence, indicating transcripts from which the intron has not yet been spliced out.

1. Basic usage

To run SPLICE-q with default parameters, it requires a BAM file and a genome annotation file provided by GENCODE or Ensembl (GTF):

$ SPLICE-q.py -b file.bam -g annotation.gtf

By default, an output file named splifing-efficiency.tsv goes to your current folder. If you wish to specify a different output file name and location:

$ SPLICE-q.py -b file.bam -g annotation.gtf -o outfile.tsv

2. Examples

Below are other examples of SPLICE-q usage.

2.1 Chromosome names

SPLICE-q was developed to parse genomic features from the annotation file of multiple species. By default, it considers chr1-720, I-XVI, 2L, 2R, 3L, 3R, Z, W. If you have different chromosome names or with to work with a subset of chromosomes, a list can be provided through the parameter --ChromsList or -x:

$ SPLICE-q.py -b file.bam -g annotation.gtf -x "1,2,3,4,X"

This is telling SPLICE-q to work with chromosomes 1, 2, 3, 4 and X.

Attention!! The list of chromosome names should be separated by comma and without spaces.

2.2 Coverage and mapping quality

By default, SPLICE-q works with uniquely mapped reads and a minimum of 10 reads spanning each splice junction. This parameters can also be adjusted. For example:

$ SPLICE-q.py -b file.bam -g annotation.gtf -c 5 -r 1 

-c 5 (also --MinCoverage 5) is telling SPLICE-q to include introns whose splice junctions are covered by a minimum of 5 reads (each side).
-r 1 (or --MinReadQuality 1) tells SPLICE-q to work also with MAPQ values for multi-mapping reads. Not recommended!

2.3 Multiprocessing

Multiple concurrent processes are used to minimize running times. The number of processes can be adjusted by the user through -p or --NProcesses:

$ SPLICE-q.py -b file.bam -g annotation.gtf -p 4  

NOTE: This option requires an index file (.bai). SPLICE-q will check if you have one available and do it for you in case you do not. You can also easily generate an index file with Samtools.

3. More

Other filters can be adjusted according to your necessities. To learn more about them, click here or run:

$ SPLICE-q.py -h