Here we describe a fast an efficient protocol for analyzing alternative splicing using SAJR.
Method Article
Alternative splicing analysis of RNA-seq data using SAJR
https://doi.org/10.1038/protex.2018.029
This work is licensed under a CC BY 4.0 License
This protocol has been posted on Protocol Exchange, an open repository of community-contributed protocols sponsored by Nature Portfolio. These protocols are posted directly on the Protocol Exchange by authors and are made freely available to the scientific community for use and comment.
posted 12 Mar, 2018
You are reading this latest protocol version
Here we describe a fast an efficient protocol for analyzing alternative splicing using SAJR.
Here we describe a fast an efficient protocol for analyzing alternative splicing using ‘cutadapt’ [1] to trim reads before alignment with ‘STAR’ [2], subsequent merging of samples using ‘samtools’ [3] and finally analysis of splicing with ‘SAJR’ [4]. We also added annotation of novel splicing events and conversion of SAJR specific ids to standard gene ids using ‘bedtools’ [5] and custom perl scripts.
First part of the protocol is preparing and mapping reads.
Align the samples to the genome using ‘STAR.’
Merge all BAM files into a single BAM file using ‘samtools merge’.
Second part of the protocol is preparing a reference as well as identifying novel splicing patterns and annotating these.
Convert a GTF reference to an SAJR specific GFF reference using SAJR’s annotation conversion mode.
Run SAJR in de novo annotation mode to find novel splice-forms using the merged BAM file and the known annotation to produce a novel annotation, novel.gff
get_genename_from_segment_overlap.pl novel_overlap_known.gff > novel2known_from_overlap.tsv
annotate_novel_segments.pl novel_overlap_known_stringent.gff > novel_overlap_known_stringent_novel.tsv
The final part of the protocol is estimating inclusion levels in each sample, and testing for differences between groups of samples.
Run SAJR in count mode for each sample using the novel.gff reference.
The whole analysis can be completed within 24 hours for 36 samples with a total of app. 450 mio reads running on 16 cores.
The expected outcome is a list of significant alternative splicing events with with optional indication of novel splicing patterns.
1 Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. 2011 17, doi:10.14806/ej.17.1.200
pp. 10-12 (2011).
2 Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15-21, doi:10.1093/bioinformatics/bts635 (2013).
3 Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078-2079, doi:10.1093/bioinformatics/btp352 (2009).
4 Mazin, P. et al. Widespread splicing changes in human brain development and aging. Molecular systems biology 9, 633, doi:10.1038/msb.2012.67 (2013).
5 Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841-842, doi:10.1093/bioinformatics/btq033 (2010).
The authors declare no conflicting financial interests.
Scripts Scripts Zip file containing perl scripts used in the protocol and example R script.
This protocol has been posted on Protocol Exchange, an open repository of community-contributed protocols sponsored by Nature Portfolio. These protocols are posted directly on the Protocol Exchange by authors and are made freely available to the scientific community for use and comment.
posted 12 Mar, 2018
You are reading this latest protocol version
© Research Square 2022