CYCLeR is a pipeline for reconstruction of circRNA transcripts from RNA-seq data and their subsequent quantification. The algorithm relies on comparison between control total RNA-seq samples and circRNA enriched samples to identify circRNA specific features. Then the selected circRNA features are used to infer the transcripts through a graph-based algorithm. Once the predicted transcript set is assembled, the transcript abundances are estimated through an EM algorithm with kallisto. CYCLeR takes as an input BAM files and back-splice junction (BSJ) files and outputs transcript infomation in different formats, including a FASTA output for abundance estimation.
The computation steps prior and post CYCLeR run are most efficiently run on HPC. It is very likely that any HPC in biological institute already has most of those tools installed. Just in case, a Docker image containing all the tools is provided.
NOTE: prior to running Docker image, make sure that *Docker is indeed installed and working: https://docs.docker.com/get-started/
- STAR - https://github.com/alexdobin/STAR
- samtools - https://sourceforge.net/projects/samtools/files/samtools/
- kallisto - http://pachterlab.github.io/kallisto/download
- bwa (needed for CIRI2) - http://bio-bwa.sourceforge.net/bwa.shtml
- CIRI2 - https://sourceforge.net/projects/ciri/files/CIRI2/
- CIRCexplorer2 - https://circexplorer2.readthedocs.io/en/latest/
#Docker image with all command line tools
sudo docker pull stiv1n/cycler.prerequisites
For a test run, I suggest using a Docker container. There, all test input and all dependencies are provided.
The Docker use requires you to mount a volume - a working directory (<local_dir>) where the output and input would be stored.
This container uses RStudio server and required login. In this case, the username is rstudio the password is guest.
Open a browser and type localhost:8787.
sudo docker pull stiv1n/cycler
sudo docker run --rm -ti -e PASSWORD=guest -v <local_dir>:/usr/workdir -p 8787:8787 stiv1n/cycler
For more information on installation, pre-processing and core tool run please see the vignette and the manual.
To test the tool, use the R script provided and just run it in the RStudio server.
The final step of the CYCLeR pipeline is running kallisto with the newly assembled transcriptome.
sudo docker run -v <local_dir>:/usr/local stiv1n/cycler.prerequisites \
kallisto index -i kallisto_index -k 31 for_kallisto.fa
sudo docker run -v <local_dir>:/usr/local stiv1n/cycler.prerequisites \
kallisto quant -i kallisto_index -o ./ <sample_name>_1.fastq <sample_name>_2.fastq