Requirements

The pipeline is Linux compatible and it was tested under Ubuntu 18 (or newer). We recommend to run the pipeline under Docker in other Linux distributions.

Workflow

Before executing, it is necessary to generate a STAR (2.6.1b) index directory from the annotation of interest (for more info visit https://github.com/alexdobin/STAR/blob/2.6.1b/doc/STARmanual.pdf).

The steps for CORALL analysis on demultiplexed FASTQ files are as follows:

1. UMI extraction
2. Adapter trimming (Cutadapt 2.5)
3. Alignment (STAR 2.6.1b)
4. UMI deduplicating (UMI-tools 1.0.0)
5. Transcript quantification (mix-square 1.4.0.2)

Pipeline set-up

Download Mix² RNA-Seq Data Analysis Software

Before you do any of the other steps for the pipeline set-up, you need to download Mix² from https://www.lexogen.com/mix-analysis-software/ and extract the binary with the name mix-square to the main folder of this repository.

Using installation in local miniconda environment

If your miniconda environment is located in ~/miniconda3 you can, for instance, install the pipeline in your local environment with

./install.sh ~/miniconda3

and run, e.g. with

./run_corall_local.sh -a gtf_file -o corall_out -s star_index_dir -m ~/miniconda3 rd1_fastq_gz [rd2_fastq_gz]

or

./run_corall_local.sh -a gtf_file -o corall_out -s star_index_dir -m ~/miniconda3 sample_csv

The first version takes one gzipped fastq file for read 1 and possibly another one for read 2, while the second version takes a csv file as input which contains on each line the path of a gzipped fastq file for read 1 and possibly another one for read 2. If two files are present on a line they are separated by a comma.

Using docker installation

For building the docker image use, for instance,

./build_corall_dockerimg.sh corall:v1.0.0-customer

This requires that environment.yml for conda installation, and the mix-square binary are in the current working directory. The command line above builds an image with tag corall:v1.0.1

For running the image use, e.g.

./run_corall_docker.sh -a gtf_file -o ${input_dir}/corall_out -s star_index_dir -d corall:v1.0.0-customer ${input_dir}/sample_fastq.csv

Note that ${input_dir} has to be an absolute path name.

In case you want make a backup of the build image and export it run

docker save corall:v1.0.0-customer | gzip > corallWF_v1.0.0-customer.tar.gz

Using an existing docker image

In case you want use our pre-build docker image just import it with

docker load < corallWF_v1.0.0-customer.tar.gz

The pipeline can then be run in the same way as shown above e.g.

./run_corall_docker.sh -a gtf_file -o ${input_dir}/corall_out -s star_index_dir -d corall:v1.0.0-customer ${input_dir}/sample_fastq.csv

Running local tests

In case you need to validate your set-up you can run

./test_data/test_pipeline.sh

from the cloned root directory. Conda needs to be accessible in order to run the tests (e.g. by running conda init or the conda folder needs to be included in PATH environment variable).