jcast 0.2.8

Jcast retrieves splice junction information and translates into amino acids

Junction Centric Alternative Splicing Translator v.0.2.6

JCAST (Junction Centric Alternative Splicing Translator) takes in alternative splicing events and returns custom protein sequence databases for isoform analysis.

Getting Started

Requirements

Install Python 3.7+ and pip. See instructions on Python website for specific instructions for your operating system.

Jcast can be installed from PyPI via pip. We recommend using a virtual environment.

$ pip install jcast

Running

Launch JCAST as a module (Usage/Help):

$ python -m jcast

Alternatively:

$ jcast

Example command:

$ python -m jcast  data/encode_human_pancreas/ data/gtf/Homo_sapiens.GRCh38.89.gtf data/gtf/Homo_sapiens.GRCh38.89.gtf data/genome/Homo_sapiens.GRCh38.dna.primary_assembly.fa -o encode_human_pancreas

To test that the installation can load test data files in tests/data (sample rMATS file and human chr 15 genome files)

$ pip install tox
$ tox

To run jcast using the test files and print the results to Desktop

$ python -m jcast {j}/tests/data/rmats {j}/tests/data/genome/Homo_sapiens.GRCh38.89.chromosome.15.gtf  {j}/tests/data/genome/Homo_sapiens.GRCh38.dna.chromosome.15.fa.gz -o ~/Desktop

where {j} is replaced by the path to jcast.

Example using JCAST to generate custom databases from ENCODE

The following is an example using ENCODE public RNA-seq dataset to generate a cardiac-specific database with JCAST.

Download RNA-Seq from ENCODE:

As an example, we will download the .fastq files from ENCODE adult human heart dataset 1 and dataset 2.

Align the FASTQ files to a reference genome

Read alignment can be done using STAR v.2.5.0, e.g.,:

$ STAR --runThreadN 10 --genomeDir path/to/STARindex --sjdbGTFfile path/to/Homo_sapiens.gtf --sjdbOverhang 100 --readFilesIn ./ENCFF781VGS.fastq.gz ./ENCFF466ZAS.fastq.gz --readFilesCommand zcat --outSAMtype BAM SortedByCoordinate --outFileNamePrefix ./STAR_aligned/b1t1/
$ STAR --runThreadN 10 --genomeDir path/to/GRCh38/STARindex --sjdbGTFfile path/to/Homo_sapiens.gtf --sjdbOverhang 100 --readFilesIn ./ENCFF731CDK.fastq.gz ./ENCFF429YOS.fastq.gz --readFilesCommand zcat --outSAMtype BAM SortedByCoordinate --outFileNamePrefix ./STAR_aligned/b2t1/

Identify transcript splice junctions

Splice junctions can be found using rMATS with the .bam files following STAR. Please refer to the rMATS instructions for latest commands. The following example was tested using rmats-turbo-0.1. Support for stringtie assembled transcripts will be implemented in a future version.

Set up a Virtual Environment for rMATS in Python 2.7 (only if needed)

Install the rMATS image

Follow instructions from rMATS and docker specific to your OS. E.g.:

$ sudo docker load -i rmats-turbo-0.1.tar

Prepare the /rMATS subdirectory

Copy the individual .bam files from STAR into the rMATS subdirectory and rename them b1t1.bam, b1t2.bam, b2t1.bam, b2t2.bam, etc. Copy the GTF file from the Genomes folder as GRCm38.gtf. Write a b1.txt file with a text editor containing the following docker virtual directories:

/data/b1t1.bam,/data/b1t2.bam

Write a b2.txt file

/data/b2t1.bam,/data/b2t2.bam

Go back to the data directory and run the rMATS image. The -v flag mounts the host directory into the docker container at /data, which corresponds to the visual directories in the b1.txt and b2.txt files.

$ sudo docker run -v path/to/data/directory:/data rmats:turbo01 --b1 /data/b1.txt --b2 /data/b2.txt --gtf /data/GRCh38.gtf --od /data/output -t paired  --nthread 4 --readLength 101 --anchorLength 1

Run the JCAST Python program specifying the directory of the rMATS output as well as the GTF annotation file:

$ python -m jcast path/to/rMATS/output/encode_human_heart/ path/to/gtf/Homo_sapiens.GRCh38.89.gtf data/genome/Homo_sapiens.GRCh38.dna.primary_assembly.fa -o encode_human_heart

FASTA output

JCAST outputs FASTA databases which can be further filtered and combined using any scripting languages, or can be used directly for database search in virtually any shotgun proteomics database search engines (e.g., SEQUEST, Crux/Tide, MS-GF+)

Dependencies

JCAST is tested in Python 3.7 and 3.8 and uses the following packages:

biopython>=1.77
gtfparse>=1.2.0
pandas>=1.0.4
requests>=2.23.0
tqdm>=4.46.0

Contributing

Please contact us if you wish to contribute, and submit pull requests to us.

Authors

• Edward Lau, PhD - Code/design - ed-lau
• Maggie Lam, PhD - Code/design - Maggie-Lam

License

This project is licensed under the MIT License - see the LICENSE.md file for details