m⁶A-eTAM-seq

Qucik start (Run in docker environment)

• Prepare configuration file (data.yaml for example)

minimum configuration example:

genome_index: ~/reference/genome/Homo_sapiens/hisat2_tx_3n/GRCh38.release110

reference:
  contamination:
    - test/ref/contamination.fa.gz
  genes:
    - test/ref/spikein.fa
    - test/ref/human_rRNA.fa
  genome:
    - ~/reference/genome/Homo_sapiens/GRCh38.fa

samples:
  HeLa-treat-rep1:
    data:
      - R1: test/data/SRR21070405_1.fastq.gz
        R2: test/data/SRR21070405_2.fastq.gz
      - R1: test/data/SRR21070406_1.fastq.gz
  HeLa-treat-rep2:
    data:
      - R1: test/data/SRR21070404_1.fastq.gz

advanced configuration:

Refer to documentation at https://y9c.github.io/m6A-eTAMseq/

• Install apptainer and run

  apptainer run docker://y9ch/etamseq -c data.yaml -j 48

• Note: On server without external internet access, you can use the following command to pull the docker image first:

  apptainer build etamseq.sif docker://y9ch/etamseq
  apptainer run etamseq.sif -c data.yaml -j 48

• Note: If your sequencing data for reference files are under a mounted directory, you may need to add --bind /path/to/mounted/dir to the apptainer command line.

  apptainer run --bind /path/to/mounted/dir docker://y9ch/etamseq -c data.yaml -j 48

Local environment and customization

• If you want to test the code under local environment, you can clone the repository by the following command:

  git clone --recurse-submodules https://github.com/y9c/m6A-eTAMseq.git

• This package has been tested on Linux operating systems. It requires the following software dependencies:

  • Python 3.9 or higher
  • Snakemake 8.0.0 or higher
  • hisat2-3n
  • cutseq

• Run the code by snakemake

  snakemake -j 48 --configfile data.yaml -s Snakefile

Changelog

• The data pre-processing stesps are now based on trichromat

Citation

• Coming soon
• Use our previous work if you try to reproduce the results of the eTAMseq v1 protocol

 

Copyright © 2024-present Chang Y