Docker DOI:10.1038/s41587-022-01243-z Citation Badge

A docker image containing the source code and dependencies has been published for reproducibility. You can run it using the singularity container runtime.

The entire analysis can be completed in just three steps:

1. Specific the path (with label) of both rawdata and references for your project in a YAML format.

   data.yaml for example^{(Click to expand)}

   samples:
    HeLa-WT:
    input:
    rep1:
    - R1: ./rawdata/HeLa-WT-polyA-input-rep1-run1_R1.fq.gz
    R2: ./rawdata/HeLa-WT-polyA-input-rep1-run1_R2.fq.gz
    - R1: ./rawdata/HeLa-WT-polyA-input-rep1-run2_R1.fq.gz
    R2: ./rawdata/HeLa-WT-polyA-input-rep1-run2_R2.fq.gz
    rep2:
    - R1: ./rawdata/HeLa-WT-polyA-input-rep2-run1_R1.fq.gz
    R2: ./rawdata/HeLa-WT-polyA-input-rep2-run1_R2.fq.gz
    - R1: ./rawdata/HeLa-WT-polyA-input-rep2-run2_R1.fq.gz
    R2: ./rawdata/HeLa-WT-polyA-input-rep2-run2_R2.fq.gz
    treated:
    rep1:
    - R1: ./rawdata/HeLa-WT-polyA-treated-rep1-run1_R1.fq.gz
    R2: ./rawdata/HeLa-WT-polyA-treated-rep1-run1_R2.fq.gz
    - R1: ./rawdata/HeLa-WT-polyA-treated-rep1-run2_R1.fq.gz
    R2: ./rawdata/HeLa-WT-polyA-treated-rep1-run2_R2.fq.gz
    rep2:
    - R1: ./rawdata/HeLa-WT-polyA-treated-rep2-run1_R1.fq.gz
    R2: ./rawdata/HeLa-WT-polyA-treated-rep2-run1_R2.fq.gz
    - R1: ./rawdata/HeLa-WT-polyA-treated-rep2-run2_R1.fq.gz
    R2: ./rawdata/HeLa-WT-polyA-treated-rep2-run2_R2.fq.gz
   references:
    spike:
    fa: ./ref/spike_expand.fa
    bt2: ./ref/spike_expand
    spikeN:
    fa: ./ref/spike_degenerate.fa
    blast: ./ref/spike_degenerate
    rRNA:
    fa: ./ref/Homo_sapiens.GRCh38.rRNA.fa
    bt2: ./ref/Homo_sapiens.GRCh38.rRNA
    smallRNA:
    fa: ./ref/Homo_sapiens.GRCh38.smallRNA.fa
    bt2: ./ref/Homo_sapiens.GRCh38.smallRNA
    genome:
    fa: ./ref/Homo_sapiens.GRCh38.genome.fa
    star: ./ref/Homo_sapiens.GRCh38.genome
    gtf: ./ref/Homo_sapiens.GRCh38.genome.gtf
    gtf_collapse: ./ref/Homo_sapiens.GRCh38.genome.collapse.gtf
    contamination:
    fa: ./ref/contamination.fa
    bt2: ./ref/contamination

   Read the documentation on how to customize.

2. Run all the analysis by one command:

   apptainer run docker://y9ch/sacseq:latest

   Note that when you storge your input file in a mounted partition, don't forget to add --bind / -B command to mount the partition. For example, using apptainer run -B /data docker://sacseq:latest...

   default settings^{(Click to expand)}

   • default config file: data.yaml
   • default output dir: ./results
   • default jobs in parallel: 48

   Read the documentation on how to customize.

3. View the analytics report and use the m⁶A sites for downstream analysis.

   The output of all the steps will be in one folder (./results) under the current path. A webpage report of all the analysis will be in ./results/report.html ^(example).

https://y9c.github.io/m6A-SACseq/

• Ge, R., Ye, C., Peng, Y. et al. m6A-SAC-seq for quantitative whole transcriptome m6A profiling. Nat Protoc (2022). https://doi.org/10.1038/s41596-022-00765-9

Copyright © 2021-present Chang Y