m⁶A-SAC-seq

Overview of the workflow

How to use?

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).

Documentation

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

Citation

• 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

 

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