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Docker DOI

Ψ-BID-seq

Overview of the workflow

How to use it?

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

The entire analysis can be completed in just three steps:

  1. Specific the path of references (.fasta) and samples (.fastq) in a configure file (.YAML).

    data.yaml for example(Click to expand) 
    reference:
  contamination:
    fa: ./ref/contamination.fa
  genes:
    fa: ./ref/genes.fa
  genome:
    fa: /data/reference/genome/Mus_musculus/GRCm39.fa
    star: /data/reference/genome/Mus_musculus/star/GRCm39.release108

samples:
  mESCWT-rep1-input:
    data:
      - R1: ./test/IP16.fastq.gz
    group: mESCWT
    treated: false
  mESCWT-rep1-treated:
    data:
      - R1: ./test/IP4.fastq.gz
    group: mESCWT
    treated: true
  mESCWT-rep2-treated:
    data:
      - R1: ./test/IP5.fastq.gz
    group: mESCWT
    treated: true

    You can copy and edit from this template.

    Read the documentation on how to customize.

  2. Run all the analysis by one command:

    apptainer run docker://y9ch/bidseq
    The pipeline will load configure file named `data.yaml` under the current directory.(Click to expand)
    • Customized configure file with -c argument. (default: data.yaml)
    • Customized number of jobs/cores in parallel -j argument. (default: 48)

  3. View the analytics reports and filtered sites.

    3 folders are will be created in the working directory (default: `workspace`).(Click to expand) ├── align_bam ├── report_reads └── filter_sites
    • trimming, mapping, and deduping reports are in report_reads folder, with key numbers in all the steps reported in one webpage(example).
    • filtered sites for Ψ detection are in the filter_sites folder. These sites are only passed the simplest filtering, you can apply customized thresholds to them based on your data type and quality.
    • processed mapping results (.bam) are in align_bam folder. You can zoom into a location that you are interested in IGV.

Documentation

Read more

Citation

  • cite this software

    @misc{y_y9cpseudou-bidseq_2022,
  title = {y9c/{pseudoU}-{BIDseq}: v1.0},
  url = {https://zenodo.org/record/8158036},
  urldate = {2023-07-18},
  publisher = {Zenodo},
  author = {Ye, Chang},
  month = dec,
  year = {2022},
  doi = {10.5281/zenodo.8158036},
}

  • cite the protocol

    @article{dai2023quantitative,
title={Quantitative sequencing using BID-seq uncovers abundant pseudouridines in mammalian mRNA at base resolution},
author={Dai, Qing and Zhang, Li-Sheng and Sun, Hui-Lung and Pajdzik, Kinga and Yang, Lei and Ye, Chang and Ju, Cheng-Wei and Liu, Shun and Wang, Yuru and Zheng, Zhong and others},
journal={Nature Biotechnology},
volume={41},
number={3},
pages={344--354},
year={2023},
publisher={Nature Publishing Group US New York}
}

  • cite the method

    @article{dai_quantitative_2022,
  title = {Quantitative sequencing using {BID}-seq uncovers abundant pseudouridines in mammalian {mRNA} at base resolution},
  issn = {1087-0156},
  doi = {10.1038/s41587-022-01505-w},
  journal = {Nature Biotechnology},
  author = {Dai, Qing and Zhang, Li-Sheng and Sun, Hui-Lung and Pajdzik, Kinga and Yang, Lei and Ye, Chang and Ju, Cheng-Wei and Liu, Shun and Wang, Yuru and Zheng, Zhong and Zhang, Linda and Harada, Bryan T. and Dou, Xiaoyang and Irkliyenko, Iryna and Feng, Xinran and Zhang, Wen and Pan, Tao and He, Chuan},
  year = {2022},
  pages = {1--11},
}

 

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