• Published in BMC Bioinformatics

• Input read count data

  • PARS
  • SHAPE-Seq
  • icSHAPE
  • DMS-Seq (assumed to be enriched only at A or C)

• Output

  • posterior probability of being loop (enriched in case) or stem (enriched in control)

• Algorithm

  • IDR + hidden Markov Model

• python3
• numpy
• scikit-learn

Other packages are required for visualization process as follows:

• pandas
• seaborn

git clone https://github.com/carushi/reactIDR
cd reactIDR/
python setup.py  build_ext -b reactIDR/ # cython build
cd example && bash training.sh    # Run test

Please visit our wiki for further info.

• read_collapse.py
  • collapse PCR duplicates and trim barcode
  • assume gawk
• read_truncate.py
  • extract consistent paired end reads
• bed_to_pars_format.py
  • write PARS-formatted 5' end coverage data based on gtf and gff annotation or sequence location
  • format: NAME 0;1;2;3;.....
• tab_to_csv.py
  • use to append raw count (read count, coverage, ...) to the output csv file

• R. Kawaguchi, H. Kiryu, J. Iwakiri and J. Sese. "reactIDR: evaluation of the statistical reproducibility of high-throughput structural analyses towards a robust RNA structure prediction" BMC Bioinformatics 20 (Suppl 3) :130 (2019)" ー Selected for APBC '19 proceedings

• Docker image for reactIDR

  • Convert bam to read count data
  • Find scripts and how to use at https://github.com/carushi/RT_end_counter

• IDR

  • Li, Qunhua, et al. "Measuring reproducibility of high-throughput experiments", The annals of applied statistics, 2011.
  • IDR in Python
  • IDR in R

• apply to MaP analyses