MoAIMS

MoAIMS is an efficient software for detection of enriched regions of MeRIP-Seq. MoAIMS can also provide an intuitive indicator of treatment effect for the treatment MeRIP-Seq data.

Installation

• Implemented language: R
• Version: 1.0
• Dependends: MASS,mgcv,ggplot2,dplyr,GenomicRanges,GenomicAlignments,GenomicFeatures,rtracklayer,Rsubread,mosaics

  #install.packages("devtools")
  library(devtools)
  install_github("rreybeyb/MoAIMS/codes")

Example

• A simple run(test data provided in moaims_extdata)

  • Required inputs
  • sample_info_file: A sample sheet of sample information. Please see the required format in example/sample_sheet_exampe.tsv
  • gtf_file: Genome annotation file in sorted GTF format.
  • strand_specifc: Sequencing strand protocol. 0 for unstranded, 1 for fr-first, 2 for fr-second.
  • is_paired: Paired or not. TRUE or FALSE

  > moaims(sample_info_file = /absolute/path/to/sample_info_file, gtf_file =/absolute/path/to/gtf_file,
  strand_specific = 1, is_paired = F, proj_name='test')

  • Primary output(See example/output/test)

  • Enriched regions in BED12 format(sig_*.bed). Definition of some specific columns are:

    1) 5th: the highest bin count of merged regions

    2) 13th: the highest fold change of merged regions

    3) 14th: the highest -log10(pvalue) of merged regions

  • Goodness of Fitting plot(GOF_*.png)

  • A summary table of models(fitres*.tsv)

• Need to adjust the model

  • Required inputs
  • bin_info_obj: Object of class binInfo, imported using method readBinInfo.
  • bin_count_obj:  Object of class binCount, imported using method readBinCount.
  • sample_id: Sample name.
  • Output(See example/output/test/adj)

    #bin_info_*.tsv and bin_count_*.tsv are generated by the main function 'moaims' when setting output_intmd=T(default)
    > bin_info_obj=readBinInfo(in_fn = /absolute/path/to/bin_info_*.tsv)
    > bin_count_obj=readBinCount(in_fn = /absolute/path/to/bin_count_*.tsv)
    > adjFit(bin_info_obj,bin_count_obj,sample_id='test',proj_name='adj')

Reference

• Kuan, P.F. et al. A Statistical Framework for the Analysis of ChIP-Seq Data. J Am Stat Assoc 106(495), 891–903 (2011)
• Bao, Y. et al. Accounting for immunoprecipitation efficiencies in the statistical analysis of ChIP-seq data. BMC Bioinformatics 14, 169 (2013)