Please cite the paper below for the InPAS package.

Jianhong Ou, Haibo Liu, Sungmi Park, Michael R. Green, Lihua Julie Zhu. InPAS: An R/Bioconductor Package for Identifying Novel Polyadenylation Sites and Alternative Polyadenylation from Bulk RNA-seq Data. Front. Biosci. (Schol Ed) 2024, 16(4), 21. https://doi.org/10.31083/j.fbs1604021

Corresponding BibTeX entry:

  @Article{,
    title = {InPAS: An R/Bioconductor Package for Identifying Novel
      Polyadenylation Sites and Alternative Polyadenylation from Bulk
      RNA-seq Data},
    author = {Jianhong Ou and Haibo Liu and Sungmi Park and Michael R.
      Green and Lihua Julie Zhu},
    journal = {FBS},
    volume = {16},
    year = {2024},
    number = {4},
    pages = {21},
    url =
      {https://www.imrpress.com/journal/FBS/16/4/10.31083/j.fbs1604021},
    doi = {10.31083/j.fbs1604021},
    issn = {1945-0516},
    abstract = {Background: Alternative cleavage and polyadenylation
      (APA) is a crucial post-transcriptional gene regulation mechanism
      that regulates gene expression in eukaryotes by increasing the
      diversity and complexity of both the transcriptome and proteome.
      Despite the development of more than a dozen experimental methods
      over the last decade to identify and quantify APA events,
      widespread adoption of these methods has been limited by
      technical, financial, and time constraints. Consequently, APA
      remains poorly understood in most eukaryotes. However, RNA
      sequencing (RNA-seq) technology has revolutionized transcriptome
      profiling and recent studies have shown that RNA-seq data can be
      leveraged to identify and quantify APA events. Results: To fully
      capitalize on the exponentially growing RNA-seq data, we
      developed InPAS (Identification of Novel alternative
      PolyAdenylation Sites), an R/Bioconductor package for accurate
      identification of novel and known cleavage and polyadenylation
      sites (CPSs), as well as quantification of APA from RNA-seq data
      of various experimental designs. Compared to other APA analysis
      tools, InPAS offers several important advantages, including the
      ability to detect both novel proximal and distal CPSs, to fine
      tune positions of CPSs using a naive Bayes classifier based on
      flanking sequence features, and to identify APA events from
      RNA-seq data of complex experimental designs using linear models.
      We benchmarked the performance of InPAS and other leading tools
      using simulated and experimental RNA-seq data with matched 3'-end
      RNA-seq data. Our results reveal that InPAS frequently
      outperforms existing tools in terms of precision, sensitivity,
      and specificity. Furthermore, we demonstrate its scalability and
      versatility by applying it to large, diverse RNA-seq datasets.
      Conclusions: InPAS is an efficient and robust tool for
      identifying and quantifying APA events using readily accessible
      conventional RNA-seq data. Its versatility opens doors to explore
      APA regulation across diverse eukaryotic systems with various
      experimental designs. We believe that InPAS will drive APA
      research forward, deepening our understanding of its role in
      regulating gene expression, and potentially leading to the
      discovery of biomarkers or therapeutics for diseases.},
  }