APARENT PolyA Detector

PolyA Detector Loading...

Detect and score all the polyadenylation sites of the input sequence.


Detected Peaks

PolyA Site peaks detected by APARENT.

Peak # Location PAS Score

Min Prominence
Min Distance
Min Height
PolyA Site Detection

Explanation of values and graphics presented in the tool.

  • pA Intensity
    The 3' cleavage distribution of the input sequence is shown as an orange curve. The values (pA Intensity) are non-normalized (they do not sum to 100%). Rather, pA Intensity is scaled with respect to the strength of the PAS relative to the average affinity of a distal, well-separated PAS.
  • Peak Detection
    The predicted 3' cleavage distribution is smoothed, aggregated and processed, after which an automatic peak detection algorithm identifies signifant polyadenylation peaks in the signal. The detected peaks are listed in a table below the graph.
  • PAS Score
    The aggregate APA isoform log odds ('strength') of each detected peak, with respect to the average affinity of a well-separated, distal PAS, are listed in the peak detection table below the graph.

Instructions on how to use the tool.

  • Choose gene and pA site
    Select the gene of interest in the dropdown list above the cleavage curve. Choosing a gene populates the dropdown to the right with the annotated pA sites of that gene. We currently provide integration with APADB (V2) and PolyADB (V3). Choosing a site populates the genome browser fields with the corresponding coordinates. Pressing 'Fetch' loads the genomic sequence.
  • (Optional) Specify custom pA sequence
    Instead of choosing from a list of native human pA sites, you can paste your own custom sequence in the text area. Note: The sequence must be at least 205 nucleotides and at most 10,000 nucleotides.
  • Adjust prediction algorithm
    The cleavage distrbution prediction algorithm works by sliding APARENT across the entire input sequence according to a user-specific stride (default stride = 10nt), and aggregating the partially overlapping pA profiles predicted from each window. You may need to adjust the parameters (Stride, Smoothing Kernel) according to your needs. Smaller strider linearly increases prediction time. The Smoothing Kernel evens out the profile by applying a gaussian filter.
  • Adjust peak detection algorithm
    The peak detection algorithm is pre-configured with parameters that work well in most cases by detecting significant peaks with high specificity. You may need to adjust the parameters (Min Peak Height, Min Peak Distance, Min Peak Prominence) according to your needs.