Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.06.27.546788v1?rss=1
Authors: Chang, Y., Lei, T., Zhang, H.
Abstract:
Ribosome profiling is a powerful method for global survey of ribosomal footprints. Inferring the offsets of footprint 5' ends to the ribosomal P-site is essential to pinpoint codons translated by ribosomes. By convention, global or read length-specific P-site offsets are estimated by inspecting the distribution of ribosome footprints around the annotated start or stop codons. However, actual offsets might be different even for footprints of the same length due to the influence of sequence context and the cutting bias of endoribonucleases. To address this issue, we present PSite, a python package for inferring read-specific P-site offsets using a gradient boosting trees model. PSite assigned more reads to the correct reading frame than conventional methods and improved the prediction of translated ORFs by existing software. Besides, PSite is robust to ribosome profiling datasets of varying quality or using endonucleases with cutting bias for digestion.
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