DeAI Research
Source · Briefings in Bioinformatics

RTCpredictor: identification of read-through chimeric RNAs from RNA sequencing data

2024Sandeep Singh; Xinrui Shi; Samuel Haddox; Justin Elfman; Syed Basil Ahmad; Sarah Jane Lynch; Tommy Manley; Claire Piczak; Christopher Phung; Yunan Sun; Aadi Sharma; Hui Li

cid d612727ec474da31d16a414758e8f834c4a8151ba8aaa4a53e5057e6845a8a2e · original · open access · via openalex

Title: RTCpredictor: identification of read-through chimeric RNAs from RNA sequencing data
Authors: Sandeep Singh; Xinrui Shi; Samuel Haddox; Justin Elfman; Syed Basil Ahmad; Sarah Jane Lynch; Tommy Manley; Claire Piczak; Christopher Phung; Yunan Sun; Aadi Sharma; Hui Li
Venue: Briefings in Bioinformatics
Year: 2024
Canonical: doi:10.1093/bib/bbae251
URL: https://doi.org/10.1093/bib/bbae251
Collector: openalex
OA: true

ABSTRACT
Read-through chimeric RNAs are gaining attention in cancer and other research fields, yet current tools often fail in predicting them. We have thus developed the first read-through chimeric RNA specific prediction method, RTCpredictor, utilizing a fast ripgrep algorithm to search for all possible exon-exon combinations of parental gene pairs. Compared with other ten popular tools, RTCpredictor achieved top performance on both simulated and real datasets. We randomly selected up to 30 candidate read-through chimeras predicted from each software method and experimentally validated a total of 109 read-throughs and on this set, RTCpredictor outperformed all the other methods. In addition, RTCpredictor ( https://github.com/sandybioteck/RTCpredictor ) has less memory requirements and faster execution time.