Long non-coding RNAs identification and analysis. Default models are trained with human, mouse and wheat datasets by employing SVM. Features are based on intrinsic composition of sequence, EIIP value (electron-ion interaction pseudopotential), and secondary structure. This package can also extract other classic features and build new classifiers. Reference: Han S., et al. (2019)
Long Non-Coding RNA Identification and Analysis Utilizing Sequence Intrinsic Composition, Structural Information and Physicochemical Property
https://CRAN.R-project.org/package=LncFinder
https://github.com/HAN-Siyu/LncFinder/
https://bmbl.bmi.osumc.edu/lncfinder/
LncFinder: an integrated package for long non-coding RNA identification utilizing sequence intrinsic composition, structural information and physicochemical property, Briefings in Bioinformatics, 2019, 20(6):2009-2027. (doi: "https://doi.org/10.1093/bib/bby065")
The authors would be glad to hear how LncFinder is employed. You are kindly encouraged to notify Siyu HAN <[email protected]> about any work you publish.