Comparison tools for lncRNA identification: analysis among plants and humans

This article has as its main objective the evaluation of the differences between long non-coding RNAs of plants and humans. Long non-coding RNAs are also known as lncRNAs. The lncRNAS belong to the class of RNAs that do not encode proteins and are related to several biological functions, such as chromatin modifications, post-transcriptional regulation and mainly in the different development processes of diseases such as cancer. In this work, we want to verify the existence of differences in lncRNAs in plants and humans using state-of-the-art approaches to identify lncRNAs. The main reason for the study is that there are differences between the miNAs (small ncRNAs) of plants and humans, whether in biological or computational characteristics, for lncRNAs it is still an open question. To answer this question, this paper proposes to show the results of two ncRNAS prediction tools, trained with humans, and which are widely used for lncRNA prediction: CPC2 and CPAT. We will also show results from tools used to predict lncRNAS in plants, which are trained with plant data: the RNAplonc, the PlncPRO tool that contains two versions, one for monocot and one for dicot and the LGC tool that was trained with plants and humans. The results of tools trained with human data will also be displayed: PLEK, CPPRED and PredLnc-GFStack. These eight tools were applied in two sets of tests, one composed of eight species of plants (Amborella trichopoda, Brachypodium distachyon, Citrus sinensis, Manihot esculenta, Ricinus communis, Solanum tuberosum, Sorghum bicolor, Zea mays) and the other composed of human lncRNAS.