Identification of protein-coding sequences using the hybridization of 18S rRNA and mRNA during translation

LncRNA-ID: Long non-coding RNA IDentification using balanced random forests

MOTIVATION

Long non-coding RNAs (lncRNAs), which are non-coding RNAs of length above 200 nucleotides, play important biological functions such as gene expression regulation. To fully reveal the functions of lncRNAs, a fundamental step is to annotate them in various species. However, as lncRNAs tend to encode one or multiple open reading frames, it is not trivial to distinguish these long non-coding transcripts from protein-coding genes in transcriptomic data.

RESULTS

In this work, we design a new tool that calculates the coding potential of a transcript using a machine learning model (random forest) based on multiple features including sequence characteristics of putative open reading frames, translation scores based on ribosomal coverage, and conservation against characterized protein families. The experimental results show that our tool competes favorably with existing coding potential computation tools in lncRNA identification.

AVAILABILITY AND IMPLEMENTATION

The scripts and data can be downloaded at https://github.com/zhangy72/LncRNA-ID.

Methods of identification of pseudogenes based on functionality: hybridization of 18S rRNA and mRNA during translation

Protein-coding sequences are characterized by a period-3 free energy signal that arises from the interaction between the 3'-terminal nucleotides of the 18S rRNA and the mRNA. Such a signal is not present in noncoding sequences such as introns and intergenic regions and can be used for pseudogene identification.