A Model-Based Approach for Identifying Functional Intergenic Transcribed Regions and Noncoding RNAs. (15th March 2018)

• Record Type: Journal Article
• Authors: Lloyd, John P; Tsai, Zing Tsung-Yeh; Sowers, Rosalie P; Panchy, Nicholas L; Shiu, Shin-Han
• Editors: Gojobori, Jun
• Is Part Of: Molecular biology and evolution. Volume 35:Number 6(2018)
• Page Start: 1422
• Page End: 1436
• Publication Date: 2018-03-15
• Subjects: intergenic transcription -- ncRNAs -- definition of function -- molecular evolution -- machine learning -- data integration; Molecular biology -- Periodicals; Molecular evolution -- Periodicals; Evolution, Molecular -- Periodicals; Molecular Biology -- Periodicals; 572.8
• DOI: 10.1093/molbev/msy035
• Languages: English
• ISSNs: 0737-4038

Abstract

Abstract: With advances in transcript profiling, the presence of transcriptional activities in intergenic regions has been well established. However, whether intergenic expression reflects transcriptional noise or activity of novel genes remains unclear. We identified intergenic transcribed regions (ITRs) in 15 diverse flowering plant species and found that the amount of intergenic expression correlates with genome size, a pattern that could be expected if intergenic expression is largely nonfunctional. To further assess the functionality of ITRs, we first built machine learning models using Arabidopsis thaliana as a model that accurately distinguish functional sequences (benchmark protein-coding and RNA genes) and likely nonfunctional ones (pseudogenes and unexpressed intergenic regions) by integrating 93 biochemical, evolutionary, and sequence-structure features. Next, by applying the models genome-wide, we found that 4, 427 ITRs (38%) and 796 annotated ncRNAs (44%) had features significantly similar to benchmark protein-coding or RNA genes and thus were likely parts of functional genes. Approximately 60% of ITRs and ncRNAs were more similar to nonfunctional sequences and were likely transcriptional noise. The predictive framework established here provides not only a comprehensive look at how functional, genic sequences are distinct from likely nonfunctional ones, but also a new way to differentiate novel genes from genomic regions with noisy transcriptional activities.