LncMachine: a machine learning algorithm for long noncoding RNA annotation in plants

• Published in: Functional & integrative genomics Vol. 21; no. 2; pp. 195 - 204
• Main Authors: Cagirici, H. Busra, Galvez, S., Sen, Taner Z., Budak, Hikmet
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2021; Springer Nature B.V
• Subjects: Algorithms; Animal Genetics and Genomics; Biochemistry; Bioinformatics; Biomedical and Life Sciences; Cell Biology; Computational Biology; genes; genomics; High-Throughput Nucleotide Sequencing; humans; Learning algorithms; Life Sciences; Machine Learning; mice; Microbial Genetics and Genomics; Molecular Sequence Annotation; Next-generation sequencing; non-coding RNA; Original Article; Plant Genetics and Genomics; Plants - genetics; prediction; Predictions; Ribonucleic acid; RNA; RNA, Long Noncoding - classification; RNA, Long Noncoding - genetics; wheat; LncRNA; Plants; Machine learning; Random Forest
• ISSN: 1438-793X; 1438-7948; 1438-7948
• DOI: 10.1007/s10142-021-00769-w

Following the elucidation of the critical roles they play in numerous important biological processes, long noncoding RNAs (lncRNAs) have gained vast attention in recent years. Manual annotation of lncRNAs is restricted by known gene annotations and is prone to false prediction due to the incompleteness of available data. However, with the advent of high-throughput sequencing technologies, a magnitude of high-quality data has become available for annotation, especially for plant species such as wheat. Here, we compared prediction accuracies of several machine learning algorithms using a 10-fold cross-validation. This study includes a comprehensive feature selection step to refine irrelevant and repeated features. We present a crop-specific, alignment-free coding potential prediction tool, LncMachine, that performs at higher prediction accuracies than the currently available popular tools (CPC2, CPAT, and CNIT) when used with the Random Forest algorithm. Further, LncMachine with Random Forest performed well on human and mouse data, with an average accuracy of 92.67%. LncMachine only requires either a FASTA file or a TAB separated CSV file containing features as input files. LncMachine can deploy several user-provided algorithms in real time and therefore be effortlessly applied to a wide range of studies.