The high-throughput sequencing technologies have created the opportunity of large-scale transcriptome analyses and intensify attention on the study of non-coding RNAs (ncRNAs). NcRNAs pay important roles in many cellular processes. For example, transfer RNAs and ribosomal RNAs are involved in protein translation process; micro RNAs regulate gene expression; long ncRNAs are found to associate with many human diseases ranging from autism to cancer.Many ncRNAs function through both their sequences and secondary structures. Thus, accurate secondary structure prediction provides important information to understand the tertiary structures and thus the functions of ncRNAs.The state-of-the-art ncRNA identification tools are mainly based on two approaches. The first approach is a comparative structure analysis, which determines the consensus structure from homologous ncRNAs. Structure prediction is a costly process, because the size of the putative structures increases exponentially with the sequence length. Thus it is not practical for very long ncRNAs such as lncRNAs. The accuracy of current structure prediction tools is still not satisfactory, especially on sequences containing pseudoknots. An alternative identification approach that has been increasingly popular is sequence based expression analysis, which relies on next generation sequencing (NGS) technologies for quantifying gene expression on a genome-wide scale. The specific expression patterns are used to identify the type of ncRNAs. This method therefore is limited to ncRNAs that have medium to high expression levels and have the unique expression patterns that are different from other ncRNAs. In this work, we address the challenges presented in ncRNA identification using different approaches. To be specific, we have proposed four tools, grammar-string based alignment, KnotShape, KnotStructure, and lncRNA-ID. Grammar-string is a novel ncRNA secondary structure representation that encodes an ncRNA's sequence and secondary structure in the parameter space of a context-free grammar and a full RNA grammar including pseudoknots. It simplifies a complicated structure alignment to a simple grammar string-based alignment. Also, grammar-string-based alignment incorporates both sequence and structure into multiple sequence alignment. Thus, we can then enhance the speed of alignment and achieve an accurate consensus structure. KnotShape and KnotStructure focus on reducing the size of the structure search space to enhance the speed of a structure prediction process. KnotShape predicts the best shape by grouping similar structures together and applying SVM classification to select the best representative shape. KnotStructure improve the performance of structure prediction by using grammar-string based-alignment and the predicted shape output by KnotShape.lncRNA-ID is specially designed for lncRNA identification. It incorporates balanced random forest learning to construct a classification model to distinguish lncRNA from protein-coding sequences. The major advantage is that it can maintain a good predictive performance under the limited or imbalanced training data.
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