Long noncoding RNAs (lncRNAs) make up a large proportion of transcriptome in eukaryotes, and have been revealed with many regulatory functions in various biological processes. When studying lncRNAs, the first step is to accurately and specifically distinguish them from the colossal transcriptome data with complicated composition, which contains mRNAs, lncRNAs, small RNAs and their primary transcripts. In the face of such a huge and progressively expanding transcriptome data, the
in-silico
approaches provide a practicable scheme for effectively and rapidly filtering out lncRNA targets, using machine learning and probability statistics. In this review, we mainly discussed the characteristics of algorithms and features on currently developed approaches. We also outlined the traits of some state-of-the-art tools for ease of operation. Finally, we pointed out the underlying challenges in lncRNA identification with the advent of new experimental data.
Nyctinastic leaf movement of Fabaceae is driven by the tiny motor organ pulvinus located at the base of the leaf or leaflet. Despite the increased understanding of the essential role of ELONGATED PETIOLULE1 (ELP1)/PETIOLE LIKE PULVINUS (PLP) orthologs in determining pulvinus identity in legumes, key regulatory components and molecular mechanisms underlying this movement remain largely unclear. Here, we used WT pulvinus and the equivalent tissue in the elp1 mutant to carry out transcriptome and proteome experiments. The omics data indicated that there are multiple cell biological processes altered at the gene expression and protein abundance level during the pulvinus development. In addition, comparative analysis of different leaf tissues provided clues to illuminate the possible common primordium between pulvinus and petiole, as well as the function of ELP1. Furthermore, the auxin pathway, cell wall composition and chloroplast distribution were altered in elp1 mutants, verifying their important roles in pulvinus development. This study provides a comprehensive insight into the motor organ of the model legume Medicago truncatula and further supplies a rich dataset to facilitate the identification of novel players involved in nyctinastic movement.
Gene co-expression network analysis has been widely used in gene function annotation, especially for long noncoding RNAs (lncRNAs). However, there is a lack of effective cross-platform analysis tools. For biologists to easily build a gene co-expression network and to predict gene function, we developed GCEN, a cross-platform command-line toolkit developed with C++. It is an efficient and easy-to-use solution that will allow everyone to perform gene co-expression network analysis without the requirement of sophisticated programming skills, especially in cases of RNA-Seq research and lncRNAs function annotation. Because of its modular design, GCEN can be easily integrated into other pipelines.
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