Technological advances over the past decade have unraveled the remarkable complexity of RNA. The identification of small peptides encoded by long non-coding RNAs (lncRNAs) as well as regulatory functions mediated by non-coding regions of mRNAs have further complicated our understanding of the multifaceted functions of RNA. In this review, we summarize current evidence pointing to dual roles of RNA molecules defined by their coding and non-coding potentials. We also discuss how the emerging roles of RNA transform our understanding of gene expression and evolution.

In the past decade, hundreds of long noncoding RNAs (lncRNAs) have been identified as significant players in diverse types of cancer; however, the functions and mechanisms of most lncRNAs in cancer remain unclear. Several computational methods have been developed to detect associations between cancer and lncRNAs, yet those approaches have limitations in both sensitivity and specificity. With the goal of improving the prediction accuracy for associations of lncRNA with cancer, we upgraded our previously developed cancer-related lncRNA classifier, CRlncRC, to generate CRlncRC2. CRlncRC2 is an eXtreme Gradient Boosting (XGBoost) machine learning framework, including Synthetic Minority Over-sampling Technique (SMOTE)-based over-sampling, along with Laplacian Score-based feature selection. Ten-fold cross-validation showed that the AUC value of CRlncRC2 for identification of cancer-related lncRNAs is much higher than previously reported by CRlncRC and others. Compared with CRlncRC, the number of features used by CRlncRC2 dropped from 85 to 51. Finally, we identified 439 cancer-related lncRNA candidates using CRlncRC2. To evaluate the accuracy of the predictions, we first consulted the cancer-related long non-coding RNA database Lnc2Cancer v2.0 and relevant literature for supporting information, then conducted statistical analysis of somatic mutations, distance from cancer genes, and differential expression in tumor tissues, using various data sets. The results showed that our approach was highly reliable for identifying cancer-related lncRNA candidates. Notably, the highest ranked candidate, lncRNA AC074117.1, has not been reported previously; however, integrated multi-omics analyses demonstrate that it is the target of multiple cancer-related miRNAs and interacts with adjacent protein-coding genes, suggesting that it may act as a cancer-related competing endogenous RNA, which warrants further investigation. In conclusion, CRlncRC2 is an effective and accurate method for identification of cancer-related lncRNAs, and has potential to contribute to the functional annotation of lncRNAs and guide cancer therapy.

Species of Paris Sect. Marmorata are valuable medicinal plants to synthesize steroidal saponins with effective pharmacological therapy. However, the wild resources of the species are threatened by plundering exploitation before the molecular genetics studies uncover the genomes and evolutionary significance. Thus, the availability of complete chloroplast genome sequences of Sect. Marmorata is necessary and crucial to the understanding the plastome evolution of this section and facilitating future population genetics studies. Here, we determined chloroplast genomes of Sect. Marmorata, and conducted the whole chloroplast genome comparison. This study presented detailed sequences and structural variations of chloroplast genomes of Sect. Marmorata. Over 40 large repeats and approximately 130 simple sequence repeats as well as a group of genomic hotspots were detected. Inverted repeat contraction of this section was inferred via comparing the chloroplast genomes with the one of P. verticillata. Additionally, almost all the plastid protein coding genes were found to prefer ending with A/U. Mutation bias and selection pressure predominately shaped the codon bias of most genes. And most of the genes underwent purifying selection, whereas photosynthetic genes experienced a relatively relaxed purifying selection. Repeat sequences and hotspot regions can be scanned to detect the intraspecific and interspecific variability, and selected to infer the phylogenetic relationships of Sect. Marmorata and other species in subgenus Daiswa. Mutation and natural selection were the main forces to drive the codon bias pattern of most plastid protein coding genes. Therefore, this study enhances the understanding about evolution of Sect. Marmorata from the chloroplast genome, and provide genomic insights into genetic analyses of Sect. Marmorata.

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.

The CRISPR/Cas9 system, as a revolutionary genome editing tool for all areas of molecular biology, provides new opportunities for research on lncRNA’s function. However, designing a CRISPR/Cas9 single guide RNA (sgRNA) for lncRNA is not easy with an unwarrantable effectiveness. Thus, it is worthy of collecting validated sgRNAs, to assist in efficiently choosing sgRNA with an expected activity. CRISPRlnc (http://www.crisprlnc.org or http://crisprlnc.xtbg.ac.cn) is a manually curated database of validated CRISPR/Cas9 sgRNAs for lncRNAs from all species. After manually reviewing more than 200 published literature, the current version of CRISPRlnc contains 305 lncRNAs and 2102 validated sgRNAs across eight species, including mammalian, insect and plant. We handled the ID, position in the genome, sequence and functional description of these lncRNAs, as well as the sequence, protoacceptor-motif (PAM), CRISPR type and validity of their paired sgRNAs. In CRISPRlnc, we provided the tools for browsing, searching and downloading data, as well as online BLAST service and genome browse server. As the first database against the validated sgRNAs of lncRNAs, CRISPRlnc will provide a new and powerful platform to promote CRISPR/Cas9 applications for future functional studies of lncRNAs.