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) are widely involved in the initiation and development of cancer. Although some computational methods have been proposed to identify cancer-related lncRNAs, there is still a demanding to improve the prediction accuracy and efficiency. In addition, the quick-update data of cancer, as well as the discovery of new mechanism, also underlay the possibility of improvement of cancer-related lncRNA prediction algorithm. In this study, we introduced CRlncRC, a novel Cancer-Related lncRNA Classifier by integrating manifold features with five machine-learning techniques. CRlncRC was built on the integration of genomic, expression, epigenetic and network, totally in four categories of features. Five learning techniques were exploited to develop the effective classification model including Random Forest (RF), Naïve bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR) and K-Nearest Neighbors (KNN). Using ten-fold cross-validation, we showed that RF is the best model for classifying cancer-related lncRNAs (AUC = 0.82). The feature importance analysis indicated that epigenetic and network features play key roles in the classification. In addition, compared with other existing classifiers, CRlncRC exhibited a better performance both in sensitivity and specificity. We further applied CRlncRC to lncRNAs from the TANRIC (The Atlas of non-coding RNA in Cancer) dataset, and identified 121 cancer-related lncRNA candidates. These potential cancer-related lncRNAs showed a certain kind of cancer-related indications, and many of them could find convincing literature supports. Our results indicate that CRlncRC is a powerful method for identifying cancer-related lncRNAs. Machine-learning-based integration of multiple features, especially epigenetic and network features, had a great contribution to the cancer-related lncRNA prediction. RF outperforms other learning techniques on measurement of model sensitivity and specificity. In addition, using CRlncRC method, we predicted a set of cancer-related lncRNAs, all of which displayed a strong relevance to cancer as a valuable conception for the further cancer-related lncRNA function studies.

Zebrafish is a full-developed model system for studying development processes and human disease. Recent studies of deep sequencing had discovered a large number of long non-coding RNAs (lncRNAs) in zebrafish. However, only few of them had been functionally characterized. Therefore, how to take advantage of the mature zebrafish system to deeply investigate the lncRNAs’ function and conservation is really intriguing. We systematically collected and analyzed a series of zebrafish RNA-seq data, then combined them with resources from known database and literatures. As a result, we obtained by far the most complete dataset of zebrafish lncRNAs, containing 13,604 lncRNA genes (21,128 transcripts) in total. Based on that, a co-expression network upon zebrafish coding and lncRNA genes was constructed and analyzed, and used to predict the Gene Ontology (GO) and the KEGG annotation of lncRNA. Meanwhile, we made a conservation analysis on zebrafish lncRNA, identifying 1828 conserved zebrafish lncRNA genes (1890 transcripts) that have their putative mammalian orthologs. We also found that zebrafish lncRNAs play important roles in regulation of the development and function of nervous system; these conserved lncRNAs present a significant sequential and functional conservation, with their mammalian counterparts. By integrative data analysis and construction of coding-lncRNA gene co-expression network, we gained the most comprehensive dataset of zebrafish lncRNAs up to present, as well as their systematic annotations and comprehensive analyses on function and conservation. Our study provides a reliable zebrafish-based platform to deeply explore lncRNA function and mechanism, as well as the lncRNA commonality between zebrafish and human.

Recent studies demonstrated that long non-coding RNAs (lncRNAs) could be intricately implicated in cancer-related molecular networks, and related to cancer occurrence, development and prognosis. However, clinicopathological and molecular features for these cancer-related lncRNAs, which are very important in bridging lncRNA basic research with clinical research, fail to well settle to integration. After manually reviewing more than 2500 published literature, we collected the cancer-related lncRNAs with the experimental proof of functions. By integrating from literature and public databases, we constructed CRlncRNA, a database of cancer-related lncRNAs. The current version of CRlncRNA embodied 355 entries of cancer-related lncRNAs, covering 1072 cancer-lncRNA associations regarding to 76 types of cancer, and 1238 interactions with different RNAs and proteins. We further annotated clinicopathological features of these lncRNAs, such as the clinical stages and the cancer hallmarks. We also provided tools for data browsing, searching and download, as well as online BLAST, genome browser and gene network visualization service. CRlncRNA is a manually curated database for retrieving clinicopathological and molecular features of cancer-related lncRNAs supported by highly reliable evidences. CRlncRNA aims to provide a bridge from lncRNA basic research to clinical research. The lncRNA dataset collected by CRlncRNA can be used as a golden standard dataset for the prospective experimental and in-silico studies of cancer-related lncRNAs. CRlncRNA is freely available for all users at http://crlnc.xtbg.ac.cn .

Background: Long non-coding RNAs (lncRNAs) play vital roles in many important biological processes in plants. Currently, a large fraction of plant lncRNA studies center at lncRNA identification and functional analysis. Only a few plant lncRNA studies focus on understanding their evolutionary history, which is crucial for an in-depth understanding of lncRNAs. Therefore, the integration of large volumes of plant lncRNA data is required to deeply investigate the evolution of lncRNAs. Results: We present a large-scale evolutionary analysis of lncRNAs in 25 flowering plants. In total, we identified 199,796 high-confidence lncRNAs through data integration analysis, and grouped them into 5497 lncRNA orthologous families. Then, we divided the lncRNAs into groups based on the degree of sequence conservation, and quantified the various characteristics of 756 conserved Arabidopsis thaliana lncRNAs. We found that compared with non-conserved lncRNAs, conserved lncRNAs might have more exons, longer sequence length, higher expression levels, and lower tissue specificities. Functional annotation based on the A. thaliana coding-lncRNA gene co-expression network suggested potential functions of conserved lncRNAs including autophagy, locomotion, and cell cycle. Enrichment analysis revealed that the functions of conserved lncRNAs were closely related to the growth and development of the tissues in which they were specifically expressed. Conclusions: Comprehensive integration of large-scale lncRNA data and construction of a phylogenetic tree with orthologous lncRNA families from 25 flowering plants was used to provide an oversight of the evolutionary history of plant lncRNAs including origin, conservation, and orthologous relationships. Further analysis revealed a differential characteristic profile for conserved lncRNAs in A. thaliana when compared with non-conserved lncRNAs. We also examined tissue specific expression and the potential functional roles of conserved lncRNAs. The results presented here will further our understanding of plant lncRNA evolution, and provide the basis for further in-depth studies of their functions.

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.