Locate-R: Subcellular localization of long non-coding RNAs using nucleotide compositions

Knowledge of the sub-cellular localization of the most diverse class of transcribed RNA, long non-coding RNAs (lncRNAs) will lead us to identify different types of cancers and other diseases as lncRNAs play key role in related cellular functions. In recent days with the exponential growth of known records, it becomes essential to establish new machine learning based techniques to identify the new one due to faster and cheaper solutions provided compared to laboratory methods. In this paper, we propose Locate-R, a novel method for predicting the sub-cellular location of lncRNAs. We have used only n-gapped l-mer composition and l-mer composition as features and select best 655 features to build the model. This model is based locally deep support vector machines which significantly enhance the prediction accuracy with respect to exiting state-of-the-art methods. Our predictor is readily available for use as a stand-alone web application from: http://locate-r.azurewebsites.net/.     

CRlncRC: a machine learning-based method for cancer-related long noncoding RNA identification using integrated features

Background

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.

Results

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.

Conclusions

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.

     

Tissue-specific analysis of Coffea arabica L. transcriptome revealed potential regulatory roles of lncRNAs

Long non-coding RNAs (lncRNAs) play pivot roles in regulating mRNA expression in eukaryotic organisms without coding any proteins. In the current study, a comprehensive analysis of 260 published RNA-Seq datasets collected from different tissues (fruits, leaves, stems, and roots) of Coffea arabica L. was performed to discover potential lncRNAs. A total of 10,564 unique lncRNAs were identified. Our results showed that 77.14% of the lncRNAs were intergenic and 60.39% of them are located within 5 Kbp from the partner gene. In general, all the identified lncRNAs showed shorter lengths, fewer number of exons, and lower expression levels as compared to mRNAs in different studied tissues. Several lncRNAs were determined as differentially expressed (DE) in fruits as compared to leaves, stems, or roots. The functional characterization of the DE lncRNAs revealed their roles in regulating significant biological processes in different tissues of C. arabica. The current study provides a comprehensive analysis and dataset of lncRNAs in C. arabica that could be utilized in further studies concerning the roles of these molecules in plant cells.     

Identification of novel phasiRNAs loci on long non-coding RNAs in Arabidopsis thaliana

Long non-coding RNAs (lncRNAs) are the “dark matters”involved in gene regulation with complex mechanisms. However, the functions of most lncRNAs remain to be determined. Our previous work revealed a massive number of degradome-supported cleavage signatures on Arabidopsis lncRNAs. Some of them have been confirmed associated with miRNAs-like sRNAs production, while others without long stem structure remain unexplored. A systematical search for phasiRNAs generating ability of these lncRNAs was conducted. Eight novel small RNA triggered lncRNA-phasiRNA pathways were discovered and three of them were found to be conserved in Arabidopsis, Oryza sativa, Glycine max and Gossypium hirsutum. Besides, Five novel ta-siRNAs derived from these lncRNAs were further identified to be involved in the regulation of plant development, stress responses and aromatic amino acids synthesis. These results substantially expanded the gene regulation mechanisms of lncRNAs.     

Long noncoding RNA complementarity and target transcripts abundance

Eukaryotic mRNA metabolism regulates its stability, localization, and translation using complementarity with counter-part RNAs. To modulate their stability, small and long noncoding RNAs can establish complementarity with their target mRNAs. Although complementarity of small interfering RNAs and microRNAs with target mRNAs has been studied thoroughly, partial complementarity of long noncoding RNAs (lncRNAs) with their target mRNAs has not been investigated clearly. To address that research gap, our lab investigated whether the sequence complementarity of two lncRNAs, lincRNA-p21 and OIP5-AS1, influenced the quantity of target RNA expression. We predicted a positive correlation between lncRNA complementarity and target mRNA quantity. We confirmed this prediction using RNA affinity pull down, microarray, and RNA-sequencing analysis. In addition, we utilized the information from this analysis to compare the quantity of target mRNAs when two lncRNAs, lincRNA-p21 and OIP5-AS1, are depleted by siRNAs. We observed that human and mouse lincRNA-p21 regulated target mRNA abundance in complementarity-dependent and independent manners. In contrast, affinity pull down of OIP5-AS1 revealed that changes in OIP5-AS1 expression influenced the amount of some OIP5-AS1 target mRNAs and miRNAs, as we predicted from our sequence complementarity assay. Altogether, the current study demonstrates that partial complementarity of lncRNAs and mRNAs (even miRNAs) assist in determining target RNA expression and quantity.     

Long Noncoding RNAs AC009014.3 and Newly Discovered XPLAID Differentiate Aggressive and Indolent Prostate Cancers

INTRODUCTION: The molecular mechanisms underlying aggressive versus indolent disease are not fully understood. Recent research has implicated a class of molecules known as long noncoding RNAs (lncRNAs) in tumorigenesis and progression of cancer. Our objective was to discover lncRNAs that differentiate aggressive and indolent prostate cancers. METHODS: We analyzed paired tumor and normal tissues from six aggressive Gleason score (GS) 8-10 and six indolent GS 6 prostate cancers. Extracted RNA was split for poly(A)+ and ribosomal RNA depletion library preparations, followed byRNA sequencing (RNA-Seq) using an Illumina HiSeq 2000. We developed an RNA-Seq data analysis pipeline to discover and quantify these molecules. Candidate lncRNAs were validated using RT-qPCR on 87 tumor tissue samples: 28 (GS 6), 28 (GS 3+4), 6 (GS 4+3), and 25 (GS 8-10). Statistical correlations between lncRNAs and clinicopathologic variables were tested using ANOVA. RESULTS: The 43 differentially expressed (DE) lncRNAs between aggressive and indolent prostate cancers included 12 annotated and 31 novel lncRNAs. The top six DE lncRNAs were selected based on large, consistent fold-changes in the RNA-Seq results. Three of these candidates passed RT-qPCR validation, including AC009014.3 (P < .001 in tumor tissue) and a newly discovered X-linked lncRNA named XPLAID (P = .049 in tumor tissue and P = .048 in normal tissue). XPLAID and AC009014.3 show promise as prognostic biomarkers. CONCLUSIONS: We discovered several dozen lncRNAs that distinguish aggressive and indolent prostate cancers, of which four were validated using RT-qPCR. The investigation into their biology is ongoing.     

Sense-overlapping lncRNA as a decoy of translational repressor protein for dimorphic gene expression

Long noncoding RNAs (lncRNAs) are vastly transcribed and extensively studied but lncRNAs overlapping with the sense orientation of mRNA have been poorly studied. We analyzed the lncRNA DAPALR overlapping with the 5´ UTR of the Doublesex1 (Dsx1), the male determining gene in Daphnia magna. By affinity purification, we identified an RNA binding protein, Shep as a DAPALR binding protein. Shep also binds to Dsx1 5´ UTR by recognizing the overlapping sequence and suppresses translation of the mRNA. In vitro and in vivo analyses indicated that DAPALR increased Dsx1 translation efficiency by sequestration of Shep. This regulation was impaired when the Shep binding site in DAPALR was deleted. These results suggest that Shep suppresses the unintentional translation of Dsx1 by setting a threshold; and when the sense lncRNA DAPALR is expressed, DAPALR cancels the suppression caused by Shep. This mechanism may be important to show dimorphic gene expressions such as sex determination and it may account for the binary expression in various developmental processes.     

The long noncoding RNA FRILAIR regulates strawberry fruit ripening by functioning as a noncanonical target mimic

Long noncoding RNAs (lncRNAs) are emerging as important regulators in plant development, but few of them have been functionally characterized in fruit ripening. Here, we have identified 25,613 lncRNAs from strawberry ripening fruits based on RNA-seq data from poly(A)-depleted libraries and rRNA-depleted libraries, most of which exhibited distinct temporal expression patterns. A novel lncRNA, FRILAIR harbours the miR397 binding site that is highly conserved in diverse strawberry species. FRILAIR overexpression promoted fruit maturation in the Falandi strawberry, which was consistent with the finding from knocking down miR397, which can guide the mRNA cleavage of both FRILAIR and LAC11a (encoding a putative laccase-11-like protein). Moreover, LAC11a mRNA levels were increased in both FRILAIR overexpressing and miR397 knockdown fruits, and accelerated fruit maturation was also found in LAC11a overexpressing fruits. Overall, our study demonstrates that FRILAIR can act as a noncanonical target mimic of miR397 to modulate the expression of LAC11a in the strawberry fruit ripening process.