Dataset Information

PLncPRO for prediction of long non-coding RNAs (lncRNAs) in plants and its application for discovery of abiotic stress-responsive lncRNAs in rice and chickpea.

ABSTRACT: Long non-coding RNAs (lncRNAs) make up a significant portion of non-coding RNAs and are involved in a variety of biological processes. Accurate identification/annotation of lncRNAs is the primary step for gaining deeper insights into their functions. In this study, we report a novel tool, PLncPRO, for prediction of lncRNAs in plants using transcriptome data. PLncPRO is based on machine learning and uses random forest algorithm to classify coding and long non-coding transcripts. PLncPRO has better prediction accuracy as compared to other existing tools and is particularly well-suited for plants. We developed consensus models for dicots and monocots to facilitate prediction of lncRNAs in non-model/orphan plants. The performance of PLncPRO was quite better with vertebrate transcriptome data as well. Using PLncPRO, we discovered 3714 and 3457 high-confidence lncRNAs in rice and chickpea, respectively, under drought or salinity stress conditions. We investigated different characteristics and differential expression under drought/salinity stress conditions, and validated lncRNAs via RT-qPCR. Overall, we developed a new tool for the prediction of lncRNAs in plants and showed its utility via identification of lncRNAs in rice and chickpea.

SUBMITTER: Singh U

PROVIDER: S-EPMC5727461 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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PLncPRO for prediction of long non-coding RNAs (lncRNAs) in plants and its application for discovery of abiotic stress-responsive lncRNAs in rice and chickpea.

Singh Urminder U Khemka Niraj N Rajkumar Mohan Singh MS Garg Rohini R Jain Mukesh M

Nucleic acids research 20171201 22

Long non-coding RNAs (lncRNAs) make up a significant portion of non-coding RNAs and are involved in a variety of biological processes. Accurate identification/annotation of lncRNAs is the primary step for gaining deeper insights into their functions. In this study, we report a novel tool, PLncPRO, for prediction of lncRNAs in plants using transcriptome data. PLncPRO is based on machine learning and uses random forest algorithm to classify coding and long non-coding transcripts. PLncPRO has bette ...[more]

PMID: 29036354

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Project description:BackgroundLong non-coding RNAs (lncRNAs) represent a class of riboregulators that either directly act in long form or are processed into shorter microRNAs (miRNAs) and small interfering RNAs. Long noncoding RNAs (lncRNAs) are arbitrarily defined as RNA genes larger than 200 nt in length that have no apparent coding potential. lncRNAs have emerged as playing important roles in various biological regulatory processes and are expressed in a more tissue-specific manner than mRNA. Emerging evidence shows that lncRNAs participate in stress-responsive regulation.ResultsIn this study, in order to develop a comprehensive catalogue of lncRNAs in upland cotton under salt stress, we performed whole-transcriptome strand-specific RNA sequencing for three-leaf stage cotton seedlings treated with salt stress (S_NaCl) and controls (S_CK). In total we identified 1117 unique lncRNAs in this study and 44 differentially expressed RNAs were identified as potential non-coding RNAs. For the differentially expressed lncRNAs that were identified as intergenic lncRNAs (lincRNA), we analysed the gene ontology enrichment of cis targets and found that cis target protein-coding genes were mainly enriched in stress-related categories. Real-time quantitative PCR confirmed that all selected lincRNAs responsive to salt stress. We found lnc_388 was likely as regulator of Gh_A09G1182. And lnc_883 may participate in regulating tolerance to salt stress by modulating the expression of Gh_D03G0339 MS_channel. We then predicted the target mimics for miRNA in Gossypium. six miRNAs were identified, and the result of RT-qPCR with lncRNA and miRNA suggested that lnc_973 and lnc_253 may regulate the expression of ghr-miR399 and ghr-156e as a target mimic under salt stress.ConclusionsWe identified 44 lincRNAs that were differentially expressed under salt stress. These lincRNAs may target protein-coding genes via cis-acting regulation. We also discovered that specifically-expressed lincRNAs under salt stress may act as endogenous target mimics for conserved miRNAs. These findings extend the current view on lincRNAs as ubiquitous regulators under stress stress.

Dataset Information

PLncPRO for prediction of long non-coding RNAs (lncRNAs) in plants and its application for discovery of abiotic stress-responsive lncRNAs in rice and chickpea.

Publications

PLncPRO for prediction of long non-coding RNAs (lncRNAs) in plants and its application for discovery of abiotic stress-responsive lncRNAs in rice and chickpea.

Similar Datasets

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