Dataset Information

Long noncoding RNA complementarity and target transcripts abundance.

ABSTRACT: 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.

SUBMITTER: Zealy RW

PROVIDER: S-EPMC5924675 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Long noncoding RNA complementarity and target transcripts abundance.

Zealy Richard W RW Fomin Mikhail M Davila Sylvia S Makowsky Daniel D Thigpen Haley H McDowell Catherine H CH Cummings James C JC Lee Edward S ES Kwon Sang-Ho SH Min Kyung-Won KW Yoon Je-Hyun JH

Biochimica et biophysica acta. Gene regulatory mechanisms 20180206 3

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, ...[more]

PMID: 29421307

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Project description:The biological importance of changes in RNA expression is reflected by the wide variety of tools available to characterise these changes from RNA-seq data. Several tools exist for detecting differential transcript isoform usage (DTU) from aligned or assembled RNA-seq data, but few exist for DTU detection from alignment-free RNA-seq quantifications. We present the RATs, an R package that identifies DTU transcriptome-wide directly from transcript abundance estimates. RATs is unique in applying bootstrapping to estimate the reliability of detected DTU events and shows good performance at all replication levels (median false positive fraction < 0.05). We compare RATs to two existing DTU tools, DRIM-Seq & SUPPA2, using two publicly available simulated RNA-seq datasets and a published human RNA-seq dataset, in which 248 genes have been previously identified as displaying significant DTU. RATs with default threshold values on the simulated Human data has a sensitivity of 0.55, a Matthews correlation coefficient of 0.71 and a false discovery rate (FDR) of 0.04, outperforming both other tools. Applying the same thresholds for SUPPA2 results in a higher sensitivity (0.61) but poorer FDR performance (0.33). RATs and DRIM-seq use different methods for measuring DTU effect-sizes complicating the comparison of results between these tools, however, for a likelihood-ratio threshold of 30, DRIM-Seq has similar FDR performance to RATs (0.06), but worse sensitivity (0.47). These differences persist for the simulated drosophila dataset. On the published human RNA-seq dataset the greatest agreement between the tools tested is 53%, observed between RATs and SUPPA2. The bootstrapping quality filter in RATs is responsible for removing the majority of DTU events called by SUPPA2 that are not reported by RATs. All methods, including the previously published qRT-PCR of three of the 248 detected DTU events, were found to be sensitive to annotation differences between Ensembl v60 and v87.

Dataset Information

Long noncoding RNA complementarity and target transcripts abundance.

Publications

Long noncoding RNA complementarity and target transcripts abundance.

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