Dataset Information

MiR&moRe2: A Bioinformatics Tool to Characterize microRNAs and microRNA-Offset RNAs from Small RNA-Seq Data.

ABSTRACT: MicroRNA-offset RNAs (moRNAs) are microRNA-like small RNAs generated by microRNA precursors. To date, little is known about moRNAs and bioinformatics tools to inspect their expression are still missing. We developed miR&moRe2, the first bioinformatics method to consistently characterize microRNAs, moRNAs, and their isoforms from small RNA sequencing data. To illustrate miR&moRe2 discovery power, we applied it to several published datasets. MoRNAs identified by miR&moRe2 were in agreement with previous research findings. Moreover, we observed that moRNAs and new microRNAs predicted by miR&moRe2 were downregulated upon the silencing of the microRNA-biogenesis pathway. Further, in a sizeable dataset of human blood cell populations, tens of novel miRNAs and moRNAs were discovered, some of them with significantly varied expression levels among the cell types. Results demonstrate that miR&moRe2 is a valid tool for a comprehensive study of small RNAs generated from microRNA precursors and could help to investigate their biogenesis and function.

SUBMITTER: Gaffo E

PROVIDER: S-EPMC7084216 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Publications

MiR&moRe2: A Bioinformatics Tool to Characterize microRNAs and microRNA-Offset RNAs from Small RNA-Seq Data.

Gaffo Enrico E Bortolomeazzi Michele M Bisognin Andrea A Di Battista Piero P Lovisa Federica F Mussolin Lara L Bortoluzzi Stefania S

International journal of molecular sciences 20200304 5

MicroRNA-offset RNAs (moRNAs) are microRNA-like small RNAs generated by microRNA precursors. To date, little is known about moRNAs and bioinformatics tools to inspect their expression are still missing. We developed miR&moRe2, the first bioinformatics method to consistently characterize microRNAs, moRNAs, and their isoforms from small RNA sequencing data. To illustrate miR&moRe2 discovery power, we applied it to several published datasets. MoRNAs identified by miR&moRe2 were in agreement with pr ...[more]

PMID: 32143373

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Project description:BackgroundRecent advances in sequencing technology have opened a new era in RNA studies. Novel types of RNAs such as long non-coding RNAs (lncRNAs) have been discovered by transcriptomic sequencing and some lncRNAs have been found to play essential roles in biological processes. However, only limited information is available for lncRNAs in Drosophila melanogaster, an important model organism. Therefore, the characterization of lncRNAs and identification of new lncRNAs in D. melanogaster is an important area of research. Moreover, there is an increasing interest in the use of ChIP-seq data (H3K4me3, H3K36me3 and Pol II) to detect signatures of active transcription for reported lncRNAs.ResultsWe have developed a computational approach to identify new lncRNAs from two tissue-specific RNA-seq datasets using the poly(A)-enriched and the ribo-zero method, respectively. In our results, we identified 462 novel lncRNA transcripts, which we combined with 4137 previously published lncRNA transcripts into a curated dataset. We then utilized 61 RNA-seq and 32 ChIP-seq datasets to improve the annotation of the curated lncRNAs with regards to transcriptional direction, exon regions, classification, expression in the brain, possession of a poly(A) tail, and presence of conventional chromatin signatures. Furthermore, we used 30 time-course RNA-seq datasets and 32 ChIP-seq datasets to investigate whether the lncRNAs reported by RNA-seq have active transcription signatures. The results showed that more than half of the reported lncRNAs did not have chromatin signatures related to active transcription. To clarify this issue, we conducted RT-qPCR experiments and found that ~95.24% of the selected lncRNAs were truly transcribed, regardless of whether they were associated with active chromatin signatures or not.ConclusionsIn this study, we discovered a large number of novel lncRNAs, which suggests that many remain to be identified in D. melanogaster. For the lncRNAs that are known, we improved their characterization by integrating a large number of sequencing datasets (93 sets in total) from multiple sources (lncRNAs, RNA-seq and ChIP-seq). The RT-qPCR experiments demonstrated that RNA-seq is a reliable platform to discover lncRNAs. This set of curated lncRNAs with improved annotations can serve as an important resource for investigating the function of lncRNAs in D. melanogaster.

Project description:MicroRNA-offset RNAs (moRs) were first identified in simple chordates and subsequently in mouse and human cells by deep sequencing of short RNAs. MoRs are derived from sequences located immediately adjacent to microRNAs (miRs) in the primary miR (pri-miR). Currently moRs are considered to be simply a by-product of miR biosynthesis that lack biological activity. Here we show for the first time that a moR is biologically active. We now demonstrate that endogenous and over-expressed moR-21 significantly alters gene expression and inhibits the proliferation of vascular smooth muscle cells (VSMC). We report that the "seed region" of moR-21 as well as the "seed match region" in the target gene 3'UTR are indispensable for moR-21-mediated gene down-regulation. We further demonstrated that moR-21-mediated gene repression is Argonaute 2 (Ago2) dependent. In addition, we find that miR-21 and moR-21 may regulate different genes in a given pathway and can oppose each other in regulating certain genes. Taken together, these findings provide the first evidence that microRNA offset RNA regulates gene expression and is biologically active. Small RNAs were harvested from mouse aortic smooth muscle cells (AoSMCs), with three biological replicates, using miReasy kits (Qiagen) according to the manufacturerâ??s instructions. RNA was quantified using a Nanodrop spectrophotometer. Small RNA library construction and illumina HiSeq 75bp single read sequencing were performed at the Yale Center for Genome Analysis. Reads were trimmed to remove adaptor sequences, and inserts longer than 13 bases were mapped to the mouse mm10 genome using Bowtie (with settings â??n 0 -m 5 --best --strata, allowing 0 mismatches and up to 5 genomic loci with a perfect match, {Genome Biol. 2009;10(3):R25, Langmead B, Trapnell C, Pop M, Salzberg SL}). Reads that mapped to within 50 bp of more than one annotated mouse pri-miR (as defined by miRBase {Kozomara A, Griffiths-Jones S. Nucelic Acids Res 2014 42:D68-D73}), were allotted evenly between these locations. Reads that fell entirely within established miRbase 3p or 5p miR coordinates, plus or minus 3 bp, were counted as 3p or 5p miRs, respectively. Reads that fell entirely within the range of 35 bp 3â?? to 3 bp 5â?? of the last base of a 3p miR were counted as 3p moR reads. Those that fell entirely between 3bp 3â?? and 35 bp 5â?? of the first base of a 5p miR were counted as 5p moR reads. On average each library contained 17.4 million qualifying inserts, of which 13.8 million (79.3%) mapped to one of these four regions, indicating that the large majority of inserts represented miR or moR sequences. Reads were divided by the number of million reads mapping to these regions in each library to give normalized reads per million reads (RPMR) values.

Dataset Information

MiR&moRe2: A Bioinformatics Tool to Characterize microRNAs and microRNA-Offset RNAs from Small RNA-Seq Data.

Publications

MiR&moRe2: A Bioinformatics Tool to Characterize microRNAs and microRNA-Offset RNAs from Small RNA-Seq Data.

Similar Datasets

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