Computational Identification of Ciona intestinalis MicroRNAs

Keshavan, Raja; Virata, Michael J.; Keshavan, Anisha; Zeller, Robert W.

doi:10.2108/zsj.27.162

Cited by 22 publications

(11 citation statements)

References 55 publications

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“…These miRNAs sum up to 319 in total, a lower number than described in Ciona [31], suggesting that our repertoire may be incomplete. To estimate the proportion of undiscovered miRNAs, we investigated how many metazoan miRNA precursors deposited in the miRBase would pass our selection criteria used in this study in terms of precursor length, loop number, MFE, GC content, and precursor base pairing situation as shown below.…”

Section: Discussionmentioning

confidence: 83%

“…Of the 230 novel predicted mature H. roretzi miRNAs (268 hits minus 38 identified in Fig. 3), Hrore -miR-5008 was the only one with less than two mismatches to the 458 C. robusta mature miRNAs predicted by Keshavan et al [31]. When four mismatches were allowed, only five hits were detected, Harore -miR-5008 ( Cirobu- miR-13a), Harore -miR-5046 ( Cirobu- miR-200b), Harore -miR-5154_5p ( Cirobu- miR-244), Harore -miR-5165 ( Cirobu- miR-246), and Harore -miR-5214 ( Cirobu- miR-246).…”

Section: Resultsmentioning

confidence: 99%

“…When two mismatches were allowed in the mature sequences, 168 H. roretzi miRNAs were found to have a homolog in the C. robusta genome (BLASTN top 500 hits, word size of 7, and an alignment length of ≥20). When four mismatches were accepted, however, 225 out of 230 (97.8%) H. roretzi miRNAs were found to have a homolog in the C. robusta genome and 66 (29.3%) of these 225 precursors could form canonical stem-loops when the temperature parameters of RNAfold were adjusted to 18 °C [31] (Additional file 1, right most column). We have analyzed positions of the mismatches.…”

Section: Resultsmentioning

confidence: 99%

“…Over 400 miRNA candidates were predicted [31] and the expression of 380 of them was experimentally detected in C. robusta by miRNA-seq and microarray data [31, 32]. Some C. robusta miRNAs control development processes [19].…”

Section: Introductionmentioning

confidence: 99%

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Genome-wide survey of miRNAs and their evolutionary history in the ascidian, Halocynthia roretzi

et al. 2017

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BackgroundmiRNAs play essential roles in the modulation of cellular functions via degradation and/or translation attenuation of target mRNAs. They have been surveyed in a single ascidian genus, Ciona. Recently, an annotated draft genome sequence for a distantly related ascidian, Halocynthia roretzi, has become available, but miRNAs in H. roretzi have not been previously studied.ResultsWe report the prediction of 319 candidate H. roretzi miRNAs, obtained through three complementary methods. Experimental validation suggests that more than half of these candidate miRNAs are expressed during embryogenesis. The majority of predicted H. roretzi miRNAs appear specific to ascidians or tunicates, and only 32 candidates, belonging to 25 families, are widely conserved across metazoans.ConclusionOur study presents a comprehensive identification of candidate H. roretzi miRNAs. This resource will facilitate the study of the mechanisms for miRNA-controlled gene regulatory networks during ascidian development. Further, our analysis suggests that the majority of Halocynthia miRNAs are specific to ascidian or tunicates, with only a small number of widely conserved miRNAs. This result is consistent with the general notion that animal miRNAs are less conserved between taxa than plant ones.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-017-3707-5) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 83%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Genome-wide survey of miRNAs and their evolutionary history in the ascidian, Halocynthia roretzi

et al. 2017

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“…The miRDeep2 program (Friedländer et al, 2012) was used to align and quantify miRNA reads. Most of the miRNAs that we detected during OS regeneration have been described previously (Hendrix et al, 2010;Keshavan et al, 2010;Missal et al, 2005;Norden-Krichmar et al, 2007;Shi et al, 2009;Terai et al, 2012); however, miRDeep2 also predicted 52 previously undescribed miRNAs ( probability 95±3%; Table S7). The most highly expressed of these novel miRNAs, 1_2011, was found to have an identical seed sequence to hsa-miR-4709-5p and was detected at >50-fold greater number of reads than the second most highly expressed novel miRNA.…”

Section: Differential Expression During Os Regenerationmentioning

confidence: 73%

A microRNA-mRNA expression network during oral siphon regeneration in Ciona

et al. 2017

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Here we present a parallel study of mRNA and microRNA expression during oral siphon (OS) regeneration in Ciona robusta, and the derived network of their interactions. In the process of identifying 248 mRNAs and 15 microRNAs as differentially expressed, we also identified 57 novel microRNAs, several of which are among the most highly differentially expressed. Analysis of functional categories identified enriched transcripts related to stress responses and apoptosis at the wound healing stage, signaling pathways including Wnt and TGFβ during early regrowth, and negative regulation of extracellular proteases in late stage regeneration. Consistent with the expression results, we found that inhibition of TGFβ signaling blocked OS regeneration. A correlation network was subsequently inferred for all predicted microRNA-mRNA target pairs expressed during regeneration. Network-based clustering associated transcripts into 22 non-overlapping groups, the functional analysis of which showed enrichment of stress response, signaling pathway and extracellular protease categories that could be related to specific microRNAs. Predicted targets of the miR-9 cluster suggest a role in regulating differentiation and the proliferative state of neural progenitors through regulation of the cytoskeleton and cell cycle.

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Computational and Bioinformatics Methods for MicroRNA Gene Prediction

Allmer

2013

miRNomics: MicroRNA Biology and Computational Analysis

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MicroRNAs (miRNAs) have attracted ever-increasing interest in recent years. Since experimental approaches for determining miRNAs are nontrivial in their application, computational methods for the prediction of miRNAs have gained popularity. Such methods can be grouped into two broad categories (1) performing ab initio predictions of miRNAs from primary sequence alone and (2) additionally employing phylogenetic conservation. Most methods acknowledge the importance of hairpin or stem-loop structures and employ various methods for the prediction of RNA secondary structure. Machine learning has been employed in both categories with classification being the predominant method. In most cases, positive and negative examples are necessary for performing classification. Since it is currently elusive to experimentally determine all possible miRNAs for an organism, true negative examples are hard to come by, and therefore the accuracy assessment of algorithms is hampered. In this chapter, first RNA secondary structure prediction is introduced since it provides a basis for miRNA prediction. This is followed by an assessment of homology and then ab initio miRNA prediction methods.

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Computational Identification of Ciona intestinalis MicroRNAs

Cited by 22 publications

References 55 publications

Genome-wide survey of miRNAs and their evolutionary history in the ascidian, Halocynthia roretzi

Genome-wide survey of miRNAs and their evolutionary history in the ascidian, Halocynthia roretzi

A microRNA-mRNA expression network during oral siphon regeneration in Ciona

Computational and Bioinformatics Methods for MicroRNA Gene Prediction

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