Comprehensive analysis of microRNA genomic loci identifies pervasive repetitive-element origins

Borchert, Glen M.; Holton, Nathaniel W; Williams, Jonathan; Hernan, William L.; Bishop, Ian P.; Dembosky, Joel A.; Elste, James E.; Gregoire, Nathaniel S.; Kim, Jee-Ah; Koehler, Wesley W.; Lengerich, Joe C.; Medema, Arianna A.; Nguyen, Marilyn A.; Ower, Geoffrey D.; Rarick, Michelle A.; Strong, Brooke N.; Tardi, Nicholas J.; Tasker, Nathan M.; Wozniak, Darren J.; Gatto, Craig; Larson, Erik D.

doi:10.4161/mge.1.1.15766

Cited by 76 publications

(77 citation statements)

References 54 publications

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“…complementary messenger RNAs and subsequently repressing translation [7,8]. Research conducted by Borchert et al [9,10] provided significant evidence for a mechanism for the formation of microRNAs involving the fortuitous positioning of transposable elements (TEs) in the genome. Subsequent informatic analyses of individual genomes continue to identify a significant number of microRNAs arising from transposable elements.…”

Section: Molecular Genetics Backgroundmentioning

confidence: 99%

“On the job” learning: A bioinformatics course incorporating undergraduates in actual research projects and manuscript submissions

Smith

Harris

Lopez

et al. 2015

Biochem Molecular Bio Educ

Self Cite

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The sequencing of whole genomes and the analysis of genetic information continues to fundamentally change biological and medical research. Unfortunately, the people best suited to interpret this data (biologically trained researchers) are commonly discouraged by their own perceived computational limitations. To address this, we developed a course to help alleviate this constraint. Remarkably, in addition to equipping our undergraduates with an informatic toolset, we found our course design helped prepare our students for collaborative research careers in unexpected ways. Instead of simply offering a traditional lecture-or laboratorybased course, we chose a guided inquiry method, where an instructor-selected research question is examined by students in a collaborative analysis with students contributing to experimental design, data collection, and manuscript reporting. While students learn the skills needed to conduct bioinformatic research throughout all sections of the course, importantly, students also gain experience in working as a team and develop important communication skills through working with their partner and the class as a whole, and by contributing to an original research article. Remarkably, in its first three semesters, this novel computational genetics course has generated 45 undergraduate authorships across three peer-reviewed articles. More importantly, the students that took this course acquired a positive research experience, newfound informatics technical proficiency, unprecedented familiarity with manuscript preparation, and an earned sense of achievement. Although this course deals with analyses of genetic systems, we suggest the basic concept of integrating actual research projects into a 16-week undergraduate course could be applied to numerous other research-active academic fields. V C 2015 by The International Union of Biochemistry and Molecular Biology, 43(3):154-161, 2015.

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Section: Molecular Genetics Backgroundmentioning

confidence: 99%

“On the job” learning: A bioinformatics course incorporating undergraduates in actual research projects and manuscript submissions

Smith

Harris

Lopez

et al. 2015

Biochem Molecular Bio Educ

Self Cite

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“…While there are several additional implications of miRs originating from TE sequences (e.g., determining transcriptional regulation 16 ), our recently developed miR target prediction algorithm (OrbId: Origin-based identification of microRNA targets 19 ) suggests that a particularly advantageous utility for this information is the refinement of target prediction through restricting putative targets to mRNAs which contain the TE giving rise to a particular miR. Therefore, in light of our having previously defined the genomic events behind the formation of over 2,300 distinct miRs 24 and having successfully developed a novel miR target prediction algorithm utilizing this information to accurately predict mRNAs targeted by miRs clearly formed from TE sequences, 19 this report now details our continuing analysis of this topic -a comprehensive update examining the over 7,000 novel miRs described since our initial study. 28 To our surprise, in addition to characterizing over 1,000 new miR molecular origins from TEs, we also find evidence suggesting that a subset of miRs actually arose from a distinct, previously undescribed mechanism -RNA structural alteration resulting from point mutations to noncoding RNAs such as tRNAs and snoRNAs.…”

Section: Introductionmentioning

confidence: 99%

Continuing analysis of microRNA origins

Roberts

Cooper

Favreau

et al. 2013

Mobile Genetic Elements

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“…A wide range of endogenous regulatory small RNAs exist within the animal cell system, originating from antisense transcripts (nati-si RNA), being generated from degradation products (rasi-RNAs) or piwi RNAs [23][24][25][26]. A lot of regulatory small RNAs including mi RNAs have been shown to have the origin in repetitive elements [27,28]. Several non-coding RNAs like snoRNAs, tRNAs, rRNAs and other non-coding RNAs have been reported to produce endogenous regulatory small RNAs capable to influence phenotypes in vertebrates [29][30][31].…”

Section: Rna Biogenesismentioning

confidence: 99%

Peculiarities of Non Coding Micrornas Organization in H. Sapiens and some other Species

Ns¹

2017

IJMBOA

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It is universally accepted today that micro (mi) non coding (nc) RNAs are endogenous ~22 nt RNAs that can play important regulatory roles in a vast number of cellular processes including cell growth, disease, embryogenesis, gene regulation, signal transduction, receptor activation, etc. by targeting mRNAs for cleavage or translational repression. Mi RNAs comprise one of the more abundant classes of gene regulatory molecules in multi cellular organisms and likely influence the output of many protein-coding genes. The shorter lin-4 RNA is now recognized as the founding member of an abundant class of tiny regulatory RNAs called micro RNAs or mi RNAs. Now it has become clear that large number of regulatory small (rs) RNAs are compromised due to traditional approaches to identify mi RNAs mainly to hair-pin loop bred typical signs. The rsRNA-target interactions have been studied with the use of mi RNA recognition elements binding with Ago proteins (AGO-cross linking), microprocessor complex subunit DGCR8 involved in the initial step of mi RNA biogenesis cross-linking, ligation, and sequencing of hybrids (CLASH), proteome and expression data. The advent of next generation sequencing (NGS) technologies, powerful enough to detect most of the existing small (s) RNA sequences even with low abundance, gave rise to a sudden surge in the reporting of novel mi RNAs.

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Comprehensive analysis of microRNA genomic loci identifies pervasive repetitive-element origins

Cited by 76 publications

References 54 publications

“On the job” learning: A bioinformatics course incorporating undergraduates in actual research projects and manuscript submissions

“On the job” learning: A bioinformatics course incorporating undergraduates in actual research projects and manuscript submissions

Continuing analysis of microRNA origins

Peculiarities of Non Coding Micrornas Organization in H. Sapiens and some other Species

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