Dataset Information

MiFAST: A novel and rapid microRNA target capture method.

ABSTRACT: MicroRNAs (miRNAs), small 22-25 nucleotide non-coding RNAs, play important roles in cellular and tumor biology. However, characterizing miRNA function remains challenging due to an abundance of predicted targets and an experimental bottleneck in identifying biologically relevant direct targets. Here, we developed a novel technique (miFAST) to identify direct miRNA target genes. Using miFAST, we confirmed several previously reported miR-340 target genes and identified five additional novel direct miR-340 targets in melanoma cells. This methodology can also be efficiently applied for the global characterization of miRNA targets. Utilizing miFAST to characterize direct miRNA targetomes will further our understanding of miRNA biology and function.

SUBMITTER: Poenitzsch Strong AM

PROVIDER: S-EPMC8858646 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

miFAST: A novel and rapid microRNA target capture method.

Poenitzsch Strong Ashley M AM Berry Scott M SM Beebe David J DJ Li Jian-Liang JL Spiegelman Vladimir S VS

Molecular carcinogenesis 20180212 4

MicroRNAs (miRNAs), small 22-25 nucleotide non-coding RNAs, play important roles in cellular and tumor biology. However, characterizing miRNA function remains challenging due to an abundance of predicted targets and an experimental bottleneck in identifying biologically relevant direct targets. Here, we developed a novel technique (miFAST) to identify direct miRNA target genes. Using miFAST, we confirmed several previously reported miR-340 target genes and identified five additional novel direct ...[more]

PMID: 29350431

Similar Datasets

Project description:Whole genome duplication (WGD) events are common in many plant lineages, but the ploidy status and possible occurrence of intraspecific ploidy variation are unknown for most species. Standard methods for ploidy determination are chromosome counting and flow cytometry approaches. While flow cytometry approaches typically use fresh tissue, an increasing number of studies have shown that recently dried specimens can be used to yield ploidy data. Recent studies have started to explore whether high-throughput sequencing (HTS) data can be used to assess ploidy levels by analyzing allelic frequencies from single copy nuclear genes. Here, we compare different approaches using a range of yam (Dioscorea) tissues of varying ages, drying methods and quality, including herbarium tissue. Our aims were to: (1) explore the limits of flow cytometry in estimating ploidy level from dried samples, including herbarium vouchers collected between 1831 and 2011, and (2) optimize a HTS-based method to estimate ploidy by considering allelic frequencies from nuclear genes obtained using a target-capture method. We show that, although flow cytometry can be used to estimate ploidy levels from herbarium specimens collected up to fifteen years ago, success rate is low (5.9%). We validated our HTS-based estimates of ploidy using 260 genes by benchmarking with dried samples of species of known ploidy (Dioscorea alata, D. communis, and D. sylvatica). Subsequently, we successfully applied the method to the 85 herbarium samples analyzed with flow cytometry, and successfully provided results for 91.7% of them, comprising species across the phylogenetic tree of Dioscorea. We also explored the limits of using this HTS-based approach for identifying high ploidy levels in herbarium material and the effects of heterozygosity and sequence coverage. Overall, we demonstrated that ploidy diversity within and between species may be ascertained from historical collections, allowing the determination of polyploidization events from samples collected up to two centuries ago. This approach has the potential to provide insights into the drivers and dynamics of ploidy level changes during plant evolution and crop domestication.

Dataset Information

MiFAST: A novel and rapid microRNA target capture method.

Publications

miFAST: A novel and rapid microRNA target capture method.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets