scholarly journals Predicting microRNA targeting efficacy in Drosophila

2017 ◽  
Author(s):  
Vikram Agarwal ◽  
Alexander O. Subtelny ◽  
Prathapan Thiru ◽  
Igor Ulitsky ◽  
David P. Bartel
Keyword(s):  
Quantitative Study ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Quantitative Model ◽  
Valuable Resource ◽  
Mrna Targets ◽  
Experimental Organism ◽  
Gene Regulatory ◽  
Better Than

ABSTRACTImportant for understanding the regulatory roles of miRNAs is the ability to predict the mRNA targets most responsive to each miRNA. Here, we acquired datasets needed for the quantitative study of microRNA targeting in Drosophila. Analyses of these data expanded the types of sites known to be effective in flies, expanded the mRNA regions with detectable targeting to include 5′ UTRs, and identified features of site context that correlate with targeting efficacy. Updated evolutionary analyses evaluated the probability of conserved targeting for each predicted site and indicated that more than a third of the Drosophila genes are preferentially conserved targets of miRNAs. Based on these results, a quantitative model was developed to predict targeting efficacy in insects. This model performed better than existing models and will drive the next version of TargetScanFly (v7.0; targetscan.org), thereby providing a valuable resource for placing miRNAs into gene-regulatory networks of this important experimental organism.

scholarly journals An Open-Source Cloud-FPGA Gene Regulatory Accelerator

2021 ◽  
Author(s):  
Lucas Bragança ◽  
Jeronimo Penha ◽  
Michael Canesche ◽  
Dener Ribeiro ◽  
José Augusto M. Nacif ◽  
...  
Keyword(s):  
Open Source ◽  
Gene Regulatory Networks ◽  
High Performance ◽  
Regulatory Networks ◽  
Cloud Services ◽  
On Demand ◽  
Speed Up ◽  
Gene Regulatory ◽  
High Level ◽  
Better Than

FPGAs are suitable to speed up gene regulatory network (GRN) algorithms with high throughput and energy efficiency. In addition, virtualizing FPGA using hardware generators and cloud resources increases the computing ability to achieve on-demand accelerations across multiple users. Recently, Amazon AWS provides high-performance Cloud's FPGAs. This work proposes an open source accelerator generator for Boolean gene regulatory networks. The generator automatically creates all hardware and software pieces from a high-level GRN description. We evaluate the accelerator performance and cost for CPU, GPU, and Cloud FPGA implementations by considering six GRN models proposed in the literature. As a result, the FPGA accelerator is at least 12x faster than the best GPU accelerator. Furthermore, the FPGA reaches the best performance per dollar in cloud services, at least 5x better than the best GPU accelerator.

scholarly journals Predicting effective microRNA target sites in mammalian mRNAs

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Vikram Agarwal ◽  
George W Bell ◽  
Jin-Wu Nam ◽  
David P Bartel
Keyword(s):  
Regulatory Networks ◽  
Quantitative Model ◽  
Seed Region ◽  
Microrna Target ◽  
Functional Sites ◽  
Uv Crosslinking ◽  
Gene Regulatory ◽  
Mirna Seed ◽  
Better Than

MicroRNA targets are often recognized through pairing between the miRNA seed region and complementary sites within target mRNAs, but not all of these canonical sites are equally effective, and both computational and in vivo UV-crosslinking approaches suggest that many mRNAs are targeted through non-canonical interactions. Here, we show that recently reported non-canonical sites do not mediate repression despite binding the miRNA, which indicates that the vast majority of functional sites are canonical. Accordingly, we developed an improved quantitative model of canonical targeting, using a compendium of experimental datasets that we pre-processed to minimize confounding biases. This model, which considers site type and another 14 features to predict the most effectively targeted mRNAs, performed significantly better than existing models and was as informative as the best high-throughput in vivo crosslinking approaches. It drives the latest version of TargetScan (v7.0; targetscan.org), thereby providing a valuable resource for placing miRNAs into gene-regulatory networks.

scholarly journals Inference of Gene Regulatory Networks Using Bayesian Nonparametric Regression and Topology Information

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Yue Fan ◽  
Xiao Wang ◽  
Qinke Peng
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Gene Selection ◽  
Cellular Systems ◽  
Expression Data ◽  
Nonparametric Models ◽  
Spline Basis ◽  
Topology Information ◽  
Gene Regulatory ◽  
Better Than

Gene regulatory networks (GRNs) play an important role in cellular systems and are important for understanding biological processes. Many algorithms have been developed to infer the GRNs. However, most algorithms only pay attention to the gene expression data but do not consider the topology information in their inference process, while incorporating this information can partially compensate for the lack of reliable expression data. Here we develop a Bayesian group lasso with spike and slab priors to perform gene selection and estimation for nonparametric models. B-spline basis functions are used to capture the nonlinear relationships flexibly and penalties are used to avoid overfitting. Further, we incorporate the topology information into the Bayesian method as a prior. We present the application of our method on DREAM3 and DREAM4 datasets and two real biological datasets. The results show that our method performs better than existing methods and the topology information prior can improve the result.

scholarly journals The biochemical basis of microRNA targeting efficacy

2018 ◽  
Author(s):  
Sean E. McGeary ◽  
Kathy S. Lin ◽  
Charlie Y. Shi ◽  
Namita Bisaria ◽  
David P. Bartel
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Binding Affinities ◽  
Biochemical Basis ◽  
Mrna Targets ◽  
Argonaute Proteins ◽  
Relative Binding ◽  
Target Sites ◽  
Gene Regulatory ◽  
Insight Into

MicroRNAs (miRNAs) act within Argonaute proteins to guide repression of mRNA targets. Although various approaches have provided insight into target recognition, the sparsity of miRNA–target affinity measurements has limited understanding and prediction of targeting efficacy. Here, we adapted RNA bind-n-seq to enable measurement of relative binding affinities between Argonaute–miRNA complexes and all ≤12-nucleotide sequences. This approach revealed noncanonical target sites unique to each miRNA, miRNA-specific differences in canonical target-site affinities, and a 100-fold impact of dinucleotides flanking each site. These data enabled construction of a biochemical model of miRNA-mediated repression, which was extended to all miRNA sequences using a convolutional neural network. This model substantially improved prediction of cellular repression, thereby providing a biochemical basis for quantitatively integrating miRNAs into gene-regulatory networks.

Decoding the transcriptome of pre-granulosa cells during the formation of primordial follicles in the mouse

2021 ◽  
Author(s):  
Kurumi Fukuda ◽  
Masafumi Muraoka ◽  
Yuzuru Kato ◽  
Yumiko Saga
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Granulosa Cells ◽  
Regulatory Networks ◽  
Primordial Follicle ◽  
Valuable Resource ◽  
Primordial Follicles ◽  
Matrix Cell ◽  
Gene Regulatory ◽  
Underlying Mechanisms

Abstract Primordial follicles, a finite reservoir of eggs in mammalian ovaries, are composed of a single oocyte and its supporting somatic cells, termed granulosa cells. Although their formation may require reciprocal interplay between oocytes and pre-granulosa cells, precursors of granulosa cells, little is known about the underlying mechanisms. We addressed this issue by decoding the transcriptome of pre-granulosa cells during the formation of primordial follicles. We found that marked gene expression changes including extracellular matrix, cell adhesion and several signaling pathways, occur along with primordial follicle formation. Importantly, differentiation of Lgr5-EGFP-positive pre-granulosa cells to FOXL2-positive granulosa cells was delayed in mutant ovaries of the germ cell-specific genes Nanos3 and Figla, accompanied by perturbed gene expression in mutant pre-granulosa cells. These results suggest that proper development of oocytes is required for the differentiation of pre-granulosa cells. Our data provide a valuable resource for understanding the gene regulatory networks involved in the formation of primordial follicles. Summary sentence: Gene expression profile in mouse pre-granulosa cells alters coinciding with primordial follicle formation and oocyte development contributes to this process.

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