scholarly journals A Comprehensive Study of Target Prediction Algorithms for Animal MicroRNAs(miRNAs)

2012 ◽  
Vol 40 (15) ◽  
pp. 8-11 ◽  
Author(s):  
Joyshree Nath ◽  
Asoke Nath
Keyword(s):  
Target Prediction ◽  
Prediction Algorithms ◽  
Comprehensive Study

A Comprehensive Study of Route Prediction Algorithms

2019 ◽  
Vol 7 (9) ◽  
pp. 78-85
Author(s):  
Dimple Singh ◽  
Pritam . ◽  
Neelam Duhan ◽  
Komal Kumar Bhatia
Keyword(s):  
Prediction Algorithms ◽  
Route Prediction ◽  
Comprehensive Study

piCRISPR: Physically Informed Features Improve Deep Learning Models for CRISPR/Cas9 Off-Target Cleavage Prediction

2021 ◽  
Author(s):  
Florian Störtz ◽  
Jeffrey Mak ◽  
Peter Minary
Keyword(s):  
Deep Learning ◽  
Gene Editing ◽  
Target Prediction ◽  
Model Space ◽  
Chromatin Accessibility ◽  
Sequence Context ◽  
Prediction Algorithms ◽  
Cleavage Assay

CRISPR/Cas programmable nuclease systems have become ubiquitous in the field of gene editing. With progressing development, applications in in vivo therapeutic gene editing are increasingly within reach, yet limited by possible adverse side effects from unwanted edits. Recent years have thus seen continuous development of off-target prediction algorithms trained on in vitro cleavage assay data gained from immortalised cell lines. Here, we implement novel deep learning algorithms and feature encodings for off-target prediction and systematically sample the resulting model space in order to find optimal models and inform future modelling efforts. We lay emphasis on physically informed features, hence terming our approach piCRISPR, which we gain on the large, diverse crisprSQL off-target cleavage dataset. We find that our best-performing model highlights the importance of sequence context and chromatin accessibility for cleavage prediction and outperforms state-of-the-art prediction algorithms in terms of area under precision-recall curve.

scholarly journals A Preliminary Investigation of PVT1 on the Effect and Mechanisms of Hepatocellular Carcinoma: Evidence from Clinical Data, a Meta-Analysis of 840 Cases, and In Vivo Validation

2018 ◽  
Vol 47 (6) ◽  
pp. 2216-2232 ◽  
Author(s):  
Yu Zhang ◽  
Dong-yue Wen ◽  
Rui Zhang ◽  
Jia-cheng Huang ◽  
Peng Lin ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Proliferation ◽  
Meta Analysis ◽  
Target Prediction ◽  
The Cancer Genome Atlas ◽  
Gene Microarray ◽  
Prediction Algorithms ◽  
Correlation Trend

Background/Aims: Hepatocellular carcinoma (HCC) remains a difficult problem that significantly affects the survival of the afflicted patients. Accumulating evidence has demonstrated the functions of long non-coding RNA (lncRNA) in HCC. In the present study, we aimed to explore the potential roles of PVT1 in the tumorigenesis and progression of HCC. Methods: In this study, quantitative reverse transcription-polymerase chain reaction (RT-qPCR) was applied to detect the differences between PVT1 expression in HCC tissues and cell lines. Then, the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were searched to confirm the relationship between PVT1 expression and HCC. Moreover, a meta-analysis comprising TCGA, GEO, and RT-qPCR was applied to estimate the expression of PVT1 in HCC. Then, cell proliferation was evaluated in vitro. A chicken chorioallantoic membrane (CAM) model of HCC was constructed to measure the effect on tumorigenicity in vivo. To further explore the sponge microRNA (miRNA) of PVT1 in HCC, we used TCGA, GEO, a gene microarray, and target prediction algorithms. TCGA and GEO and the gene microarray were used to select the differentially expressed miRNAs, and the different target prediction algorithms were applied to predict the target miRNAs of PVT1. Results: We found that PVT1 was markedly overexpressed in HCC tissue than in normal liver tissues based on both RT-qPCR and data from TCGA, and the overexpression of PVT1 was closely related to the gender and race of the patient as well as to higher HCC tumor grades. Also, a meta-analysis of 840 cases from multiple sources (TCGA, GEO and the results of our in-house RT-qPCR) showed that PVT1 gained moderate value in discriminating HCC patients from normal controls, confirming the results of RT-qPCR. Additionally, the upregulation of PVT1 could promote HCC cell proliferation in vitro and vivo. Based on the competing endogenous RNA (ceRNA) theory, the PVT1/miR-424-5p/INCENP axis was finally selected for further research. The in silico prediction revealed that there were complementary sequences between PVT1 and miR-424-5p as well as between miR-424-5p and INCENP. Furthermore, a negative correlation trend was found between miR-424-5p and PVT1 based on RT-qPCR, whereas a positive correlation trend was found between PVT1 and INCENP based on data from TCGA. Also, INCENP small interfering RNA (siRNA) could significantly inhibit cell proliferation and viability. Conclusions: We hypothesized that PVT1 could affect the biological function of HCC cells via targeting miR-424-5p and regulating INCENP. Focusing on the new insight of the PVT1/miR-424-5p/INCENP axis, this study provides a novel perspective for HCC therapeutic strategies.

Target Prediction Algorithms and Bioinformatics Resources for miRNA Studies

2012 ◽  
pp. 29-48 ◽  
Author(s):  
Colin Clarke ◽  
Niall Barron ◽  
Mark Gallagher ◽  
Michael Henry ◽  
Paula Meleady ◽  
...  
Keyword(s):  
Target Prediction ◽  
Prediction Algorithms

scholarly journals MicroRNAs in the Aging Female Brain: A Putative Mechanism for Age-Specific Estrogen Effects

Endocrinology ◽  
2013 ◽  
Vol 154 (8) ◽  
pp. 2795-2806 ◽  
Author(s):  
Yathindar S. Rao ◽  
Natasha N. Mott ◽  
Yanru Wang ◽  
Wilson C.J. Chung ◽  
Toni R. Pak
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Dorsal Hippocampus ◽  
Cognitive Disorders ◽  
Target Prediction ◽  
Female Rats ◽  
Dependent Manner ◽  
Small Noncoding Rnas ◽  
Prediction Algorithms ◽  
Age Related

Abstract Menopause is characterized by the rapid age-related decline of circulating 17β-estradiol (E2) levels in women, which can sometimes result in cognitive disorders such as impaired memory and increased anxiety. Hormone therapy (HT) is a widely used treatment for the adverse effects associated with menopause; however, evidence suggests that HT administered to postmenopausal women age 65 years and over can lead to increased risks for cognitive disorders. We hypothesized that these age-related changes in E2 action are due to posttranscriptional gene regulation by microRNAs (miRNAs). miRNAs are a class of small noncoding RNAs that regulate gene expression by binding to the 3′-untranslated region of target mRNAs and subsequently target these transcripts for degradation. In the present study, 3- and 18-month-old female rats were oophorectomized (OVX) and treated 1 week after surgery with 2.5 μg E2 once per day for 3 days. Total RNA was isolated from the ventral and dorsal hippocampus, central amygdala, and paraventricular nucleus. Our results showed that E2 differentially altered miRNA levels in an age- and brain region-dependent manner. Multiple miRNA target prediction algorithms revealed putative target genes that are important for memory and stress regulation, such as BDNF, glucocorticoid receptor, and SIRT-1. Indeed, quantitative RT-PCR analyses of some of the predicted targets, such as SIRT1, showed that the mRNA expression levels were the inverse of the targeting miRNA, thereby confirming the prediction algorithms. Taken together, these data show that E2 regulates miRNA expression in an age- and E2-dependent manner, which we hypothesize results in differential gene expression and consequently altered neuronal function.

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