Simultaneous Analysis of Spatio-Temporal Gene Expression for Cephamycin Biosynthesis in Streptomyces clavuligerus

2001 ◽  
Vol 17 (6) ◽  
pp. 1000-1007 ◽  
Author(s):  
Y.S. Kyung ◽  
D.H. Sherman ◽  
W.-S. Hu
Keyword(s):  
Gene Expression ◽  
Streptomyces Clavuligerus ◽  
Simultaneous Analysis ◽  
Temporal Gene Expression ◽  
Spatio Temporal

scholarly journals Regulation of spatial and temporal gene expression in an animal germline

2018 ◽  
Author(s):  
Asija Diag ◽  
Marcel Schilling ◽  
Filippos Klironomos ◽  
Salah Ayoub ◽  
Nikolaus Rajewsky
Keyword(s):  
Gene Expression ◽  
Regulatory Mechanisms ◽  
Rna Expression ◽  
Temporal Expression ◽  
Temporal Gene Expression ◽  
Cell Proliferation And Differentiation ◽  
Proliferation And Differentiation ◽  
Gene Regulatory ◽  
Spatio Temporal

SUMMARYIn animal germlines, regulation of cell proliferation and differentiation is particularly important but poorly understood. Here, using a cryo-cut approach, we mapped RNA expression along the Caenorhabditis elegans germline and, using mutants, dissected gene regulatory mechanisms that control spatio-temporal expression. We detected, at near single-cell resolution, > 10,000 mRNAs, > 300 miRNAs and numerous novel miRNAs. Most RNAs were organized in distinct spatial patterns. Germline-specific miRNAs and their targets were co-localized. Moreover, we observed differential 3’ UTR isoform usage for hundreds of mRNAs. In tumorous gld-2 gld-1 mutants, gene expression was strongly perturbed. In particular, differential 3’ UTR usage was significantly impaired. We propose that PIE-1, a transcriptional repressor, functions to maintain spatial gene expression. Our data also suggest that cpsf-4 and fipp-1 control differential 3’ UTR usage for hundreds of genes. Finally, we constructed a “virtual gonad” enabling “virtual in situ hybridizations” and access to all data (https://shiny.mdc-berlin.de/spacegerm/).

scholarly journals Machine learning algorithms for the identification of potential non-syndromic intellectual disability associated miRNAs

2021 ◽  
Author(s):  
Julián González Betancur ◽  
José Guevara-Coto ◽  
Adarli Romero
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Developmental Disorders ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Machine Learning Algorithms ◽  
Stochastic Gradient Descent ◽  
Temporal Gene Expression ◽  
Interesting Approach ◽  
Spatio Temporal

Abstract Background: Intellectual disabilities (IDs) are a group of developmental disorders with high phenotypic and genotypic heterogeneity. Association of genetic elements to IDs has typically been empirically accomplished, however recently, machine learning (ML) has proved to be an excellent instrument to elucidate these associations. miRNAs are short non-coding molecules that participate in spatiotemporal gene regulation, making them relevant for the understanding ID causality. Methods: In this study we used the BrainSpan spatio-temporal expression database to develop a series of machine learning predictors: SVM, RF, FF-ANN, and Stochastic Gradient Descent Classifier. These models were capable of recognizing gene expression profiles. The best classifier was used to label miRNAs associated with NS-IDs using the BrainSpan expression profiles. Results: The model with the best performance was a FF-ANN with 0.78 of F1-score, 0.78 of weighted recall and 0.78 of weighted precision. We used this model to identify miRNAs with high probability to be associated with NS-IDs using the spatio-temporal gene expression profile in the human brain. Labeled miRNAs that were annotated were associated with processes related to either IDs and-or neurodevelopmental processes. Conclusions: The development of a machine learning framework that identified potential NS-ID miRNAs represents an interesting approach for the identification of a potential list of on genes that could be subject for further experimental validation. This study also reinforces the potential of machine learning frameworks in their discovery of potential biomarkers that could improve disease detection and management.

scholarly journals Machine Learning Algorithms for the Identification of Potential Non-Syndromic Intellectual Disability Associated miRNAs

2021 ◽  
Author(s):  
Julián González Betancur ◽  
José A Guevara-Coto ◽  
Adarli Romero
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Intellectual Disability ◽  
Developmental Disorders ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Machine Learning Algorithms ◽  
Stochastic Gradient Descent ◽  
Temporal Gene Expression ◽  
Spatio Temporal

Abstract Background: Intellectual disabilities (IDs) are a group of developmental disorders with high phenotypic and genotypic heterogeneity. Association of genetic elements to IDs has typically been empirically accomplished, however recently, machine learning (ML) has proved to be an excellent instrument to elucidate these associations. miRNAs are short non-coding molecules that participate in spatiotemporal gene regulation, making them relevant for the understanding ID causality. Methods: In this study we used the BrainSpan spatio-temporal expression database to develop a series of machine learning predictors: SVM, RF, FF-ANN, and Stochastic Gradient Descent Classifier. These models were capable of recognizing gene expression profiles. The best classifier was used to label miRNAs associated with NS-IDs using the BrainSpan expression profiles. Results: The model with the best performance was a FF-ANN with 0.78 of F1-score, 0.78 of weighted recall and 0.78 of weighted precision. We used this model to identify miRNAs with high probability to be associated with NS-IDs using the spatio-temporal gene expression profile in the human brain. Labeled miRNAs that were annotated were associated with processes related to either IDs and-or neurodevelopmental processes. Conclusions: The development of a machine learning framework that identified potential NS-ID miRNAs represents an interesting approach for the identification of a potential list of on genes that could be subject for further experimental validation. This study also reinforces the potential of machine learning frameworks in their discovery of potential biomarkers that could improve disease detection and management. Keywords: miRNA association; artificial intelligence; machine learning; intellectual disability; biomarker

scholarly journals Spatio-temporal gene expression analysis from cryosection using laser microdissection

2020 ◽  
Vol 32 (1) ◽  
pp. 39-43
Author(s):  
◽  
Miyuki Nakanowatari ◽  
Kazuki Yamada ◽  
Emi Yumoto ◽  
Shinobu Satoh
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Laser Microdissection ◽  
Temporal Gene Expression ◽  
Spatio Temporal

scholarly journals SuperFly: a comparative database for quantified spatio-temporal gene expression patterns in early dipteran embryos

2014 ◽  
Vol 43 (D1) ◽  
pp. D751-D755 ◽  
Author(s):  
Damjan Cicin-Sain ◽  
Antonio Hermoso Pulido ◽  
Anton Crombach ◽  
Karl R. Wotton ◽  
Eva Jiménez-Guri ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Temporal Gene Expression ◽  
Spatio Temporal

scholarly journals Accurate genome-wide predictions of spatio-temporal gene expression during embryonic development

PLoS Genetics ◽  
2019 ◽  
Vol 15 (9) ◽  
pp. e1008382 ◽  
Author(s):  
Jian Zhou ◽  
Ignacio E. Schor ◽  
Victoria Yao ◽  
Chandra L. Theesfeld ◽  
Raquel Marco-Ferreres ◽  
...  
Keyword(s):  
Gene Expression ◽  
Embryonic Development ◽  
Temporal Gene Expression ◽  
Genome Wide ◽  
Spatio Temporal
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