scholarly journals Peer Review #2 of "TEffectR: an R package for studying the potential effects of transposable elements on gene expression with linear regression model (v0.2)"

2019 ◽  
Keyword(s):  
Gene Expression ◽  
Linear Regression ◽  
Transposable Elements ◽  
Regression Model ◽  
Peer Review ◽  
Linear Regression Model ◽  
R Package

scholarly journals TEffectR: an R package for studying the potential effects of transposable elements on gene expression with linear regression model

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8192 ◽  
Author(s):  
Gökhan Karakülah ◽  
Nazmiye Arslan ◽  
Cihangir Yandım ◽  
Aslı Suner
Keyword(s):  
Gene Expression ◽  
Linear Regression ◽  
Transposable Elements ◽  
Regression Model ◽  
Rna Sequencing ◽  
Linear Regression Model ◽  
R Package ◽  
Breast Cancer Patients ◽  
Sequencing Data ◽  
Data Set

Introduction Recent studies highlight the crucial regulatory roles of transposable elements (TEs) on proximal gene expression in distinct biological contexts such as disease and development. However, computational tools extracting potential TE –proximal gene expression associations from RNA-sequencing data are still missing. Implementation Herein, we developed a novel R package, using a linear regression model, for studying the potential influence of TE species on proximal gene expression from a given RNA-sequencing data set. Our R package, namely TEffectR, makes use of publicly available RepeatMasker TE and Ensembl gene annotations as well as several functions of other R-packages. It calculates total read counts of TEs from sorted and indexed genome aligned BAM files provided by the user, and determines statistically significant relations between TE expression and the transcription of nearby genes under diverse biological conditions. Availability TEffectR is freely available at https://github.com/karakulahg/TEffectR along with a handy tutorial as exemplified by the analysis of RNA-sequencing data including normal and tumour tissue specimens obtained from breast cancer patients.

scholarly journals liureg: A Comprehensive R Package for the Liu Estimation of Linear Regression Model with Collinear Regressors

The R Journal ◽  
2017 ◽  
Vol 9 (2) ◽  
pp. 232
Author(s):  
Muhammad Imdadullah ◽  
Muhammad Aslam ◽  
Saima Altaf
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
R Package

Physico-Chemical Analysis of Groundwater and Effect of Climatic Change in Angul-Talcher Area

2018 ◽  
Vol 6 (12) ◽  
pp. 26
Author(s):  
Aliva Bera ◽  
D.P. Satapathy
Keyword(s):  
Water Resources ◽  
Linear Regression ◽  
Regression Model ◽  
Climatic Change ◽  
Linear Regression Model ◽  
Ann Model ◽  
Quality Of Water ◽  
Physico Chemical ◽  
Simulation Based

In this paper, the linear regression model using ANN and the linear regression model using MS Excel were developed to estimate the physico-chemical concentrations in groundwater using pH, EC, TDS, TH, HCO3 as input parameters and Ca, Mg and K as output parameters. A comparison was made which indicated that ANN model had the better ability to estimate the physic-chemical concentrations in groundwater. An analytical survey along with simulation based tests for finding the climatic change and its effect on agriculture and water bodies in Angul-Talcher area is done. The various seasonal parameters such as pH, BOD, COD, TDS,TSS along with heavy elements like Pb, Cd, Zn, Cu, Fe, Mn concentration in water resources has been analyzed. For past 30 years rainfall data has been analyzed and water quality index values has been studied to find normal and abnormal quality of water resources and matlab based simulation has been done for performance analysis. All results has been analyzed and it is found that the condition is stable. 

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