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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

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Validating a re-implementation of an algorithm to integrate transcriptome and ChIP-seq data.

Mahmoud Ahmed¹, Deok Ryong Kim¹

¹Department of Biochemistry and Convergence Medical Sciences and Institute of Medical Sciences, College of Medicine, Gyeongsang National University, Jinju, South Korea.

|October 25, 2023

Summary

This summary is machine-generated.

This study successfully reimplemented the Binding and Expression Target Analysis (BETA) algorithm in R, replicating original findings. The R version of BETA accurately predicts gene expression regulation by transcription factors and is robust to parameter choices.

Keywords:

Competitive-binding Cooperative-binding DNA-binding R-package Reproducible-research Transcription-factor

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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Transcription factors regulate gene expression by binding to specific DNA regions.
Predicting the functional impact of transcription factor binding requires integrating binding and gene expression data.
The Binding and Expression Target Analysis (BETA) algorithm was previously developed in Python for this purpose.

Purpose of the Study:

To reimplement the BETA algorithm in the R programming language.
To validate the accuracy and robustness of the R implementation of BETA.
To assess the performance of BETA using existing datasets and varying input parameters.

Main Methods:

Modeled regulatory potential based on transcription factor binding site proximity to transcription start sites using a decay function.
Incorporated differential gene expression statistics from experiments perturbing transcription factor activity.
Combined regulatory potential and expression effects using the rank product to identify key regulatory targets.

Main Results:

Successfully replicated original BETA findings using the new R implementation on identical datasets.
Demonstrated that the R implementation's results were appropriately influenced by input variations.
Confirmed the robustness of the BETA method to different statistical testing cutoffs.

Conclusions:

The R reimplementation of BETA accurately predicts transcription factor-mediated gene expression regulation.
The R version offers advantages for downstream analyses by leveraging existing R data structures and tools.
The BETA algorithm, in both Python and R, is a reliable method for analyzing transcription factor function.