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Reverse Engineering of Regulatory Networks

Overview of attention for book
Cover of 'Reverse Engineering of Regulatory Networks'

Table of Contents

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    Book Overview
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    Chapter 1 Molecular Modeling Techniques and In-Silico Drug Discovery
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    Chapter 2 Systems Biology Approach to Analyze Microarray Datasets for Identification of Disease-Causing Genes: Case Study of Oral Squamous Cell Carcinoma
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    Chapter 3 Fluorescence Spectroscopy: A Useful Method to Explore the Interactions of Small Molecule Ligands with DNA Structures
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    Chapter 4 Inference of Dynamic Growth Regulatory Network in Cancer Using High-Throughput Transcriptomic Data
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    Chapter 5 Implementation of Exome Sequencing to Identify Rare Genetic Diseases
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    Chapter 6 Emerging Trends in Big Data Analysis in Computational Biology and Bioinformatics in Health Informatics: A Case Study on Epilepsy and Seizures
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    Chapter 7 New Insights into Clinical Management for Sickle Cell Disease: Uncovering the Significant Pathways Affected by the Involvement of Sickle Cell Disease
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    Chapter 8 A Review of Computational Approach for S-system-based Modeling of Gene Regulatory Network
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    Chapter 9 Big Data in Bioinformatics and Computational Biology: Basic Insights
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    Chapter 10 Identification of Culprit Genes for Different Diseases by Analyzing Microarray Data
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    Chapter 11 Big Data Analysis in Computational Biology and Bioinformatics
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    Chapter 12 Prediction and Analysis of Transcription Factor Binding Sites: Practical Examples and Case Studies Using R Programming.
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    Chapter 13 Hubs and Bottlenecks in Protein-Protein Interaction Networks
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    Chapter 14 Next-Generation Sequencing to Study the DNA Interaction.
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    Chapter 15 Deep Learning for Predicting Gene Regulatory Networks: A Step-by-Step Protocol in R
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    Chapter 16 Computational Inference of Gene Regulatory Network Using Genome-wide ChIP-X Data
  18. Altmetric Badge
    Chapter 17 Reverse Engineering in Biotechnology: The Role of Genetic Engineering in Synthetic Biology
Attention for Chapter 12: Prediction and Analysis of Transcription Factor Binding Sites: Practical Examples and Case Studies Using R Programming.
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Chapter title
Prediction and Analysis of Transcription Factor Binding Sites: Practical Examples and Case Studies Using R Programming.
Chapter number 12
Book title
Reverse Engineering of Regulatory Networks
Published in
Methods in molecular biology, January 2024
DOI 10.1007/978-1-0716-3461-5_12
Pubmed ID
Book ISBNs
978-1-07-163460-8, 978-1-07-163461-5
Authors

Muley, Vijaykumar Yogesh

Abstract

Transcription factors (TFs) bind to specific regions of DNA known as transcription factor binding sites (TFBSs) and modulate gene expression by interacting with the transcriptional machinery. TFBSs are typically located upstream of target genes, within a few thousand base pairs of the transcription start site. The binding of TFs to TFBSs influences the recruitment of the transcriptional machinery, thereby regulating gene transcription in a precise and specific manner. This chapter provides practical examples and case studies demonstrating the extraction of upstream gene regions from the genome, identification of TFBSs using PWMEnrich R/Bioconductor package, interpretation of results, and preparation of publication-ready figures and tables. The EOMES promoter is used as a case study for single DNA sequence analysis, revealing potential regulation by the LHX9-FOXP1 complex during embryonic development. Additionally, an example is presented on how to investigate TFBSs in the upstream regions of a group of genes, using a case study of differentially expressed genes in response to human parainfluenza virus type 1 (HPIV1) infection and interferon-beta. Key regulators identified in this context include the STAT1:STAT2 heterodimer and interferon regulatory factor family proteins. The presented protocol is designed to be accessible to individuals with basic computer literacy. Understanding the interactions between TFs and TFBSs provides insights into the complex transcriptional regulatory networks that govern gene expression, with broad implications for several fields such as developmental biology, immunology, and disease research.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 50%
Student > Bachelor 1 50%
Readers by discipline Count As %
Unspecified 1 50%
Agricultural and Biological Sciences 1 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 October 2023.
All research outputs
#17,095,949
of 25,117,541 outputs
Outputs from Methods in molecular biology
#5,942
of 14,117 outputs
Outputs of similar age
#88,732
of 175,095 outputs
Outputs of similar age from Methods in molecular biology
#93
of 215 outputs
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So far Altmetric has tracked 14,117 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 42nd percentile – i.e., 42% of its peers scored the same or lower than it.
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