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High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

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Target analysis by integration of transcriptome and ChIP-seq data with BETA.

Su Wang¹, Hanfei Sun, Jian Ma

¹Department of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai, China.

Nature Protocols

|November 23, 2013

Summary

This summary is machine-generated.

Binding and Expression Target Analysis (BETA) is a new software tool that combines ChIP-seq and gene expression data to identify transcription factor (TF) targets. This efficient package predicts TF function and target genes, aiding gene regulation research.

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Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Understanding gene expression regulation is crucial in molecular biology.
ChIP-seq and transcriptome analysis are powerful tools for studying gene regulation.
Existing methods for predicting transcription factor (TF) targets often lack efficient software packages.

Purpose of the Study:

To introduce Binding and Expression Target Analysis (BETA), an integrated software package.
To enable the prediction of direct target genes for transcription factors and chromatin regulators.
To provide tools for predicting TF function (activation/repression) and identifying regulatory motifs.

Main Methods:

Integration of ChIP-seq data with differential gene expression data.
Development of a software package (BETA) for target gene inference.
Application of BETA to analyze multiple datasets to demonstrate its features.

Main Results:

BETA successfully integrates ChIP-seq and gene expression data.
The software predicts TF activating or repressive functions.
BETA identifies direct target genes and associated motifs with high efficiency.
The analysis procedure is computationally efficient, requiring ~1 GB RAM and 20 minutes.

Conclusions:

BETA provides an efficient and accessible open-source software package for the research community.
The tool facilitates the unraveling of gene expression regulation by TFs and chromatin regulators.
BETA aids in predicting TF targets, functions, and collaborative motifs, advancing genomic research.