beta-binding-analysis 2.0.0

BETA: Binding and Expression Target Analysis - Integrative analysis of ChIP-seq and RNA-seq data

BETA2: Binding and Expression Target Analysis

BETA is a computational tool for integrative analysis of ChIP-seq and RNA-seq/microarray data to predict transcription factor (TF) direct target genes and identify whether the TF primarily functions as a transcriptional activator or repressor.

Overview

BETA integrates ChIP-seq binding data with differential gene expression data to:

1. Predict direct target genes by combining:

   • Regulatory potential scores based on distance to transcription start sites (TSS)
   • Binding peak signals
   • Gene expression changes

2. Infer TF function as activator or repressor through:

   • Regulatory potential analysis
   • Gene set enrichment analysis

3. Identify enriched motifs in target regions (optional)

Key Features

• Integrative Analysis: Combines ChIP-seq peaks with gene expression data
• Regulatory Potential Scoring: Distance-weighted scoring system
• Statistical Assessment: Kolmogorov-Smirnov test and permutation-based FDR
• Motif Analysis: Optional motif scanning and enrichment analysis
• Multiple Input Formats: Supports LIMMA, Cuffdiff, and custom formats
• Genome Support: Human (hg38, hg19, hg18) and Mouse (mm10, mm9)

Installation

Requirements

• Python 3.8 or higher
• C compiler (gcc) for motif scanning module

From Source

git clone https://github.com/yourusername/BETA2.git
cd BETA2
pip install -e .

Using pip (when available)

pip install beta-binding-analysis

Quick Start

Basic Analysis

Predict TF target genes and function (activator/repressor):

beta basic \
  -p peaks.bed \
  -e diff_expr.txt \
  -k LIM \
  -g hg38 \
  -n my_experiment \
  -o output_dir

Plus Mode (with Motif Analysis)

Include motif analysis:

beta plus \
  -p peaks.bed \
  -e diff_expr.txt \
  -k LIM \
  -g hg38 \
  --gs hg38.fa \
  -n my_experiment \
  -o output_dir

Minus Mode (Peaks Only)

Analyze binding data without expression data:

beta minus \
  -p peaks.bed \
  -g hg38 \
  -n my_experiment \
  -o output_dir

Input Files

ChIP-seq Peaks (required)

BED format file (3 or 5 columns):

chr1    1000    2000
chr1    5000    6000    peak1    100

Differential Expression (required for basic/plus modes)

Supported formats:

1. LIMMA (-k LIM): Standard LIMMA output
2. Cuffdiff (-k CUF): Cuffdiff gene_exp.diff format
3. BETA Standard Format (-k BSF):

   GeneSymbol    log2FoldChange    FDR
   TP53          2.5               0.001
   MYC           -1.8              0.01
   

4. Other (-k O): Specify columns with --info

Genome Sequence (for plus mode)

FASTA format genome sequence file (required for motif analysis)

Output Files

Basic Mode

• {name}_targets.txt: Predicted target genes with statistics
• {name}_uptarget.txt: Up-regulated targets
• {name}_downtarget.txt: Down-regulated targets
• {name}_function.pdf: TF function prediction plot

Plus Mode

Additional files:

• {name}_motif.html: Motif enrichment results
• {name}_motif_logo/: Motif logos
• Motif scanning results

Algorithm

Regulatory Potential Score

For each gene, BETA calculates a regulatory potential score based on nearby binding peaks:

Score = Σ exp(-0.5 - 4 × distance/max_distance)

Where distance is from peak center to TSS.

Target Prediction

1. Rank genes by regulatory potential
2. Rank genes by differential expression
3. Combine rankings using Kolmogorov-Smirnov test
4. Calculate FDR through permutation testing

Function Prediction

Assess enrichment of up-regulated vs down-regulated genes among predicted targets using one-sided KS test.

Command-line Options

Common Options

Option	Description	Default
-p, --peakfile	ChIP-seq peaks (BED format)	Required
-g, --genome	Genome assembly (hg38/hg19/hg18/mm10/mm9)	Required
-n, --name	Output prefix	"NA"
-o, --output	Output directory	Current directory
-d, --distance	Distance from TSS (bp)	100000
--pn	Number of peaks to consider	10000

Expression Options

Option	Description
-e, --diff_expr	Differential expression file
-k, --kind	Expression file format (LIM/CUF/BSF/O)
--info	Column specification (for -k O)
--df	FDR threshold
--da	Top genes to consider (fraction or number)

Advanced Options

Option	Description
--method	Scoring method (score/distance)
-c, --cutoff	P-value cutoff for targets
--bl	Use CTCF boundary filtering
--gname2	Gene IDs are gene symbols

Examples

Example 1: Basic TF Analysis (hg38)

beta basic \
  -p ERalpha_peaks.bed \
  -e ERalpha_treatment_vs_control.txt \
  -k LIM \
  -g hg38 \
  -n ERalpha \
  -d 100000 \
  -c 0.001

Example 2: With Custom Expression Format

beta basic \
  -p TF_peaks.bed \
  -e expression.txt \
  -k O \
  --info 1,3,7 \
  -g hg38 \
  -n TF_experiment

(Column 1: gene ID, Column 3: log2FC, Column 7: FDR)

Example 3: Mouse Analysis with Motif Scanning

beta plus \
  -p mm10_peaks.bed \
  -e mm10_expression.txt \
  -k CUF \
  -g mm10 \
  --gs mm10.fa \
  -n mouse_TF \
  --mn 20

Migration from BETA 1.x

This is a modernized Python 3 version of BETA. Key changes:

• Python 3.8+ required (was Python 2.6/2.7)
• Default genome: hg38 (was hg19)
• Improved performance: Optimized algorithms
• Better logging: Structured logging instead of print statements
• Modern packaging: Uses pyproject.toml and pip installable

Command Compatibility

All BETA 1.x commands should work with BETA 2.0 without changes. However, you may need to update:

• Genome references to hg38
• Python environment to 3.8+

Reference Data

BETA includes reference gene annotations for:

• Human: hg38 (default), hg19, hg18
• Mouse: mm10, mm9

Default CTCF boundary data included for hg19 and mm9.

Custom Genomes

For other genome assemblies, provide your own reference:

beta basic \
  -p peaks.bed \
  -e expression.txt \
  -k LIM \
  -r custom_refseq.txt \
  -n experiment

RefSeq format: tab-delimited with columns:

bin name chrom strand txStart txEnd cdsStart cdsEnd exonCount exonStarts exonEnds score name2 ...

Citation

If you use BETA in your research, please cite:

Wang S, Sun H, Ma J, et al. Target analysis by integration of transcriptome and ChIP-seq data with BETA. Nature Protocols. 2013;8(12):2502-2515. doi:10.1038/nprot.2013.150

License

BETA is distributed under the Artistic License 2.0.

Support

• Documentation: http://cistrome.org/BETA/tutorial.html
• Issues: https://github.com/yourusername/BETA2/issues
• Original paper: https://doi.org/10.1038/nprot.2013.150

Authors

• Original Author: Su Wang (wangsu0623atgmail.com)
• Python 3 Port: Tommy Tang (tangming2005atgmail.com)

Changelog

Version 2.0.0 (2025)

• Python 3.8+ support (dropped Python 2)
• Modern project structure and packaging
• Default genome changed to hg38
• Improved code quality and type hints
• Enhanced logging and error handling
• Updated dependencies
• Performance optimizations

Version 1.0.7 (2015)

• Original Python 2 version
• Basic, plus, and minus modes
• Support for hg18, hg19, mm9, mm10