BROCKMAN

BROCKMAN analyzes genomics and epigenomics data by converting genomic regions linked to chromatin marks into DNA k-mer words to compare samples and infer transcription factor-associated chromatin variability, with emphasis on single-cell ATAC-seq.

Key Features:

• K-mer Representation: Transforms genomic regions associated with chromatin marks into sets of k-mers (short DNA sequences) for downstream analysis.
• Single-cell ATAC-seq Focus: Targets single-cell genomics applications and has been extensively applied to single-cell ATAC-seq datasets.
• Unsupervised TF Activity Inference: Employs unsupervised methods to infer variation in transcription factor (TF) activity by representing each sample as a vector of k-mer frequencies.
• Matrix Decomposition: Applies matrix decomposition techniques to k-mer frequency matrices to uncover hidden structure and heterogeneity across samples.
• Sample Grouping and TF Identification: Groups samples unsupervisedly and identifies transcription factors that distinguish groups, enabling detection of cell types, treatments, batch effects, experimental artifacts, and cycling cells.
• Cooperative TF Binding Insights: Provides theoretical and empirical insight into cooperative transcription factor binding variability, exemplified by AP-1–driven chromatin variability in K562 cells.

Scientific Applications:

• Single-cell ATAC-seq analysis: Analyzes chromatin accessibility variability in single-cell ATAC-seq using k-mer-based representations.
• Cell type discrimination: Differentiates distinct cell populations based on k-mer signatures associated with chromatin marks.
• Treatment effect detection: Identifies chromatin changes associated with experimental treatments.
• Batch effect and artifact recognition: Detects technical variation and experimental artifacts in epigenomic datasets.
• Cell cycle analysis: Detects cycling cells and cell-cycle–related chromatin states.
• K562 chromatin variability: Reveals the substantial influence of AP-1 transcription factors on chromatin accessibility variability in K562 cells.

Methodology:

Convert genomic regions linked to chromatin marks into k-mers, construct per-sample k-mer frequency vectors, and apply unsupervised analyses including matrix decomposition to infer transcription factor-associated chromatin variability.