Abstract Prostate cancer (PCa) is one of the most frequently diagnosed cancers in men. PCa growth is dependent on the androgen receptor (AR) which regulates transcription directly as well as through interactions with other transcription factors (TFs). While androgen deprivation therapy is often successful upon progression, a fraction of PCa cases possess constitutively active AR that functions independently of hormonal regulation. Understanding the TF regulatory landscape, especially androgen-dependent versus androgen-independent PCa, is critical to developing better therapeutic strategies. Here we investigated the epigenomic landscape of two prostate cancer cell lines, VCaP (androgen-dependent) and 22Rv1 (androgen-independent), using ATAC-seq to map chromatin accessibility and a high-resolution version of ChIP-seq (ChIP-exo) that characterizes binding of proteins to the genome at base-pair resolution. We identified distinct AR binding between cell lines in response to androgen-treatment, with a subset of AR sites showing strong enhancer enrichment and preferential colocalization with FoxA1 at characteristic spacing, consistent with cooperative AR–FoxA1 regulation. Interestingly, many FoxA1 binding events occurred independently of AR and were enriched in quiescent chromatin, suggesting a context-dependent role for FoxA1. Together, these findings highlight how differential AR–FoxA1 crosstalk and chromatin states contribute to hormone sensitivity versus resistance in PCa.

MOTIVATION: The wide range of cellular complexity created by multicellular organisms is due in large part to the intricate and synergistic interplay of regulatory complexes throughout the eukaryotic genome. These regulatory elements "enhance" specific gene programs and have been shown to operate in diverse networks that are distinct across cell states of the same organism. Attempts to characterize and predict enhancers have typically focused on leveraging information-dense DNA sequence in parallel with epigenomic assays. We examined the viability of enhancer prediction using only a minimal set of epigenomic datasets without direct DNA information. RESULTS: We demonstrate that chromatin datasets are sufficient to identify enhancers genome-wide with high accuracy. By training networks leveraging data from multiple cell types simultaneously, we generated a cell-type invariant enhancer prediction platform that utilized only the patterns of protein binding for inference. We also showed the utility of swarm-based adversarial attacks [adversarial particle swarm optimization (APSO)] to deconvolute trained genomic neural networks for the first time. Critically, unlike saliency mapping or other game-theory based approaches, APSO is completely network-architecture independent and can be applied to any prediction engine to derive the features that drive inference. AVAILABILITY AND IMPLEMENTATION: All software and code for data downloading, processing, enhancer inference, eXplainable AI (XAI), and complete figure generation are publicly available on GitHub at https://github.com/EpiGenomicsCode/ChromEnhancer and Zenodo at https://doi.org/10.5281/zenodo.15652797.

The ability to aggregate experimental data analysis and results into a concise and interpretable format is a key step in evaluating the success of an experiment. This critical step determines baselines for reproducibility and is a key requirement for data dissemination. However, in practice it can be difficult to consolidate data analyses that encapsulates the broad range of datatypes available in the life sciences. We present STENCIL, a web templating engine designed to organize, visualize, and enable the sharing of interactive data visualizations. STENCIL leverages a flexible web framework for creating templates to render highly customizable visual front ends. This flexibility enables researchers to render small or large sets of experimental outcomes, producing high-quality downloadable and editable figures that retain their original relationship to the source data. REST API based back ends provide programmatic data access and supports easy data sharing. STENCIL is a lightweight tool that can stream data from Galaxy, a popular bioinformatic analysis web platform. STENCIL has been used to support the analysis and dissemination of two large scale genomic projects containing the complete data analysis for over 2,400 distinct datasets. Code and implementation details are available on GitHub: https://github.com/CEGRcode/stencil.

MOTIVATION: Regulatory proteins associate with the genome either by directly binding cognate DNA motifs or via protein-protein interactions with other regulators. Each recruitment mechanism may be associated with distinct motifs and may also result in distinct characteristic patterns in high-resolution protein-DNA binding assays. For example, the ChIP-exo protocol precisely characterizes protein-DNA crosslinking patterns by combining chromatin immunoprecipitation (ChIP) with 5' → 3' exonuclease digestion. Since different regulatory complexes will result in different protein-DNA crosslinking signatures, analysis of ChIP-exo tag enrichment patterns should enable detection of multiple protein-DNA binding modes for a given regulatory protein. However, current ChIP-exo analysis methods either treat all binding events as being of a uniform type or rely on motifs to cluster binding events into subtypes. RESULTS: To systematically detect multiple protein-DNA interaction modes in a single ChIP-exo experiment, we introduce the ChIP-exo mixture model (ChExMix). ChExMix probabilistically models the genomic locations and subtype memberships of binding events using both ChIP-exo tag distribution patterns and DNA motifs. We demonstrate that ChExMix achieves accurate detection and classification of binding event subtypes using in silico mixed ChIP-exo data. We further demonstrate the unique analysis abilities of ChExMix using a collection of ChIP-exo experiments that profile the binding of key transcription factors in MCF-7 cells. In these data, ChExMix identifies possible recruitment mechanisms of FoxA1 and ERα, thus demonstrating that ChExMix can effectively stratify ChIP-exo binding events into biologically meaningful subtypes. AVAILABILITY AND IMPLEMENTATION: ChExMix is available from https://github.com/seqcode/chexmix. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: The rapid advancement of genomic technology has revealed the enormous complexity and combinatorial nature of chromatin modifications. To facilitate interpretation of the combinatorial nature of chromatin, we have developed a novel method to integrate all chromatin datasets into distinct nucleosome types (nucleosome alphabet). We have applied this approach to Saccharomyces cerevisiae, generating a nucleosome alphabet, which forms chromatin motifs when mapped back to the genome. By applying novel chromatin alignment and global word search approaches, we have defined distinctive chromatin motifs for introns, origins of replication, tRNAs, antisense transcripts, double-strand-break hotspots and DNase hypersensitive sites, and can distinguish genes by expression level. We have also uncovered strong associations between transcription factor binding and specific types of nucleosomes. Our results demonstrate the uses and functionality of defining a chromatin alphabet and provide a unique and novel framework for exploring chromatin architecture. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: The extension of mapped sequence tags is a common step in the analysis of single-end next-generation sequencing (NGS) data from protein localization and chromatin studies. The optimal extension can vary depending on experimental and technical conditions. Improper extension of sequence tags can obscure or mislead the interpretation of NGS results. We present an algorithm, ArchTEx (Architectural Tag Extender), which identifies the optimal extension of sequence tags based on the maximum correlation between forward and reverse tags and extracts and visualizes sites of interest using the predicted extension. AVAILABILITY AND IMPLEMENTATION: ArchTEx requires Java 1.6 or newer. Source code and the compiled program are freely available at http://sourceforge.net/projects/archtex/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.