SNAP: Streamlined Nextflow Analysis Pipeline for Immunoprecipitation-Based Epigenomic Profiling of Circulating Chromatin
Zhang, Z.; Da Silva Cordeiro, P.; Chhetri, S. B.; Fortunato, B.; Jin, Z.; El Hajj Chehade, R.; Semaan, K.; Gulati, G.; Lee, G. G.; Hemauer, C.; Bian, W.; Sotudian, S.; Zhang, Z.; Osei-Hwedieh, D.; Heim, T. E.; Painter, C.; Nawfal, R.; Eid, M.; Vasseur, D.; Canniff, J.; Savignano, H.; Phillips, N.; Seo, J.-H.; Weiss, K. R.; Freedman, M. L.; Baca, S. C.
Show abstract
Epigenomic profiling of circulating chromatin is a powerful and minimally invasive approach for detecting and monitoring disease, but there are no bioinformatics pipelines tailored to the unique characteristics of cell-free chromatin. We present SNAP (Streamlined Nextflow Analysis Pipeline), a reproducible, scalable, and modular workflow specifically designed for immunoprecipitation-based methods for profiling cell-free chromatin. SNAP incorporates quality control metrics optimized for circulating chromatin, including enrichment score and fragment count thresholds, as well as direct estimation of circulating tumor DNA (ctDNA) content from fragment length distributions. It also includes SNP fingerprinting to enable sample identity verification. When applied to cfChIP-seq and cfMeDIP-seq data across multiple cancer types, SNAPs quality filters significantly improved classification performance while maintaining high data retention. Independent validation using plasma from patients with osteosarcoma confirmed the detection of tumor-associated epigenomic signatures that correlated with ctDNA levels and reflected disease biology. SNAPs modular architecture enables straightforward extension to additional cell-free immunoprecipitation-based assays, providing a robust framework to support studies of circulating chromatin broadly. SNAP is compatible with cloud and high-performance computing environments and is publicly available at https://github.com/prc992/SNAP/. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/694452v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@47c734org.highwire.dtl.DTLVardef@674af8org.highwire.dtl.DTLVardef@16ae938org.highwire.dtl.DTLVardef@1f57a01_HPS_FORMAT_FIGEXP M_FIG C_FIG
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- CUT&Tag recovers up to half of ENCODE ChIP-seq histone acetylation peaks 96%
- A multi-omic dissection of super-enhancer driven oncogenic gene expression programs in ovarian cancer 96%
- Cross-dataset pan-cancer detection: Correlating cell-free DNA fragment coverage with open chromatin sites across cell types 95%
Similar papers in this journal
- Modeling methyl-sensitive transcription factor motifs with an expanded epigenetic alphabet 95%
- RAMEN: Dissecting individual, additive and interactive gene-environment contributions to DNA methylome variability in cord blood 94%
- HiCognition: a visual exploration and hypothesis testing tool for 3D genomics 94%
Similar papers in this journal
- Y chromosome sequence and epigenomic reconstruction across human populations 95%
- Multi-sample Full-length Transcriptome Analysis of 22 Breast Cancer Clinical Specimens with Long-Read Sequencing 94%
- Single-cell somatic copy number variants in brain using different amplification methods and reference genomes 94%
Similar papers in this journal
- Chemoenzymatic labeling of DNA methylation patterns for single-molecule epigenetic mapping 95%
- Multi-resolution characterization of molecular taxonomies in bulk and single-cell transcriptomics data 95%
- Predicting gene expression from histone marks using chromatin deep learning models depends on histone mark function, regulatory distance and cellular states 94%
Similar papers in this journal
- Short and long-read genome sequencing methodologies for somatic variant detection; genomic analysis of a patient with diffuse large B-cell lymphoma 94%
- Molecular counting enables accurate and precise quantification of methylated ctDNA for tumor-naive cancer therapy response monitoring 94%
- Correspondence analysis for dimension reduction, batch integration, and visualization of single-cell RNA-seq data 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.