CUT&Tag

Ryder normalizes epigenomic bigWig tracks against stable internal reference regions such as invariant CTCF sites, correcting background and signal separately, recovering expected perturbation effects that depth-only normalization obscured.

A web atlas counts evidence from 24 types for 188,634,118 variant-gene pairs across 50 human tissues; 1,530,420 pairs pass a high-confidence cutoff, 87% of them tissue-specific.

Over 8,000 gapless human centromeres reveal population-stratified satellite-array, inversion and haplotype variation, multiple candidate kinetochore sites in 16.8% of centromeres, and single-base substitution rates comparable to flanking pericentromeres.

This study identifies subtype-specific molecular and immune features associated with recurrence risk in breast cancer using multi-omic analysis of 340 tumors and validates predictive models in an external cohort.

This paper introduces SONAR, a platform for detecting nucleic acids and triggering gene expression in human cells, demonstrating its ability to sense DNA and RNA and express various protein payloads.

This study identifies chromatin reorganization and histone mark changes in HGPS VSMCs, linking them to vascular pathology through disrupted enhancer-promoter interactions.

This paper integrates multiple transcriptomics assays to identify cancer biomarkers, finding GBP4 as a predictor of recurrence and immune remodeling.

This study generates genomic and epigenomic maps of mouse centromeres and pericentromeres using long-read sequencing and epigenetic analyses, revealing satellite DNA composition and chromatin state differences.

This paper introduces DLVPM, a deep learning method for integrating multi-modal cancer data, showing improved performance in mapping associations between genetic, epigenetic, and histological features.

POCALI identifies cancer lncRNAs by integrating 44 multi-omics features, showing improved sensitivity and strong associations with cancer phenotypes.

The paper identifies a 101-biomarker signature predicting radiotherapy response in colorectal cancer using multi-omics and supervised learning, achieving 89% accuracy.

CLCluster uses contrastive learning with multi-omics data to cluster cancer subtypes, showing improved performance over existing methods and identifying AS events linked to patient survival.

This study benchmarks 35 AI classifiers across 153 cancer datasets to evaluate their performance in molecular subtype classification, identifying optimal data configurations and model types for different omics modalities and cancer types.

The paper introduces two new methods, ChrODIE and ANDIE, for mapping chromatin accessibility and histone modifications without enzymatic cleavage, enabling multi-omics integration.

The paper uses G4-DNABERT to map G-quadruplexes to enhancer and promoter regions, supporting their role in chromatin looping and gene regulation.

DiffModeler uses diffusion models to accurately model protein complex structures from low-resolution cryo-EM maps, achieving high TM-Scores across various resolutions.

The paper identifies alternative promoters in cancer transcriptomes that are associated with epigenetic misregulation due to DNA methylation changes in CpG-depleted regions.

The paper develops a deep learning model integrating multi-omics data to stratify breast cancer patients by prognosis and identify potential therapeutic targets.

FILER provides a large-scale, harmonized functional genomics data catalog with streamlined access to over 50,000 datasets and efficient querying capabilities.

This paper uses multi-omics topic modeling to classify breast cancer subtypes, showing improved accuracy by integrating mRNA, microRNA, and copy number variation data.

The paper develops an integrated resource to analyze small RNA profiles in mosquitoes, revealing evolutionary dynamics and host responses to viruses and transposons.

MESSI infers coevolution rates of base-pairing interactions in nucleic acid sequences and identifies functionally important substructures.

The paper analyzes testis-specific genes to identify regulatory features and patterns associated with differentiation levels in human testis cells.

This paper introduces AMARETTO, a multi-omics integration algorithm that identifies pancancer driver genes related to smoking and antiviral responses by analyzing eleven cancer types.

The paper introduces seqOutBias to correct enzymatic sequence bias in sequencing data, improving the identification of molecular signatures from protein/DNA interactions.

OncoScape integrates five genomic data types across 11 cancer types to identify new candidate cancer genes and reveals discrepancies between cell line and tumor aberrations.