scATAC-seq

Single-Cell Sequencing in Pediatric Acute Leukemia: A Systematic Review of Biologic Insights and Translational Potential

This review evaluates single-cell sequencing findings in pediatric acute leukemia, highlighting insights into clonal evolution, chemoresistance, and immune dynamics, while noting limitations in study design and bias.

multiomeR: a pipeline for reproducible analysis of large-scale single-nucleus Epi Multiome datasets

multiomeR provides a reproducible R pipeline for analyzing large-scale single-nucleus Epi Multiome datasets, improving efficiency and reducing memory usage compared to existing workflows.

Quantifying distribution shifts in single-cell data with scXMatch

scXMatch quantifies distribution shifts between single-cell data from different conditions using a non-parametric graph-based test, showing robust performance across gene expression, chromatin accessibility, and morphology data.

DeepSCENIC: transfer learning from sequence-to-function models enables causal gene regulatory network inference

DeepSCENIC uses transfer learning from sequence-to-function models to infer causal gene regulatory networks from single-cell multiome data.

Move BeTween modAlities (MBTA) employs flow matching to predict single cell data modalities

MBTA uses flow matching to address structural mismatch between molecular modalities, improving cross-modal translation and enabling multimodal analysis in single-cell data.

High-Specificity Detection of Chromosomal Mosaicism Reveals Cell-Type-Specific Genomic Alteration Patterns in Aging Tissues

CHASM detects chromosomal copy number alterations in single-cell data, revealing age-related genomic instability in specific cell types and states.

simPIC: flexible simulation of single-cell ATAC-seq paired-insertion counts from individuals to populations

simPIC simulates single-cell ATAC-seq count data across individuals and populations to enable controlled evaluation of chromatin accessibility QTL mapping methods.

An Integrative Single-Cell Epigenomic Atlas Annotates the Regulatory Genome of the Adult Mouse Brain

The paper constructs an integrative single-cell epigenomic atlas of the adult mouse brain, mapping chromatin states and gene regulatory programs across 100 cell subclasses.

scWeave: A deep learning model that bidirectionally translates between gene expression and chromatin structure at single cell resolution

scWeave translates between gene expression and chromatin structure at single-cell resolution using deep learning, improving cross-modality inference across multiple datasets.

Semantic fragment representations for coordinate-free analysis of genomics data

The paper introduces LEAF-1, a fragment-level foundation model for genomics data analysis, showing it can classify cell types and detect cancer from sparse datasets and cell-free DNA.

A Simple, Cost-Effective, High-Throughput Method for Measuring Chromatin Accessibility and Gene Expression in Single Nuclei

The paper introduces and benchmarks two droplet-based single-nucleus assays for chromatin accessibility and gene expression, showing they are cost-effective and produce high-quality data.

Disentangling covariate effects on single cell-resolved epigenomes with DeepDive

DeepDive disentangles covariate effects on single-cell epigenomes, enabling counter-factual analyses and identifying diabetes-linked beta cell subtypes.

Somatic variant detection in normal tissues from single-cell sequencing data

This study evaluates somatic variant calling in normal tissues using single-cell RNA and ATAC sequencing, showing Monopogen outperforms other tools in accuracy and sensitivity.

Robust footprinting with sample-specific Tn5 bias correction for bulk and single cell ATAC-seq

This paper introduces TraceBIND, a method to correct Tn5 bias in ATAC-seq data, improving TF footprint detection and revealing age-related regulatory changes in scATAC-seq.

Alignment-free integration of single-nucleus ATAC-seq across species with sPYce

sPYce integrates single-nucleus ATAC-seq data across species using k-mer histograms, revealing conserved and divergent regulatory programs without requiring genome alignments.

Disagreement between demultiplexing methods reveals structured cell quality gradients in multiplexed single-cell data

This paper introduces Split-flow, a pipeline that uses concordance between demultiplexing methods to classify cell quality in multiplexed single-cell data, revealing structured quality gradients.

Quantifying Cross-Modal Association Confidence for Single-Cell RNA-ATAC Integration

The paper introduces CLIC scores to quantify confidence in gene-peak associations for single-cell RNA-ATAC integration, improving integration accuracy and biological interpretability.

Predicting gene-specific regulation with transcriptomic andepigenetic single-cell data

The paper introduces MetaFR, a method that links scATAC-seq open-chromatin variation to scRNA-seq gene expression, showing improved performance over existing methods.

SPEAR: Predicting Gene Expression from Single-Cell Chromatin Accessibility

SPEAR predicts gene expression from chromatin accessibility in single-cell multiome data, showing that transformer models achieve the highest correlation across two biological systems.

scMultiPreDICT: A single-cell predictive framework with transcriptomic and epigenetic signatures

scMultiPreDICT compares the predictive power of transcriptional and epigenetic features for gene expression using single-cell multiomics data, showing gene-specific contributions of each layer.

OmicClaw: executable and reproducible natural-language multi-omics analysis over the unified OmicVerse ecosystem.

OmicClaw provides a natural-language framework for reproducible multi-omics analysis using the OmicVerse ecosystem, improving performance over baseline models in complex workflows.

Integration of single-cell multi-omic data with graph-based topic modelling

This paper introduces a graph-based topic modelling method for single-cell multi-omic data, showing improved performance in clustering and biological interpretation.

BARTsc identifies key transcriptional regulators from single-cell omics data

BARTsc identifies key transcriptional regulators from single-cell omics data by integrating ChIP-seq profiles and outperforms existing methods in multiple biological systems.

Longitudinal modality prediction learns gene regulatory patterns: insights from a single-cell competition

The paper presents a longitudinal multimodal benchmarking dataset and competition to evaluate modality prediction methods for gene regulatory inference, showing top models outperform existing approaches.

CIRCE: a scalable Python package to predict cis-regulatory DNA interactions from single-cell chromatin accessibility data

CIRCE predicts cis-regulatory DNA interactions from single-cell chromatin accessibility data, improving speed and memory use compared to Cicero.

CLM-X: A multimodal single-cell foundation model with flexible multi-way Transformer for unified scRNA-seq and scATAC-seq analysis

CLM-X is a multimodal single-cell foundation model that outperforms existing methods in cross-modal translation and perturbation prediction across scRNA-seq and scATAC-seq datasets.

A Comprehensive Benchmarking Study on Computational Tools for Cross-omics Label Transfer from Single-cell RNA to ATAC Data

This study benchmarks 27 computational tools for transferring cell type labels from scRNA-seq to scATAC-seq data across human and mouse tissues, finding Bridge and GLUE as top performers.

coelsch: Platform-agnostic single-cell analysis of meiotic recombination events

The paper benchmarks single-cell methods for meiotic recombination mapping in Arabidopsis thaliana and introduces tools to detect crossovers and haplotypes from sparse sequencing data.

An Integrated Single-Cell and Epigenomic Resource for Comparative Analysis of the Basal Ganglia

The paper presents an integrated single-cell and epigenomic resource for exploring basal ganglia datasets across species, enabling multi-omics and comparative analysis.

HyDrop v2: Scalable atlas construction for training sequence-to-function models

The paper introduces HyDrop v2, a microfluidic system for generating large scATAC-seq atlases, and shows its data is comparable to commercial platforms for training sequence-to-function models.