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PETRO improves Perturb-seq analysis by reducing off-target bias and enabling robust target-level perturbation effect estimation.

MINTsC identifies multi-way chromatin interactions from scHi-C data, revealing gene regulation by multiple enhancers and reducing multiple-testing burden in molecular QTL studies.

CMAPS performs causal mediation analysis on perturbation screens to identify molecular intermediates, showing accurate mediation estimates and robust mediator identification in Perturb-seq data.

ADAPRE infers causal gene regulatory networks from Perturb-seq data by using adaptive instrumental variable modeling, improving accuracy over existing methods.

The paper identifies sex-specific DNA methylation differences in blood samples from individuals with mild cognitive impairment and Alzheimer's disease, showing distinct epigenetic regulation by sex.

This paper introduces BasCoD, a framework for selecting optimal backgrounds in contrastive dimension reduction, improving contrast and interpretability in single-cell data.

The paper uses whole genome methylation sequencing in blood samples to identify differentially methylated positions associated with cognitive status in mild cognitive impairment and Alzheimer's disease.

This study identifies IL11's dual role in heart regeneration and fibrosis, showing that combining IL11a induction with ERK inhibition can enhance regeneration while reducing fibrosis.

Muscle jointly models single cell 3D genome and DNA methylation data to improve cell type delineation and modality associations.

FlyVISTA is a machine learning platform that identifies sleep-associated microbehaviors in Drosophila by analyzing high-resolution video data and annotating body parts.

MuDCoD detects dynamic gene communities across multiple subjects and time points in scRNA-seq data, revealing subject- and time-specific network variations.

Dozer debiases gene-gene correlations in scRNA-seq data to improve co-expression network accuracy across individuals.

AdaLiftOver identifies orthologous regulatory elements across species by improving upon sequence conservation methods with epigenomic and sequence grammar features.

The paper identifies regulatory elements and chromatin structures in the RCA gene cluster, revealing long-range interactions and a TAD boundary within CR1 that influence gene expression and autoimmune disease susceptibility.

scGAD provides a dimension reduction tool for scHi-C data to explore gene-level chromatin interactions and integrate with other single-cell modalities.

The paper shows that prenatal stress in Cntnap2 heterozygous mice disrupts 5hmC levels and alters gene expression, leading to neurodevelopmental behaviors similar to knockout mice.

MLG improves clustering accuracy in multi-condition scRNA-seq data by integrating multiple dimensionality reduction methods.

INFIMA identifies effector genes of GWAS variants by integrating multi-omics data from mice and applying it to diabetes-associated variants.

This paper introduces BandNorm and 3DVI to normalize and de-noise scHi-C data, improving performance in cell type separation and 3D genomic structure recovery.

The paper identifies novel risk genes for autism spectrum disorder using transcriptome-wide transmission disequilibrium analysis across 7,805 ASD proband-parent trios.

PRAM improves transcript discovery from RNA-seq data by combining datasets in a single step, outperforming two-step methods and identifying new transcripts in human and mouse data.

The paper introduces SURF, a tool for integrating CLIP-seq and RNA-seq data to identify differential alternative transcriptional regulation events linked to RNA-binding proteins.

FreeHi-C simulates high-fidelity Hi-C data for benchmarking and augmentation, improving detection precision while controlling false discovery rates.

mHi-C improves Hi-C analysis by handling multi-mapping reads, increasing sequencing depth and reproducibility of contact matrices and interactions.