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Characterizing epigenetic heterogeneity at the cellular level is a critical problem in the modern genomics era. Assays such as single cell ATAC-seq (scATAC-seq) offer an opportunity to interrogate cellular level epigenetic heterogeneity through patterns of variability in open chromatin. However, these assays exhibit technical variability that complicates clear classification and cell type identification in heterogeneous populations. We present scABC , an R package for the unsupervised clustering of single-cell epigenetic data, to classify scATAC-seq data and discover regions of open chromatin specific to cell identity. Single cell ATAC-seq (scATAC-seq) data reveals cellular level epigenetic heterogeneity but its application in delineating distinct subpopulations is still challenging. Here, the authors develop scABC, a statistical method for unsupervised clustering of scATAC-seq data and identification of open chromatin regions specific to cell identity.

When different types of functional genomics data are generated on single cells from different samples of cells from the same heterogeneous population, the clustering of cells in the different samples should be coupled. We formulate this “coupled clustering” problem as an optimization problem and propose the method of coupled nonnegative matrix factorizations (coupled NMF) for its solution. The method is illustrated by the integrative analysis of single-cell RNA-sequencing (RNA-seq) and single-cell ATAC-sequencing (ATAC-seq) data.

Combinatorial CRISPR technologies have emerged as a transformative approach to systematically probe genetic interactions and dependencies of redundant gene pairs. However, the performance of different functional genomic tools for multiplexing sgRNAs vary widely. Here, we generate and benchmark ten distinct pooled combinatorial CRISPR libraries targeting paralog pairs to optimize digenic knockout screens. Libraries composed of dual Streptococcus pyogenes Cas9 (spCas9), orthogonal spCas9 and Staphylococcus aureus (saCas9), and enhanced Cas12a from Acidaminococcus were evaluated. We demonstrate a combination of alternative tracrRNA sequences from spCas9 consistently show superior effect size and positional balance between the sgRNAs as a robust combinatorial approach to profile genetic interactions of multiple genes. Combinatorial CRISPR screens can be utilized to identify genetic interactions and functional redundancies of multiple genes. Here, the authors benchmark ten digenic CRISPR technologies and identify novel Cas9 tracrRNA combinations that show superior performance.

Systems for CRISPR-based combinatorial perturbation of two or more genes are emerging as powerful tools for uncovering genetic interactions. However, systematic identification of these relationships is complicated by sample, reagent, and biological variability. We develop a variational Bayes approach (GEMINI) that jointly analyzes all samples and reagents to identify genetic interactions in pairwise knockout screens. The improved accuracy and scalability of GEMINI enables the systematic analysis of combinatorial CRISPR knockout screens, regardless of design and dimension. GEMINI is available as an open source R package on GitHub at https://github.com/sellerslab/gemini .

Drug-Induced Liver Injury (DILI) remains one of the most critical challenges in drug development, causing patient safety concerns, clinical trial failures and drug withdrawals. We introduce ToxPredictor , a toxicogenomics framework combining RNA-seq data from primary human hepatocytes with pharmacokinetic data to predict dose-resolved DILI risks and safety margins. At its core is DILImap , an RNA-seq library tailored for DILI research, comprising 300 compounds at multiple concentrations. ToxPredictor achieves 88% sensitivity at 100% specificity in blind validation, outperforming state-of-the-art methods. It flagged recent phase III clinical failures, including Evobrutinib, TAK-875, and BMS-986142, overlooked by animal studies. Beyond prediction, ToxPredictor provides mechanistic insights into hepatotoxic pathways, enabling early de-risking and actionable safety decisions. Unlike single-endpoint readouts—even from 3D models—transcriptomics offers a multi-dimensional system-level view of hepatocyte responses, capable of detecting diverse DILI mechanisms not captured by conventional assays. Scalable, actionable, and integrated into a broader AI/ML drug discovery platform, this work establishes toxicogenomics as a promising tool for developing safer therapeutics and addressing one of the most pressing challenges in toxicology. Drug-induced liver injury is a major cause of patient harm, trial failures, and drug withdrawals. Here, the authors show that a toxicogenomics resource of 300 drugs enables the prediction of liver injury with 88% sensitivity at 100% specificity and reveals mechanisms for safer drug development.

Dimension reduction methods are commonly applied to high-throughput biological datasets. However, the results can be hindered by confounding factors, either biological or technical in origin. In this study, we extend principal component analysis (PCA) to propose AC-PCA for simultaneous dimension reduction and adjustment for confounding (AC) variation. We show that AC-PCA can adjust for (i) variations across individual donors present in a human brain exon array dataset and (ii) variations of different species in a model organism ENCODE RNA sequencing dataset. Our approach is able to recover the anatomical structure of neocortical regions and to capture the shared variation among species during embryonic development. For gene selection purposes, we extend AC-PCA with sparsity constraints and propose and implement an efficient algorithm. The methods developed in this paper can also be applied to more general settings. The R package and MATLAB source code are available at https://github.com/linzx06/AC-PCA.

Large panels of comprehensively characterized human cancer models, including the Cancer Cell Line Encyclopedia (CCLE), have provided a rigorous framework with which to study genetic variants, candidate targets, and small-molecule and biological therapeutics and to identify new marker-driven cancer dependencies. To improve our understanding of the molecular features that contribute to cancer phenotypes, including drug responses, here we have expanded the characterizations of cancer cell lines to include genetic, RNA splicing, DNA methylation, histone H3 modification, microRNA expression and reverse-phase protein array data for 1,072 cell lines from individuals of various lineages and ethnicities. Integration of these data with functional characterizations such as drug-sensitivity, short hairpin RNA knockdown and CRISPR–Cas9 knockout data reveals potential targets for cancer drugs and associated biomarkers. Together, this dataset and an accompanying public data portal provide a resource for the acceleration of cancer research using model cancer cell lines. The original Cancer Cell Line Encyclopedia (CCLE) is expanded with deeper characterization of over 1,000 cell lines, including genomic, transcriptomic, and proteomic data, and integration with drug-sensitivity and gene-dependency data.

Although single-gene perturbation screens have revealed a number of new targets, vulnerabilities specific to frequently altered drivers have not been uncovered. An important question is whether the compensatory relationship between functionally redundant genes masks potential therapeutic targets in single-gene perturbation studies. To identify digenic dependencies, we developed a CRISPR paralog targeting library to investigate the viability effects of disrupting 3,284 genes, 5,065 paralog pairs and 815 paralog families. We identified that dual inactivation of DUSP4 and DUSP6 selectively impairs growth in NRAS and BRAF mutant cells through the hyperactivation of MAPK signaling. Furthermore, cells resistant to MAPK pathway therapeutics become cross-sensitized to DUSP4 and DUSP6 perturbations such that the mechanisms of resistance to the inhibitors reinforce this mechanism of vulnerability. Together, multigene perturbation technologies unveil previously unrecognized digenic vulnerabilities that may be leveraged as new therapeutic targets in cancer. A CRISPR paralog targeting library profiling 815 paralog families across 11 cell lines identifies DUSP4 and DUSP6 as paralog pairs whose combined inactivation confers sensitivity to cells resistant to MAPK inhibitors or cells harboring NRAS or BRAF mutations.

Unsupervised methods, including clustering methods, are essential to the analysis of single-cell genomic data. Model-based clustering methods are under-explored in the area of single-cell genomics, and have the advantage of quantifying the uncertainty of the clustering result. Here we develop a model-based approach for the integrative analysis of single-cell chromatin accessibility and gene expression data. We show that combining these two types of data, we can achieve a better separation of the underlying cell types. An efficient Markov chain Monte Carlo algorithm is also developed.

Although single-gene perturbation screens have revealed a number of new targets, vulnerabilities specific to frequently altered drivers have not been uncovered. An important question is whether the compensatory relationship between functionally redundant genes masks potential therapeutic targets in single-gene perturbation studies. To identify digenic dependencies, we developed a CRISPR paralog targeting library to investigate the viability effects of disrupting 3,284 genes, 5,065 paralog pairs and 815 paralog families. We identified that dual inactivation of DUSP4 and DUSP6 selectively impairs growth in NRAS and BRAF mutant cells through the hyperactivation of MAPK signaling. Furthermore, cells resistant to MAPK pathway therapeutics become cross-sensitized to DUSP4 and DUSP6 perturbations such that the mechanisms of resistance to the inhibitors reinforce this mechanism of vulnerability. Together, multigene perturbation technologies unveil previously unrecognized digenic vulnerabilities that may be leveraged as new therapeutic targets in cancer.