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Aggregating similar single cells into metacells is a common heuristic for sparse data, but risks mixing dissimilar cells. Here, authors present mcRigor, which detects and filters heterogeneous metacells, optimizes metacell partitioning, and improves reliability in single-cell omics studies.

RNAtracker is a computational pipeline that distinguishes variants associated with allele-specific RNA stability from those associated with allele-specific RNA transcription. Variants affecting RNA stability are enriched in immune-related genes and contribute to disease risk.

Dissecting the causal relationship between genotype and phenotype can be challenging, Here, the authors develop Worm Perturb-Seq, a high throughput sequencing and analytical framework to assess transcriptomic changes upon gene perturbation in whole C. elegans.

In two independent studies, Song et al. and Jiang, Dalgarno et al. present computational frameworks, perturbation-response score and Mixscale, respectively, to determine individual cellular variation in response to perturbation.

Genomic profiling of tumours can help tailer treatments to the patient, however, it often fails to accurately predict therapeutic outcomes. Here, the authors combine molecular and functional characterisation via BH3 profiling to identify therapeutically targetable vulnerabilities in glioma.

The FarmGTEx Project aims to understand genetic control of gene activity under diverse biological and environmental contexts in domestic animals, providing a foundation for improving animal precision breeding, adaptation and human health.

Despite being widely performed in exploring cell heterogeneity and gene expression stochasticity, single cell RNA-seq analysis is complicated by excess zero counts (dropouts). Here, Li and Li develop scImpute for statistical imputation of dropouts in scRNA-seq data.

Here, the authors introduce Cell Heterogeneity–Adjusted cLonal Methylation (CHALM) as a methylation quantification method that considers the heterogeneity of sequenced bulk cells. They apply CHALM to methylation datasets to detect differentially methylated genes that exhibit distinct biological functions supporting underlying mechanisms.

The global pattern of the mammalian methylome is formed by changes in methylation and demethylation. Here the authors describe a metric methylation concurrence that measures the ratio of unmethylated CpGs inside the partially methylated reads and show that methylation concurrence is associated with epigenetically regulated tumour suppressor genes.

Uniform processing and detailed annotation of human, worm and fly RNA-sequencing data reveal ancient, conserved features of the transcriptome, shared co-expression modules (many enriched in developmental genes), matched expression patterns across development and similar extent of non-canonical, non-coding transcription; furthermore, the data are used to create a single, universal model to predict gene-expression levels for all three organisms from chromatin features at the promoter.

2D visualisation of single-cell data is highly impacted by the hyperparameter setting of the 2D embedding method, such as t-SNE and UMAP. Here, authors develop a statistical method scDEED to detect dubious cell embeddings and optimise the hyperparameter setting for trustworthy visualisation.

Benchmarking computational tools for analysis of single-cell sequencing data demands simulation of realistic sequencing reads. However, none of the few existing read simulators aim to mimic real data. Here, the authors introduce scReadSim, a single-cell RNA-seq and ATAC-seq read simulator that works by mimicking real data.

In spatial transcriptomics data analysis, identifying spatially variable genes (SVGs) is crucial for understanding tissue organization and function. The authors categorize 34 computational methods for SVG detection, exploring their definitions, methodologies—including statistical approaches—and applications, while proposing future research directions.

This overview of the ENCODE project outlines the data accumulated so far, revealing that 80% of the human genome now has at least one biochemical function assigned to it; the newly identified functional elements should aid the interpretation of results of genome-wide association studies, as many correspond to sites of association with human disease.

The challenge of simulating multiomic single-cell data is addressed by a probabilistic model.

A map of genome-wide binding locations of 165 human, 93 worm and 52 fly transcription-regulatory factors (almost 50% presented for the first time) from diverse cell types, developmental stages, or conditions reveals that gene-regulatory properties previously observed for individual factors may be general principles of metazoan regulation that are well preserved.