Comprehensive Guide to Gene Expression Analysis from Bulk and Single-Nucleus Transcriptomes.

Precision-Based Filtering Facilitates Cross-Referencing of Conventional and Single-Nucleus Transcriptomes to Identify Time- and Temperature-Sensitive Cell Populations.

Transcriptome analysis via RNA sequencing (RNAseq) has become a ubiquitous method of molecular characterization from whole organisms, dissected tissues, and single cells. These experiments continue to provide an extraordinary volume of data describing molecular states and responses to many conditions. However, standard...

A Framework for Analyzing the Epigenetic and Transcriptomic Landscape of Cancers Using Single-Cell Multiomic Data.

Accurate de novo transcription unit annotation from run-on and sequencing data

This work developed a convolutional neural network, called convolutional discovery of gene anatomy using PRO-seq (CGAP), trained to identify different anatomical features of a transcription unit, which were then stitched together into transcript annotations using a hidden Markov model (HMM).

Cross-chemistry single-nucleus RNA-seq identifies gene length and CpG-island promoters as determinants of transcriptional noise

Cell-to-cell transcriptional heterogeneity, or noise, is an intrinsic property of the transcriptome with implications for development, disease progression, and aging. Bulk RNA-seq masks this variability by averaging gene expression across cells, whereas single-cell RNA sequencing (scRNA-seq) resolves it. Nevertheless,...

Systematic benchmarking of commercial workflows for isoform-resolved single-nucleus transcriptomics

Short-read sequencing-based single-cell transcriptomics represents the current gold standard for studying cellular transcriptomes but remains limited in its ability to resolve full-length transcript isoforms and splicing patterns. Long-read single-cell and single-nucleus RNA sequencing (LR sc/snRNA-seq) enables the tra...

MicroRNAs and Their Profiling via Single-Cell Sequencing Technologies.