Ultrafast and reference-free sequence discovery in single-cell data.

MKMC enables reference-free transcriptomic analysis using k-mer representations

MKMC (Multi-sample Kmer Counter), a scalable, reference-free toolkit for RNA-seq analysis that leverages k-mer–based statistics to detect biological variation without requiring alignment, is presented.

Single-cell long-read transcriptomics: from technologies to biological insights

This review examines the experimental and computational foundations of scLR-seq, including platform selection, library design, cell barcode and unique molecular identifier recovery, transcript discovery, and isoform quantification, and summarize emerging insights into isoform usage, alternative splicing, transcription start and end site selection, allele-specific expression, fusion transcripts, transposable element-derived transcripts, and RNA modifications.

Uncovering novel regulators of immune response in rhesus macaque single-cell RNA-seq data 2260001

Single-cell RNA-seq (scRNA-seq) analyses rely on accurate gene annotations, a challenge for many species with less completely curated genomes. Rhesus macaque (RM), a widely used model for human biomedical research, is one such case where missing gene annotations have hindered the study of immune responses. We aim to develop a computational framework to identify and reintegrate missing gene features, improving immune response characterization in RM. In a preliminary analysis, we computationally searched in a RM peripheral blood mononuclear cell (PBMC) scRNA-seq dataset from a kidney allograft study for unannotated but transcriptionally active regions (uTARs). We then performed cell clustering twice, once on annotated-gene expression and again on uTAR expression, and assessed uTAR expression for cell-type specificity and association with immune-related pathways. We identified >5,500 uTARs, indicating that numerous features–e.g., long non-coding RNAs or alternative transcripts of existing genes–are missing from current RM annotations. uTARs exhibit cell-type-specific expression and, when used to group cells, separate major cell types, paralleling cell clustering using annotated rhesus genes. These findings illustrate substantial gaps in the RM genome annotation and highlight the biological relevance of these missing genes or transcripts. uTARs likely harbor many previously unannotated genes or transcripts that are involved in immune regulation in RM. Ongoing work will denoise the signals in scRNA-seq data and prioritize a subset of uTARs as candidate transcriptional regulators. We will also infer regulatory relationships between candidate regulators and downstream targets. Our immediate goal is to identify drivers of transplant rejection. However, this framework is broadly applicable to scRNA-seq datasets across species and experimental contexts. NIAID U19 AI131471 Technological Innovations in Immunology (TECH)

REFCON: Reference-free and robust copy number inference in single-cell tumor transcriptomes

High-resolution reconstruction of cell-type-specific transcriptional regulatory processes from bulk sequencing samples.

Single-cell sequencing methods such as scRNA-seq and scATAC-seq have advanced our understanding of individual cellular functions but experimentally adapting genome-wide assays measuring other genomic features to achieve single-cell resolution remains a technical challenge. Here we introduce deep-learning-based deconvolution of tissue profiles with accurate interpretation of locus-specific signals (DeepDETAILS), a quasisupervised framework performing cross-modality deconvolution using scATAC-seq reference libraries for other bulk datasets. DeepDETAILS enables base-pair-resolution mapping of genomic signals across diverse cell types, with great versatility for various omics datasets, including nascent transcript sequencing (such as PRO-cap and PRO-seq) and ChIP-seq for chromatin modifications. Using DeepDETAILS, we generated a compendium of high-resolution nascent transcription and histone modification signals across 39 diverse human tissues and 86 distinct cell types. Furthermore, we applied our compendium to fine-map risk variants associated with primary sclerosing cholangitis, a progressive cholestatic liver disorder, and revealed a potential etiology of the disease.

Cell-Hub: a graphical interface for end-to-end single-cell RNA sequencing analysis

Single-cell and single-nucleus RNA sequencing have become increasingly widespread, creating a significant demand for accessible analysis tools in research laboratories. Despite this need, the bioinformatics expertise required for such analyses remains rare. Cell-Hub addresses this gap by enabling single-cell data analysis for all researchers, regardless of computational background. Cell-Hub is a comprehensive, free, and open-source framework built on R/Shiny and distributed as a Docker image, integrating Seurat 5, CellChat 2, and Monocle 3 within a unified graphical interface. It supports all essential steps of single-cell RNA-seq analysis: data loading, quality control, normalization, clustering, multi-dataset integration, differential expression, and biomarker detection. Cell-Hub further incorporates ligand-receptor interaction inference powered by GaspouDB, a consolidated database of 11,563 mouse and 9,604 human interactions derived from CellChat, CellPhoneDB, CellTalkDB, and MultiNicheNet as well as trajectory inference via Monocle 3 and spatial transcriptomics analysis for 10X Visium datasets. All analyses produce publication-ready visualizations with flexible export options. By integrating these analytical frameworks into a single, intuitive interface requiring no programming expertise, Cell-Hub represents a significant step toward democratizing single- cell genomics for the broader research community.