Matthew E. Ritchie

Alternative splicing and transcript isoform diversity expand the regulatory information captured by transcriptome profiling beyond gene-level expression. However, the reliable detection and quantification of isoforms and alternative splicing using RNA sequencing is complex and not determined by a single platform or computational tool. It is a workflow-centered measurement problem in which the analytical endpoint, RNA integrity, enrichment and library construction, sequencing strategy, computational analysis, benchmarking, and validation jointly determine the confidence of a research finding. This review reframes transcript-resolved sequencing in terms of endpoint-aware workflows, distinguishing among isoform discovery, isoform quantification, splicing event detection, differential isoform expression, differential transcript usage, and differential alternative splicing events. We compare short-read, long-read, and hybrid sequencing strategies across the experimental and computational decisions that most strongly affect reliability. We further discuss RNA reference materials, matched reference datasets, quality metrics, and orthogonal validation as recurring elements of workflow evaluation. An endpoint-aware reliability framework can help transcript-resolved studies progress from feature detection to reproducible, benchmark-supported, and biologically interpretable inference.

Microfold (M) cells transcytose luminal antigens to initiate mucosal adaptive immunity, but their role in organizing innate responses in Peyer’s patches is unclear. Here we showed that Peyer’s patch M cells organized an epithelial–group 3 innate lymphoid cell (ILC3) axis, establishing a spatial niche within the dome epithelium that drove ILC3 localization, proliferation and IL-22 production. We found that epithelial, but not hematopoietic, SPI-B was required for intestinal IgA responses to establish this niche. Single-cell profiling of intestinal SPI-B ⁺ epithelial cells revealed that M cells were highly heterogeneous, displaying tissue- and pathogen-specific transcriptional programs. Using subset-specific genetic perturbation and whole-mount imaging, we found that this circuit relied on CCR6-dependent positioning cues and RANK–RANKL signaling to ILC3 to regulate Peyer’s patch ILC3 homeostasis. Together, these findings identified Peyer’s patch M cells as organizers that spatially coordinated innate cell localization, proliferation and cytokine production to maintain mucosal barrier defense.

Visualizing and troubleshooting long-read sequencing data is a common task when analyzing novel protocols, but identifying several primer sequences from reads exceeding 2 kb with fuzzy matching is challenging for the human eye. Here, we present seqsizzle , a cross-platform terminal user interface (TUI) based tool for convenient visualization of long-read sequencing data directly from the command-line, applicable to all sequencing technologies that produce FASTQ or FASTA output. seqsizzle allows primer sequences to be matched with user-specified fuzzy matching thresholds and highlighted with configurable colors. Additional styling can be applied to distinguish mismatches and quality scores, allowing convenient quality control of long-read sequencing data. To further aid troubleshooting, seqsizzle includes a built-in k -mer enrichment analysis, enabling the detection of unknown primer sequences or unexpected artifacts, which can then be reviewed via manual inspection. seqsizzle is implemented in Rust and is available open-source from https://github.com/ChangqingW/SeqSizzle .

Spatial transcriptomics (ST) has rapidly expanded with the introduction of multiple high-resolution platforms, yet cross-platform benchmarking remains limited and largely focused on technical performance. Here we present SpatialBench , a matched multi-platform resource comprising Visium HD, Xenium and MERSCOPE data together with single-cell and single-nucleus references from a malaria-challenged wild-type and B cell-specific Tbx21 knockout mouse spleen model. In this system, loss of T-bet in B cells disrupts germinal center (GC) polarization and antibody maturation, providing a biologically grounded benchmark for technology comparison. We leveraged this system to systematically evaluate ST platform performance using technical and biological readouts. Across platforms, immune organization and Tbx21 -associated programs were consistently recovered, indicating robustness of major biological signals. Platforms instead differed in the level of biological resolution accessible. Visium HD enabled transcriptome-scale GC characterization and, together with Xenium, resolved dark and light zone organization, whereas GC zonation was not resolved in MERSCOPE, consistent with differences in transcript detection sensitivity. SpatialBench provides a biologically defined reference dataset for evaluation of ST technologies, method development, computational benchmarking, and studies of GC spatial organization in lymphoid tissue.

The D4Z4 locus is a macrosatellite array on Chromosome 4q normally comprising 8 to >100 3.3-kb repeat units. Its size and repetitiveness render it refractory to most sequencing technologies; consequently, its genetic and epigenetic architectures remain incompletely understood despite their relevance to facioscapulohumeral muscular dystrophy (FSHD). Current FSHD molecular testing relies on complex, multistep and low-resolution assays, which aim to identify contractions on permissive haplotypes (FSHD type 1) or epigenetic reactivation due to pathogenic variants in the epigenetic machinery, most often in SMCHD1 (FSHD type 2). Recent guideline updates highlight the need for more accurate and comprehensive diagnostic approaches. Here, we leverage ultra-long whole-genome and Cas9-targeted sequencing to develop a fast and accurate workflow, D4Z4End2End, for comprehensive genetic and methylation analysis of D4Z4 alleles. We apply it to samples from two controls, four FSHD1 patients, four FSHD2 patients, and two patients with Bosma arhinia microphthalmia syndrome (BAMS) caused by SMCHD1 variants, as well as publicly available data from 30 B-lymphoblastoid cell lines from the 1000 Genomes Project and Human Pangenome Reference Consortium. We attain high-depth sequencing of full-length D4Z4 arrays of up to 40 repeat units (∼132 kb), accurately capture contracted arrays, genetic mosaicism, and pathogenic SMCHD1 variants, and generate consensus sequences of all D4Z4 alleles. We identify new allelic variants, analyze complex D4Z4 rearrangements including in- cis duplications, and reveal length- and SMCHD1 -dependent methylation patterns across the D4Z4 array. Our findings offer insights into D4Z4 genetics and epigenetics, and demonstrate the potential of long-read nanopore sequencing to accelerate FSHD research and diagnostics.

Nanobodies have emerged as promising tools for many biotechnological applications due to their small size, high stability and remarkable binding specificity. Next-Generation Sequencing (NGS) enables deep profiling of large nanobody libraries and panning campaigns; however, the scale and diversity of nanobody NGS datasets presents a significant bioinformatic challenge. To this end, we have developed alpseq, an optimized, open-source software pipeline designed specifically for the efficient and accurate processing of NGS data from nanobody libraries and panning campaigns. alpseq is also paired with a PCR-free sequencing library preparation protocol to allow researchers to easily generate their own data while avoiding biases. The alpseq software pipeline is composed of two parts: a pre-processing module written in Nextflow efficiently handles raw nanobody reads in a single line of code. These results are then fed into the analysis module, which contains a comprehensive suite of functions for quality control, diversity analysis, identification of enriched sequences and clustering. alpseq also creates a user-friendly interactive report which empowers scientists to explore their data without the need for extensive bioinformatic experience. Sophisticated panning campaign designs are supported, such as replicates and comparisons between different pans to find cross-binding leads. alpseq thus generates insights into the nanobody selection process and delivers a list of lead candidates for further experimental validation and downstream applications. alspeq is available at https://github.com/kzeglinski/alpseq.

Long-read RNA sequencing (lrRNA-seq) enables full-length transcript profiling but is confounded by technical batch effects that compromise quantification and prevent data integration across platforms, protocols, and laboratories. The lack of a transcriptome-wide biological ground truth has hindered objective benchmarking. To address these dual challenges, we leveraged certified Quartet reference materials to generate one of the largest multi-center lrRNA-seq resources to date: over one billion long reads from 144 libraries across four PacBio and Nanopore protocols in four independent laboratories. We first establish that ratio-based quantification against built-in reference samples effectively removes technical noise, revealing underlying biological signals. We then constructed the first ratio-based reference datasets for full-length transcripts—comprising 10,218 isoforms and 6,032 alternative splicing (AS) events—and orthogonally validated them with RT–qPCR. Finally, a comprehensive benchmark using these ground truths reveals that a hybrid strategy integrating long- and short-read data (hybrid-seq) achieves the highest quantification accuracy for both isoforms and AS events. Our work provides a foundational framework and resource for evaluating lrRNA-seq technologies and accelerating the standardization of full-length transcriptomics for research and clinical applications.

The wide variety of protocols and applications for DNA and RNA sequencing makes flexible tools for read processing an important step in sequence analysis. Beyond trimming and demultiplexing, custom read-level processing is commonly required for data exploration, QC and analysis. Existing tools are often task-specific and don’t generalise to new bioinformatic problems. Thus, there is a need for a tool flexible enough to handle the full variety of read processing tasks, and fast and scalable enough to retain high performance on growing sequencing datasets. We introduce matchbox , a read processor that enables fluent manipulation and analysis of FASTA/FASTQ/SAM/BAM files. With a lightweight scripting language designed around error-tolerant pattern-matching, users can write their own matchbox scripts to tackle a wide variety of bioinformatic problems, and incorporate them into existing pipelines and work-flows. We demonstrate matchbox ’s versatility in a number of contexts: demultiplexing long-read scRNA-seq data with 10X or SPLiT-seq barcodes; restranding RNA-seq reads; assessing CRISPR editing efficiency; and haplotyping macrosatel-lite repeat regions. matchbox achieves a computational performance comparable to existing tools, while addressing a broader range of bioinformatic needs, representing a new state-of-the-art in sequence processing. matchbox is implemented in Rust and available open-source at https://github.com/jakob-schuster/matchbox .