Biology subjects

Prawer, Y. D. J.

Publications and source records attributed to Prawer, Y. D. J..

7 recordsLinked to original sources

bioRxiv2026

GenomeProt: User friendly proteogenomics for canonical and non-canonical proteoform characterisation

Quantifying the diversity of RNAs and proteins produced by cells is fundamental to the biological and clinical sciences. However, many RNAs and proteins remain uncharacterised, especially proteins translated from alternate RNA isoforms; untranslated regions of mRNAs and non-coding RNAs, as well as the effects of DNA variation on protein sequences. Proteogenomics aims to characterise the complete proteome by integrating genomics and/or transcriptomics with proteomics, but current tools have limitations in useability, analysis features and visualisation of resulting data. To address these gaps, we developed GenomeProt, a user-friendly GUI-based tool for integrative proteogenomic analysis. We demonstrate its utility by integrating long-read RNA sequencing with mass-spectrometry-based proteomics to pinpoint proteoform expression generated by alternative splicing; discover novel, unannotated proteins in human brain samples; and quantify variant-containing peptides associated with treatment resistance in a melanoma xenograft model. GenomeProt brings the discovery power of proteogenomics to biologists, illuminating the hidden proteome.

bioinformatics↗

bioRxiv2025

Igniting full-length isoform analysis in single-cell and spatial RNA-seq data with FLAMESv2

Long-read single-cell RNA-sequencing enables the profiling of RNA isoform expression and alternative splicing at single cell resolution. However, diverse single-cell technologies and sparse isoform data demand flexible and accurate analysis tools. We introduce FLAMESv2, a highly modular and protocol-agnostic R/Bioconductor package for long-read single-cell RNA-seq data analysis. FLAMESv2 supports a wide range of single-cell and spatial protocols, is highly configurable, scales to allow multi-sample analysis and provides versatile visualisation and analysis outputs. We demonstrate its compatibility with both droplet-based and combinatorial barcoding single-cell methods, as well as spatial transcriptomics workflows. Benchmarking confirms FLAMESv2 achieves field-leading performance across key analysis tasks. Applying FLAMESv2 to in vitro differentiation of stem cells into neurons, we identify cell-types, differentiation trajectories, expression of annotated and novel isoforms and isoform expression diversity and heterogeneity within individual cells. FLAMESv2 provides a comprehensive, flexible approach to analysing long-read single-cell RNA-sequencing, unlocking this powerful methodology for RNA isoform characterisation.

bioinformatics↗

bioRxiv2025

Benchmarking long-read RNA-sequencing technologies with LongBench: a cross-platform reference dataset profiling cancer cell lines with bulk and single-cell approaches

Long-read RNA sequencing enables full-length transcript profiling and improved isoform resolution, but variable platforms and evolving chemistries demand careful benchmarking for reliable application. We present LongBench, a matched, multi-platform reference dataset spanning bulk, single-cell, and single-nucleus transcriptomics across eight human lung cancer cell lines with synthetic spike-in controls. LongBench in-corporates three state-of-the-art long-read protocols alongside Illumina short reads: Oxford Nanopore Technologies (ONT) PCR-cDNA, ONT direct RNA, and PacBio Kinnex. We systematically evaluate transcript capture, quantification accuracy, differential expression, isoform usage, variant detection, and allele-specific analyses. Our results show high concordance in gene-level differential analyses across protocols, but reduced consistency for transcript-level and isoform analyses due to length- and platform-dependent biases. Single-cell long-read data are highly concordant with bulk for high-confidence features, though single-nuclei data show reduced feature detection. LongBench provides one of the largest publicly available long-read benchmarking resources, enabling rigorous cross-platform evaluation and guiding technology selection for transcriptomic research.

genomics↗

bioRxiv2023

Discovery of NRG1-VII: a novel myeloid-derived class of NRG1 isoforms

The growth factor Neuregulin-1 (NRG1) has pleiotropic roles in proliferation and differentiation of the stem cell niche in different tissues. It has been implicated in gut, brain and muscle development and repair. Six isoform classes of NRG1 and over 28 protein isoforms have been previously described. Here we report a new class of NRG1, designated NRG1-VII to denote that these NRG1 isoforms arise from a myeloid-specific transcriptional start site (TSS) previously uncharacterized. Long-read sequencing was used to identify eight high-confidence NRG1-VII transcripts. These transcripts presented major structural differences from one another, through the use of cassette exons and alternative stop codons. Expression of NRG1-VII was confirmed in primary human monocytes and tissue resident macrophages and iPSC-derived macrophages. Isoform switching via cassette exon usage and alternate polyadenylation was apparent during monocyte maturation and macrophage differentiation. NRG1-VII is the major class expressed by the myeloid lineage, including tissue-resident macrophages. Analysis of public gene expression data indicates that monocytes and macrophages are a primary source of NRG1, suggesting that NRG1-VII is the most common class of NRG1 in most adult human tissues, except brain. The size and structure of type VII isoforms suggests that they may be more diffusible through tissues than other NRG1 classes. However, the specific roles of type VII variants in tissue homeostasis and repair have not yet been determined.

genomics↗

bioRxiv2022

Identification of cell barcodes from long-read single-cell RNA-seq with BLAZE

Single-cell RNA sequencing (scRNA-seq) has revolutionised our ability to profile gene expression. However, short-read (SR) scRNAseq methodologies such as 10x are restricted to sequencing the 3 or 5 ends of transcripts, providing accurate gene expression but little information on the RNA isoforms expressed in each cell. Newly developed long-read (LR) scRNA-seq enables the quantification of RNA isoforms in individual cells but LR scRNA-seq using the Oxford Nanopore platform has largely relied upon matched short-read data to identify cell barcodes and allow single cell analysis. Here we introduce BLAZE (Barcode identification from long-reads for AnalyZing single-cell gene Expression), which accurately and efficiently identifies 10x cell barcodes using only nanopore LR scRNA-seq data. We compared BLAZE to existing tools, including cell barcodes identified from matched SR scRNA-seq, on differentiating stem cells and 5 cancer cell lines. BLAZE outperforms existing tools and provides a more accurate representation of the cells present in LR scRNA-seq than using matched short-reads. BLAZE provides accurate cell barcodes over a wide range of experimental read depths and sequencing accuracies, while other methodologies commonly identify false-positive barcodes and cell clusters, disrupting biological interpretation of LR scRNA-seq results. In conclusion, BLAZE eliminates the requirement for matched SR scRNA-seq to interpret LR scRNA-seq, simplifying procedures and decreasing costs while also improving LR scRNA-seq results. BLAZE is compatible with downstream tools accepting a cell barcode whitelist file and is available at https://github.com/shimlab/BLAZE.

bioinformatics↗

bioRxiv2022

Proliferation is a driver of quorum sensing in an in vitro model of tissue resident macrophages

Macrophages coordinate the initial host inflammatory response to tissue infection, as well as mediating the reparative phase, by producing growth factors that promote tissue repair. One model of this functional dichotomy is that peripherally recruited monocyte-derived macrophages drive acute inflammatory responses to infection, whereas tissue-resident macrophages are responsible for tissue repair. Alternatively, inflammation and repair may be inter-dependent molecular programs, such that both recruited and resident cells have equivalent capacity to contribute. Repeated exposure to pathogenic challenge results in innate tolerance, which may also alter the contributions of discrete macrophage populations to inflammation or repair. In this study a village model of tissue resident and recruited macrophages was created using induced pluripotent stem cell-derived macrophages and peripheral blood monocyte-derived macrophages, respectively. Population responses to repeated exposure to lipopolysaccharide were assessed with single-cell RNA sequencing and donors demultiplexed with Vireo. A subset of genes escaped classical tolerance programs in the iPSC, but not monocyte-derived macrophages, and this was associated with differences in their proliferative capacity. This suggests that targeting the proliferative resident macrophages would be most effective to limit inflammatory signaling.

systems biology↗

bioRxiv2020

The long and the short of it: unlocking nanopore long-read RNA sequencing data with short-read tools

Application of Oxford Nanopore Technologies long-read sequencing platform to transcriptomic analysis is increasing in popularity. However, such analysis can be challenging due to small library sizes and high sequence error, which decreases quantification accuracy and reduces power for statistical testing. Here, we report the analysis of two nanopore sequencing RNA-seq datasets with the goal of obtaining gene-level and isoform-level differential expression information. A dataset of synthetic, spliced, spike-in RNAs ("sequins") as well as a mouse neural stem cell dataset from samples with a null mutation of the epigenetic regulator Smchd1 were analysed using a mix of long-read specific tools for preprocessing together with established short-read RNA-seq methods. We used limma-voom to perform differential gene expression analysis, and the novel FLAMES pipeline to perform isoform identification and quantification, followed by DRIMSeq and limma-diffSplice (with stageR) to perform differential transcript usage analysis. We compared results from the sequins dataset to the ground truth, and results of the mouse dataset to a previous short-read study on equivalent samples. Overall, our work shows that transcriptomic analysis of long-read nanopore data using short-read software and methods that are already in wide use can yield meaningful results.

bioinformatics↗