Biology subjects

Nyaga, D. M.

Publications and source records attributed to Nyaga, D. M..

3 recordsLinked to original sources

bioRxiv2026

A comprehensive assessment of tandem repeat genotyping methods for Nanopore long-read genomes

BackgroundTandem repeats (TRs) play critical roles in human disease and phenotypic diversity but are among the most challenging classes of genomic variation to measure accurately. While it is possible to identify TR expansions using short-read sequencing, these methods are limited because they often cannot accurately determine repeat length or sequence composition. Long-read sequencing (LRS) has the potential to accurately characterize long TRs, including the identification of non-canonical motifs and complex structures. However, while there are an increasing number of genotyping methods available, no systematic effort has been undertaken to evaluate their length and sequence-level accuracy, performance across motifs from STRs to VNTRs and across allele lengths, and, critically, how usable these tools are in practice. ResultsWe reviewed 25 available bioinformatic tools, and selected seven that are actively maintained for benchmarking using publicly available Oxford Nanopore genome sequencing data from more than 100 individuals. Our benchmarking catalog included [~]43k TR loci genome-wide, selected to represent a range of simple and challenging TR loci. As no "truth" exists for this purpose, we used four complementary strategies to assess accuracy: concordance with high-quality haplotype-resolved Human Pangenome Reference Consortium (HPRC) assemblies, Mendelian consistency in Genome in a Bottle trios, cross-tool consistency, and sensitivity in individuals with pathogenic TR expansions confirmed by molecular methods. For all comparisons, we assess both total allele length and full sequence similarity using the Levenshtein distance. We also evaluated installation, documentation, computational requirements, and output characteristics to reflect real-world use. We provide a complete analysis workflow for all tools to support community reuse. Tool performance varied substantially across both accuracy and usability. Most methods achieved high concordance with HPRC assemblies, with higher accuracy when using the R10 ONT pore chemistry. Accuracy generally declined with increasing allele length, and most tools performed worse on homopolymers, likely reflecting underlying sequencing accuracy. Tools generally performed worse at heterozygous loci and at alleles that differed from the reference genome. Interestingly, concordance with assembly in population samples did not predict sensitivity to pathogenic expansions, with different genotypers performing best in each category. Similarly, Mendelian consistency was highest in the tool that performed worst in assembly concordance. ConclusionsNo single genotyper emerged as consistently best across all assessments, but strong contenders emerged in each. Our results demonstrate that length accuracy (a typical benchmarking approach) alone overestimates TR genotyping performance. Sequence-level benchmarking is essential for selecting tools best-suited for population studies and clinical diagnostics. This work provides practical guidance for tool selection and highlights key priorities for future long-read TR genotyping method development.

bioinformatics↗

bioRxiv2020

A transcription regulatory network within the ACE2 locus may promote a pro-viral environment for SARS-CoV-2 by modulating expression of host factors.

IntroductionA novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was recently identified as the pathogen responsible for the COVID-19 outbreak. SARS-CoV-2 triggers severe pneumonia, which leads to acute respiratory distress syndrome and death in severe cases. As reported, SARS-CoV-2 is 80% genetically identical to the 2003 SARS-CoV virus. Angiotensin-converting enzyme 2 (ACE2) has been identified as the main receptor for entry of both SARS-CoV and SARS-CoV-2 into human cells. ACE2 is normally expressed in cardiovascular and lung type II alveolar epithelial cells, where it positively modulates the RAS system that regulates blood flow, pressure, and fluid homeostasis. Thus, virus-induced reduction of ACE2 gene expression is considered to make a significant contribution to severe acute respiratory failure. Chromatin remodeling plays a significant role in the regulation of ACE2 gene expression and the activity of regulatory elements within the genome. MethodsHere, we integrated data on physical chromatin interactions within the genome organization (captured by Hi-C) with tissue-specific gene expression data to identify spatial expression quantitative trait loci (eQTLs) and thus regulatory elements located within the ACE2 gene. ResultsWe identified regulatory elements within ACE2 that control the expression of PIR, CA5B, and VPS13C in the lung. The gene products of these genes are involved in inflammatory responses, de novo pyrimidine and polyamine synthesis, and the endoplasmic reticulum, respectively. ConclusionOur study, although limited by the fact that the identification of the regulatory interactions is putative until proven by targeted experiments, supports the hypothesis that viral silencing of ACE2 alters the activity of gene regulatory regions and promotes an intra-cellular environment suitable for viral replication.

genomics↗

bioRxiv2018

Type 1 diabetes mellitus-associated genetic variants contribute to overlapping immune regulatory networks

Type 1 diabetes (T1D) is a chronic metabolic disorder characterised by the autoimmune destruction of insulin-producing pancreatic islet beta cells in genetically predisposed individuals. Genome-wide association studies (GWAS) have identified over 60 risk loci across the human genome, marked by single nucleotide polymorphisms (SNPs), which confer genetic predisposition to T1D. There is increasing evidence that disease-associated SNPs can alter gene expression through spatial interactions that involve distal loci, in a tissue-and development-specific manner. Here, we used three-dimensional (3D) genome organization data to identify genes that physically co-localized with DNA regions that contained T1D-associated SNPs in the nucleus. Analysis of these SNP-gene pairs using the Genotype-Tissue Expression database identified a subset of SNPs that significantly affected gene expression. We identified 298 spatially regulated genes including HLA-DRB1, LAT, MICA, BTN3A2, CTLA4, CD226, NOTCH1, TRIM26, CLEC2B, TYK2, and FLRT3, which exhibit tissue-specific effects in multiple tissues. We observed that the T1D-associated variants interconnect through networks that form part of the immune regulatory pathways, including immune-cell activation, cytokine signalling, and programmed cell death protein-1 (PD-1). These pathways have been implicated in the pancreatic beta-cell inflammation and destruction as observed in T1D. Our results demonstrate that T1D-associated variants contribute to adaptive immune signalling, and immune-cell proliferation and activation through tissue and cell-type specific regulatory networks.\n\nAuthor SummaryAlthough genome-wide association studies have identified risk regions across the human genome that predispose individuals to the development of type 1 diabetes (T1D), the mechanisms through which these regions contribute to disease is unclear. Here, we used population-based genetic data from genome-wide association studies (GWAS) to understand how the three-dimensional (3D) organization of the DNA contributes to the differential expression of genes involved in immune system dysregulation as observed in T1D. We identified interconnected regulatory networks that affect immune pathways (adaptive immune signalling and immune-cell proliferation and activation) in a tissue and cell-type specific manner. Some of these pathways are implicated in the pancreatic beta-cell destruction. However, we observed other regulatory changes in tissues that are not typically considered to be central to the pathology of T1D, which represents a novel insight into the disease. Collectively, our data represent a novel resource for the hypothesis-driven development of diagnostic, prognostic and therapeutic interventions in T1D.

genetics↗