Biology subjects

Oliveros, W.

Publications and source records attributed to Oliveros, W..

5 recordsLinked to original sources

bioRxiv2025

rDNAcaller: a fast and robust pipeline to call ribosomal DNA variants

Ribosomal DNA (rDNA) genes are essential components of the ribosome, organized as tandem repeats on the mammalian genomes. Extensive genetic and copy number variation in rDNA has been reported both within and between individuals, contributing to phenotypic diversity. However, previous rDNA variant calling strategies have relied on methods designed for diploid regions and have not been systematically benchmarked. With the recent availability of a telomere-to-telomere (T2T) genome assembly, rDNA regions have been fully assembled for the first time, enabling benchmarking and optimization of rDNA variant calling strategies. We developed a customized simulator that replicates real intra- and inter-individual rDNA variation based on the T2T assembly to benchmark the performance of commonly used variant callers, including GATK, Mutect2, and Lofreq. Additionally, we optimized the preprocessing and mapping steps to remove pseudogenes and significantly improve accuracy. Based on these optimizations, we introduce rDNAcaller, a novel pipeline for accurate rDNA variant detection using short-read whole-genome sequencing. rDNAcaller integrates optimized preprocessing with the top-performing variant caller and achieves 94% precision on experimental data from the T2T cell line. Applying our pipeline to data from the 1000 Genomes Project, we identify 5,607 novel rDNA variant positions across human populations, with African individuals showing the highest number of variants. Overall, rDNAcaller is a robust and versatile tool for analyzing rDNA variation, addressing the limitations of existing methods in handling high ploidies. By enabling accurate detection of rDNA variants, it facilitates deeper exploration of rDNAs role in phenotypic diversity, supporting future genomic studies and broadening our understanding of rDNA biology in health and disease. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=72 SRC="FIGDIR/small/653643v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@148419borg.highwire.dtl.DTLVardef@cb3b42org.highwire.dtl.DTLVardef@9fa802org.highwire.dtl.DTLVardef@1cf4ff0_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

CRISPRi perturbation screens and eQTLs provide complementary and distinct insights into GWAS target genes

Most genetic variants associated with human traits and diseases lie in noncoding regions of the genome 1, and a key challenge is determining which genes they affect 2,3. A common approach has been to leverage associations between natural genetic variation and gene expression to identify expression quantitative trait loci (eQTLs) in the population 4,5. At the same time, a novel approach uses pooled CRISPR interference (CRISPRi) perturbations of noncoding loci with single-cell transcriptome sequencing 6,7. Here, we systematically harmonized and compared the results from these approaches across hundreds of genomic regions associated with blood cell traits. We find that while the two approaches sometimes identify the same target genes, there are considerable differences that affect biological inferences made from the data. CRISPRi preferentially maps highly proximal, constraint-enriched genes, whereas eQTLs recover multiple, often distal targets. By benchmarking against 1,075 gold-standard CRE-gene pairs linked to blood traits, we show that the two approaches identify largely distinct targets; when combined, they achieve a balance between accuracy and completeness of gene discovery. Our results offer guidance for improved design of CRISPRi and eQTL studies and highlight their joint potential as a powerful toolkit for interpreting disease-associated loci.

genomics↗

bioRxiv2025

Long-read transcriptomics of a diverse human cohort reveals widespread ancestry bias in gene annotation

Accurate gene annotations are fundamental for interpreting genetic variation, cellular function, and disease mechanisms. However, current human gene annotations are largely derived from transcriptomic data of individuals with European ancestry, introducing potential biases that remain uncharacterized. Here, we generate over 800 million full-length reads with long-read RNA-seq in 43 lymphoblastoid cell line samples from eight genetically-diverse human populations and build a cross-ancestry gene annotation. We show that transcripts from non-European samples are underrepresented in reference gene annotations, leading to systematic biases in allele-specific transcript usage analyses. Furthermore, we show that personal genome assemblies enhance transcript discovery compared to the generic GRCh38 reference assembly, even though genomic regions unique to each individual are heavily depleted of genes. These findings underscore the urgent need for a more inclusive gene annotation framework that accurately represents global transcriptome diversity.

genomics↗

bioRxiv2022

Deterioration of the human transcriptome with age due to increasing intron retention and spurious splicing

Adult aging is characterized by a progressive deterioration of biological functions at physiological, cellular and molecular levels, but its damaging effects on the transcriptome are not well characterized. Here, by analyzing splicing patterns in [~]1,000 human subjects sampled across multiple tissues, we found that splicing fidelity declines with age. Most prominently, genuine introns fail to be spliced out, manifesting as a broad surge in intron retention, and this is exacerbated by the increase in diverse spurious exon-exon junctions with age. Both of these effects are prominently detected in the majority of human tissues. Collectively, they result in the progressive deterioration of the active transcriptome, wherein functional mRNAs are increasingly diluted with non-functional splicing isoforms. We discuss the concept of "splicing damage" and formulate methods to quantify it. Using these tools, we show that splicing damage increases both with age and with the incidence of diseases. Altogether, this work uncovers transcriptome damage as a critical molecular indicator of human aging and healthspan.

genomics↗

bioRxiv2019

Analysis of regulatory element evolution between human and mouse reveals a lack of cis-trans compensation

Gene expression differences between species are driven by both cis and trans effects. Whereas cis effects are caused by genetic variants in close proximity to the target gene, trans effects are due to distal genetic variants that affect diffusible elements such as transcription factors. Previous studies have mostly assessed the impact of cis and trans effects at the gene level. However, how cis and trans effects differentially impact regulatory elements such as enhancers and promoters remains poorly understood. Here, we used massively parallel reporter assays to directly measure cis and trans effects between human and mouse embryonic stem cells at thousands of individual regulatory elements. Our approach revealed that cis effects are widespread across regulatory elements, and the strongest cis effects are associated with the disruption of motifs recognized by strong transcriptional activators. Conversely, we found that trans effects are rare but stronger in enhancers than promoters, and can be attributed to a subset of transcription factors that are differentially expressed between human and mouse. While previous studies have found extensive co-occurrence of cis and trans effects in opposite directions that stabilize gene expression throughout evolution, we find that cis-trans compensation is uncommon within individual regulatory elements. Thus, our results are consistent with a model wherein compensatory cis-trans effects at the gene level are explained by cis and trans effects that separately impact several regulatory elements rather than cis-trans effects that occur simultaneously within a single regulatory element. Together, these results indicate that studying the evolution of individual regulatory elements is pivotal to understand the tempo and mode of gene expression evolution.

genomics↗