Biology subjects

Yepez, V. A.

Publications and source records attributed to Yepez, V. A..

5 recordsLinked to original sources

bioRxiv2025

Aberrant splicing prediction during human organ development

Developmental disorders constitute a major class of genetic diseases, yet tools to identify splicing-disruptive variants during development are lacking. To address this need we extended the AbSplice framework to incorporate splicing dynamics across developmental stages. Moreover, we introduced several improvements including a refined ground truth from the aberrant splicing caller FRASER2, a continuous representation of splice site usage, and integration of the rich set of predictions from the sequence-based model Pangolin. These advances improve the performance of the original model at predicting aberrant splicing events and enable predictions across embryonic, childhood, and adult tissues. Genome-wide scores for all single-nucleotide variants and a web interface to score indels are available to facilitate the exploration of the predictions. Our genome-wide predictions reveal a class of variants with splicing-disruptive effects confined to early development, particularly abundant in the brain (>18,000 variants). These variants are enriched in loss-of-function intolerant genes and neurodevelopmental disorder genes. Within the rare-disease cohort Solve-RD high-impact brain-specific predictions are significantly enriched in individuals affected by neurodevelopmental disorders (NDD) in NDD-linked genes. Furthermore, the predicted developmental timing of the splicing disruption correlates with the clinical age of onset. Analysis of genomes of individuals with a suspected Mendelian disorder from Genomics England identified 26 unique variants in disease-linked genes, with stronger predicted effects during development than in adulthood, including a candidate new diagnosis in the gene FGFR1. Altogether, these results improve the accuracy of splice-disruptive variant prediction and provide tissue and developmental context to aid interpretation in rare disease diagnostics.

genomics↗

bioRxiv2023

Aberrant expression prediction across human tissues

Despite the frequent implication of aberrant gene expression in diseases, algorithms predicting aberrantly expressed genes of an individual are lacking. To address this need, we compiled an aberrant expression prediction benchmark covering 8.2 million rare variants from 633 individuals across 49 tissues. While not geared toward aberrant expression, the deleteriousness score CADD and the loss-of-function predictor LOFTEE showed mild predictive ability (1-1.6% average precision). Leveraging these and further variant annotations, we next trained AbExp, a model that yielded 12% average precision by combining in a tissue-specific fashion expression variability with variant effects on isoforms and on aberrant splicing. Integrating expression measurements from clinically accessible tissues led to another two-fold improvement. Furthermore, we show on UK Biobank blood traits that performing rare variant association testing using the continuous and tissue-specific AbExp variant scores instead of LOFTEE variant burden increases gene discovery sensitivity and enables improved phenotype predictions.

genetics↗

bioRxiv2023

Cellular energy regulates mRNA translation and degradation in a codon-specific manner

BackgroundCodon optimality is a major determinant of mRNA translation and degradation rates. However, whether and through which mechanisms its effects are regulated remains poorly understood. ResultsHere we show that codon optimality associates with up to 2-fold change in mRNA stability variations between human tissues, and that its effect is attenuated in tissues with high energy metabolism and amplifies with age. Biochemical modeling and perturbation data through oxygen deprivation and ATP synthesis inhibition reveal that cellular energy variations non-uniformly affect the decoding kinetics of different codons. ConclusionsThis new mechanism of codon effect regulation, independent of tRNA regulation, provides a fundamental mechanistic link between cellular energy metabolism and eukaryotic gene expression.

systems biology↗

bioRxiv2019

Detection of aberrant splicing events in RNA-seq data with FRASER

Aberrant splicing is a major cause of rare diseases, yet its prediction from genome sequence remains in most cases inconclusive. Recently, RNA sequencing has proven to be an effective complementary avenue to detect aberrant splicing. Here, we developed FRASER, an algorithm to detect aberrant splicing from RNA sequencing data. Unlike existing methods, FRASER captures not only alternative splicing but also intron retention events. This typically doubles the number of detected aberrant events and identified a pathogenic intron retention in MCOLN1. FRASER automatically controls for latent confounders, which are widespread and substantially affect sensitivity. Moreover, FRASER is based on a count distribution and multiple testing correction, reducing the number of calls by two orders of magnitude over commonly applied z score cutoffs, with a minor sensitivity loss. The application to rare disease diagnostics is demonstrated by reprioritizing a pathogenic aberrant exon truncation in TAZ from a published dataset. FRASER is easy to use and freely available.

bioinformatics↗

bioRxiv2018

OUTRIDER: A statistical method for detecting aberrantly expressed genes in RNA sequencing data

RNA sequencing (RNA-seq) is gaining popularity as a complementary assay to genome sequencing for precisely identifying the molecular causes of rare disorders. A powerful approach is to identify aberrant gene expression levels as potential pathogenic events. However, existing methods for detecting aberrant read counts in RNA-seq data either lack assessments of statistical significance, so that establishing cutoffs is arbitrary, or rely on subjective manual corrections for confounders. Here, we describe OUTRIDER (OUTlier in RNA-seq fInDER), an algorithm developed to address these issues. The algorithm uses an autoencoder to model read count expectations according to the co-variation among genes resulting from technical, environmental, or common genetic variations. Given these expectations, the RNA-seq read counts are assumed to follow a negative binomial distribution with a gene-specific dispersion. Outliers are then identified as read counts that significantly deviate from this distribution. The model is automatically fitted to achieve the best correction of artificially corrupted data. Precision-recall analyses using simulated outlier read counts demonstrated the importance of combining correction for co-variation and significance-based thresholds. OUTRIDER is open source and includes functions for filtering out genes not expressed in a data set, for identifying outlier samples with too many aberrantly expressed genes, and for the P-value-based detection of aberrant gene expression, with false discovery rate adjustment. Overall, OUTRIDER provides a computationally fast and scalable end-to-end solution for identifying aberrantly expressed genes, suitable for use by rare disease diagnostic platforms.

bioinformatics↗