Harold Pimentel

This study uses deep mutational pharmacology to show how different residues in the -opioid receptor determine the efficacy of opioids by shaping distinct activation mechanisms.

This paper identifies that truncating variants in KAT6A cause two distinct neurodevelopmental disorders with opposing gene regulatory and metabolic effects, highlighting the need for variant-level stratification in therapy development.

The paper introduces a cell village-based fitness screening approach and a Dirichlet regression framework to analyze genetic factors affecting cell proliferation and environmental sensitivity in human neural progenitor cells.

This study identifies genetic and transcriptomic factors associated with severe coccidioidomycosis, finding a founder variant in NLRX1 linked to African ancestry and increased disease risk.

This study shows that using multiple guide RNAs in CRISPR screens can reduce cell numbers needed while maintaining screening effectiveness.

keju improves inference in MPRA data by accounting for batch and modality-specific uncertainty, achieving higher sensitivity and lower false positive rates.

Lilace improves variant effect estimation in FACS-based DMS by providing uncertainty quantification and better FDR control.

Cosmos is a Bayesian framework that identifies direct and indirect effects of protein variants in multi-phenotype DMS data by inferring causal relationships and enabling counterfactual interpretation.

The paper uses deep mutational scanning to analyze MET kinase domain mutations and their resistance to 11 inhibitors, identifying resistance and sensitizing mutations and improving prediction with a protein language model.

The paper describes using kallisto, bustools, and kb-python to quantify RNA-seq data from bulk, single-cell, and single-nucleus samples.

The paper develops a Bayesian model and software for analyzing CRISPR screens and validates its effectiveness in maintaining statistical power under low coverage conditions.

Rosace improves analysis of deep mutational scanning data by using Bayesian shrinkage and position information, outperforming existing methods in simulations and real data.

This study shows that accounting for isoform expression in eQTL mapping increases power and highlights issues with current methods in real data.

The paper tests for genetic interactions affecting causal variant effect sizes in gene expression and LDL-C across European and African American populations, finding significant evidence in gene expression but not LDL-C.

The paper shows that DNA methylation persists in Cryptococcus neoformans despite the loss of a de novo methyltransferase, with methylation patterns maintained through replication and natural selection.

The paper introduces absSimSeq, a tool for simulating RNA-Seq data that accounts for compositional data properties, and shows that compositional normalization improves differential analysis performance.

This paper identifies intron retention as a new source of cancer neoantigens and shows that retained intron neoantigen burden correlates with immune checkpoint blockade response.

The paper introduces a method to perform gene-level differential analysis with transcript-level resolution by aggregating transcript-level results and p-value aggregation methods, enhancing sensitivity and enabling application to perturbation analysis.

Read pairs that fail kallisto pseudoalignment because of conflicting matches are filtered by pizzly to call, assemble and quantify gene fusions from raw RNA-seq in minutes rather than hours.

The paper analyzes RNA-Seq data from Zika-infected human neural progenitor cells to identify transcriptional changes and isoform divergence in neurodevelopmental pathways.

The Lair provides tools to analyze published RNA-Seq data from the Short Read Archive, enabling accessible and extendable exploration without technical expertise.

The paper presents a probabilistic method for RNA-seq quantification that achieves near-optimal accuracy.