Nicolas Alcala

This study identifies three distinct spliceosomic profiles in pancreatic neuroendocrine tumors linked to clinical and molecular characteristics and potential therapeutic targets.

PACMOS projects query cancer samples into pretrained MOFA latent factor spaces for classification.

The paper presents a large multi-omic dataset of lung neuroendocrine tumours, including genomics, transcriptomics, methylation, spatial proteomics, and histopathology, enabling comprehensive molecular and morphological analysis.

The paper introduces AMSD, a signature-agnostic metric to detect differences in mutation spectra between groups, showing its effectiveness in identifying carcinogen and ancestry effects.

This paper presents a comprehensive molecular characterization of Malignant Pleural Mesothelioma using multi-omics data from 120 patients, integrating with existing datasets to create an interactive molecular phenotype map.

The paper derives mathematical constraints on FST for multiallelic markers across multiple populations and applies them to explain microsatellite differentiation patterns.

DECONbench benchmarks deconvolution methods for quantifying tumor cell-type heterogeneity by providing datasets, tools, and performance evaluation.

Needlestack is a variant caller that improves sensitivity for low abundance mutations by learning sequencing error rates from multi-sample data.

The paper uses coalescent theory to analyze migration network motifs and their impact on genetic diversity and differentiation in small population networks.

The paper analyzes mathematical constraints on FST for multiple populations, showing how FST depends on allele frequency and subpopulation number, and applies findings to human genetic data.