Siddharth Jayaraman

The paper presents a reusable TWAS workflow for cattle to identify gene-trait associations using GWAS and transcriptomic data, improving variant calling and detecting novel associations.

This study constructs a water buffalo pangenome using graph genomics, revealing extensive structural variation and population-specific selection signatures in 711 global samples.

This paper develops a framework to predict regulatory variants in cattle using machine learning, achieving high performance and demonstrating enrichment for functional variants linked to important traits.

This study presents a continent-wide genomic analysis of African buffalo, revealing three main lineages and signatures of selection related to immune response.

The paper creates a graph genome incorporating global cattle diversity, showing it includes more sequence and improves mapping and variant calling.

This study uses long-read sequencing to analyze VSG diversity in T. brucei infections, revealing higher expressed diversity and early mosaic gene formation than previously observed.