Computational biologist | Statistics, multi-omics, and scientific computing

Hi, I'm Yixuan 👋 — a computational biologist and Ph.D. candidate in Bioinformatics at North Carolina State University, advised by Benjamin Callahan, with M.S. and B.S. training in Statistics.

My work combines statistical modeling, reproducible workflows, and scientific software to study complex biological data. My research spans meta-omics, the gut–brain axis, plant pathogen genomics, and molecular evolution, with particular interest in how measurement and study design shape biological inference.

• Quantitative methods: Statistical inference, study design, causal inference, compositional data analysis, differential expression and abundance, machine learning
• Biological data: Metagenomics, metaproteomics, PacBio HiFi sequencing, amplicon sequencing, RNA-seq
• Scientific computing: Python, R/Bioconductor, Nextflow, containers (Docker, Singularity), scalable computing (HPC/Slurm, AWS), SQL, Parquet, MuData

With Lina Quesada and Benjamin Callahan · Manuscript in preparation

A modular Nextflow DSL2 workflow for recovering target eukaryotic genomes from highly contaminated PacBio HiFi reads. It integrates assembly, multi-stage decontamination, quality control, reproducible HPC execution, and RNA-seq-supported protein validation.

With Benjamin Callahan and Karen R. Muñana

Reproducible analyses for a household-matched case-control study of gut microbiome alterations in 98 dogs. The study evaluated community structure and six differential-abundance methods, identifying household as the dominant source of microbiome variation.

With Jeff Thorne and Xiang Ji

Statistical modeling of interlocus gene conversion, natural selection, and paralog homogenization. This work extended the MG94 codon framework with an IGC component and evaluated competing evolutionary hypotheses using maximum-likelihood estimation and likelihood-ratio tests.

Replaced the original grid search for pattern matching with a DIRECT-based optimization approach, improving runtime by approximately 4,000×. TrIdent is an R/Bioconductor package for detecting, classifying, and characterizing active transduction events in microbiomes from sequencing-coverage patterns.

• Yang, Y., Nettifee, J., Azcarate-Peril, M. A., Muñana, K. R., & Callahan, B. (2026). Gut microbiome alterations in canine idiopathic epilepsy: a pairwise case-control study. Animal Microbiome. doi:10.1186/s42523-026-00594-1
• Yang, Y., Xu, T., Conant, G. C., Kishino, H., Thorne, J. L., & Ji, X. (2023). Interlocus gene conversion, natural selection, and paralog homogenization. Molecular Biology and Evolution, 40, msad198. doi:10.1093/molbev/msad198

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Always happy to chat about computational biology, statistics, multi-omics, and reproducible research.