Xiaoming Liu

Stairway Plot v3 infers nonparametric effective population size trajectories from the site frequency spectrum at biobank scale, achieving high resolution with large samples.

MetaSplice improves pathogenicity prediction for non-canonical intronic variants by combining splicing predictions with conservation and gene-level constraints, achieving higher accuracy than existing tools.

PathoBench provides a benchmark registry for pathogen bioinformatics tools, enabling standardized evaluation and comparison across 10 pathogens using curated datasets and metrics.

The paper identifies distinct DNA methylation mechanisms for common and rare noncoding genetic risk in brain disorders using deep learning and single-nucleus DNAm data.

MalariaSED predicts chromatin profiles in malaria parasites and identifies noncoding variants linked to invasion and drug resistance.

This paper introduces MetaRNN and MetaRNN-indel, deep learning models that differentiate rare pathogenic from benign variants, outperforming existing methods and providing interpretable scores.

The paper identifies the prevalence and phenotypic impact of loss of function mutations in the MC4R gene in a UK birth cohort, finding a 0.30% carrier rate with significant effects on body weight and BMI.

The paper infers genealogical history and social behaviors from IBD segments in UK Biobank data, revealing population-scale genetic connections and regional family preferences.

Over 400 million variants, 97% below 1% frequency, emerge from deep whole-genome sequencing of 53,831 diverse TOPMed participants; TOPMed haplotypes enable imputation down to ~0.01% frequency.

dbMTS compiles putative human microRNA target site SNVs with functional annotations to aid in identifying SNVs affecting miRNA targeting.

The paper introduces SMMAT, a variant-set mixed model association test for continuous and binary traits in large-scale WGS studies, showing it controls type I error rates and applies to population structured and related samples.

The paper identifies novel genes for tooth development by associating CRC risk variants with tooth agenesis and showing gene expression in developing teeth.

RaPID detects identity by descent in large biobank-scale cohorts using a randomized positional BWT approach, achieving linear scaling and high accuracy.

iCAGES identifies personalized cancer driver genes and predicts therapies by integrating genomic data and biological knowledge, achieving high accuracy in mutation and gene classification.