Zhuoqing Fang

The paper creates a tandem repeat atlas for 39 inbred mouse strains using long-read sequencing, showing TRs are abundant but differ from human TRs in expansion and transposable element association.

This study uses an AI co-scientist to identify and validate anti-fibrotic drugs in human hepatic organoids, finding that epigenomic modifiers play a key role in liver fibrosis.

The paper creates a database of structural variants in 40 inbred mouse strains and identifies an SV linked to lymphoma susceptibility in SJL mice.

GenomeMUSter integrates and harmonizes mouse genetic variation data across populations to enable multi-trait, multi-population analyses and improve variant-phenotype association studies.

This study uses a large language model to analyze gene-phenotype relationships in mouse genetic models, identifying causative genetic factors and generating novel hypotheses for spontaneous hearing loss.

This study identifies Nav1 as a gene regulating learning, memory, and responses to addictive drugs through synaptic balance changes in the cortex and hippocampus.

AIMHIGH identifies novel disease-causing genes by analyzing inbred mouse strain data and machine learning-driven literature-based discovery.

This study evaluates the impact of population structure on murine GWAS and finds minimal effects in most datasets, with some cases where PS correction may reject true signals.

The paper analyzes structural variation in inbred mouse strains to identify genetic factors associated with autism-related traits.

The paper applies haplotype-based computational genetic mapping to large-scale mouse phenotypic datasets to identify genetic factors affecting biomedical traits.

Bioconda provides a sustainable, comprehensive distribution of bioinformatics software using Conda for reproducible analysis across platforms.