Dongdong Lin

This paper uses long-read sequencing to characterize rAAV genomes and impurities, enabling better quality control in gene therapy production.

This study investigates the association between oral microbiome composition and brain functional connectivity in schizophrenia, finding significant microbiome shifts and correlations with brain network alterations.

OmicsView provides an interactive platform for analyzing omics data, enabling researchers to explore disease pathology and identify biomarkers using preprocessed datasets.

CellDepot provides a web platform for uploading, querying, and comparing scRNA-seq datasets with visualization tools to support data sharing and analysis.

CHOmics is a web-based tool for analyzing and visualizing multi-omics data from CHO cell lines, offering integrated analysis, customization, and visualization features.

The paper identifies and replicates expression and methylation modules in schizophrenia patients and healthy controls, showing their association with astrocyte and oligodendrocyte functions and genetic risk.

Cellxgene VIP enhances single cell data analysis by providing interactive visualization, advanced analytical tools, and multi-modal data integration for scRNA-seq, spatial transcriptomics, and multiome datasets.

This study identifies 45 structural-functional component maps across 9 functional domains using independent component analysis in two cohorts, showing varying S-F coherence in different brain regions.

figureComposer is a web-based tool that allows users to create multi-panel publication-quality figures without programming skills.

This study analyzes age-related changes in brain structure and function across 5967 individuals from 13 to 72 years old, revealing nonlinear patterns in gray matter volume, structural network correlation, and functional network connectivity.

This paper introduces a method to fuse functional and structural MRI data by integrating temporal information, identifying linked variability and covariation in schizophrenia.

This study compares GIG-ICA and STR for identifying brain functional networks in schizophrenia, finding GIG-ICA more sensitive to group differences and better at classification.