Wenming Xiao

This paper introduces Quartet DNA reference materials to comprehensively evaluate germline variant calling accuracy across the genome, including difficult regions and batch effects.

The Quartet Data Portal provides access to multi-omics reference materials and datasets, along with tools for quality control and community integration.

This paper introduces ratio-based multiomics profiling using universal reference materials to improve data integration across omics types and batches.

The paper introduces RNA reference materials and ratio-based datasets to improve the reliability and benchmarking of transcriptomic profiling.

The paper presents whole-genome and whole-exome sequencing datasets from a multi-center, cross-platform study to benchmark NGS technologies and bioinformatics pipelines in cancer genomics.

The paper establishes reference samples with high-coverage whole-genome sequencing to create truth sets for somatic mutation and germline variant detection using NGS technologies.

This study evaluates reproducibility and accuracy of cancer mutation detection using whole-genome and whole-exome sequencing across multiple sequencing centers and bioinformatics pipelines.

This study compares somatic mutation detection accuracy using personalized genome assembly versus standard reference in cancer samples, showing improved results with personalized assemblies.

This study evaluates somatic CNV callers using NGS data from 91 samples with varying factors, finding that caller choice and tumor purity significantly impact call quality.

The paper presents a multi-center cross-platform scRNA-seq reference dataset for benchmarking bioinformatics methods across different platforms and centers.

This study benchmarks scRNA-seq reproducibility across platforms and pipelines, finding that batch correction methods significantly affect results.

The paper evaluates epigenetic methods using reference materials, finding high concordance between assays and differences in performance across platforms.

This study develops and validates deep learning strategies for robust cancer mutation detection using tumor-normal sequencing data across multiple technologies and scenarios.