Shengkang Li

DCSMAP: A Hybrid Linear-Pangenome Framework for Accurate and Efficient Read Alignment

DCSMAP improves read alignment accuracy and efficiency by selectively applying pangenome graph alignment to reads with unreliable linear placements.

OmniCell: Unified Foundation Modeling of Single-Cell and Spatial Transcriptomics for Cellular and Molecular Insights

OmniCell integrates single-cell and spatial transcriptomics to model tissue context, revealing how gene programs operate in different anatomical environments.

DCS Tools: A high-performance, resource-efficient and scalable computing suite for population-scale genomic analysis and data compression

DCS Tools provides a high-performance, resource-efficient genomic analysis suite with significant speedup and storage reduction for population-scale sequencing data.

DPGT: A spark based high-performance joint variant calling tool for large cohort sequencing

DPGT is a high-performance joint variant calling tool for large cohorts, offering comparable accuracy to existing methods with improved speed and scalability.

Stereopy: modeling comparative and spatiotemporal cellular heterogeneity via multi-sample spatial transcriptomics

Stereopy provides a framework for analyzing multi-sample spatial transcriptomics data to model comparative and spatiotemporal cellular heterogeneity through three key algorithms.

SAW: An efficient and accurate data analysis workflow for Stereo-seq spatial transcriptomics

SAW improves the efficiency and accuracy of Stereo-Seq spatial transcriptomics analysis by optimizing key steps and reducing runtime.

CellBin: a highly accurate single-cell gene expression processing pipeline for high-resolution spatial transcriptomics

CellBin processes high-resolution spatial transcriptomics data to generate accurate single-cell gene expression profiles, improving accuracy and efficiency in mouse brain tissue analysis.