Ka Yee Yeung

The paper introduces an interactive Data Integrator for uniform processing and integration of bulk RNA-seq datasets from the MorPhiC program and external sources.

STARgrid is an open-source pipeline that processes Visium HD sequencing data to generate count matrices, showing high agreement with Space Ranger while improving efficiency and resolution.

Open-source STAR binary for five transcriptomic assays runs 3.9- to 6.2-fold faster than Cell Ranger 9.0.1 for scRNA-seq and Perturb-seq, 17- to 41-fold for Flex, with closely concordant counts.

The paper presents a serverless cloud computing approach for scRNA-seq data processing, demonstrating significant speedup for large datasets.

RapidMACS provides a faster narrow peak caller for ATAC-seq and related data, achieving 3.5 to 51x speed improvements over MACS3 while producing identical output.

The paper introduces an interactive spatial proteomics workflow for analyzing imaging data, enabling reproducible and customizable analysis of protein expression and localization in tissues.

Chromap Suite provides an open-source, single-binary platform for RNA + ATAC multiomic profiling with improved speed and memory efficiency compared to proprietary tools.

This paper introduces a user-friendly scheduler for big data processing in bioinformatics, reducing cost and time for bulk RNA sequencing workflows using a hybrid architecture and supercomputing.

The paper integrates RNA-seq datasets to identify molecular patterns distinguishing acute and chronic phases of COVID-19, revealing pathway signatures for disease progression and PASC diagnosis.

The paper converts GDC SOPs into reproducible, containerized workflows to harmonize datasets across repositories and improve data interpretation.

The paper presents a cloud-based Fiji platform with GPU acceleration for image processing, achieving 29x faster execution with reproducible results across AWS and IBM clouds.

The paper introduces a cloud-enabled platform for reproducible and modular image processing workflows that integrates interactive graphics with software containers.

The paper introduces a GPU-enabled graphical workflow for processing long-read sequencing data, achieving significant speedup and cost reduction in basecalling.

The paper introduces a serverless computing method that drastically reduces RNA-seq alignment time and provides an interactive interface for real-time parameter adjustment.

The paper introduces Bwb, a tool for creating and executing reproducible bioinformatics workflows via a drag-and-drop interface using Docker containers.

The paper presents a Bayesian method integrating external data with knockdown data to improve gene network inference in human cell lines.

The paper optimizes an RNA-seq workflow for multi-threaded environments, achieving significant speed improvements by optimizing all steps, not just alignment.

The paper introduces a method to reduce cloud computing costs for bioinformatics by hot-starting software containers, avoiding repetitive initialization steps.

The paper introduces nbdocker, a Jupyter notebook extension that embeds Docker containers to enhance reproducibility of data science workflows.

The paper introduces BiocImageBuilder, a tool that enables reproducible bioinformatics workflows through interactive notebooks and containers.

fastBMA improves network inference by being faster, more accurate, and scalable, with a transitive reduction method for denser networks.

This paper demonstrates that RNA-seq analysis can depend on the computing environment and shows that software containers improve reproducibility.