Biology subjects

Lai, W. K.

Publications and source records attributed to Lai, W. K..

6 recordsLinked to original sources

bioRxiv2026

ScriptManager: a platform for scalable and reproducible high-resolution analysis of genomics datasets

BackgroundThe growing diversity of genomic and epigenomic assays has driven a parallel expansion in data formats, analysis workflows, and figure-generation tools. However, tools for analyzing data and assembling publication-quality figures are often specialized to a specific assay, dramatically limiting their interoperability and reproducibility. ResultsWe present the v1.0 release of ScriptManager, a Java-based framework for modular and reproducible analysis and visualization workflows of genomics and epigenomics data. Unlike existing tools specialized for individual assay types, ScriptManager provides a unified and extensible framework for cross-assay visualization and workflow reproducibility. The v1.0 release adds novel analytical modules, GUI session logging, automated unit and integration testing, tutorials, and expanded documentation. It also integrates with the broader reproducibility ecosystem through Singularity containers, Anaconda packaging, and Galaxy XML wrappers. We demonstrate ScriptManagers TagPileup scaling from local single-core execution to a 10,305-job analysis distributed across the Open Science Grid (OSG), with the full workload completing in <2 hours of wall-clock time. ConclusionsScriptManager v1.0 enhances workflow portability, transparency, and reproducibility across a diverse range of high-resolution genomic assays. By coupling a flexible module design with modern reproducibility standards, ScriptManager provides a bridge between exploratory data analysis and formal, publication-ready figure generation. These improvements enable researchers to build, share, and reproduce genomic analyses across diverse computational infrastructures with minimal configuration.

bioinformatics↗

bioRxiv2025

Refining liquid chromatography conditions across platforms to democratize single-cell proteomics

Single-cell proteomics has emerged as a powerful approach for characterizing cellular heterogeneity. Here, we present an optimized scProteomic workflow that enhances proteome coverage and quantification by refining liquid chromatography (LC) conditions across platforms (both Evosep and nanoElute2). Importantly, our results show that using our optimized LC conditions, even with Bruker timsTOF HT, a machine not designed for scProteomics applications, we achieved solid performance with single cell samples that allows meaningful biological discoveries. First, we compared power to detect differentially-expressed genes/proteins between scRNA-seq and scProteomics at single cell level, and demonstrated that scProteomics showed an excellent performance at small sample sizes. To further validate this finding, we applied our optimized workflow to study the proteomic response to oxidative stress using a limited number of single cells. Our scProteomics results successfully detected 3 distinct cell populations, reflecting correctly the 3 cell lines used, and captured the dysregulation of antioxidant enzymes, molecular chaperones and ubiquitin-proteasome system, reflecting a multi-faceted response to oxidative stress that is uniform across distinct cell lines. Our results highlighted the potential of scProteomics to resolve subtle perturbations and provide a readout of cellular states for the broader community, including users operating non-scProteomics-dedicated machines.

systems biology↗

bioRxiv2025

Protein structure prediction and design for high-throughput computing

Recent advances in structural biology and machines learning have resulted in a revolution in molecular biology. This revolution is driven by protein structure prediction and design tools such as Alphafold3, Chai-1, and Boltz-2 which are now able to accurately model protein structures as well as predict protein-complex formation with a variety of substrates at atomic resolution (i.e., DNA, RNA, small ligands, post-translational modifications). The impact of these protein-structure prediction algorithms has been matched by the emergence of in silico protein design platforms (RFdiffusion), which now promise to revolutionize synthetic biology and novel disease therapeutics. Despite their potential to transform molecular biology, the adoption of these algorithms is hindered in part, not only by their high computational requirements, but also by the difficulty in deploying these algorithms on available systems. To help address these barriers, we developed containerized solutions for AlphaFold3, Chai-1, Boltz-2, and RFdiffusion, optimized across a variety of computational architectures (e.g., x86 and ARM). Additionally, we present OmniFold, an optimized wrapper-platform with automatic QC report generation that enables AlphaFold3, Chai-1, and Boltz-2 to perform simultaneously while more efficiently utilizing GPU systems. Precompiled containers and their definition files are available as open source through Sylabs and GitHub. We hope that these containers and repos will help to facilitate reproducibility, accessibility, and accelerate scientific discovery. Availability and implementationSource code for containers is available at: https://github.com/EpiGenomicsCode/ProteinStruct-Containers https://github.com/EpiGenomicsCode/ProteinDesign-Containers https://github.com/EpiGenomicsCode/OmniFold

bioinformatics↗

bioRxiv2023

GenoPipe: identifying the genotype of origin within (epi)genomic datasets

Confidence in experimental results is critical for discovery. As the scale of data generation in genomics has grown exponentially, experimental error has likely kept pace despite the best efforts of many laboratories. Technical mistakes can and do occur at nearly every stage of a genomics assay (i.e., cell line contamination, reagent swapping, tube mislabelling, etc.) and are often difficult to identify post-execution. However, the DNA sequenced in genomic experiments contains certain markers (e.g., indels) encoded within and can often be ascertained forensically from experimental datasets. We developed the Genotype validation Pipeline (GenoPipe), a suite of heuristic tools that operate together directly on raw and aligned sequencing data from individual high-throughput sequencing experiments to characterize the underlying genome of the source material. We demonstrate how GenoPipe validates and rescues erroneously annotated experiments by identifying unique markers inherent to an organisms genome (i.e., epitope insertions, gene deletions, and SNPs).

bioinformatics↗

bioRxiv2021

PEGR: a flexible management platform for reproducible epigenomic and genomic research

Reproducibility is a significant challenge in (epi)genomic research due to the complexity of experiments composed of traditional biochemistry and informatics. Recent advances have exacerbated this challenge as high-throughput sequencing data is generated at an unprecedented pace. Here we report on our development of a Platform for Epi-Genomic Research (PEGR), a web-based project management platform that tracks and quality controls experiments from conception to publication-ready figures, compatible with multiple assays and bioinformatic pipelines. It supports rigor and reproducibility for biochemists working at the wet bench, while continuing to fully support reproducibility and reliability for bioinformaticians through integration with the Galaxy platform.

bioinformatics↗

bioRxiv2018

Characterizing protein-DNA binding event subtypes in ChIP-exo data

MotivationRegulatory proteins associate with the genome either by directly binding cognate DNA motifs or via protein-protein interactions with other regulators. Each recruitment mechanism may be associated with distinct motifs and may also result in distinct characteristic patterns in high-resolution protein-DNA binding assays. For example, the ChIP-exo protocol precisely characterizes protein-DNA crosslinking patterns by combining chromatin immunoprecipitation (ChIP) with 5 [->] 3 exonuclease digestion. Since different regulatory complexes will result in different protein-DNA crosslinking signatures, analysis of ChIP-exo tag enrichment patterns should enable detection of multiple protein-DNA binding modes for a given regulatory protein. However, current ChIP-exo analysis methods either treat all binding events as being of a uniform type or rely on motifs to cluster binding events into subtypes.\n\nResultsTo systematically detect multiple protein-DNA interaction modes in a single ChIP-exo experiment, we introduce the ChIP-exo mixture model (ChExMix). ChExMix probabilistically models the genomic locations and subtype memberships of binding events using both ChIP-exo tag distribution patterns and DNA motifs. We demonstrate that ChExMix achieves accurate detection and classification of binding event subtypes using in silico mixed ChIP-exo data. We further demonstrate the unique analysis abilities of ChExMix using a collection of ChIP-exo experiments that profile the binding of key transcription factors in MCF-7 cells. In these data, ChExMix identifies possible recruitment mechanisms of FoxA1 and ER, thus demonstrating that ChExMix can effectively stratify ChIP-exo binding events into biologically meaningful subtypes.\n\nAvailabilityChExMix is available from https://github.com/seqcode/chexmix\n\nContactmahony@psu.edu

bioinformatics↗