Biology subjects

Hung, L.-H.

Publications and source records attributed to Hung, L.-H..

At least 19 recordsLinked to original sources

bioRxiv2026

Interactive Data Integrator for Uniform Data Processing in the MorPhiC Program

We present an interactive Data Integrator to facilitate uniform data processing and integration with datasets generated by the NIH funded MorPhiC (Molecular Phenotypes of Null Alleles in Cells) program. In the dataset identification step, users can select published bulk RNA sequencing (RNA-seq) datasets generated by the MorPhiC program, specify external datasets from the Gene Expression Omnibus (GEO) repository, or upload raw RNA-seq data. Subsequently, users can select a subset of samples and indicate experimental design by assigning groups to samples. External data will then be processed using the MorPhiC approved bulk RNA-seq analytical pipeline, and differential expression will be inferred. Users also have the option to download the uniformly processed counts table.

bioinformatics↗

bioRxiv2026

STARgrid: an open-source pipeline from Visium HD sequencing reads to count matrices

In Visium HD, transcript positions are computed from sequenced barcodes, which are matched to a list of coordinate barcodes that map to a grid of capture locations. Quantifying reads and assigning their positions have relied on Space Ranger, whose end-user licence prohibits modification and whose published source cannot be redistributed. We present STARgrid, the first open-source pipeline that takes Visium HD FASTQ files from Flex or RNA-seq based quantitation to count matrices at 2, 8 and 16 um; it is part of STAR Suite. On three human slides its output agrees closely with Space Ranger's (correlations of gene totals are at least 0.9989), and STARgrid takes 20-35% less wall time under the tested configurations. STARgrid assigns each read barcode to the coordinate barcode with the most similar sequence, as assessed by edit distance. In an examination of Space Ranger's per-read output on one further slide, Space Ranger does the same for most reads, with two differences. First, where two coordinate barcodes overlap, the shorter being the longer without its first base, Space Ranger records the shorter barcode, although the longer one matches with fewer edits; in all, Space Ranger's location is not among the closest for 0.5980% of the reads that our decoder can place. Second, Space Ranger settles each read on its own, recording one location or none, and leaves 0.2916% of these reads without a location; STARgrid keeps every matching location and chooses among them for each molecule, using all its reads. Our molecule totals are 1.9-7.6% above Space Ranger's. The larger source of this difference is probably that we resolve location for each molecule and not for each read; STAR Suite's own gene assignment and molecule counting may also contribute. The counts that STARgrid adds behave like signal. On H&E images of the same sections they fall in cells at nearly the rate of the counts that both pipelines report. Against an adjacent Xenium section of one slide, our counts are closer to Xenium than Space Ranger's for 15 of the 20 genes of a cancer gene panel that Xenium measures most abundantly (P = 0.0414). The added counts therefore appear to improve the measurement of cancer-associated expression. These results are preliminary, but the method is now open, and every comparison reported here can be repeated, extended and improved upon.

bioinformatics↗

bioRxiv2026

RapidMACS: MACS3-identical peak calling, 50x faster

Motivation: MACS3 is a comprehensive peak-calling toolkit whose subcommands span single- and paired-end data, narrow and broad peaks, and a range of signal-track utilities. However, many ATAC-seq and multiomic pipelines, including our own, use just one of those capabilities: narrow peak calling. A single analysis may call peaks under different conditions, so a per-call saving is multiplied and the time recovered can be substantial. Furthermore, we wanted an efficient and embeddable narrow peak caller that could be integrated directly with our Chromap Suite aligner, so we wrote RapidMACS. Results: RapidMACS is a narrow peak caller optimized for this purpose, building its signal tracks in a single lazy sweep that avoids a global sort, processing chromosomes in parallel, and keeping intermediates in memory rather than creating temporary files. In benchmarks involving single-cell ATAC-seq, bulk ATAC-seq, ChIP-seq, and CUT&RUN, it is 3.5 to 51 times faster than MACS3 v3.0.3 depending on the applications and number of threads used. More importantly, the output is byte-identical. This means that any downstream analysis using RapidMACS will produce identical results to those using MACS3. The speed gains are largely due to these algorithmic changes rather than the choice of language, because MACS3's peak-calling code is itself compiled (Cython). RapidMACS depends only on htslib and zlib and links as a small static archive without a Python or Cython runtime. Availability and implementation: RapidMACS is open source under the MIT license at https://github.com/morphic-bio/rapidmacs, with both a standalone CLI executable (rapidmacs) and a linkable C++ library. Prebuilt containers for x86-64 and arm64 are published as biodepot/rapidmacs on Docker Hub.

bioinformatics↗

bioRxiv2026

Chromap Suite: an open-source single-binary platform for agentic multiomic RNA + ATAC profiling

BackgroundSingle-cell multiomic profiling of RNA expression and chromatin accessibility is now a standard tool for resolving regulatory state in single cells, but existing analysis toolchains have lagged. Cell Ranger ARC, the proprietary multiomic pipeline, uses a custom broad peak caller rather than the MACS3 narrow peaks that the ATAC field has consolidated on, and its restrictive end-user licence forbids redistribution of analysis pipelines that include it. A fully open-source, permissively-licensed alternative anchored on community-standard methods--Chromap for ATAC alignment and MACS3 for narrow peak calling--has been impractical to assemble because the two codebases are written in different languages with incompatible runtimes, leaving practitioners to chain them together with ad-hoc scripts. ResultsWe present Chromap Suite, the chromatin-accessibility side of an open-source multiomic stack built in support of the NIH Molecular Phenotypes of Null Alleles in Cells (MorPhiC) consortiums multiomic production pipeline. We extended Chromap with native BAM output and coordinate sorting, in-process narrow peak calling, optional Y-chromosome filtering, and native input from the compressed binary CBQ sequencing format alongside FASTQ, and hardened the result with a regression-test matrix that auto-validates the four upstream Chromap presets (bulk ATAC, scATAC, ChIP-seq, Hi-C). We reimplemented MACS3s narrow peak caller in portable C++ as libMACS3, byte-identical to MACS3 v3.0.3 and free of any Python interpreter dependency. Finally, we extracted Chromaps alignment and fragment-generation paths into a callable C++ library (libchromap) and embedded both libchromap and libMACS3 into STAR Suite, so that one STAR invocation runs alignment, peak calling, and cell calling for both RNA and ATAC modalities concurrently--to our knowledge the first true single-binary RNA + ATAC multiomic implementation. On the public 3K PBMC Multiome at 32 threads, the platform completes in 18 minutes 55 seconds wall time and 44.6 GB peak resident memory, against 40 minutes 4 seconds and 79.1 GB resident memory for Cell Ranger ARC v2.2.0--a 2.12 x wall speedup with 1.8x less peak memory--and produces 50,274 peaks that are byte-identical to MACS3 v3.0.3. To support deployment by both research scientists and the AI agents increasingly used in bioinformatics analysis, Chromap Suite ships a Model Context Protocol (MCP) server and a browser-based Launchpad driven by a shared set of composable YAML recipes that humans and agents drive the same way. ConclusionsChromap Suite delivers a unified, freely redistributable multiomic pipeline that produces the MACS3 narrow peaks downstream ATAC analyses already rely on, with substantially lower wall time and memory than the proprietary alternative. The MIT- and BSD-3-licensed code carries no redistribution restrictions, the constituent libraries are independently embeddable in other open-source tools, and the MCP server plus Launchpad recipes make the platform straightforward to drive both by humans and by AI agents.

bioinformatics↗

bioRxiv2026

STAR Suite: Integrating transcriptomics through AI software engineering in the NIH MorPhiC consortium

Background. STAR is the aligner underlying most transcriptomics processing, and is used inside Cell Ranger. However, the surrounding steps - adapter trimming, sorting, feature-barcode assignment, probe handling, SLAM-seq analysis, quantification, and quality control - run as separate tools scripted around it. Cell Ranger hides this complexity but is proprietary, restricted to 10x products, and barred by its license from redistribution, so it cannot serve as a shared processing layer. While STARsolo supports scRNA-seq, it lacks the feature-barcode detection needed for Perturb-seq, where short tags such as CRISPR guides or lineage barcodes are read alongside the transcriptome. No open-source tool processes Perturb-seq feature barcodes at production scale, and the only open-source processor for 10x Flex - a probe-based assay for fixed and FFPE tissue that multiplexes several samples in one lane - is a recent standalone k-mer tool. Results. STAR Suite compiles these steps into the STAR codebase to create a drop-in executable for bulk RNA-seq, scRNA-seq, Perturb-seq, 10x Flex, and SLAM-seq. It adds no dependencies beyond OpenSSL's libcrypto, and legacy behavior is preserved. On consortium and public benchmarks it is up to 41-fold faster than Cell Ranger 9.0.1 for Flex (20- to 41-fold from binary CBQ input, 17- to 23-fold from FASTQ) and 3.9- to 6.2-fold faster for scRNA-seq and Perturb-seq, timed on one modest server (24 cores, 128 GiB, local SSD) except for the largest dataset, which ran on a cloud instance limited to the same size. The concordance to Cell Ranger is high: gene-level Spearman and Pearson 0.98-1.0, cell-level Pearson on per-cell total counts 0.9999-1.0, feature-UMI Pearson 0.999-1.000, Jaccard agreement of the called-cell sets 0.97-0.995, and 99-100% CRISPR-call agreement. It also needs far less disk: its peak use on the largest Flex dataset is 16.9 GiB, against 1.8 TiB for Cell Ranger. These gains arise from running the steps in one process rather than as separate programs exchanging files, and from four new computational techniques: fast-Hamming, a vectorized exact Hamming-distance search, used for Flex probe matching and optionally for feature barcodes; a disambiguated hash cascade that assigns Flex probes without alignment; a permit-based thread scheduler that interleaves alignment and feature assignment; and variance-based auto-trimming for SLAM-seq conversion calling, which finds the reliable region of each read from how variable the conversion rate is along it. The four modules add 183,606 lines of C/C++ to STAR's 28,228. STAR Suite is the NIH MorPhiC consortium's production processor. The same binary is run by people, by AI agents and by cluster schedulers, and every production run is recorded with its commands, checksums and outputs. It has been built and maintained over eight months and 25 releases under a human-directed, AI-implemented workflow, with its full design and benchmark record public. Conclusions. STAR Suite provides the first production-ready open-source implementation of Perturb-seq feature-barcode processing, and an open end-to-end 10x Flex pipeline matched to Cell Ranger, delivered as a drop-in replacement for the STAR aligner. The design also extends: downstream analysis now done in Python or R packages can be built into the same binary, and further modalities added alongside, as a companion preprint does for ATAC-seq. Source code, workflow recipes, and per-run provenance are released under the MIT license.

bioinformatics↗

bioRxiv2025

User-friendly scheduler Using a hybrid architecture and supercomputing for big data processing

The exponential growth of omics data requires novel strategies for storage, transfer, and processing of said data. We present a scheduler based on the Temporal.io workflow framework which enables two key optimizations of bioinformatics workflows. Firstly, we enable users to transparently map workflow steps to diverse execution environments, including high-performance computing (HPC) resources managed by the SLURM resource manager through an easy-to-use graphical user interface. Secondly, we enable asynchronous execution of workflows, a feature which guarantees that workflows will achieve reasonable resource utilization even when the scheduler cannot make use of a systems full RAM and CPU resources. Thirdly, we propose a universal, platform agnostic JSON representation of workflows that allows platform-specific execution details to be abstracted away from the core scientific logic. Our work includes a custom executor plugin that supports translation of workflows from an external language, such as Nextflow, to our universal JSON format. Finally, we develop a graphical user interface to make our scheduler easy-to-use for non-technical users. When benchmarked on a bulk RNA sequencing workflow, these features reduced the cost and time requirements. We illustrated the merits of our cross-platform method using credit allocations from federally funded supercomputers.

bioinformatics↗

bioRxiv2025

Uncovering the Pathophysiological Pattern of Expression from Integrated Analysis across Uniformly Processed RNA Sequencing COVID-19 Datasets

BackgroundPost-acute sequelae of SARS-CoV-2 infection (PASC) affects millions globally, yet the molecular mechanisms underlying acute COVID-19 and its chronic sequelae remain poorly understood. MethodsWe performed an integrative transcriptomic analysis of three independent RNA-seq datasets, capturing the complete COVID-19 pathophysiology from health through acute severe infection to post-acute sequelae and mortality (n=142 total samples). We implemented a containerized analytical pipeline from data download, quantification, differential gene expression to uniformly process these three RNA-seq datasets. ResultsOur analysis reveals striking molecular dichotomies contrasting disease phases with profound clinical implications. Acute severe/critical COVID-19 reveals predominant enrichment of TNF- signaling via NF-{kappa}B pathways (normalized enrichment score >2.5, FDR <0.001), reflecting a cytokine storm pathophysiology characterized by rapid inflammatory developments involving IL-6, TNF-, and anti-apoptotic responses. In contrast, PASC patients exhibit dominant enrichment of Myc Targets V1 and Oxidative Phosphorylation pathways (NES >2.2, FDR <0.005), indicating important shifts toward cellular adaptation. Pathway signature analysis identifies core differentially expressed genes that reliably distinguish disease phases, thereby offering objective biomarkers for precision diagnosis and monitoring. ConclusionsThese findings establish a comprehensive molecular framework distinguishing acute inflammatory from chronic metabolic COVID-19 phases, with potential clinical applicability. TNF-/NF-{kappa}B pathway signatures identify patients at risk for severe disease progression, while Myc/OXPHOS signatures allow objective PASC diagnosis, addressing current reliance on subjective and eliminative diagnosis. This integrative analytical framework has utility beyond COVID-19, offering an applicable approach for precision medicine implementation across other diseases processes. Clinical SignificanceThis study transforms COVID-19 from a symptom-based to a molecularly-defined disease spectrum, enabling precision diagnosis, prognostic monitoring, classification, and targeted therapeutic possibilities based on pathway-specific biomarkers rather than subjective clinical assessments.

genomics↗

bioRxiv2025

Graphical and Interactive Spatial Proteomics Image Analysis Workflow

Spatial proteomics provides a spatially resolved view of protein expression and localization within cells and tissues by mapping the location and abundance of proteins. There is a need for fully-integrated end-to-end imaging workflows for spatial proteomic analysis that are flexible, high-throughput, and support graphical and interactive visualizations. We present a modular and interactive spatial proteomic image analysis workflow with individual steps containerized that empowers biomedical researchers to reproducibly execute and customize complex analyses. Our workflow consists of cell segmentation, unsupervised clustering, validation of clusters on the image, and cell type clustering results visualization. Users can utilize a form-based graphical interface to execute and customize multi-step workflows with a single click or interactively adjust image processing steps within the workflow, apply workflows to various datasets, and modify input parameters as needed. We illustrated the functionality of our workflow using a cancer imaging dataset consisting of a tissue microarray (TMA) stained by high-plex immunohistochemistry. This TMA contained a variety of cancer and tissue cell types to assess the broad applicability of this workflow to different biopsy and tissue types.

bioinformatics↗

bioRxiv2025

Singe cell RNA sequencing data processing using cloud-based serverless computing

Singe cell RNA sequencing (scRNA-seq) has become a routine method for measuring cell activities. Processing large scRNA-seq datasets requires high-performance computing resources. The emergence of cloud computing allows us to leverage its on-demand capabilities without major investment in infrastructure. Serverless computing provides cost efficiency by allowing users to pay only for actual resource usage, eliminating the necessity for pre-allocated server capacities. Additionally, there is no requirement to set up servers in advance. We present a novel and generalizable methodology using serverless cloud computing to accelerate computationally intensive workflows. We create an on-demand "supercomputer" using rapidly deployable cloud serverless functions as automatically provisioned computation units. We tested our methodology of optimizing a scRNA-seq workflow by leveraging serverless functions on the cloud using two publicly available peripheral blood mononuclear cell (PBMC) datasets. In addition, we demonstrate our approach using data generated by the NIH MorPhiC program, where we process a 450 GB human scRNA-seq dataset across 86 cell lines designed to study the temporal impact of perturbations on pancreatic differentiation. We compared the total execution time of the scRNA-seq serverless workflow with the traditional workflow without using serverless functions, and demonstrate major speedup for large scRNA-seq datasets.

bioinformatics↗

bioRxiv2022

Accessible, interactive and cloud-enabled genomic workflows integrated with the NCI Genomic Data Commons

Cancer data is widely available in repositories such as the National Cancer Institute (NCI) Genomic Data Commons (GDC). These datasets could serve as controls or comparisons in compendium analyses with user data, avoiding the expense and time of generating additional datasets. However, the user must be able to process their new data in the same manner for these comparisons to be useful. This can be non-trivial. Although the executables themselves are usually available in repositories, the GDC pipelines that describe that entire analysis workflow are currently published as text-based standard operating procedures (SOPs). It is difficult to document a computational workflow to the level of detail and accuracy required to reproduce the results. Discrepancies between versions and exclusions of details accumulate as the documentation inevitably lags behind code revisions. We address this problem by converting the SOPs into a downloadable and executable format. Specifically, we converted the GDC DNA sequencing (DNA-Seq) and the GDC mRNA sequencing (mRNA-Seq) SOPs into reproducible, self-installing, containerized, and interactive graphical workflows. These can be applied to reproducibly process user data and to harmonize datasets across repositories. Using our publicly available graphical workflows, we harmonize raw RNA-Seq datasets from the GDC and the Genotype-Tissue Expression (GTEx) project that were originally processed using different methodologies to illustrate the importance of uniform processing of control and treatment data for accurate inference of differentially expressed genes. By disseminating the analytical methodology in a reproducible and easily executed form, we greatly increase the utility of the GDC by enabling researchers to uniformly process custom data and datasets across multiple repositories to enhance data interpretation. Our approach and open-source executable workflows of making the analytical process as readily available as the data can be applied to other data repositories to increase their impact on scientific research.

bioinformatics↗

bioRxiv2022

Accelerated and Reproducible Fiji for image processing using GPUs on the cloud

SummaryGraphical processing units can greatly accelerate image processing but adoption has been hampered by the need for specialized hardware and software. The cloud offers inexpensive on-demand instances that can be pre-configured with the necessary software. Specifically, we use the Biodepot-workflow-builder (Bwb) to deploy a containerized version of Fiji that includes the CLIJ package to use GPUs on the cloud. In addition, we provide an Amazon Machine Image (AMI) with the correct drivers and Docker images pre-loaded. We demonstrate the portability and reproducibility of the platform by deploying an interactive Fiji/CLIJ workflow on both Amazon Web Services and IBM cloud. The workflows produce identical results while providing a 29-fold reduction in execution time.

bioinformatics↗

bioRxiv2021

Cloud-enabled Fiji for reproducible, integrated and modular image processing

Modern biomedical image analyses workflows contain multiple computational processing tasks giving rise to problems in reproducibility. In addition, image datasets can span both spatial and temporal dimensions, with additional channels for fluorescence and other data, resulting in datasets that are too large to be processed locally on a laptop. For omics analyses, software containers have been shown to enhance reproducibility, facilitate installation and provide access to scalable computational resources on the cloud. However, most image analyses contain steps that are graphical and interactive, features that are not supported by most omics execution engines. We present the containerized and cloud-enabled Biodepot-workflow-builder platform that supports graphics from software containers and has been extended for image analyses. We demonstrate the potential of our modular approach with multi-step workflows that incorporate the popular and open-source Fiji suite for image processing. One of our examples integrates fully interactive Fiji macros with Jupyter notebooks. Our second example illustrates how the complicated cloud setup of an computationally intensive process such as stitching 3D digital pathology datasets using BigStitcher can be automated and simplified. In both examples, users can leverage a form-based graphical interface to execute multi-step workflows with a single click, using the provided sample data and preset input parameters. Alternatively, users can interactively modify the image processing steps in the workflow, apply the workflows to their own data, change the input parameters and macros. By providing interactive graphics support to software containers, our modular platform supports reproducible image analysis workflows, simplified access to cloud resources for analysis of large datasets, and integrated workflows across imaging, genomics and transcriptomics data.

bioinformatics↗

bioRxiv2021

A graphical, interactive and GPU-enabled workflow to process long-read sequencing data

We present a graphical cloud-enabled workflow for fast, interactive analysis of nanopore sequencing data using GPUs. Users customize parameters, monitor execution and visualize results through an accessible graphical interface. To facilitate reproducible deployment, we use Docker containers and provide an Amazon Machine Image (AMI) with all software and drivers pre-installed for GPU computing on the cloud. We observe a 34x speedup and a 109x reduction in costs for the rate-limiting basecalling step in the analysis of blood cancer cell line data. The graphical interface and greatly simplified deployment facilitate the adoption of GPUs for rapid, cost-effective analysis of long-read sequencing.

bioinformatics↗

bioRxiv2019

Rapid RNA sequencing data analysis using serverless computing

We present a novel method that yields 1100-fold computational speedup and allows biomedical scientists to interactively adjust alignment parameters in real time to iteratively improve final analytical results. Specifically, the alignment time for a 640 million read human RNA-sequencing dataset is reduced from 19 hours to 1 minute using serverless cloud computing. We provide a graphical interface for the accelerated workflows, thus making our methodology accessible to non-cloud experts.

bioinformatics↗

bioRxiv2018

Combinatorial recognition of clustered RNA elementsby a multidomain RNA-binding protein, IMP3

How multidomain RNA-binding proteins recognize their specific target sequences, based on a combinatorial code, represents a fundamental unsolved question and has not been studied systematically so far. Here we focus on a prototypical multidomain RNA-binding protein, IMP3 (also called IGF2BP3), which contains six RNA-binding domains (RBDs): four KH and two RRM domains. We have established an integrative systematic strategy, combining single-domain-resolved SELEX-seq, motif-spacing analyses, in vivo iCLIP, functional validation assays, and structural biology. This approach identifies the RNA-binding specificity and RNP topology of IMP3, involving all six RBDs and a cluster of up to five distinct and appropriately spaced CA-rich and GGC-core RNA elements, covering a >100 nucleotide-long target RNA region. Our generally applicable approach explains both specificity and flexibility of IMP3-RNA recognition, providing a paradigm for the function of multivalent interactions with multidomain RNA-binding proteins in gene regulation.

molecular biology↗

bioRxiv2018

Holistic optimization of RNA-seq workflow for multi-threaded environments

SummaryFor many next-generation sequencing pipelines, the most computationally intensive step is the alignment of reads to a reference sequence. As a result, alignment software such as the Burrows-Wheeler Aligner (BWA) is optimized for speed and and is often executed in parallel on the cloud. However, there are other less demanding steps that can also be optimized and significantly increase the speed especially when using many threads. We demonstrate this using a Unique-molecular-identifier (UMI) RNA sequencing pipeline consisting of 3 steps: split, align, and merge. Optimization of all three steps yields a 40% increase in speed when executed using a single thread. However, when executed using 16 threads, we observe a 4-fold improvement over the original parallel imple-mentation and more than an 8-fold improvement over the original single-threaded implementation. In contrast, optimizing only the alignment step results in just a 13% improvement over the original parallel workflow using 16 threads.

bioinformatics↗

bioRxiv2018

Embedding containerized workflows inside data science notebooks enhances reproducibility

Data science notebooks, such as Jupyter, combine text documentation with dynamically editable and executable code and have become popular for sharing computational methods. We present nbdocker, an extension that integrates Docker software containers into Jupyter notebooks. nbdocker transforms notebooks into autonomous, self-contained, executable and reproducible modules that can document and disseminate complicated data science workflows containing code written in different languages and executables requiring different software environments.

bioinformatics↗

bioRxiv2017

Hot-starting software containers for bioinformatics analyses

Using software containers has become standard practice to reproducibly deploy and execute biomedical workflows on the cloud. We demonstrate that hot-starting, from containers that have been frozen after the application has already begun execution, reduces the costs of cloud computing by avoiding repetitive initialization steps. The method is widely applicable and can provide substantial savings both for small jobs and for large-scale deployments using automated schedulers.

bioinformatics↗