Biology subjects

Lin, H.-J.

Publications and source records attributed to Lin, H.-J..

4 recordsLinked to original sources

bioRxiv2026

Generative design of programmable asymmetric β-barrel nanopores

Protein nanopores are powerful tools for molecular sensing, sequencing, and separation, but designing pores with programmable function remains challenging. Native homo-oligomeric transmembrane {beta} barrels (TMBs) are used for these applications, but their uniform lumens limit spatial resolution and analyte discrimination. Although monomeric TMBs can be designed using energy-based methods, these approaches remain highly manual and limited to structural design rather than function. Here, we present a generative AI framework for TMB design, with backbone and sequence design models trained on a curated distillation set. We characterized 48 designs spanning 0.7-1.5 nm in diameter, diverse lumen chemistries, and hydrophobic thicknesses. Crystal structures closely match the design models. We demonstrate that our method produces customizable nanopores for ion sensing, DNA translocation, and transport across polymer membranes.

biochemistry↗

bioRxiv2020

SparkINFERNO: A scalable high-throughput pipeline for inferring molecular mechanisms of non-coding genetic variants

SummaryWe report SparkINFERNO (Spark-based INFERence of the molecular mechanisms of NOn-coding genetic variants), a scalable bioinformatics pipeline characterizing noncoding GWAS association findings. SparkINFERNO prioritizes causal variants underlying GWAS association signals and reports relevant regulatory elements, tissue contexts, and plausible target genes they affect. To achieve this, the SparkINFERNO algorithm integrates GWAS summary statistics with large-scale collection of functional genomics datasets spanning enhancer activity, transcription factor binding, expression quantitative trait loci, and other functional datasets across more than 400 tissues and cell types. Scalability is achieved by an underlying API implemented using Apache Spark and Giggle-based genomic indexing. We evaluated SparkINFERNO on large GWAS studies and show that SparkINFERNO is more than 60-times efficient and scales with data size and amount of computational resources. AvailabilitySparkINFERNO runs on clusters or a single server with Apache Spark environment, and is available at https://bitbucket.org/wanglab-upenn/SparkINFERNO or https://hub.docker.com/r/wanglab/spark-inferno. Contactlswang@pennmedicine.upenn.edu

bioinformatics↗

bioRxiv2019

Sterol-O acyltransferase 1 is inhibited by gga-miR-181a-5p and gga-miR-429-3p through the TGFβ pathway in endodermal epithelial cells of Japanese quail

Nutrients are utilized and re-constructed by endodermal epithelial cells (EECs) in yolk sac membranes in avian species. Sterol-O acyltransferase 1 (SOAT1) is the key enzyme to convert cholesterol to cholesteryl ester for delivery to growing embryos. During development, absorption of yolk is matched with significant changes of SOAT1 mRNA and enzyme activity. miRNAs regulate angiogenesis and metabolism during mammalian development. However, the involvement of miRNAs in lipid utilization during avian embryogenesis remains ambiguous.\n\nUsing a miRNA sequencing technique, we found several candidate miRNAs and confirmed expression patterns with real time PCR. They were selected for as candidates targeting the receptor (TGF{beta} receptor type 1, TGFBR1) that may regulate SOAT1. Similar to SOAT1 mRNA accumulation, the gga-miR-181a-5p expression was gradually elevated during development, but the concentration of gga-miR-429-3p was in the opposite direction. Transfection with gga-miR-181a-5p or gga-miR-429-3p inhibited TGFBR1 and SOAT1 in EECs. The 3 untranslated region (3UTR) of TGFBR1 was then confirmed to be one of the targets of gga-miR-181a-5p and gga-miR-429-3p. Taken together, expression of miRNAs during embryonic development regulates SOAT1 expression by inhibiting the 3UTR of TGFBR1. This is indicative of possible regulation of avian yolk lipid utilization and modification of hatchability by changing miRNA expressions.

molecular biology↗

bioRxiv2018

VCPA: genomic variant calling pipeline and data management tool for Alzheimer’s Disease Sequencing Project

Summary: We report VCPA, our SNP/Indel Variant Calling Pipeline and data management tool used for analysis of whole genome and exome sequencing (WGS/WES) for the Alzheimers Disease Sequencing Project. VCPA consists of two independent but linkable components: pipeline and tracking database. The pipeline is coded in Workflow Description Language and is fully optimized for the Amazon elastic compute cloud environment. This includes steps for processing raw sequence reads including read alignment, and all the way up to variant calling using GATK. The tracking database allows users to dynamically view the statuses of jobs running and the quality metrics reported by the pipeline. Users can thus monitor the production process and diagnose if any problem arises during the procedure. All quality metrics (>100 collected per processed genome) are stored in the database, thus facilitating users to compare, share and visualize the results. To summarize, VCPA is functional equivalent to the CCDG/TOPMed pipeline. Together with the dockerized database (also available as Amazon Machine Image), users can easily process any WGS/WES data on Amazon cloud with minimal installation.\n\nAvailability: VCPA is released under the MIT license and is available for academic and nonprofit use for free. The pipeline source code and step-by-step instructions are available from the National Institute on Aging Genetics of Alzheimers Disease Data Storage Site (http://www.niagads.org/VCPA).\n\nContact: yyee@pennmedicine.upenn.edu or lswang@pennmedicine.upenn.edu\n\nSupplementary information: Supplementary data are available at Bioinformatics online.

bioinformatics↗