Biology subjects

Ng, J. K.

Publications and source records attributed to Ng, J. K..

5 recordsLinked to original sources

bioRxiv2026

Multi-omics Differential Inference for Functional Interpretation (MoDIFI): A Statistical Framework to Prioritize Cell Lines for Neurodevelopmental Variants

Noncoding variants contribute to neurodevelopmental disorders (NDDs), but their regulatory effects are often cell-type specific, making it difficult to choose an in vitro model for high-throughput assays such as massively parallel reporter assays. We asked: given a set of noncoding variants, which cell line and regulatory regions are most likely to reveal measurable allele-specific effects? We generated matched multiomics profiles across commonly used NDD in vitro models: human neuronal lines (i.e., IMR-32, SH-SY5Y, SK-N-SH), mouse neuronal lines (i.e., HT-22, Neuro-2a), and a non-neuronal line (i.e., HEK-293), using RNA-seq, ATAC-seq, and Hi-C under consistent conditions. To integrate these orthogonal data types, we developed MoDIFI (Multi-omics Differential Inference for Functional Interpretation), a Bayesian framework that quantifies cell-line-specific regulatory activity by computing posterior inclusion probabilities (PIPs) for differential gene-loop interactions. MoDIFI identifies regulatory regions supported by coordinated 3D contacts, accessibility, and transcriptional output, producing cell-line-resolved regulatory maps that highlight both shared synaptic programs and context-dependent mechanisms. These results provide a practical strategy for prioritizing the most informative cell lines and candidate regulatory elements for targeted functional testing of NDD-relevant noncoding variation.

genomics↗

bioRxiv2025

Generation and Characterization of a Knockout Mouse of an Enhancer of EBF3

Genomic studies of autism and other neurodevelopmental disorders have identified several relevant protein-coding and noncoding variants. One gene with an excess of protein-coding de novo variants is EBF3 that also is the gene underlying the Hypotonia, Ataxia, and Delayed Development Syndrome (HADDS). In previous work, we have identified noncoding de novo variants in an enhancer of EBF3 called hs737 and further showed that there was an enrichment of deletions of this enhancer in individuals with neurodevelopmental disorders. In this present study, we generated a novel mouse line that deletes the highly conserved, orthologous mouse region of hs737 within the Rr169617 regulatory region, and characterized the molecular and phenotypic aspects of this mouse model. This line contains a 1,160 bp deletion within Rr169617 and through heterozygous crosses we found a deviation from Mendelian expectation (p = 0.02) with a significant depletion of the deletion allele (p = 5.8 x 10-4). Rr169617+/- mice had a reduction of Ebf3 expression by 10% and Rr169617-/- mice had a reduction of Ebf3 expression by 20%. Differential expression analyses in E12.5 forebrain, midbrain, and hindbrain in Rr169617+/+versus Rr169617-/- mice identified dysregulated genes including histone genes (i.e., Hist1h1e, Hist1h2bk, Hist1h3i, Hist1h2ao) and other brain development related genes (e.g., Chd5, Ntng1). A priori phenotyping analysis (open field, hole board and light/dark transition) identified sex-specific differences in behavioral traits when comparing Rr169617-/- males versus females; whereby, males were observed to be less mobile, move slower, and spend more time in the dark. Furthermore, both sexes when homozygous for the enhancer deletion displayed body composition differences when compared to wild-type mice. Overall, we show that deletion within Rr169617 reduces the expression of Ebf3 and results in phenotypic outcomes consistent with potential sex specific behavioral differences. This enhancer deletion line provides a valuable resource for others interested in noncoding regions in neurodevelopmental disorders and/or those interested in the gene regulatory network downstream of Ebf3.

genetics↗

bioRxiv2023

HAT: de novo variant calling for highly accurate short-read and long-read sequencing data

Motivationde novo variant (DNV) calling is challenging from parent-child sequenced trio data. We developed Hare And Tortoise (HAT) to work as an automated workflow to detect DNVs in highly accurate short-read and long-read sequencing data. Reliable detection of DNVs is important for human genetics studies (e.g., autism, epilepsy). ResultsHAT is a workflow to detect DNVs from short-read and long read sequencing data. This workflow begins with aligned read data (i.e., CRAM or BAM) from a parent-child sequenced trio and outputs DNVs. HAT detects high-quality DNVs from short-read whole-exome sequencing, short-read wholegenome sequencing, and highly accurate long-read sequencing data. Availabilityhttps://github.com/TNTurnerLab/HAT Contacttychele@wustl.edu Supplementary informationSupplementary data are available at bioRxiv.

bioinformatics↗

bioRxiv2021

ACES: Analysis of Conservation with Expansive Species

MotivationAn abundance of new reference genomes are becoming available through large-scale sequencing efforts. While the reference FASTA for each genome is available, there is currently no automated mechanism to query a specific sequence across all new reference genomes. ResultsWe developed ACES (Analysis of Conservation with Expansive Species) as a computational workflow to query specific sequences of interest (e.g., enhancers, promoters, exons) against reference genomes with an available reference FASTA. This automated workflow generates BLAST hits against each of the reference genomes, a multiple sequence alignment file, a graphical fragment assembly file, and a phylogenetic tree file. These data files can then be used by the researcher in several ways to provide key insights into conservation of the query sequence. AvailabilityACES is available at https://github.com/TNTurnerLab/ACES Contacttychele@wustl.edu Supplementary informationSupplementary Figure 1 is available on bioRxiv.

bioinformatics↗

bioRxiv2021

de novo variant calling identifies cancer mutation profiles in the 1000 Genomes Project

Detection of de novo variants (DNVs) is critical for studies of disease-related variation and mutation rates. We developed a GPU-based workflow to rapidly call DNVs (HAT) and demonstrated its effectiveness by applying it to 4,216 Simons Simplex Collection (SSC) whole-genome sequenced parent-child trios from DNA derived from blood. In our SSC DNV data, we identified 78 {+/-} 15 DNVs per individual, 18% {+/-} 5% at CpG sites, 75% {+/-} 9% phased to the paternal chromosome of origin, and an average allele balance of 0.49. These calculations are all in line with DNV expectations. We sought to build a control DNV dataset by running HAT on 602 whole-genome sequenced parent-child trios from DNA derived from lymphoblastoid cell lines (LCLs) from the publicly available 1000 Genomes Project (1000G). In our 1000G DNV data, we identified 740 {+/-} 967 DNVs per individual, 14% {+/-} 4% at CpG sites, 61% {+/-} 11% phased to the paternal chromosome of origin, and an average allele balance of 0.41. Of the 602 trios, 80% had > 100 DNVs and we hypothesized the excess DNVs were cell line artifacts. Several lines of evidence in our data suggest that this is true and that 1000G does not appear to be a static reference. By mutation profile analysis, we tested whether these cell line artifacts were random and found that 40% of individuals in 1000G did not have random DNV profiles; rather they had DNV profiles matching B-cell lymphoma. Furthermore, we saw significant excess of protein-coding DNVs in 1000G in the gene IGLL5 that has already been implicated in this cancer. As a result of cell line artifacts, 1000G has variants present in DNA repair genes and at Clinvar pathogenic or likely-pathogenic sites. Our study elucidates important implications of the use of sequencing data from LCLs for both reference building projects as well as disease-related projects whereby these data are used in variant filtering steps.

genomics↗