Biology subjects

Huang, J.-H.

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

15 recordsLinked to original sources

bioRxiv2025

Quantum algorithm for identifying RNA 3D motifs by processing RNA secondary structure graphs

The identification of RNA motifs plays a crucial role in understanding biological functions, predicting RNA 3D structures, and designing RNA-based therapeutics. One of the efficient strategies approaches this task by analyzing secondary structure graphs annotated with non-canonical base interactions, effectively reframing the problem as a two-dimensional graph-theoretic challenge. However, both the graph isomorphism problem and the maximum common subgraph problem in this domain are classified as NP-complete. Classical computing architectures struggle to address these problems efficiently due to their substantial computational resource demands. In this work, we propose a quantum algorithm specifically designed for processing RNA secondary structure graphs. By encoding the adjacency matrix into a quantum circuit and incorporating base interaction families as edge weights, our approach achieves a resource efficiency of [O](log2 N) in terms of qubits. Numerical simulations demonstrate the algorithms capability to identify 15 distinct motifs across three structural categories: hairpin loops, internal loops, and three-way junction loops. Furthermore, the method successfully detects the common motif between two complete RNA chains. Finally, 29 high conserved RNA motifs were identified in four cell lines of a key lung cancer biomarker gene (MIF), and their potential roles as drug targets were explored. This work presents a highly promising quantum computing framework for addressing the RNA motif identification problem.

bioinformatics↗

bioRxiv2024

Toward Automatic Variant Interpretation: Discordant Genetic Interpretation Across Variant Annotations for ClinVar Pathogenic Variants

PurposeHigh-throughput sequencing has revolutionized genetic disorder diagnosis, but variant pathogenicity interpretation is still challenging. Even though the Human Genome Variation Society (HGVS) provides recommendations for variant nomenclature, discrepancies in annotation remain a significant hurdle. MethodsThis study evaluated the annotation concordance between three tools-- ANNOVAR, SnpEff, and Variant Effect Predictor (VEP)--using 164,549 two-star variants from ClinVar. The analysis used HGVS nomenclature string-match comparisons to assess annotation consistency from each tool, corresponding coding impacts, and associated ACMG criteria inferred from the annotations. ResultsThe analysis revealed variable concordance rates, with 58.52% agreement for HGVSc, 84.04% for HGVSp, and 85.58% for the coding impact. SnpEff showed the highest match for HGVSc (0.988), while VEP bettered for HGVSp (0.977). The substantial discrepancies were noted in the Loss-of-Function (LoF) category. Incorrect PVS1 interpretations affected the final pathogenicity and downgraded PLP variants (ANNOVAR 55.9%, SnpEff 66.5%, VEP 67.3%), risking false negatives of clinically relevant variants in reports. ConclusionsThese findings highlight the critical challenges in accurately interpreting variant pathogenicity due to discrepancies in annotations. To enhance the reliability of genetic variant interpretation in clinical practice, standardizing transcript sets and systematically cross-validating results across multiple annotation tools is essential. Graphic abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=120 SRC="FIGDIR/small/617756v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@9f6378org.highwire.dtl.DTLVardef@3b9a2corg.highwire.dtl.DTLVardef@106fb58org.highwire.dtl.DTLVardef@15f70c8_HPS_FORMAT_FIGEXP M_FIG This study examined the consistency of variant annotations produced by three widely used open-source toolsANNOVAR, SnpEff, and VEPagainst 164,549 ClinVar two starts variants. The investigation covers HGVS-based transcript, protein nomenclature and coding impact annotation. The results showed that none of the tools were fully consistent with ClinVar across all coding impact categories, particularly in the LoF category, which exhibited the poorest consistency. This inconsistency may lead to discrepancies in PVS1 interpretation, affecting the final pathogenicity assessment. PVS1 loss resulted in a significant downgrading of PLP variants, potentially leading to the omission of clinically relevant variants in reports. C_FIG

genomics↗

bioRxiv2024

Telomere-to-telomere sheep genome assembly reveals new variants associated with wool fineness trait

Ongoing efforts to improve sheep reference genome assemblies still leave many gaps and incomplete regions, resulting in a few common failures and errors in sheep genomic studies. Here, we report a complete, gap-free telomere-to-telomere (T2T) genome of a ram (T2T-sheep1.0) with a size of 2.85 Gb, including all autosomes and chromosomes X and Y. It adds 220.05 Mb of previously unresolved regions (PURs) and 754 new genes to the most updated reference assembly, ARS-UI_Ramb_v3.0, and contains four types of repeat units (SatI, SatII, SatIII, and CenY) in the centromeric regions. T2T-sheep1.0 exhibits a base accuracy of >99.999%, corrects several structural errors in previous reference assemblies, and improves structural variant (SV) detection in repetitive sequences. We identified 192,265 SVs, including 16,885 new SVs in the PURs, from the PacBio long-read sequences of 18 global representative sheep. With the whole-genome short-read sequences of 810 wild and domestic sheep representing 158 global populations and seven wild species, the use of T2T-sheep1.0 as the reference genome has improved population genetic analysis based on [~]133.31 million SNPs and 1,265,266 SVs, including 2,664,979 novel SNPs and 196,471 novel SVs. T2T-sheep1.0 improves selective tests by detecting several novel genes and variants, including those associated with domestication (e.g., ABCC4) and selection for the wool fineness trait (e.g., FOXQ1) in tandemly duplicated regions.

genomics↗

bioRxiv2024

Telomere-to-telomere genome assembly of a male goat reveals novel variants associated with cashmere traits

A complete goat (Capra hircus) reference genome enhances analyses of genetic variation, thus providing novel insights into domestication and selection in goats and related species. Here, we assembled a telomere-to-telomere (T2T) gap-free genome (2.86 Gb) from a cashmere goat (T2T-goat1.0), which included a Y chromosome of 20.9 Mb. With a base accuracy of >99.999%, T2T-goat1.0 corrected numerous genome-wide structural and base errors in previous assemblies and added 288.5 Mb of previously unresolved regions (PURs) and 446 newly assembled genes to the reference genome. We sequenced the genomes of five representative goat breeds for PacBio reads, and used T2T-goat1.0 as a reference to identify a total of 63,417 structural variations (SVs) with up to 4711 SVs (7.41%) in the PURs and 507 overlapping genes significantly enriched (Padj. < 0.05) in the keratin filament pathway. T2T-goat1.0 was applied in population analyses of global wild and domestic goats, which revealed 32,419 SVs and 25,397,794 SNPs, including 870 SVs and 545,026 SNPs in the PURs. Also, our analyses revealed a set of novel selective variants and genes associated with domestication (e.g., NKG2D and ABCC4) and cashmere traits (e.g., ABCC4 and ASIP).

genomics↗

bioRxiv2023

Robust trigger wave speed in Xenopus cytoplasmic extracts

Self-regenerating trigger waves can spread rapidly through the crowded cytoplasm without diminishing in amplitude or speed, providing consistent, reliable, long-range communication. The macromolecular concentration of the cytoplasm varies in response to physiological and environmental fluctuations, raising the question of how or if trigger waves can robustly operate in the face of such fluctuations. Using Xenopus extracts, we found that mitotic and apoptotic trigger wave speeds are remarkably invariant. We derived a model that accounts for this robustness and for the eventual slowing at extremely high and low cytoplasmic concentrations. The model implies that the positive and negative effects of cytoplasmic concentration (increased reactant concentration vs. increased viscosity) are nearly precisely balanced. Accordingly, artificially maintaining a constant cytoplasmic viscosity during dilution abrogates this robustness. The robustness in trigger wave speeds may contribute to the reliability of the extremely rapid embryonic cell cycle.

biophysics↗

bioRxiv2023

A time-resolved multi-omics atlas of transcriptional regulation in response to high-altitude hypoxia across whole-body tissues

High-altitude hypoxia acclimatization requires whole-body physiological regulation in highland immigrants, but the underlying genetic mechanism has not been clarified. Here we used sheep as an animal model for plain-to-plateau transplantation. We generated multi-omics data including time-resolved bulk RNA-Seq, ATAC-Seq and single-cell RNA-Seq from multiple tissues as well as phenotypic data from 20 bio-indicators. We characterized transcriptional changes of all genes in each tissue, and examined multi-tissue temporal dynamics and transcriptional interactions among genes. In particular, we identified critical functional genes regulating the short response to hypoxia in each tissue (e.g., PARG in the cerebellum and HMOX1 in the colon). We further identified TAD-constrained cis-regulatory elements, which suppressed the transcriptional activity of most genes under hypoxia. Phenotypic and transcriptional evidence indicated that antenatal hypoxia could improve hypoxia tolerance in offspring. Furthermore, we provided time-series expression data of candidate genes associated with human mountain sickness (e.g., BMPR2) and high-altitude adaptation (e.g., HIF1A). Our study provides valuable resources and insights for future hypoxia-related studies in mammals.

genetics↗

bioRxiv2023

Somatic mutation detection workflow validity distinctly influences clinical decision.

Evaluating robustness of somatic mutation detections is essential when utilizing whole exome sequencing (WES) for treatment decision-making. A comprehensive evaluation was conducted using tumor WES from the FDA-led Sequencing Quality Control Phase 2 (SEQC2) project, in which multiple library kits sequenced identical DNA materials across three labs to benchmark analytical validity. These workflows included various read aligner (BWA, Bowtie2, DRAGEN-Aligner, DRAGMAP, and HISAT2) and mutation caller (Mutect2, TNscope, DRAGEN-Caller, and DeepVariant) combinations. The results revealed that DRAGEN exhibited superior performance, achieving mean F1-scores of 0.966 and 0.791 for SNV and INDEL detection, respectively. Among open-source software, BWA Mutect2 and HISAT2 Mutect2 combinations showed the highest mean F1-scores for SNV (0.949) and IN-DEL (0.722), respectively. The analyses indicated that high-quality data can be analyzed as having worse results, and vice versa. Evaluations of COSMIC reported mutations unveiled discrepancies across enrichment kits. IDT enrichment kits showed a higher false negative rate, while Agilent WES kits tended to miss mutations in CBL and IDH1, and Roche library kits tended to miss the mutations in PIK3CB. For drug-related biomarkers, Sentieon TNscope tended to underestimate tumor mutation burden and overlook crucial drug-resistance mutations such as FLT3 (c.G1879A: p.A627T) for cytarabine resistance in leukemia and MAP2K1 (c.G199A:p.D67N) for BRAF inhibitors in melanoma. The findings highlight the importance of robust bioinformatic analysis in identifying tumor mutations and guiding clinical decision-making. HighlightsO_LIMutation callers had a significantly higher effect on overall sensitivity than aligners. C_LIO_LIBenchmarking analyses demonstrated that high-quality sequencing reads can be analyzed as having worse results, and vice versa. C_LIO_LIDRAGEN exhibited the best performance among other aligner-caller combinations. C_LIO_LIThe combination of BWA with Mutect2 and HISAT2 with Mutect2 yielded the highest mean F1 scores for detecting SNVs and INDELs by open-source software, respectively. C_LIO_LISentieon TNscope tended to underestimate the tumor mutation burden and missed several drug-resistant mutations. C_LI

bioinformatics↗

bioRxiv2023

VRP Assembler: haplotype-resolved de novo assembly of diploid and polyploid genomes using quantum computing

Precision medicines emphasis on individual genetic variants highlights the importance of haplotype-resolved assembly, a computational challenge in bioinformatics given its combinatorial nature. While classical algorithms have made strides in addressing this issue, the potential of quantum computing remains largely untapped. Here, we present the VRP assembler: a novel approach that transforms this task into a vehicle routing problem, a combinatorial optimization problem solvable on a quantum computer. We demonstrate its potential and feasibility through a proof of concept on short synthetic diploid and triploid genomes using a D-Wave quantum annealer. To tackle larger-scale assembly problems, we integrate the VRP assembler with Googles OR-Tools, achieving a haplotype-resolved assembly across the human major histocompatibility complex (MHC) region. Our results show encouraging performance compared to hifiasm with the phasing accuracy first approaching the theoretical limit, underscoring the promising future of quantum computing in bioinformatics.

bioinformatics↗

bioRxiv2023

Divide-and-conquer quantum algorithm for hybrid de novo genome assembly of short and long reads

Computational biology holds immense promise as a domain that can leverage quantum advantages due to its involvement in a wide range of challenging computational tasks. Researchers have recently explored the applications of quantum computing in genome assembly implementation. However, the issue of repetitive sequences remains unresolved. In this paper, we propose a hybrid assembly quantum algorithm using high-accuracy short reads and error-prone long reads to deal with sequencing errors and repetitive sequences. The proposed algorithm builds upon the variational quantum eigensolver and utilizes divide-and-conquer strategies to approximate the ground state of larger Hamiltonian while conserving quantum resources. Using simulations of 10-qubit quantum computers, we address problems as large as 140 qubits, yielding optimal assembly results. The convergence speed is significantly improved via the problem-inspired ansatz based on the known information about the assembly problem. Besides, entanglement within quantum circuits is qualitatively verified to notably accelerate the assembly path optimization.

bioinformatics↗

bioRxiv2023

Protein homeostasis from diffusion-dependent control of protein synthesis and degradation

It has been proposed that the concentration of proteins in the cytoplasm maximizes the speed of important biochemical reactions. Here we have used the Xenopus extract system, which can be diluted or concentrated to yield a range of cytoplasmic protein concentrations, to test the effect of cytoplasmic concentration on mRNA translation and protein degradation. We found that protein synthesis rates are maximal in [~]1x cytoplasm, whereas protein degradation continues to rise to an optimal concentration of [~]1.8x. This can be attributed to the greater sensitivity of translation to cytoplasmic viscosity, perhaps because it involves unusually large macromolecular complexes like polyribosomes. The different concentration optima sets up a negative feedback homeostatic system, where increasing the cytoplasmic protein concentration above the 1x physiological level increases the viscosity of the cytoplasm, which selectively inhibits translation and drives the system back toward the 1x set point.

systems biology↗

bioRxiv2023

PGSbuilder: An end-to-end platform for human genome association analysis and polygenic risk score predictions

Understanding the genetic basis of human complex diseases is increasingly important in the development of precision medicine. Over the last decade, genome-wide association studies (GWAS) have become a key technique for detecting associations between common diseases and single nucleotide polymorphisms (SNPs) present in a cohort of individuals. Alternatively, the polygenic risk score (PRS), which often applies results from GWAS summary statistics, is calculated for the estimation of genetic propensity to a trait at the individual level. Despite many GWAS and PRS tools being available to analyze a large volume of genotype data, most clinicians and medical researchers are often not familiar with the bioinformatics tools and lack access to a high-performance computing cluster resource. To fill this gap, we provide a publicly available web server, PGSbuilder, for the GWAS and PRS analysis of human genomes with variant annotations. The user-friendly and intuitive PGSbuilder web server is developed to facilitate the discovery of the genetic variants associated with complex traits and diseases for medical professionals with limited computational skills. For GWAS analysis, PGSbuilder provides the most renowned analysis tool PLINK 2.0 package. For PRS, PGSbuilder provides six different PRS methods including Clumping and Thresholding, Lassosum, LDPred2, GenEpi, PRS-CS, and PRSice2. Furthermore, PGSbuilder provides an intuitive user interface to examine the annotated functional effects of variants from known biomedical databases and relevant literature using advanced natural language processing approaches. In conclusion, PGSbuilder offers a reliable platform to aid researchers in advancing the public perception of genomic risk and precision medicine for human disease genetics. PGSbuilder is freely accessible at http://pgsb.tw23.org.

bioinformatics↗

bioRxiv2023

Predicting splicing patterns from the transcription factor binding sites in the promoter with deep learning

BackgroundAlternative splicing is a crucial mechanism of post-transcriptional modification responsible for the transcriptome plasticity and proteome diversity of a metazoan cell. Although many splicing regulations around the exon/intron regions have been discovered, the relationship between promoter-bound transcription factors and the downstream alternative splicing remains largely unexplored. ResultsIn this study, we present computational approaches to decipher the regulation relationship connecting the promoter-bound transcription factor binding sites (TFBSs) and the splicing patterns. We curated a fine data set, including DNase I hypersensitive sites sequencing and transcriptome in fifteen human tissues from ENCODE. Specifically, we proposed different representations of TF binding context and splicing patterns to tackle the associations between the promoter and downstream splicing events. Our results demonstrated that the convolutional neural network (CNN) models learned from the TF binding changes in the promoter to predict the splicing pattern changes. Furthermore, through an in silico perturbation-based analysis of the CNN models, we identified several TFs that considerably reduced the model performance of splicing prediction. ConclusionIn conclusion, our finding highlights the potential role of promoter-bound TFBSs in influencing the regulation of downstream splicing patterns and provides insights for discovering alternative splicing regulations.

bioinformatics↗

bioRxiv2022

Evaluating the analytical validity of mutation calling pipeline for tumor whole exome sequencing

Detecting somatic mutations from the patients tumor tissues has the clinical impacts in medical decision making. Library preparation methods, sequencing platforms, read alignment tools and variant calling algorithms are the major factors to influence the data analysis results. Understanding the performance of the tool combinations of the somatic variant calling pipelines has become an important issue in the use of the whole exome sequences (WES) analysis in clinical actions. In this study, we selected four state-of-the-art sequence aligners including BWA, Bowtie2, DRAGMAP, DRAGEN aligner (DragenA) and HISAT2. For the variant callers, we chose GATK Mutect2, Sentieon TNscope, DRAGEN caller (DragenC) and DeepVariant. The benchmarking tumor whole exome sequencing data released from the FDA-led Sequencing and Quality Control Phase 2 (SEQC2) consortium was applied as the true positive variants to evaluate the overall performance. Multiple combinations of the aligners and variant callers were used to assess the variation detection capability. We measured the recall, precision and F1-score for each combination in both single nucleotide variants (SNVs) and short insertions and deletions (InDels) variant detections. We also evaluated their performances in different variant allele frequencies (VAFs) and the base pair length. The results showed that the top recall, precision and F1-score in the SNVs detection were generated by the combinations of BWA+DragenC(0.9629), Bowtie2+TNscope(0.9957) and DRAGMAP+DragenC(0.9646), respectively. In the InDels detection, BWA+DragenC(0.9546), Hisat2+TNscope(0.7519) and DragenA+DragenC(0.8081) outperformed the other combinations in the recall, precision and F1-Score, respectively. In addition, we found that the variant callers could bias the variant calling results. Finally, although some combinations yielded high accuracies of variant detection, but some variants still could not be detected by these outperformed combinations. The results of this study provided the vital information that no single combination could achieve superior results in detecting all the variants of the benchmarking dataset. In conclusion, applying both merged-based and ensemble-based variants detection approaches is encouraged to further detect variants comprehensively.

bioinformatics↗

bioRxiv2021

Identification and comparative analysis of long non-coding RNAs in the brain of fire ant queens in two different reproductive states

BackgroundMany long non-coding RNAs (lncRNAs) have been extensively identified in many higher eukaryotic species. The function of lncRNAs has been reported to play important roles in diverse biological processes, including developmental regulation and behavioral plasticity. However, there are no reports of systematic characterization of long non-coding RNAs in the fire ant Solenopsis invicta. ResultsIn this study, we performed a genome-wide analysis of lncRNAs in the brains of S. invicta from RNA-seq. In total, 1,393 novel lncRNA transcripts were identified in the fire ant. In contrast to the annotated lncRNA transcripts having at least two exons, novel lncRNAs are monoexonic transcripts with a shorter length. Besides, the transcriptome from virgin alate and dealate mated queens were analyzed and compared. The results showed 295 differentially expressed mRNA genes (DEGs) and 65 differentially expressed lncRNA genes (DELs) between virgin and mated queens, of which 17 lncRNAs were highly expressed in the virgin alates and 47 lncRNAs were highly expressed in the mated dealates. By identifying the DEL:DEG pairs with high association in their expression (Spearmans |rho| > 0.8 and p-value < 0.01), many DELs were co-regulated with DEGs after mating. Furthermore, several remarkable lncRNAs (MSTRG.6523, MSTRG.588, and nc909) that were found to associate with particular coding genes may play important roles in the regulation of brain gene expression in reproductive transition in fire ants. ConclusionThis study provides the first genome-wide identification of S. invicta lncRNAs in the brains in different reproductive states and will contribute to a fuller understanding of the transcriptional regulation underpinning reproductive changes.

genomics↗

bioRxiv2018

The borders of cis-regulatory DNA sequences harbor the divergent transcription factor binding motifs in the human genome

Changes in the cis-regulatory DNA sequences and transcription factor (TF) repertoires provide major sources that shape the gene regulatory evolution in eukaryotes. However, it is currently unclear how dynamic change of DNA sequences introduce various divergence level of TF binding motifs in the genome over evolutionary time. Here, we estimated the evolutionary divergence level of the TF binding motifs, and quantified their occurrences in the DNase I hypersensitive sites. Results from our in silico motif scan and empirical TF-ChIP (chromatin immunoprecipitation) demonstrate that the divergent motifs tend to be introduced at the borders of the cis-regulatory regions, that are likely accompanied with the expansion through evolutionary time. Accordingly, we propose that an expansion by incorporating divergent motifs within the cis-regulatory regions provides a rationale for the evolutionary divergence of regulatory circuits.

genomics↗