Biology subjects

Chen, C.-Y.

Publications and source records attributed to Chen, C.-Y..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Comparative snRNAseq study of C9orf72, SOD1, and sALS spinal cord

Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease characterized by the loss of motor neurons, yet the cell-type specific molecular alterations within the spinal cords are not well characterized. In this study, we conducted deep molecular profiling of spinal cord tissues donated by people living with sporadic, C9orf72, and SOD1-ALS using single-nucleus RNA sequencing (snRNAseq). We observed numerous distinct gene expression patterns and enriched pathways among ALS types. However, when focusing on common features, we identified activation of stress-response and inflammatory pathways in specific microglia subtypes, as well as disrupted vesicle transport and synaptic function in a ventral inhibitory neuronal subtype. Notably, CPLX3, a SNARE regulator, was uniquely expressed in alpha-motor neurons and was commonly downregulated across all ALS types. While this study uncovers the molecular heterogeneity underlying ALS, it also highlights shared pathways within specific cell types, especially in the ventral inhibitory neurons that have been less explored in ALS research.

neuroscience↗

bioRxiv2025

GeneSys: Generative Modeling of Developmental System

Temporal single-cell transcriptomics enables the reconstruction of dynamic gene expression changes during development. Yet, its analytical power is often limited by data sparsity, technical noise, and imbalanced representation of cell types across time points. To overcome these challenges, we present GeneSys (Generative Modeling of Developmental System), a generative deep learning model that simulates single-cell transcriptomic landscapes under developmental constraints, which is informed by prior biological knowledge or user-defined hypotheses. GeneSys integrates a temporal variational autoencoder with a cell-type classifier, requiring a lineage blueprint as input, which enables it to model the temporal transitions of transcriptional states with cell-type specificity. Leveraging data from Arabidopsis thaliana roots and mouse embryos, we show that GeneSys learns robust developmental trajectories, generates imputed and representative transcriptomes, and enhances gene prioritization accuracy compared to unregularized scRNA-seq data. By applying gene masking and augmentation, GeneSys reveals interpretable gene expression programs (GEPs) and serves as an in silico platform to test the impact of specific genes or gene sets on user-defined developmental outcomes. Additionally, GeneSys computes linear interaction matrices (LIMAs) to infer dynamic gene networks and prioritize transcription factors with spatiotemporal resolution. These features enable GeneSys to nominate key genes governing state transitions in a developmental system, supporting both mechanistic insight and hypothesis generation. Together, GeneSys provides a flexible and extensible framework to denoise single-cell data and simulate transcriptomic developmental landscape guided by known or hypothesized developmental constraints, empowering the discovery of regulatory mechanisms from high-dimensional single-cell datasets.

systems biology↗

bioRxiv2025

Genetic parameters and genome-wide association analysis of service sire effect on litter size and its relationship with boar semen quality in three terminal sire lines

This study aimed to estimate the genetic parameters for service sire effects on the number born alive (NBA) and its relationship with semen quality traits. Data of 6,416, 23,188, and 48,890 litter size records collected between 2020 and 2024 from three purebred terminal sire lines were analyzed. The number of sows was 3,071, 11,819, and 24,089, with 197, 554, and 891 service sires for the three respective lines. There were 1,424,858, 4,344,630, and 2,146,583 animals in the pedigree of which 67,990, 259,250, and 365,392 were genotyped and imputed up to 50K SNPs. The service sire and dam effects were modeled as additive genetic effects, considering a covariance structure among them, given by the relationship matrix. The model also included fixed effects of contemporary group and parity group with random permanent environmental effects for service sire and dam. Genetic parameters were estimated using the AIREML option in the BLUPF90+ software, and GEBVs were generated by ssGBLUP with the algorithm for proven and young (APY). Heritability for the service sire effect ranged from 0.01 to 0.03, and the heritability for the dam effect ranged from 0.09 to 0.15, with genetic correlations ranging from -0.20 to 0.37. A single-step genome-wide association study (ssGWAS) was also performed, and no strong signals were detected for service sire effects on NBA. Sperm motility (MOT) and total abnormal morphology (ABN_MOR) GEBVs from the three lines were estimated based on about 57,500 to 608,996 ejaculates recorded using the computer-assisted semen analysis (CASA) system from 1,698 to 29,095 boars collected from 2013 to 2025. Heritability estimates ranged from 0.10 to 0.12 for motility and from 0.17 to 0.28 for total abnormal morphology. The correlations between service sire GEBV for NBA and semen quality were low but in the favorable direction. Results suggest that although paternal genetic contributions to litter size were small compared to maternal genetic contributions, selecting on service sire effects on litter size in addition to semen quality traits will improve overall reproductive success.

genetics↗

bioRxiv2025

Unmasking Human T Cell Receptor Germline Diversity: 335 Novel Alleles Identified in 47 Pangenome Reference Individuals Using the gAIRR Suite

The adaptive immune receptor repertoire (AIRR), composed of V(D)J-recombined T cell receptors (TR) and immunoglobulins (IG), is central to adaptive immunity. Accurate AIRR profiling depends on a comprehensive and diverse germline gene set encoding AIRR (gAIRR). Furthermore, gAIRR alleles themselves are associated with immune-related phenotypes and diseases. However, for TR genes, the IMGT database - the primary gAIRR reference - has not been updated since 2020 and lacks sufficient population diversity and complete flanking sequences. To address these limitations, we investigated 47 high-quality genomes from the Human Pangenome Reference Consortium (HPRC) using the gAIRR Suite and identified 335 novel TR alleles - 305 TRV and 30 TRJ - representing 91.6% and 30.9% increases over IMGT records. All novel alleles were crosschecked using two orthogonal pipelines. We also established a comprehensive flanking sequence database, including recombination signal sequences (RSS). We have made all resources publicly available to support immunogenomics research and clinical applications.

genomics↗

bioRxiv2025

Deciphering lung adenocarcinoma evolution and the role of LINE-1 retrotransposition

Understanding lung cancer evolution can identify tools for intercepting its growth. In a landscape analysis of 1024 lung adenocarcinomas (LUAD) with deep whole-genome sequencing integrated with multiomic data, we identified 542 LUAD that displayed diverse clonal architecture. In this group, we observed an interplay between mobile elements, endogenous and exogenous mutational processes, distinct driver genes, and epidemiological features. Our results revealed divergent evolutionary trajectories based on tobacco smoking exposure, ancestry, and sex. LUAD from smokers showed an abundance of tobacco-related C:G>A:T driver mutations in KRAS plus short subclonal diversification. LUAD in never smokers showed early occurrence of copy number alterations and EGFR mutations associated with SBS5 and SBS40a mutational signatures. Tumors harboring EGFR mutations exhibited long latency, particularly in females of European-ancestry (EU_N). In EU_N, EGFR mutations preceded the occurrence of other driver genes, including TP53 and RBM10. Tumors from Asian never smokers showed a short clonal evolution and presented with heterogeneous repetitive patterns for the inferred mutational order. Importantly, we found that the mutational signature ID2 is a marker of a previously unrecognized mechanism for LUAD evolution. Tumors with ID2 showed short latency and high L1 retrotransposon activity linked to L1 promoter demethylation. These tumors exhibited an aggressive phenotype, characterized by increased genomic instability, elevated hypoxia scores, low burden of neoantigens, propensity to develop metastasis, and poor overall survival. Re-activated L1 retrotransposition-induced mutagenesis can contribute to the origin of the mutational signature ID2, including through the regulation of the transcriptional factor ZNF695, a member of the KZFP family. The complex nature of LUAD evolution creates both challenges and opportunities for screening and treatment plans.

genomics↗

bioRxiv2024

Same-Slide Spatial Multi-Omics Integration Reveals Tumor Virus-Linked Spatial Reorganization of the Tumor Microenvironment

The advent of spatial transcriptomics and spatial proteomics have enabled profound insights into tissue organization to provide systems-level understanding of diseases. Both technologies currently remain largely independent, and emerging same slide spatial multi-omics approaches are generally limited in plex, spatial resolution, and analytical approaches. We introduce IN-situ DEtailed Phenotyping To High-resolution transcriptomics (IN-DEPTH), a streamlined and resource-effective approach compatible with various spatial platforms. This iterative approach first entails single-cell spatial proteomics and rapid analysis to guide subsequent spatial transcriptomics capture on the same slide without loss in RNA signal. To enable multi-modal insights not possible with current approaches, we introduce k-bandlimited Spectral Graph Cross-Correlation (SGCC) for integrative spatial multi-omics analysis. Application of IN-DEPTH and SGCC on lymphoid tissues demonstrated precise single-cell phenotyping and cell-type specific transcriptome capture, and accurately resolved the local and global transcriptome changes associated with the cellular organization of germinal centers. We then implemented IN-DEPTH and SGCC to dissect the tumor microenvironment (TME) of Epstein-Barr Virus (EBV)-positive and EBV-negative diffuse large B-cell lymphoma (DLBCL). Our results identified a key tumor-macrophage-CD4 T-cell immunomodulatory axis differently regulated between EBV-positive and EBV-negative DLBCL, and its central role in coordinating immune dysfunction and suppression. IN-DEPTH enables scalable, resource-efficient, and comprehensive spatial multi-omics dissection of tissues to advance clinically relevant discoveries.

systems biology↗

bioRxiv2024

Harnessing Environmental DNA (eDNA) to Explore Frugivorous Interactions: A Case Study in Papaya (Carica papaya) and Pineapple (Ananas comosus)

Plant-animal interactions (PAIs) are critical in ecosystem function, mediating energy flow and species interactions. Traditional methods of tracking PAIs, such as morphological identification and camera trapping, are limited in speed and scalability, posing challenges for comprehensive biodiversity monitoring. Recently, environmental DNA (eDNA) metabarcoding has emerged as a promising technique for detecting species interactions non-destructively. This pilot study explores the application of eDNA metabarcoding to investigate interactions involving partially consumed and intact fruits of Carica papaya and Ananas comosus. eDNA metabarcoding were performed from of 36 partially consumed and 6 intact fruit samples. Metabarcoding of mitochondrial COI gene fragments revealed a diverse range of taxa, with Arthropoda, particularly insects, being the most abundant. Results indicated significant differences in taxonomic composition between pineapple and papaya samples, where both the fruit hold some unique as well as shared taxa. Furthermore, the diversity also differed between consumed and intact fruits, suggesting that partially consumed fruits serve as rich eDNA sources, capturing interactions with various frugivores and decomposers. Signal from various organisms detected through eDNA metabarcoding from consumed and damaged fruits allowed us to capture a wide array of taxa, revealing insights into species composition and ecological relationships. The unique ASVs associated with each fruit type suggest that certain taxa may showing preferences based on fruit characteristics such as sugar content, texture, or chemical profile. Present work highlighted the importance of eDNA based methods in unraveling the taxonomic composition of fruit-associated plant-animal interactions. This method needs limited taxonomic expertise, less labors, fast and effective, which can be implemented in monitoring ecological and economical species interactions.

ecology↗

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

AutoMorFi: Automated Whole-image Morphometry in Fiji/ImageJ for Diverse Image Analysis Needs

Running on the highly popular and accessible ImageJ/Fiji platform for biological image analysis, we have established AutoMorFi as a streamlined interface for automated whole-image morphometric analysis that generates at least 47 measurements per cell or object in under 1 minute. We performed multiple validated cluster and principal component analyses on nonredundant morphometric parameters derived from AutoMorFi for various cell types, objects, and organisms. We used images of rodent macrophages, human postmortem brain tissues from multiple sclerosis (MS) and Alzheimers disease (AD) patients, iPSC/animal models for Downs syndrome and autism spectrum disorder (ASD), and organisms such as sea anemone and corals. AutoMorFis adaptability extends across diverse imaging modalities including brightfield, confocal, or widefield fluorescence microscopy as well as underwater photography. Due to its unlimited and unbiased sampling across any image and high potential for modification and customization, using AutoMorFi has led to the discovery of new distinguishing features in previously studied cell types and organisms as well as the development of rapid diagnostic approaches. AutoMorFi represents a transformative tool that will accelerate morphometric analysis and offer broad relevance in biological studies.

cell biology↗

bioRxiv2024

ChromBERT: Uncovering Chromatin State Motifs in the Human Genome Using a BERT-based Approach

Chromatin states, which are defined by specific combinations of histone post-translational modifications, are fundamental to gene regulation and cellular identity. Despite their importance, comprehensive patterns within chromatin state sequences, which could provide insights into key biological functions, remain largely unexplored. In this study, we introduce ChromBERT, a BERT-based model specifically designed to detect distinct chromatin state patterns as "motifs." We pre-trained ChromBERT on 15-state chromatin annotations from 127 human cell and tissue types from the ROADMAP consortium. This pre-trained model can be fine-tuned for various downstream tasks, and obtained high-attention chromatin state patterns are extracted as motifs. To account for the variable-length nature of chromatin state motifs, ChromBERT uses Dynamic Time Warping to cluster similar motifs and identify meaningful representative patterns. In this study, we evaluated the performance of the model on several tasks, including binary and quantitative gene expression prediction, cell type classification, and three-dimensional genome feature classification. Our analyses yielded biologically grounded results and revealed the associated chromatin state motifs. This workflow facilitates the discovery of specific chromatin state patterns across different biological contexts and offers a new framework for exploring the dynamics of epigenomic states.

bioinformatics↗

bioRxiv2024

Microscale Spatial Dysbiosis in Oral biofilms Associated with Disease

Microbiome dysbiosis has largely been defined using compositional analysis of metagenomic sequencing data; however, differences in the spatial arrangement of bacteria between healthy and diseased microbiomes remain largely unexplored. In this study, we measured the spatial arrangement of bacteria in dental implant biofilms from patients with healthy implants, peri-implant mucositis, or peri-implantitis, an oral microbiome-associated inflammatory disease. We discovered that peri-implant biofilms from patients with mild forms of the disease were characterized by large single-genus patches of bacteria, while biofilms from healthy sites were more complex, mixed structures. Based on these findings, we propose a model of peri-implant dysbiosis where changes in biofilm spatial architecture allow the colonization of new community members. This model indicates that spatial structure could be used as a potential biomarker for community stability and has implications in diagnosis and treatment of peri-implant diseases. These results enhance our understanding of peri-implant disease pathogenesis and may be broadly relevant for spatially structured microbiomes.

microbiology↗

bioRxiv2024

Redox control of the deubiquitinating enzyme Ubp2 regulates translation during stress

Protein ubiquitination is essential to govern cells ability to cope with harmful environments by regulating many aspects of protein dynamics from synthesis to degradation. As important as the ubiquitination process, the reversal of ubiquitin chains mediated by deubiquitinating enzymes (DUBs) is critical for proper recovery from stress and re-establishment of proteostasis. Although it is known that ribosomes are decorated with K63-linked polyubiquitin (K63-ub) chains that control protein synthesis under stress, the mechanisms by which these ubiquitin chains are reversed and regulate proteostasis during stress recovery are still illusive. Here, we showed in budding yeast that the DUB Ubp2 is redox regulated during oxidative stress in a reversible manner, which determines the levels of K63-ub chains present on ribosomes. We also demonstrate that Ubp2 is a processive enzyme whose activity is modulated by a series of repeated domains and the formation of important disulfide bonds. By combining, cellular, biochemical, and proteomics analyses, we showed that Ubp2 is crucial for restoring translation after stress cessation, indicating an important role in determining cellular response to oxidative stress. Our work demonstrates a novel role for Ubp2, revealing that a range of signaling pathways can be controlled by redox regulation of DUB activity in eukaryotes, which in turn will define cellular states of health and diseases.

biochemistry↗

bioRxiv2024

Baseline microbiome composition impacts resilience to and recovery following antibiotics

The gut microbiome of healthy individuals naturally undergoes temporal changes linked to the dynamics of its community components1. These dynamics are only observable in longitudinal studies; they are particularly relevant to understanding ecosystem responses to external environment disturbances. External exposures, such as antibiotic treatment, significantly reshape the gut microbiome, impacting both pathogen and commensal microbes2. The gut microbiome plays pivotal roles in digestion, nutrient absorption, and mental health, influencing immune systems, obesity, and various diseases3-6. Consequently, beyond the short-term effects on the host gut microbiome dynamics, alterations resulting from antibiotic exposure also have enduring repercussions on human health and physiological equilibrium7. Therefore, enhancing gut microbiome resilience during antibiotic treatment is essential, with the goal of mitigating prolonged adverse effects. Here, we explored the impact of pre-antibiotic microbial and functional profiles on resilience, suggesting that specific baseline features exhibit greater resilience to antibiotics-induced changes. Our results identified an uncultured Faecalibacterium prausnitzii taxon as a species at baseline associated with diminished resilience. We demonstrated that this association could be linked to the role of this F. prausnitzii taxon as a keystone species. Additionally, we observed the influence of other commensal bacteria, such as Bifidobacterium animalis and Lactobacillus acidophilus, as well as functional modules, such as multidrug resistance efflux pump, on resilience. This lays the foundations for designing targeted strategies to promote a resilient gut microbiome before antibiotic treatment, alleviating possible prolonged effects on human health.

microbiology↗

bioRxiv2024

Trabectedin Enhances Oncolytic Virotherapy by Reducing Barriers to Virus Spread and Cytotoxic Immunity in Preclinical Pediatric Bone Sarcoma

We previously reported that the DNA alkylator and transcriptional-blocking chemotherapeutic agent trabectedin enhances oncolytic herpes simplex viroimmunotherapy in human sarcoma xenograft models, though the mechanism remained to be elucidated. Here we report trabectedin disrupts the intrinsic cellular anti-viral response which increases viral transcript spread throughout the human tumor cells. We also extended our synergy findings to syngeneic murine sarcoma models, which are poorly susceptible to virus infection. In the absence of robust virus replication, we found trabectedin enhanced viroimmunotherapy efficacy by reducing immunosuppressive macrophages and stimulating granzyme expression in infiltrating T and NK cells to cause immune-mediated tumor regressions. Thus, trabectedin enhances both the direct virus-mediated killing of tumor cells and the viral-induced activation of cytotoxic effector lymphocytes to cause tumor regressions across models. Our data provide a strong rationale for clinical translation as both mechanisms should be simultaneously active in human patients.

cancer biology↗

bioRxiv2024

The unseen invaders: tracking phylogeographic dynamics and genetic diversity of cryptic Pomacea canaliculata and P. maculata (Golden Apple Snails) across Taiwan

The cryptic invasion of golden apple snails (Pomacea canaliculata and P. maculata) in Taiwan has caused significant ecological and economical damage over last few decades, however, their management remains difficult due to inadequate taxonomic identification, complex phylogeny and limited population genetic information. We aim to understand the current distribution, putative population of origin, genetic diversity and potential path of cryptic invasion of Pomacea canaliculata and P. maculata across Taiwan to aid in improved mitigation approaches. The present investigation conducted a nationwide survey with 254 samples collected from 41 locations from 14 counties or cities across Taiwan. We identified P. canaliculata and P. maculata based on mitochondrial COI and compared their genetic diversity across Taiwan, as well as other introduced and native countries (based on publicly available COI data) to understand the possible paths of invasion in Taiwan. Based on mitochondrial COI barcoding, sympatric and heterogeneous distributions of invasive P. canaliculata and P. maculata were noted. Our haplotype analysis and mismatch distribution suggested multiple introductions of P. canaliculata in Taiwan was likely originated directly from Argentina, whereas P. maculata was probably introduced from a single, or a few, introduction event(s) from Argentina and Brazil. Our population genetic data further demonstrated a higher haplotype and genetic diversity for P. canaliculata and P. maculata in Taiwan compared to other introduced regions. Based on our current understanding, the establishment of P. canaliculata and P. maculata is alarming and widespread beyond geopolitical borders, requiring a concerted and expedited national and international invasive species mitigation program.

ecology↗

bioRxiv2024

Histone acetyltransferase HAF2 associates with PDC to control H3K14ac and H3K23ac in ethylene response

Ethylene plays its essential roles in plant development, growth, and defense responses by controlling the transcriptional reprogramming, in which EIN2-C-directed regulation of histone acetylation is the first key-step for chromatin to perceive ethylene signaling. However, the histone acetyltransferase in this process remains unknown. Here, we identified histone acetyltransferase HAF2, and mutations in HAF2 confer plants with ethylene insensitivity. Furthermore, we found that HAF2 interacts with EIN2-C in response to ethylene. Biochemical assays demonstrated that the bromodomain of HAF2 binds to H3K14ac and H3K23ac peptides with a distinct affinity for H3K14ac; the HAT domain possesses acetyltransferase catalytic activity for H3K14 and H3K23 acetylation, with a preference for H3K14. ChIP-seq results provide additional evidence supporting the role of HAF2 in regulating H3K14ac and H3K23ac levels in response to ethylene. Finally, our findings revealed that HAF2 co-functions with pyruvate dehydrogenase complex (PDC) to regulate H3K14ac and H3K23ac in response to ethylene in an EIN2 dependent manner. Overall, this research reveals that HAF2 as a histone acetyltransferase that forms a complex with EIN2-C and PDC, collectively governing histone acetylation of H3H14ac and H3K23ac, preferentially for H3K14 in response to ethylene.

plant biology↗

bioRxiv2023

Graph-KIR: Graph-based KIR Copy Number Estimation and Allele Calling Using Short-read Sequencing Data

MotivationThe Killer-cell Immunoglobulin-like Receptor (KIR) is a highly polymorphic region in the human genome, associated with autoimmune diseases and organ transplantation. The sequences of KIR genes are highly similar among star alleles as well as in between individual genes, with the copy number of each KIR gene typically ranging from 0 to 4. In this study, we introduce a tool Graph-KIR that aims to estimate the copy number of genes and to call full-resolution (7-digit) KIR alleles from a whole genome sequencing (WGS) sample. ResultsGraph-KIR, unlike most KIR tools, is capable of independently typing KIR alleles per sample with no reliance on the distribution of any framework gene in a cohort. In a set of 100 simulated samples, Graph-KIR demonstrated 100% accuracy in copy number estimation and high accuracy of allele typing: 91.2% at 7-digit resolution, 97.0% at 5-digit resolution, 97.2% at 3-digit resolution, and 99.6% at gene-level resolution. Graph-KIR outperforms existing tools such as PINGs WGS version (91.9% accuracy) and T1K (84.6% accuracy) at 5-digit resolution. By analyzing the results on 44 HPRC samples, Graph-KIR achieves an accuracy of 85.0%, better than PINGs WGS version (75.5% accuracy) at 5-digit resolution. The release of Graph-KIR adds another valuable tool to assist users in accurately estimating copy numbers and calling alleles of KIR genes from WGS samples, ensuring reliable performance. AvailabilityThe Graph-KIR and paper-related pipeline codes are available at https://github.com/linnil1/KIR_graph.

bioinformatics↗

bioRxiv2023

Evaluating the performance of protein structure prediction in detecting structural changes of pathogenic nonsynonymous single nucleotide variants

AO_SCPLOWBSTRACTC_SCPLOWProtein structure prediction serves as an efficient tool, saving time and circumventing the need for laborious experimental endeavors. Distinguished methodologies, including AlphaFold, RoseTTAFold, and ESMFold, have proven their precision through rigorous evaluation based on the last Critical Assessment of Protein Structure Prediction (CASP14). The success of protein structure prediction raises the following question: can the prediction tools discern structural alterations resulting from single amino acid changes? In this regard, the objective of this study is to assess the performance of existing structure prediction tools on mutated sequences. In this study, we posited that a specific fraction of the pathogenic nonsynonymous single nucleotide variants (nsSNVs) would experience structural alterations following amino acid mutations. We meticulously assembled an extensive dataset by initially sourcing data from ClinVar and subsequently applying multiple filters, resulting in 964 alternative sequences and their corresponding reference sequences. Utilizing UniProt, we acquired reference sequences and generated the corresponding alternative sequences based on variant information. This study performed three tools of structure prediction on both the reference and alternative sequences and expected some structural changes upon mutations. Our findings affirm AlphaFold as the foremost prediction tool presently; nonetheless, our experimental results underscore persistent challenges in accurately predicting structural alterations induced by nsSNVs. Discrepancies between the predicted structures of reference and alternative sequences, when observed, often stem from a lack of confidence in the predictions or the spatial separation between compact domains interrupted by disordered regions, posing challenges to successful alignment.

bioinformatics↗