Biology subjects

Lee, Y.-L.

Publications and source records attributed to Lee, Y.-L..

6 recordsLinked to original sources

bioRxiv2025

ClairS-TO: A deep-learning method for long-read tumor-only somatic small variant calling

Accurate identification of somatic variants in tumor is crucial but challenging, and typically requires a matched normal sample for reliable detection, which is often unavailable in real-world research and clinical scenarios, necessitating proficient algorithms to tell real somatic variants from germline variants and background noises. However, existing tumor-only somatic variant callers that were designed for short-read data dont work well with long-read. To fill the gap, we present ClairS-TO, a deep-learning-based method for long-read tumor-only somatic variant calling. ClairS-TO uses an ensemble of two disparate neural networks that were trained from the same samples but for opposite tasks - how likely/not likely a candidate is a somatic variant. ClairS-TO also applies multiple post-calling filters, including 1) nine hard-filters, 2) four public plus any number of user-supplied PoNs, and 3) a module that statistically separates somatic and germline variants using tumor purity and copy number profile. Benchmarks using COLO829 and HCC1395 show that ClairS-TO outperforms DeepSomatic in long-read. ClairS-TO is also applicable to short-read and outperforms Mutect2, Octopus, Pisces, and DeepSomatic. Extensive experiments across various sequencing coverages, VAF ranges, and tumor purities support that ClairS-TO has a broad coverage of usage scenarios. ClairS-TO is open-source, available at https://github.com/HKU-BAL/ClairS-TO.

bioinformatics↗

bioRxiv2024

Clair3-RNA: A deep learning-based small variant caller for long-read RNA sequencing data

Variant calling using long-read RNA sequencing (lrRNA-seq) can be applied to diverse tasks, such as capturing full-length isoforms and gene expression profiling. It poses challenges, however, due to higher error rates than DNA data, the complexities of transcript diversity, RNA editing events, etc. In this paper, we propose Clair3-RNA, the first deep learning-based variant caller tailored for lrRNA-seq data. Clair3-RNA leverages the strengths of the Clair series pipelines and incorporates several techniques optimized for lrRNA-seq data, such as uneven coverage normalization, refinement of training materials, editing site discovery, and the incorporation of phasing haplotype to enhance variant-calling performance. Clair3-RNA is available for various platforms, including PacBio and ONT complementary DNA sequencing (cDNA), and ONT direct RNA sequencing (dRNA). Our results demonstrated that Clair3-RNA achieved a [~]91% SNP F1-score on the ONT platform using the latest ONT SQK-RNA004 kit (dRNA004) and a [~]92% SNP F1-score in PacBio Iso-Seq and MAS-Seq for variants supported by at least four reads. The performance reached a [~]95% and [~]96% F1-score for ONT and PacBio, respectively, with at least ten supporting reads and disregarding the zygosity. With read phased, the performance reached [~]97% for ONT and [~]98% for PacBio. Extensive evaluation of various GIAB samples demonstrated that Clair3-RNA consistently outperformed existing callers and is capable of distinguishing RNA high-quality editing sites from variants accurately. Clair3-RNA is open-source and available at (https://github.com/HKU-BAL/Clair3-RNA).

bioinformatics↗

bioRxiv2023

ClairS: a deep-learning method for long-read somatic small variant calling

Identifying somatic variants in tumor samples is a crucial task, which is often performed using statistical methods and heuristic filters applied to short-read data. However, with the increasing demand for long-read somatic variant calling, existing methods have fallen short. To address this gap, we present ClairS, the first deep-learning-based, long-read somatic small variant caller. ClairS was trained on massive synthetic somatic variants with diverse coverages and variant allele frequencies (VAF), enabling it to accurately detect a wide range of somatic variants from paired tumor and normal samples. We evaluated ClairS using the latest Nanopore Q20+ HCC1395-HCC1395BL dataset. With 50-fold/25-fold tumor/normal, ClairS achieved a 93.01%/86.86% precision/recall rate for Single Nucleotide Variation (SNVs), and 66.54%/66.89% for somatic insertions and deletions (Indels). Applying ClairS to short-read datasets from multiple sources showed comparable or better performance than Strelka2 and Mutect2. Our findings suggest that improved read phasing enabled by long-read sequencing is key to accurate long-read SNV calling, especially for variants with low VAF. Through experiments across various coverage, purity, and contamination settings, we demonstrated that ClairS is a reliable somatic variant caller. ClairS is open-source at https://github.com/HKU-BAL/ClairS.

bioinformatics↗

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↗

bioRxiv2021

A 12 kb multi-allelic copy number variation encompassing a GC gene enhancer is associated with mastitis resistance in dairy cattle

Clinical mastitis (CM) is an inflammatory disease occurring in the mammary glands of lactating cows. CM is under genetic control, and a prominent CM resistance QTL located on chromosome 6 was reported in various dairy cattle breeds. Nevertheless, the biological mechanism underpinning this QTL has been lacking. Herein, we mapped, fine-mapped, and discovered the putative causal variant underlying this CM resistance QTL in the Dutch dairy cattle population. We identified a ~12 kb multi-allelic copy number variant (CNV), that is in perfect linkage disequilibrium with a GWAS lead SNP, as a promising candidate variant. By implementing a genome-wide association study (GWAS) and through expression QTL mapping, we showed that the group-specific component gene (GC), a gene encoding a vitamin D binding protein, is an excellent candidate causal gene for the QTL. The multiplicated alleles are associated with increased GC expression and low CM resistance. Ample evidence from functional genomics data supports the presence of an enhancer within this CNV, which would exert cis-regulatory effect on GC. We observed that strong positive selection swept the region near the CNV, and haplotypes associated with the multiplicated allele were strongly selected for. Moreover, the multiplicated allele showed pleiotropic effects for increased milk yield and reduced fertility, hinting that a shared underlying biology for these effects may revolve around the vitamin D pathway. These findings together suggest a putative causal variant of a CM resistance QTL, where a cis-regulatory element located within a CNV can alter gene expression and affect multiple economically important traits. Author summaryClinical mastitis (CM) is an inflammatory disease that negatively influences dairy production and compromises animal welfare. Although one major genetic locus for CM resistance was mapped on bovine chromosome 6, a mechanistic description of this association has been lacking. Herein, we report a 12-kb multiallelic copy number variant (CNV), encompassing a strong enhancer for group-specific component gene (GC), as a likely causal variant for this locus. This CNV is associated with high GC expression and low CM resistance. We speculate that upregulation of GC leads to a large amount of vitamin D binding protein, which in turn, reduces biologically available vitamin D, resulting in vitamin D deficiency and low CM resistance. Despite the negative effect on CM resistance, the CNV contributes to increased milk production, hinting at balancing selection. Our results highlight how multiplication of a regulatory element can shape economically important traits in dairy cattle, both in favourable and unfavourable directions.

genomics↗

bioRxiv2020

Hyperpolyploidization of hepatocyte initiates preneoplastic lesion formation in the liver

Hepatocellular carcinoma (HCC) is the most predominant primary malignancy in the liver. Genotoxic and genetic models have revealed that HCC cells are derived from hepatocytes, but where the critical region for tumor foci emergence is and how this transformation occurs are still unclear. Here, hyperpolyploidization of hepatocytes around the centrilobular (CL) region was demonstrated to be closely linked with the development of HCC cells after diethylnitrosamine treatment. We identified the CL region as a dominant lobule for accumulation of hyperpolyploid hepatocytes and preneoplastic tumor foci formation. We also demonstrated that upregulation of Aurkb plays a critical role in promoting hyperpolyploidization. Increase of AURKB phosphorylation was detected on the midbody during cytokinesis, causing abscission failure and hyperpolyploidization. Pharmacological inhibition of AURKB dramatically reduced nucleus size and tumor foci number surrounding the CL region in diethylnitrosamine-treated liver. Our work reveals an intimate molecular link between pathological hyperpolyploidy of CL hepatocytes and transformation into HCC cells.

pathology↗