Biology subjects

Lui, W. W.

Publications and source records attributed to Lui, W. W..

3 recordsLinked to original sources

bioRxiv2026

SpliSync: Genomic language model-driven splice site correction of long RNA sequencing reads

Long RNA sequencing reads are rapidly replacing short reads in transcriptomic analyses, enabling full-length transcript sequencing and better identification of isoforms, alternative splicing events, and other transcript variants. However, their higher sequencing error rates can cause misalignments, especially at splice junctions, reducing the accuracy of transcript reconstruction and analysis. We developed SpliSync, a genomic language model-driven method for splice site correction that integrates a pre-trained genomic sequence model (HyenaDNA), alignment data, and a U-net architecture to predict splice sites at nucleotide resolution. SpliSync substantially improved the precision of RNA long-read alignments by 27%-194% across diverse datasets and consistently outperformed competing tools. As a preprocessing step, it increased alternative splicing detection accuracy by 26%-330%. In contrast, its benefit for transcript reconstruction was limited, likely due to the tools built-in correction mechanisms. The code was developed in Python using the PyTorch package, and is freely available at https://github.com/splicebox/SpliSync.

bioinformatics↗

bioRxiv2024

MntJULiP and Jutils: Differential splicing analysis of RNA-seq data with covariates

Differences in alternative splicing patterns can reveal important markers of phenotypic differentiation, including biomarkers of disease. Emerging large and complex RNA-seq datasets from disease and population studies include multiple confounders such as sex, age, ethnicity and clinical attributes, which demand highly specialized data analysis tools. However, few methods are equipped to handle the new challenges. We describe an implementation of our programs MntJULiP and Jutils for differential splicing detection and visualization from RNA-seq data that takes into account covariates. MntJULiP detects intron-level differences in alternative splicing from RNA-seq data using a Bayesian mixture model. Jutils visualizes alternative splicing variation with heatmaps, PCA and sashimi plots, and Venn diagrams. Our tools are scalable and can process thousands of samples within hours. We applied our methods to the collection of GTEx brain RNA-seq samples to deconvolute the effects of sex and age at death on the splicing patterns. In particular, clustering of covariate adjusted data identifies a subgroup of individuals undergoing a distinct splicing program during aging. MntJULiP and Jutils are implemented in Python and are available from https://github.com/splicebox/.

bioinformatics↗

bioRxiv2022

Evaluation of Mycobacterium Tuberculosis enrichment in metagenomic samples using ONT adaptive sequencing and amplicon sequencing for identification and variant calling

Sensitive detection of Mycobacterium Tuberculosis (TB) in small percentages in metagenomic samples is essential for microbial classification and drug resistance prediction, and assisting in diagnosis and treatment planning. However, traditional methods, such as bacterial culture and microscopy, are time-consuming and sometimes have limited TB detection sensitivity. Oxford Nanopore Technologies MinION sequencing allows rapid and simple sample preparation for whole genome and amplicon sequencing. Its recently developed adaptive sequencing selects reads from targets, while allowing real-time base-calling during sequencing to achieve sequence enrichment or depletion. Another common enrichment method is PCR amplification of the target TB genes. In this study, we compared both methods with MinION sequencing for TB detection and variant calling in metagenomic samples using both simulation runs and those with synthetic and patient samples. We found that both methods effectively enrich TB reads from a high percentage of human and other microbial DNA. We provide a simple automatic analysis framework, including quality filtering, taxonomic classification, variant calling, and antimicrobial resistance prediction, to support these detection workflows for clinical use, openly available at https://github.com/HKU-BAL/ONT-TB-NF. Depending on the patients medical condition and sample type (commonly including bronchoalveolar lavage fluid, blood samples, sputum, and tissues), we discuss the findings and recommend that users optimize their workflow to improve the detection limit.

bioinformatics↗