Author

Chak-Lim Wong

University of Hong Kong

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Research Focus:variant calling· 3 Organism:human· 2 Approach:statistical model· 2 Assay:whole-genome sequencing· 2 Approach:deep learning· 1 Assay:whole-exome sequencing· 1

Frequent Co-Authors

Ruibang LuoiD· 3 shared Tak‐Wah LamiD· 3 shared Henry C. M. Leung· 2 shared Ho‐Ming LukiD· 2 shared Ivan F. M. LoiD· 2 shared Amy Wing-Sze LeungiD· 1 shared Chi-Ian Tang· 1 shared Chi-Man Liu· 1 shared

Activity by Year

2020-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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3 shared papersBioinformatics

Ruibang LuoORCID

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Author profile →

3 papers

Recent·Most cited

SENSV: Detecting Structural Variations with Precise Breakpoints using Low-Depth WGS Data from a Single Oxford Nanopore MinION Flowcell

Henry C. M. Leung, Huijing Yu, Yifan Zhang, Wing Sze Leung, Ivan F. M. Lo, Ho‐Ming Luk, Wai-Chun Law, Ka Kui, Chak-Lim Wong, Yat Sing Wong, Ruibang Luo, Tak‐Wah Lam

SENSV detects structural variations with high sensitivity and precise breakpoints using low-depth Oxford Nanopore WGS data from a single MinION flowcell, outperforming existing tools in pathogenic SV detection.

Scientific Reports · Mar 16, 2022Peer-reviewedBioinformatics

ECNano: A Cost-Effective Workflow for Target Enrichment Sequencing and Accurate Variant Calling on 4,800 Clinically Significant Genes Using a Single MinION Flowcell

Amy Wing-Sze Leung, Henry C. M. Leung, Chak-Lim Wong, Zhenxian Zheng, Wui Wang Lui, Ho‐Ming Luk, Ivan F. M. Lo, Ruibang Luo, Tak‐Wah Lam

bioRxiv · Apr 5, 2021 · PreprintBioinformatics

Clair: Exploring the limit of using a deep neural network on pileup data for germline variant calling

Ruibang Luo, Chak-Lim Wong, Yat-Sing Wong, Chi-Ian Tang, Chi-Man Liu, Chi-Ming Leung, Tak‐Wah Lam

Clair is a germline small variant caller using single-molecule sequencing data that achieves high precision, recall, and speed, approaching the accuracy limit for pileup-based deep learning methods.

Nature Machine Intelligence · Apr 6, 2020Peer-reviewedBioinformatics