Author

Xiaoxiao Sun

University of ArizonaORCID 0000-0002-9689-9385Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Assay:long-read sequencing· 3 Approach:statistical model· 2 Research Focus:differential expression· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Organism:mouse· 1 Research Focus:variant calling· 1 Organism:yeast· 1

Frequent Co-Authors

Hao Helen ZhangiD· 3 shared Hongxu DingiD· 3 shared Jianwen QueiD· 3 shared Yinshan FangiD· 3 shared Ning HaoiD· 2 shared Ziyang LiuiD· 2 shared Ziyuan WangiD· 2 shared Ai-Ming Yu· 1 shared

Activity by Year

2024-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 39 mutual co-author links

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3 shared papersBioinformatics

Hao Helen ZhangORCID

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3 papers

Recent·Most cited

Training Data Diversity Enhances the Basecalling of Novel RNA Modification-Induced Nanopore Sequencing Readouts

Ziyuan Wang, Ziyang Liu, Yinshan Fang, Hao Helen Zhang, Xiaoxiao Sun, Ning Hao, Jianwen Que, Hongxu Ding

This study shows that diverse training data improves basecalling accuracy for novel RNA modifications in nanopore sequencing.

Nature Communications · Jan 15, 2025Peer-reviewedBioinformatics

An Iterative Approach to Polish the Nanopore Sequencing Basecalling for Therapeutic RNA Quality Control

Ziyuan Wang, Mei‐Juan Tu, Ziyang Liu, Katherine K. Wang, Yinshan Fang, Ning Hao, Hao Helen Zhang, Jianwen Que, Xiaoxiao Sun, Ai-Ming Yu, Hongxu Ding

This paper introduces an iterative method to improve nanopore sequencing basecalling accuracy for therapeutic RNA quality control, enabling precise analysis of modified RNAs.

bioRxiv · Sep 12, 2024 · PreprintBioinformatics

Adapting Nanopore Sequencing Basecalling Models for Modification Detection via Incremental Learning and Anomaly Detection

Ziyuan Wang, Yinshan Fang, Ziyang Liu, Ning Hao, Hao Helen Zhang, Xiaoxiao Sun, Jianwen Que, Hongxu Ding

This paper adapts nanopore sequencing basecalling models for modification detection using incremental learning and anomaly detection, achieving single-molecule and single-nucleotide resolution across multiple species and modification types.

Nature Communications · Aug 21, 2024Peer-reviewedBioinformatics