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Kun Qu

dblp:83/11189 · DBLP profile ↗
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5ranked-venue papers
2as first author
3since 2021 · last 2026
0000-0002-5555-8437ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 3 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 2 · 2 first-author · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
High-performance computing · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › metagenomics
pathogen detection
0.112012
Rapid identification of non-human sequences in high-throughput sequencing datasets · Bioinform. 2012
High-performance computing
sequence alignment
0.012012
Rapid identification of non-human sequences in high-throughput sequencing datasets · Bioinform. 2012

Methods — techniques the papers use, named apart from their topics

assembly · 0.3alignment · 0.3
YearPublicationVenuePosition
2026 Multi-view data-driven ensemble kernel ridge regression via multi-kernel optimization
Kun Qu, Emmanuel Ntaye, Ernest Domanaanmwi Ganaa, Xiangjun Shen
Multim. Tools Appl.1
2025 An extremely fast deep spectral clustering method in Fourier domain for large-scale data
Kun Qu, Yang Yang 0001, Hao Xue 0001, Xiangjun Shen
Multim. Syst.1
2024 eccDNA-pipe: an integrated pipeline for identification, analysis and visualization of extrachromosomal circular DNA from high-throughput sequencing data
abstract
Extrachromosomal circular DNA (eccDNA) is currently attracting considerable attention from researchers due to its significant impact on tumor biogenesis. High-throughput sequencing (HTS) methods for eccDNA identification are continually evolving. However, an efficient pipeline for the integrative and comprehensive analysis of eccDNA obtained from HTS data is still lacking. Here, we introduce eccDNA-pipe, an accessible software package that offers a user-friendly pipeline for conducting eccDNA analysis starting from raw sequencing data. This dataset includes data from various sequencing techniques such as whole-genome sequencing (WGS), Circle-seq and Circulome-seq, obtained through short-read sequencing or long-read sequencing. eccDNA-pipe presents a comprehensive solution for both upstream and downstream analysis, encompassing quality control and eccDNA identification in upstream analysis and downstream tasks such as eccDNA length distribution analysis, differential analysis of genes enriched with eccDNA and visualization of eccDNA structures. Notably, eccDNA-pipe automatically generates high-quality publication-ready plots. In summary, eccDNA-pipe provides a comprehensive and user-friendly pipeline for customized analysis of eccDNA research.
Minghao Fang, Jingwen Fang, Songwen Luo, Qiaoni Yu, Jiaxuan Yang, Youyang Zhou, Zongkai Li, Ruoming Sun, Chuang Guo, Kun Qu
Briefings Bioinform.11
2019 ATAC-pipe: general analysis of genome-wide chromatin accessibility
abstract
Assay of Transposase-Accessible Chromatin by deep sequencing (ATAC-seq) has been widely used to profile the chromatin accessibility genome-wide. For the absence of an integrated scheme for deep data mining of specific biological issues, here we present ATAC-pipe, an efficient pipeline for general analysis of chromatin accessibility data obtained from ATAC-seq experiments. ATAC-pipe captures information includes not only the quality of original data and genome-wide chromatin accessibility but also signatures of significant differential peaks, transcription factor (TF) occupancy and nucleosome positions around regulatory sites. In addition, ATAC-pipe automatically converts statistic results into intuitive plots at publication quality, such as the read length distribution, heatmaps of sample clustering and cell-type-specific regulatory elements, enriched TF occupancy with motifs footprints and TF-driven regulatory networks. ATAC-pipe provides convenient workflow for researchers to study chromatin accessibility and gene regulation. Availability https://github.com/QuKunLab/ATAC-pipe.
Zuqi Zuo, Yonghao Jin, Kun Qu
Briefings Bioinform.6
2012 Rapid identification of non-human sequences in high-throughput sequencing datasets
abstract
UNLABELLED: Rapid identification of non-human sequences (RINS) is an intersection-based pathogen detection workflow that utilizes a user-provided custom reference genome set for identification of non-human sequences in deep sequencing datasets. In <2 h, RINS correctly identified the known virus in the dataset SRR73726 and is compatible with any computer capable of running the prerequisite alignment and assembly programs. RINS accurately identifies sequencing reads from intact or mutated non-human genomes in a dataset and robustly generates contigs with these non-human sequences (Supplementary Material). AVAILABILITY: RINS is available for free download at http://khavarilab.stanford.edu/resources.html.
Aparna Bhaduri, Kun Qu, Carolyn S. Lee, Alexander Ungewickell, Paul A. Khavari
Bioinform.2