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Kailing Tu

dblp:425/3282 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2024
0000-0002-5879-1357ORCID · reported

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

Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 1 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%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › epigenomics
chromatin accessibility
0.812024
A data-adaptive methods in detecting exogenous methyltransferase accessible chromatin in human genome using nanopore sequencing · Bioinform. 2024
Bioinformatics and computational biology › epigenomics › DNA methylation
DNA methylation analysis
0.812024
A data-adaptive methods in detecting exogenous methyltransferase accessible chromatin in human genome using nanopore sequencing · Bioinform. 2024
Bioinformatics and computational biology
epigenomics
0.812024
A data-adaptive methods in detecting exogenous methyltransferase accessible chromatin in human genome using nanopore sequencing · Bioinform. 2024
Bioinformatics and computational biology
genomics
0.812024
A data-adaptive methods in detecting exogenous methyltransferase accessible chromatin in human genome using nanopore sequencing · Bioinform. 2024
Bioinformatics and computational biology › sequence analysis
nanopore sequencing
0.812024
A data-adaptive methods in detecting exogenous methyltransferase accessible chromatin in human genome using nanopore sequencing · Bioinform. 2024

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

data-adaptive comparison · 0.8base-calling error correction · 0.8
YearPublicationVenuePosition
2024 A data-adaptive methods in detecting exogenous methyltransferase accessible chromatin in human genome using nanopore sequencing
abstract
MOTIVATION: Identifying chromatin accessibility is one of the key steps in studying the regulation of eukaryotic genomes. The combination of exogenous methyltransferase and nanopore sequencing provides an strategy to identify open chromatin over long genomic ranges at the single-molecule scale. However, endogenous methylation, non-open-chromatin-specific exogenous methylation and base-calling errors limit the accuracy and hinders its application to complex genomes. RESULTS: We systematically evaluated the impact of these three influence factors, and developed a model-based computational method, methyltransferase accessible genome region finder (MAGNIFIER), to address the issues. By incorporating control data, MAGNIFIER attenuates the three influence factors with data-adaptive comparison strategy. We demonstrate that MAGNIFIER is not only sensitive to identify the open chromatin with much improved accuracy, but also able to detect the chromatin accessibility of repetitive regions that are missed by NGS-based methods. By incorporating long-read RNA-seq data, we revealed the association between the accessible Alu elements and non-classic gene isoforms. AVAILABILITY AND IMPLEMENTATION: Freely available on web at https://github.com/Goatofmountain/MAGNIFIER.
Kailing Tu, Dan Xie 0001
Bioinform.1