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Yanwen Xiong

dblp:226/5027 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · unresolved

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

Applied, interdisciplinary, general and emerging computing · 1

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 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › statistical genetics › genotype calling
allele calling
0.312018
Genome-by-genome approach for fast bacterial genealogical relationship evaluation · Bioinform. 2018
Bioinformatics and computational biology › genomics › microbial genomics
bacterial genomics
0.312018
Genome-by-genome approach for fast bacterial genealogical relationship evaluation · Bioinform. 2018
Bioinformatics and computational biology › population genetics
genealogical inference
0.312018
Genome-by-genome approach for fast bacterial genealogical relationship evaluation · Bioinform. 2018

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

whole-genome sequencing · 0.3genome-by-genome algorithm · 0.3
YearPublicationVenuePosition
2018 Genome-by-genome approach for fast bacterial genealogical relationship evaluation
abstract
Motivation: Large-scale whole-genome sequencing dataset-based studies are becoming increasingly common in pathogen surveillance and outbreak investigations. A highly discriminative and time-efficient bioinformatics tool is needed to transform large amounts of sequencing data into usable biological information. To replace the intuitive, yet inefficient, way of gene-by-gene allele calling algorithm, a new algorithm using genome-by-genome approach was developed. Results: Tests showed that the program equipped with the new algorithm achieved significant improvements in allele calling efficiency compared to a conventional gene-by-gene approach. The new program, Fast-GeP, rendered a fast and easy way to infer high-resolution genealogical relationships between bacterial isolates using whole-genome sequencing data. Availability and implementation: FAST-GeP is freely available from: https://github.com/jizhang-nz/fast-GeP. Supplementary information: Supplementary data are available at Bioinformatics online.
Ji Zhang 0026, Yanwen Xiong, Lynn Rogers, Glen P. Carter, Nigel French
Bioinform.2