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Heejung Koo

dblp:426/1114 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0009-3715-9713ORCID · reported

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

Applied, interdisciplinary, general and emerging computing · 1 · 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 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
deconvolution
0.912025
StrainR2 accurately deconvolutes strain-level abundances in synthetic microbial communities · Bioinform. 2025
Bioinformatics and computational biology
metagenomics
0.912025
StrainR2 accurately deconvolutes strain-level abundances in synthetic microbial communities · Bioinform. 2025
Bioinformatics and computational biology › metagenomics
strain-level profiling
0.912025
StrainR2 accurately deconvolutes strain-level abundances in synthetic microbial communities · Bioinform. 2025
Bioinformatics and computational biology › computational microbiology
microbiome analysis
0.312025
StrainR2 accurately deconvolutes strain-level abundances in synthetic microbial communities · Bioinform. 2025

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

shotgun metagenomic sequencing · 0.916S rRNA gene sequencing · 0.9
YearPublicationVenuePosition
2025 StrainR2 accurately deconvolutes strain-level abundances in synthetic microbial communities
abstract
MOTIVATION: Synthetic microbial communities offer an opportunity to conduct reductionist research in tractable model systems. However, deriving abundances of highly related strains within these communities is currently unreliable. 16S rRNA gene sequencing does not resolve abundance at the strain level and other methods such as quantitative polymerase chain reaction (qPCR) scale poorly and are resource prohibitive for complex communities. We present StrainR2, which utilizes shotgun metagenomic sequencing to provide high accuracy strain-level abundances for all members of a synthetic community, provided their genomes. RESULTS: Both in silico, and using sequencing data derived from gnotobiotic mice colonized with a synthetic fecal microbiota, StrainR2 resolves strain abundances with greater accuracy and efficiency than other tools utilizing shotgun metagenomic sequencing reads. We demonstrate that StrainR2's accuracy is comparable to that of qPCR on a subset of strains resolved using absolute quantification. AVAILABILITY AND IMPLEMENTATION: Software is available at GitHub and implemented in C, R, and Bash. Software is supported on Linux and MacOS, with packages available on Bioconda or as a Docker container. The source code at the time of publication is also available on figshare at the doi: 10.6084/m9.figshare.29420780.
Kerim Heber, Shuchang Tian, Daniela Betancurt-Anzola, Heejung Koo, Jordan E. Bisanz
Bioinform.4