Samuel H. Church

dblp:370/0634 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
0000-0002-8451-103XORCID · 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 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › sequence analysis › sequence assembly › genome assembly › de novo assembly
de bruijn graph assembly
1.012026
Sharkmer: repurposing PCR primers for targeted genome assembly using in silico PCR · Bioinform. 2026
Bioinformatics and computational biology › sequence analysis › sequence assembly
genome assembly
1.012026
Sharkmer: repurposing PCR primers for targeted genome assembly using in silico PCR · Bioinform. 2026

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

in silico PCR · 1.0
YearPublicationVenuePosition
2026 Sharkmer: repurposing PCR primers for targeted genome assembly using in silico PCR
abstract
SUMMARY: We introduce an in silico PCR (sPCR) method for the assembly of specific genomic regions spanned by PCR primers using raw sequence reads. This allows a user to quickly isolate the exact regions that are abundant in public archives of gene sequences, leveraging the decades of work that have gone into optimizing primer sequences for benchtop PCR. We implement sPCR in sharkmer as a targeted de Bruijn graph assembler seeded with the forward primer sequence and terminated with the reverse primer sequence. This is useful for a variety of routine tasks, including validating the species identity of a dataset, identifying contaminants, and quickly building phylogenies from raw sequence data. AVAILABILITY AND IMPLEMENTATION: sharkmer is written in Rust. Code, instructions for installation and use, tests, and other resources are available in the GitHub repository at https://github.com/caseywdunn/sharkmer and at Zenodo with DOI 10.5281/zenodo.19020708. It can also be installed via bioconda.
Casey W. Dunn, Samuel H. Church
Bioinform.2