PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 41955310

Sharkmer: repurposing PCR primers for targeted genome assembly using in silico PCR.

Casey W Dunn·Samuel H Church
View original source ↗Export metadata ↓

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.

Explore related subjects

SoftwarePolymerase Chain ReactionDNA PrimersGenomicsGenomeSequence Analysis, DNAAnimalsComputer SimulationJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Casey W Dunn, Samuel H Church. 2026-04-07. Sharkmer: repurposing PCR primers for targeted genome assembly using in silico PCR.. https://doi.org/10.1093/bioinformatics%2Fbtag163

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

FANTASIA suite: a reproducible and configurable framework for embedding-based functional annotation of proteins.

Embedding-based annotation transfer is increasingly used for protein function inference due to protein language models capture sequence, structural, and functional signals that may extend beyond conventional pairwise similarity. However, systematic application of these approaches requires control over model choice, reference composition, lookup parameters, evidence traceability, and output formats. We developed the FANTASIA suite, a configurable framework for embedding-based functional annotation of proteins. The suite combines a database-backed implementation for reproducible and extensible analyses with a portable flat-file implementation for rapid local annotation and pipeline integration. Using non-model and model-organism proteomes, we show that larger neighbourhood sizes remain practical for proteome-scale analyses and that taxonomy and sequence-identity filtering support leakage-aware benchmarking. We also compare the supported models with baseline methods through external CAFA5 evaluation and provide practical guidance based on empirical evidence variables. FANTASIA provides a controlled, scalable, and reproducible framework for extending functional annotation across the rapidly expanding diversity of sequenced organisms.

Software↗
PubMed2026

SNPannotator: automated functional annotation of genetic variants and linked proxies.

SUMMARY: Genome-wide association studies (GWASs) have identified thousands of genetic variants associated with complex traits and diseases. However, explaining the mechanisms underlying phenotypic variation remains challenging. Here, we introduce SNPannotator, an automated post-GWAS analysis software package designed to streamline the interpretation of GWAS findings. Our pipeline implements a multi-step process that identifies proxy variants in high linkage disequilibrium (LD) with associated lead variants, then queries comprehensive resources (including Ensembl, the GTEx Portal, the eQTL Catalog, and STRING DB) for genomic position, deleteriousness, regulatory annotations, clinical significance, trait associations, expression (eQTLs) and splicing quantitative trait loci (sQTLs), and functional enrichment analyses and compiles the results into user-friendly reports. This package is implemented in the R programming language and includes auxiliary functions for variant lookup and LD exploration. SNPannotator provides a practical framework for efficiently deriving biologically meaningful insights from GWAS data and for assisting researchers in prioritizing candidate variants for functional validation. AVAILABILITY AND IMPLEMENTATION: The SNPannotator package is available from the Comprehensive R Archive Network (CRAN) at https://cran.r-project.org/web/packages/SNPannotator. The development version and tutorial is available on GitHub (https://github.com/omicslaboratory/SNPannotator). The online version of the package is available at https://omicslab.org/snpannotator.

Software↗
PubMed2026

plinkQC: an integrated tool for ancestry inference, sample selection, and quality control in population genetics.

MOTIVATION: Population genetic analyses rely on high quality datasets that pass rigorous controls for sample and marker quality. Many analyses also require additional processing including identification of ancestry and sample relatedness. A software package that addresses all these common, yet crucial tasks is missing. RESULTS: We have developed plinkQC, an R/CRAN package that combines these functionalities into a single software package with detailed vignettes for example applications. plinkQC determines the ancestry of study samples via a pre-trained random forest classifier that reaches 98% performance accuracy with just 5% of marker overlap between reference and user data. To obtain the maximal set of unrelated study samples, we developed a graph-based pruning method, taking both relationship estimates and sample quality into account. We demonstrate optimal sample selection on the 1000 Genomes project, where we retain an additional 71 samples compared to publicly available exclusion lists. Finally, plinkQC bundles these results together with per-individual and per-marker quality control checks into three simple functions and returns both the quality controlled dataset and quality control report about each step of the analysis. AVAILABILITY AND IMPLEMENTATION: plinkQC is available as an R/CRAN package. The documentation and code are available on github: https://meyer-lab-cshl.github.io/plinkQC/ and https://github.com/meyer-lab-cshl/plinkQC_manuscript.

Software↗