Evidence map›Paper›PMID 39661475›Full record

ArticleBioinformatics (Oxford, England)2024

SCARAP: scalable cross-species comparative genomics of prokaryotes.

Stijn Wittouck, Tom Eilers, Vera van Noort, Sarah Lebeer

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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  7. Draft genome sequences of a strain ofMicrobiology resource announcements · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Stijn WittouckLab of Applied Microbiology and Biotechnology, Department of Bioscience Engineering, University of Antwerp, Antwerpen 2020, Belgium.ORCID 0000-0003-3913-5677
Tom EilersLab of Applied Microbiology and Biotechnology, Department of Bioscience Engineering, University of Antwerp, Antwerpen 2020, Belgium.ORCID 0000-0002-7509-2902
Vera van NoortFaculty of Bioscience Engineering, KU Leuven, Leuven 3001, Belgium.ORCID 0000-0002-8436-6602
Sarah LebeerLab of Applied Microbiology and Biotechnology, Department of Bioscience Engineering, University of Antwerp, Antwerpen 2020, Belgium.ORCID 0000-0002-9400-6918

Funding

European Research Council 852600Foundation-Flanders
6 · The paper itself

Abstract

motivationMuch of prokaryotic comparative genomics currently relies on two critical computational tasks: pangenome inference and core genome inference. Pangenome inference involves clustering genes from a set of genomes into gene families, enabling genome-wide association studies and evolutionary history analysis. The core genome represents gene families present in nearly all genomes and is required to infer a high-quality phylogeny. For species-level datasets, fast pangenome inference tools have been developed. However, tools applicable to more diverse datasets are currently slow and scale poorly.

resultsHere, we introduce SCARAP, a program containing three modules for comparative genomics analyses: a fast and scalable pangenome inference module, a direct core genome inference module, and a module for subsampling representative genomes. When benchmarked against existing tools, the SCARAP pan module proved up to an order of magnitude faster with comparable accuracy. The core module was validated by comparing its result against a core genome extracted from a full pangenome. The sample module demonstrated the rapid sampling of genomes with decreasing novelty. Applied to a dataset of over 31 000 Lactobacillales genomes, SCARAP showcased its ability to derive a representative pangenome. Finally, we applied the novel concept of gene fixation frequency to this pangenome, showing that Lactobacillales genes that are prevalent but rarely fixate in species often encode bacteriophage functions. AVAILABILITY AND IMPLEMENTATION: The SCARAP toolkit is publicly available at https://github.com/swittouck/scarap.

Indexed as

Genome, BacterialGenomicsPhylogenySoftware

Identifiers

PMID39661475
PMCPMC11681940

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.