Qarles: a web server for the quick characterization of large sets of genes

The characterization of gene sets is a recurring task in computational biology. Identifying specific properties of a hit set compared to a reference set can reveal biological roles and mechanisms, and can lead to the prediction of new hits. However, collecting the features to evaluate can be time consuming, and implementing an informative but compact graphical representation of the multiple comparisons can be challenging, particularly for bench scientists. Here, I present Qarles (quick characterization of large sets of genes), a web server that annotates <i>Saccharomyces cerevisiae</i> gene sets by querying a database of 31 features widely used by the yeast community and that identifies their specific properties, providing publication-ready figures and reliable statistics. Qarles has a deliberately simple user interface with all the functionality in a single web page and a fast response time to facilitate adoption by the scientific community. Qarles provides a rich and compact graphical output, including up to five gene set comparisons across 31 features in a single dotplot, and interactive boxplots to enable the identification of outliers. Qarles can also predict new hit genes by using a random forest trained on the selected features. The web server is freely available at https://qarles.org.

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Publication Details

Journal
NAR Genomics and Bioinformatics
Published
2025-03-29
DOI
https://doi.org/10.1093/nargab/lqaf030
Citations
1
Primary Topic
Bioinformatics and Genomic Networks
Type
article
Field-Weighted Citation Impact
0.81
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article

Qarles: a web server for the quick characterization of large sets of genes

Carles Pons
1 citations
NAR Genomics and Bioinformatics
Bioinformatics and Genomic Networks
0.81
article

Qarles: a web server for the quick characterization of large sets of genes

Carles Pons
article en
1 citations

Abstract

The characterization of gene sets is a recurring task in computational biology. Identifying specific properties of a hit set compared to a reference set can reveal biological roles and mechanisms, and can lead to the prediction of new hits. However, collecting the features to evaluate can be time consuming, and implementing an informative but compact graphical representation of the multiple comparisons can be challenging, particularly for bench scientists. Here, I present Qarles (quick characterization of large sets of genes), a web server that annotates <i>Saccharomyces cerevisiae</i> gene sets by querying a database of 31 features widely used by the yeast community and that identifies their specific properties, providing publication-ready figures and reliable statistics. Qarles has a deliberately simple user interface with all the functionality in a single web page and a fast response time to facilitate adoption by the scientific community. Qarles provides a rich and compact graphical output, including up to five gene set comparisons across 31 features in a single dotplot, and interactive boxplots to enable the identification of outliers. Qarles can also predict new hit genes by using a random forest trained on the selected features. The web server is freely available at https://qarles.org.

NAR Genomics and BioinformaticsVol. 7(2)
Barcelona Institute of Science and Technology (ES)
Life in Land
Openalex Percentile: Top 30%
Bioinformatics and Genomic Networks
0.81
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