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- W4290973443 endingPage "e1010348" @default.
- W4290973443 startingPage "e1010348" @default.
- W4290973443 abstract "Pathway enrichment analysis (PEA) is a computational biology method that identifies biological functions that are overrepresented in a group of genes more than would be expected by chance and ranks these functions by relevance. The relative abundance of genes pertinent to specific pathways is measured through statistical methods, and associated functional pathways are retrieved from online bioinformatics databases. In the last decade, along with the spread of the internet, higher availability of computational resources made PEA software tools easy to access and to use for bioinformatics practitioners worldwide. Although it became easier to use these tools, it also became easier to make mistakes that could generate inflated or misleading results, especially for beginners and inexperienced computational biologists. With this article, we propose nine quick tips to avoid common mistakes and to out a complete, sound, thorough PEA, which can produce relevant and robust results. We describe our nine guidelines in a simple way, so that they can be understood and used by anyone, including students and beginners. Some tips explain what to do before starting a PEA, others are suggestions of how to correctly generate meaningful results, and some final guidelines indicate some useful steps to properly interpret PEA results. Our nine tips can help users perform better pathway enrichment analyses and eventually contribute to a better understanding of current biology." @default.
- W4290973443 created "2022-08-13" @default.
- W4290973443 creator A5011556172 @default.
- W4290973443 creator A5078020905 @default.
- W4290973443 date "2022-08-11" @default.
- W4290973443 modified "2023-10-01" @default.
- W4290973443 title "Nine quick tips for pathway enrichment analysis" @default.
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