以基因为重点的计算庞基因组分析的七个快速提示。

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Biodata Mining Pub Date : 2024-09-03 DOI:10.1186/s13040-024-00380-2
Vincenzo Bonnici, Davide Chicco
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引用次数: 0

摘要

泛基因组学(Pangenomics)是一个相对较新的科学领域,研究一个支系所有基因组的结合。在古希腊语中,"pan "意为万物;"pangenomics "一词最初指细菌基因组,后来也指人类基因组。现代生物信息学为分析泛基因组学数据提供了多种工具,为我们称之为计算泛基因组学的新兴领域铺平了道路。目前生物信息学界可用的计算能力使计算庞基因组学分析变得容易执行,但庞基因组学分析的更高可及性也增加了犯错和产生误导性或夸大结果的机会,尤其是初学者。为了解决这个问题,我们在此介绍一些快速窍门,以高效、正确地进行计算庞基因组学分析,重点是细菌庞基因组学,介绍该领域应避免的常见错误和应遵循的最佳实践经验。我们相信,我们的建议能帮助读者进行更稳健、更合理的庞基因组分析,并得出更可靠的结果。
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Seven quick tips for gene-focused computational pangenomic analysis.

Pangenomics is a relatively new scientific field which investigates the union of all the genomes of a clade. The word pan means everything in ancient Greek; the term pangenomics originally regarded genomes of bacteria and was later intended to refer to human genomes as well. Modern bioinformatics offers several tools to analyze pangenomics data, paving the way to an emerging field that we can call computational pangenomics. Current computational power available for the bioinformatics community has made computational pangenomic analyses easy to perform, but this higher accessibility to pangenomics analysis also increases the chances to make mistakes and to produce misleading or inflated results, especially by beginners. To handle this problem, we present here a few quick tips for efficient and correct computational pangenomic analyses with a focus on bacterial pangenomics, by describing common mistakes to avoid and experienced best practices to follow in this field. We believe our recommendations can help the readers perform more robust and sound pangenomic analyses and to generate more reliable results.

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来源期刊
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
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