Adaptive adjustment of profile HMM significance thresholds improves functional and metabolic insights into microbial genomes.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Bioinformatics advances Pub Date : 2025-03-21 eCollection Date: 2025-01-01 DOI:10.1093/bioadv/vbaf039
Kathryn Kananen, Iva Veseli, Christian J Quiles Pérez, Samuel E Miller, A Murat Eren, Patrick H Bradley
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引用次数: 0

Abstract

Motivation: Gene function annotation in microbial genomes and metagenomes is a fundamental in silico first step toward understanding metabolic potential and determinants of fitness. The Kyoto Encyclopedia of Genes and Genomes publishes a curated list of profile hidden Markov models to identify orthologous gene families (KOfams) with roles in metabolism. However, the computational tools that rely upon KOfams yield different annotations for the same set of genomes, leading to different downstream biological inferences.

Results: Here, we apply three open-source software tools that can annotate KOfams to genomes of phylogenetically diverse bacterial families from host-associated and free-living biomes. We use multiple computational approaches to benchmark these methods and investigate individual case studies where they differ. Our results show that despite their fundamental similarities, these methods have different annotation rates and quality. In particular, a method that adaptively tunes sequence similarity thresholds substantially improves sensitivity while maintaining high accuracy. We observe particularly large improvements for protein families with few reference sequences, or when annotating genomes from nonmodel organisms (such as gut-dwelling Lachnospiraceae). Our findings show that small improvements in annotation workflows can maximize the utility of existing databases and meaningfully improve in silico characterizations of microbial metabolism.

Availability and implementation: Anvi'o is available at https://anvio.org under the GNU GPL license. Scripts and workflow are available at https://github.com/pbradleylab/2023-anvio-comparison under the MIT license.

剖面HMM显著性阈值的自适应调整提高了对微生物基因组的功能和代谢见解。
动机:微生物基因组和宏基因组的基因功能注释是理解代谢潜力和适应性决定因素的基础和第一步。《京都基因与基因组百科全书》出版了一份精心策划的隐马尔可夫模型清单,用于识别在新陈代谢中起作用的同源基因家族(KOfams)。然而,依赖于KOfams的计算工具对同一组基因组产生不同的注释,导致不同的下游生物学推断。结果:在这里,我们应用了三个开源软件工具,可以将KOfams注释到来自宿主相关和自由生活的生物群落的系统发育不同的细菌家族的基因组中。我们使用多种计算方法对这些方法进行基准测试,并调查不同的个别案例研究。我们的研究结果表明,尽管这些方法基本相似,但它们的注释率和质量不同。特别是,自适应调整序列相似阈值的方法在保持较高精度的同时大大提高了灵敏度。我们观察到有少量参考序列的蛋白质家族,或者当注释来自非模式生物(如寄生在肠道中的毛螺科)的基因组时,有特别大的改进。我们的研究结果表明,注释工作流程的微小改进可以最大限度地利用现有数据库,并有意义地改善微生物代谢的计算机表征。可用性和实现:Anvi'o在GNU GPL许可下可从https://anvio.org获得。在MIT许可下,可以在https://github.com/pbradleylab/2023-anvio-comparison上获得脚本和工作流。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
1.60
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