nf-core/proteinfamilies: a scalable pipeline for the generation of protein families.

IF 5 2区 生物学 Q1 MULTIDISCIPLINARY SCIENCES
Evangelos Karatzas, Martin Beracochea, Fotis A Baltoumas, Eleni Aplakidou, Lorna Richardson, James A Fellows Yates, Daniel Lundin, Aydin Buluç, Nikos C Kyrpides, Ilias Georgakopoulos-Soares, Georgios A Pavlopoulos, Robert D Finn
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引用次数: 0

Abstract

The growth of metagenomics-derived amino acid sequence data has transformed our understanding of protein function, microbial diversity, and evolutionary relationships. However, the vast majority of these proteins remain functionally uncharacterized. Grouping the millions of such uncharacterized sequences with the few experimentally characterized ones allows the transfer of annotations, while the inspection of conserved residues with multiple sequence alignments can provide clues to function, even in the absence of existing functional information. To address the challenges associated with this data surge and the need to group sequences, we present a scalable, open-source, parametrizable Nextflow pipeline (nf-core/proteinfamilies) that generates nascent protein families or assigns new proteins to existing families. The computational benchmarks demonstrated that resource usage scales approximately linearly with input size, and the biological benchmarks showed that the generated protein families closely resemble manually curated families in widely used databases.

nf-core/proteinfamilies:一个可扩展的蛋白质家族生成管道。
元基因组学衍生的氨基酸序列数据的增长改变了我们对蛋白质功能、微生物多样性和进化关系的理解。然而,这些蛋白质中的绝大多数在功能上仍未被表征。将数百万这样的未表征序列与少数实验表征序列分组允许注释的转移,而使用多个序列比对检查保守残基可以提供功能线索,即使在缺乏现有功能信息的情况下。为了应对与数据激增相关的挑战和对序列进行分组的需求,我们提出了一个可扩展、开源、可参数化的Nextflow管道(nf-core/proteinfamilies),它可以生成新生的蛋白质家族或将新的蛋白质分配给现有的家族。计算基准测试表明,资源使用与输入大小呈近似线性关系,生物基准测试表明,生成的蛋白质家族与广泛使用的数据库中人工筛选的家族非常相似。
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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来源期刊
GigaScience
GigaScience MULTIDISCIPLINARY SCIENCES-
CiteScore
15.50
自引率
1.10%
发文量
119
审稿时长
1 weeks
期刊介绍: GigaScience seeks to transform data dissemination and utilization in the life and biomedical sciences. As an online open-access open-data journal, it specializes in publishing "big-data" studies encompassing various fields. Its scope includes not only "omic" type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale shareable data.
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