InParanoid-DIAMOND：使用 InParanoid 算法进行更快的同源分析。
InParanoid-DIAMOND: faster orthology analysis with the InParanoid algorithm.
机构信息
Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, 17121 Solna, Sweden.
出版信息
Bioinformatics. 2022 May 13;38(10):2918-2919. doi: 10.1093/bioinformatics/btac194.
Abstract
SUMMARY
Predicting orthologs, genes in different species having shared ancestry, is an important task in bioinformatics. Orthology prediction tools are required to make accurate and fast predictions, in order to analyze large amounts of data within a feasible time frame. InParanoid is a well-known algorithm for orthology analysis, shown to perform well in benchmarks, but having the major limitation of long runtimes on large datasets. Here, we present an update to the InParanoid algorithm that can use the faster tool DIAMOND instead of BLAST for the homolog search step. We show that it reduces the runtime by 94%, while still obtaining similar performance in the Quest for Orthologs benchmark.
AVAILABILITY AND IMPLEMENTATION
The source code is available at (https://bitbucket.org/sonnhammergroup/inparanoid).
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
摘要
摘要
预测直系同源物，即不同物种中具有共同祖先的基因，是生物信息学中的一项重要任务。为了在可行的时间内分析大量数据，需要使用准确且快速的直系同源物预测工具。InParanoid 是一种用于直系同源分析的知名算法，在基准测试中表现良好，但存在一个主要限制，即在大型数据集上运行时间长。在这里，我们对 InParanoid 算法进行了更新，使其可以在同源搜索步骤中使用更快的工具 DIAMOND 代替 BLAST。我们表明，它将运行时间缩短了 94%，同时在 Quest for Orthologs 基准测试中仍获得了类似的性能。
可用性和实现
源代码可在 (https://bitbucket.org/sonnhammergroup/inparanoid) 获得。
补充信息
补充数据可在 Bioinformatics 在线获得。
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