Emms David M, Kelly Steven
Department of Plant Sciences, University of Oxford, South Parks Road, Oxford, OX1 3RB, UK.
Genome Biol. 2015 Aug 6;16(1):157. doi: 10.1186/s13059-015-0721-2.
Identifying homology relationships between sequences is fundamental to biological research. Here we provide a novel orthogroup inference algorithm called OrthoFinder that solves a previously undetected gene length bias in orthogroup inference, resulting in significant improvements in accuracy. Using real benchmark datasets we demonstrate that OrthoFinder is more accurate than other orthogroup inference methods by between 8 % and 33 %. Furthermore, we demonstrate the utility of OrthoFinder by providing a complete classification of transcription factor gene families in plants revealing 6.9 million previously unobserved relationships.
识别序列之间的同源关系是生物学研究的基础。在此,我们提供了一种名为OrthoFinder的新型直系同源组推断算法,该算法解决了直系同源组推断中以前未被发现的基因长度偏差问题,从而显著提高了准确性。使用真实的基准数据集,我们证明OrthoFinder比其他直系同源组推断方法的准确性高8%至33%。此外,我们通过提供植物转录因子基因家族的完整分类,揭示了690万个以前未观察到的关系,证明了OrthoFinder的实用性。