BACKGROUND Many of the commonly used methods for orthology detection start from mutually most similar pairs of genes (reciprocal best hits) as an approximation for evolutionary most closely related pairs of genes (reciprocal best matches). This approximation of best matches by best hits becomes exact for ultrametric dissimilarities, i.e., under the Molecular Clock Hypothesis. It fails, however, whenever there are large lineage specific rate variations among paralogous genes. In practice, this introduces a high level of noise into the input data for best-hit-based orthology detection methods. RESULTS If additive distances between genes are known, then evolutionary most closely related pairs can be identified by considering certain quartets of genes provided that in each quartet the outgroup relative to the remaining three genes is known. A priori knowledge of underlying species phylogeny greatly facilitates the identification of the required outgroup. Although the workflow remains a heuristic since the correct outgroup cannot be determined reliably in all cases, simulations with lineage specific biases and rate asymmetries show that nearly perfect results can be achieved. In a realistic setting, where distances data have to be estimated from sequence data and hence are noisy, it is still possible to obtain highly accurate sets of best matches. CONCLUSION Improvements of tree-free orthology assessment methods can be expected from a combination of the accurate inference of best matches reported here and recent mathematical advances in the understanding of (reciprocal) best match graphs and orthology relations. AVAILABILITY Accompanying software is available at https://github.com/david-schaller/AsymmeTree.

中文翻译：

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从最相似的序列对到系统发育最佳匹配。

背景技术许多常用的直系同源检测方法从相互最相似的基因对（相互最佳匹配）开始，作为进化最密切相关的基因对（相互最佳匹配）的近似值。最佳匹配的最佳匹配近似值对于超度量差异而言变得精确，即在分子钟假设下。然而，每当旁系同源基因之间存在大的谱系特异性比率变化时，它就会失败。在实践中，这会在输入数据中引入高水平的噪声，以用于基于最佳命中的正交检测方法。结果 如果已知基因之间的加性距离，则可以通过考虑某些四重奏基因来识别进化最密切相关的对，前提是每个四重奏中相对于其余三个基因的外群是已知的。潜在物种系统发育的先验知识极大地促进了所需外群的识别。尽管由于无法在所有情况下可靠地确定正确的外群，工作流程仍然是一种启发式方法，但具有谱系特定偏差和速率不对称的模拟表明可以实现近乎完美的结果。在现实环境中，距离数据必须从序列数据中估计出来，因此噪声很大，仍然可以获得高度准确的最佳匹配集。结论 结合此处报告的最佳匹配的准确推断和最近在理解（互易）最佳匹配图和正交关系方面的数学进展，可以预期无树正交评估方法的改进。可用性随附的软件可在 https://github 获得。