Alignment-free methods, more time and memory efficient than alignment-based methods, have been widely used for comparing genome sequences or raw sequencing samples without assembly. However, in this study, we show that alignment-free dissimilarity calculated based on sequencing samples can be overestimated compared with the dissimilarity calculated based on their genomes, and this bias can significantly decrease the performance of the alignment-free analysis. Here, we introduce a new alignment-free tool, Alignment-Free methods Adjusted by Neural Network (Afann) that successfully adjusts this bias and achieves excellent performance on various independent datasets. Afann is freely available at https://github.com/GeniusTang/Afann.

中文翻译：

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Afann：使用神经网络回归基于测序数据进行无比对序列比较的偏差调整

与基于比对的方法相比，无需比对的方法在时间和内存方面的效率更高，已广泛用于比较基因组序列或未经组装的原始测序样本。然而，在本研究中，我们表明，与基于基因组计算的差异相比，基于测序样本计算的无比对差异可能被高估，这种偏差会显着降低无比对分析的性能。在这里，我们介绍了一种新的无对齐工具，即神经网络调整的无对齐方法（Afann），它成功地调整了这种偏差并在各种独立数据集上取得了出色的性能。Afann 可在 https://github.com/GeniusTang/Afann 上免费获得。