Afann: bias adjustment for alignment-free sequence comparison based on sequencing data using neural network regression

Abstract

Abstract 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.

Keywords

• Alignment-free
• Neural network regression
• kmer
• d 2 ∗ , d 2 s $d_{2}^{*}, \protect d_{2}^{s}$
• NGS
• Bias adjustment