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

• Published in: Genome Biology 2019-12, Vol.20 (1), p.266-266, Article 266
• Main Authors: Tang, Kujin, Ren, Jie, Sun, Fengzhu
• Format: Article
• Language: English
• Subjects: Alignment-free; Animals; Bias; Bias adjustment; d 2 ∗ , d 2 s $d_{2}^{}, \protect d_{2}^{s}; data collection; genome; Genomes; Genomics - methods; High-Throughput Nucleotide Sequencing; kmer; Method; Methods; Neural network regression; Neural networks; Neural Networks, Computer; NGS; nucleotide sequences; Primates - genetics; Quercus - genetics; Regression Analysis; sequence analysis; Software
• ISSN: 1474-760X; 1474-7596; 1474-760X
• DOI: 10.1186/s13059-019-1872-3

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.