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

Genome Biology

Kujin Tang¹  Jie Ren¹  Fengzhu Sun¹ 

[1] Quantitative and Computational Biology Program, Department of Biological Sciences, University of Southern California;

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

DOI  :  10.1186/s13059-019-1872-3

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown