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

Autor:	Kujin Tang, Jie Ren, Fengzhu Sun
Jazyk:	angličtina
Rok vydání:	2019
Předmět:	Alignment-free Neural network regression kmer d 2 ∗ d 2 s $d_{2}^{*} \protect d_{2}^{s}$ NGS Bias adjustment Biology (General) QH301-705.5 Genetics QH426-470
Zdroj:	Genome Biology, Vol 20, Iss 1, Pp 1-17 (2019)
Druh dokumentu:	article
ISSN:	1474-760X
DOI:	10.1186/s13059-019-1872-3
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/c2b78009b83249cf82db3d2ff5275dda Zobrazit plný text záznamu Full text from SpringerLink View record in DOAJ