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


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.

SUBMITTER: Tang K 

PROVIDER: S-EPMC6891986 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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

Tang Kujin K   Ren Jie J   Sun Fengzhu F  

Genome biology 20191204 1


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-f  ...[more]

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