research data . Dataset . 2019

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

Kujin Tang; Ren, Jie; Fengzhu Sun;
  • Published: 05 Dec 2019
  • Publisher: Springer Science and Business Media LLC
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...
Subjects
free text keywords: Biochemistry, Evolutionary Biology, FOS: Biological sciences, 69999 Biological Sciences not elsewhere classified, 80699 Information Systems not elsewhere classified, FOS: Computer and information sciences
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...
Subjects
free text keywords: Biochemistry, Evolutionary Biology, FOS: Biological sciences, 69999 Biological Sciences not elsewhere classified, 80699 Information Systems not elsewhere classified, FOS: Computer and information sciences
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