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

Kujin Tang¹, Jie Ren¹, Fengzhu Sun²

¹Quantitative and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA.

|December 6, 2019

Summary

Alignment-free genome comparison methods can overestimate sequence dissimilarity. A new tool, Afann (Alignment-Free methods Adjusted by Neural Network), corrects this bias, improving analysis performance across datasets.

Keywords:

Alignment-free Bias adjustment NGS Neural network regression kmer

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Alignment-free methods offer computational efficiency for genome sequence comparison.
These methods are valuable for analyzing large sequencing datasets without assembly.

Purpose of the Study:

To identify and address the overestimation bias in alignment-free dissimilarity calculations from sequencing samples.
To introduce a novel tool that corrects this bias and enhances analytical performance.

Main Methods:

Developed a neural network-based approach named Afann (Alignment-Free methods Adjusted by Neural Network).
Validated Afann's performance on diverse, independent biological datasets.

Main Results:

Demonstrated that alignment-free dissimilarity from sequencing samples can be significantly overestimated compared to genome-based calculations.
Afann effectively adjusted this bias, leading to more accurate comparisons.
Achieved excellent performance across various independent datasets.

Conclusions:

The identified bias can negatively impact the reliability of alignment-free analyses.
Afann provides a robust solution for accurate alignment-free genome sequence comparison.
The tool is freely available, promoting wider adoption in genomic research.