DNA sequence alignment datasets based on NW algorithm

This study presented six datasets for DNA/RNA sequence alignment for one of the most common alignment algorithms, namely, the Needleman–Wunsch (NW) algorithm. This research proposed a fast and parallel implementation of the NW algorithm by using machine learning techniques. This study is an extension and improved version of our previous work . The current implementation achieves 99.7% accuracy using a multilayer perceptron with ADAM optimizer and up to 2912 giga cell updates per second on two real DNA sequences with a of length 4.1 M nucleotides. Our implementation is valid for extremely long sequences by using the divide-and-conquer strategy.

本研究提出六个用于DNA/RNA序列比对的数据集，针对最常用的比对算法之一——Needleman-Wunsch (NW) 算法。本研究通过运用机器学习技术，提出了NW算法的快速并行实现方案。该研究是对我们先前工作的扩展与优化。当前实现版本采用多层感知器与ADAM优化器，在两个长度为4.1百万核苷酸的真实DNA序列上，实现了99.7%的准确率，并且每秒最多可以进行2912吉字节细胞的更新。我们的实现方案通过采用分而治之的策略，适用于极长序列。