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DNA sequence alignment datasets based on NW algorithm

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Citation Author(s):
Amr Rashed (Lecturer with the Computer Engineering Department, Faculty of Computers and Information Technology, Taif University, Saudi Arabia)
Submitted by:
Amr Rashed
Last updated:
DOI:
10.21227/45dr-8p86
Data Format:
*.mat *.txt *.zip
Research Article Link:
Sequence Alignment Using Machine Learning-Based Needleman–Wunsch Algorithm
Links:
presentation
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Categories:
Keywords:
bioinformatics
DNA
RNA
Pairwise sequence alignment (PWSA)
Needleman–Wunsch (NW) algorithm
Machine learning (ML) algorithms
Multilayer perceptron (MLP)
Extreme gradient boosting algorithm (XGBoost).
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Abstract

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.

Instructions:

these datasets are illustrated in a manuscript submitted to IEEE OPEN ACCESS entitled “Parallel Implementation of the Needleman–Wunsch Algorithm Using Machine Learning Algorithms”.

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DATASET SCRIPTS

create indexed output based on matrix5.mat to be suited for ML use
test our best model (MLP network)
another test to our best model (MLP network)
check all ML models (train/test)
check all ML models (cross validation)
check all ML models (cross validation) update

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