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 an of length 4.1 M nucleotides. Our implementation is valid for extremely long sequences by using the divide-and-conquer strategy. dataset1 is titled csvlist.txt (in zip file) and so on. Dataset 3T is called csv3testdata.csv and Dataset 6T is called csv6testdata.csv for more details about the dataset, please see the references.In addition, If you use this dataset, kindly cite these references.

video tutorial https://youtube.com/playlist?list=PLAI6JViu7XmfZWy3wtE4A-dPCelzgwO3U presentation https://www.slideshare.net/AmrRashed3/implementation-of-dna-sequence-alignment-algorithms-using-fpga-mland-cnn?from_m_app=android IEEE DATAPORT LINK FOR DATASET https://ieee-dataport.org/documents/dna-sequence-alignment-datasets-based-nw-algorithm

References: 1- Rashed, A. E. E. D., Amer, H. M., El-Seddek, M., & Moustafa, H. E. D. (2021). Sequence Alignment Using Machine Learning-Based Needleman–Wunsch Algorithm. IEEE Access, 9, 109522-109535.‏ 2- Rashed, A. E. E. D., Obaya, M., El, H., & Moustafa, D. (2021). Accelerating DNA pairwise sequence alignment using FPGA and a customized convolutional neural network. Computers & Electrical Engineering, 92, 107112.‏

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