In most of the recent whole-genome sequencing cases, whole-genome sequences are generated using next-generation sequencing (NGS).
There does not yet exist a proven method to generate chaotic sequences with desired correlation properties, rather the sequences are generated, and their properties are compared post generation [28].
This is especially important for new sequencing technologies, i.e. next-generation sequencing, where millions of sequences are generated per sample and fast predictions are needed to be applicable in routine diagnostics.
(i) generation of pseudo-random sequences (pseudo-random sequences are generated by linear feedback shift register and standard chaotic map); (ii) permutation and XORing using pseudo-random sequences; and (iii) steganography using the improved diagonal queues.
The proposed algorithm comprises four stages, generation of pseudo-random sequences (pseudo-random sequences are generated by linear feedback shift register and standard chaotic map), permutation and XORing using pseudo-random sequences, encryption using Rabin cryptosystem, and steganography using the improved diagonal queues.
High-throughput sequencing or next generation sequencing (NGS) is a process where many millions of sequences are generated simultaneously, often from mixed slurries of material.
First, by the dynamic analysis of this system, two different chaotic sequences are generated.
The second assumes that the sequences are generated by the same model having pdf.
The information sequences are generated from Markov sources with different state transition probabilities.
The signature sequences are generated pseudo-randomly, each with a certain seed [26].
For transmission in a QAM transmitter, pseudo noise (PN) sequences are generated to serve as the message signal.
