Maletskyi, Denys2024-04-222024-04-2220232024Maletskyi D. Program synthesis for genome compression : explanatory note to the bachelor's level qualification thesis : 122 «Computer Science» / Denys Maletskyi ; Lviv Polytechnic National University. – Lviv, 2023. – 61 p.https://ena.lpnu.ua/handle/ntb/61830In the era where genomic data is being generated at an unprecedented pace, the imperative to develop efficient methods for its compression cannot be overstated. This is particularly critical for large-scale genomic projects where the sheer volume of data presents substantial challenges in terms of storage and analysis. This thesis delves into the realm of genome data compression, addressing its significance and exploring innovative solutions to overcome the associated challenges. Central to this discourse is the introduction and exploration of program synthesis as a formidable tool for data compression. Program synthesis, in this context, is leveraged to create sophisticated programs capable of efficiently representing and reproducing genomic data. This technique emerges as a promising approach for genome compression, particularly due to its ability to process large datasets effectively while maintaining data integrity. Throughout this thesis, readers will gain a comprehensive understanding of program synthesis – its mechanics, applications, and how it can be specifically tailored for genome data compression. A significant focus is placed on elucidating how program synthesis can enhance the compression of genome sequences, offering not only a more efficient alternative to traditional methods but also ensuring faster processing times and reduced computational demands. Moreover, the insights and methodologies discussed extend beyond the confines of genomic data. The principles and techniques expounded upon in this thesis have broader applications and can be adapted for compressing various data types. This universality provides a fresh and expansive perspective to the ongoing conversation around data compression strategies, making the findings of this research relevant to a wider audience. In conclusion, this thesis presents a novel approach to genome data compression using program synthesis, specifically the equality saturation approach. The proposed method stands as a testament to the potential of cross-disciplinary 4 innovation in tackling the challenges posed by the ever-growing expanse of genomic data. As the field of genomics continues to evolve, so too must the strategies for managing its data, and this research contributes a pivotal piece to that evolving puzzle. The total volume of work: 61 pages, containing 5 figures and 17 references.enProbabilistic Program Synthesis, Compression, Encoding, Decoding, Genome, DNAStudents_diploma61