摘要：Ongoing improvements in Computational Biology (CB) research have generated massive amounts of Protein-Protein Interactions (PPIs) data set. In this regards, the availability of PPI data for several organisms provoke the discovery of computational methods for measurements, analysis, modeling, comparisons, clustering and alignments of biological data networks. Nevertheless, fixed network comparison is computationally stubborn and as a result several methods have been used instead. It is very crucial to utilize the memory of computing devices for Protein- Protein Interactions (PPIs) data set. We have compared the memory uses using Pushdown Automata and de Bruijn graph based Bloom Filter for global proteins network alignment. De Bruijn graph is regularly used in Next Generation Sequencing (NGS) for large scale data set. De novo genome assembler utilizes the memory. Bloom filter and Pushdown Automat perform better to reduce memory. We have noticed that Pushdown Automata outperform Bloom filter in memory saving but it takes more time than Bloom filter. The result shows that Bloom filter software Mania implements full de novo assembly of human genome data set using 6.5 GB memory in 27 hours, on the other hand Pushdown Automat performs same results in 1 GB memory of 31 hours.
