An optimal genome reassembling technique by Artificial Bees System for small genome sequences

Fragment assembling problem (FAP) is an NP-complete problem. The present article presents an Artificial Bees Colony (ABC) learning system to solve Genome sequence reassembling techniques. Reference Genome sequence which is taken 99% analogous to a Genome from same organism, because of the fact the sequences from the similar organism usually have approximately 99.9% resemblance. We have used the sequences from NCBI database². Then we have cloned each sequence and shear the clone to a numeral short reads. Here, we have used a different perception in Genome reassembling by Synthetic Bees System where nectar amount is relative to the accuracy of assembled reads with some reference genome sequences inside the similar creature. For local heuristics information, we have introduced local alignment of short reads instead local overlapping among the reads. The outcome depict that our methodology is more accurate than an existing Bee Colony Algorithm. Genome reassembling methodology require a huge concurrency and vast storage because of size of Genome sequences of mammalian group is ~ 10⁹bp, and ABC is inherently concurrent in nature. We have run LSBCO in 64 bit O.S in HP proliant server with 16GB RAM, 2-quad core processor. We have computed our methodology for the Genome length up to 127429bp. We have simulated hierarchical sequencing, and finally stitched the each segments to get back the actual Genome sequence.