Research Of Metagenomics Contigs Converging And Gene Tagging Algorithm

Metagenomics makes the studies of microorganism that cannot be purified and cultured individually in lab possible. Read assembly plays an important role in metagenomics. Limited by species abundance and homologous genes, etc. current read assembly algorithms usually generate a lot of short contigs which represent short gene fragments.However, gene prediction and DNA annotation tools cannot deal with short contigs effectively. In practical applications, these contigs was abandoned and couldn’t be fully utilized.This paper proposes a new algorithm to converge the contigs produced by different read assemblers from different kinds of reads, and to specify gene sequences in the fused contigs by network matching optimization algorithm. Since different sequencing techniques have different advantages, the integration of the assembled contigs can not only provide more path selections for the follow-up network matching but also fix gaps between contigs of the assemblies, and then optimize the tagging result. Because network matching algorithm compares the graph composed of contigs with reference gene sequence, and then searchs the most similar path between the graph and the refrence sequence, so that it can make full use of short contigs. Extensive experimental results show that the algorithm can effectively utilize long contigs generated bydifferent kinds of assembly tools. Compared with GeneStitch, our algorithm takes advantage of contigs convergenece algorithm and network matching optimization algorithm, and can detect more gene sequences with less misassemble rate.To provide a humanized gene fragment test tool, we design a full-featured visualization tool. It combines a variety of popular biological software, provides comprehensive test measures and presents test results graphically.