A Next-generation Sequence Clustering Method for E. Coli through Proteomics-genomics Data Mapping

Abstract Recent publications of various ‘omics’ data have provided new challenges and opportunities to the development of novel approaches to the assembly of next-generation sequences. As an attempt to improve the quality of assembled sequences, we developed a next-generation sequence clustering method by using the interdependency between genomics and proteomics data, which has not been well utilized so far in this field. Given a set of next-generation read sequences with a number of protein sequences, our method clusters the read sequences by mapping to the protein sequences. As a preliminary research, we selected Escherichia coli ( E. coli ) as our target species and simulated next-generation reads of E. coli to evaluate our method by analyzing the actual adjacency of the clustered reads in the E. coli genome. We found that ( i ) read base matching (RBM) ratio, which represents the amount of bases in a read that are mapped to a protein sequence, higher than 50∼70% is a useful criterion for effective read clustering and ( ii ) higher RBM ratio does not always lead to better quality of clusters in the case of E. coli . These preliminary results demonstrate that the integrative approach is simple yet has great potential for clustering adjacent reads in a genome.