Analysis of common k-mers for whole genome sequences using SSB-tree.
As sequenced genomes become larger and sequencing process becomes faster, there is a need to develop a tool to analyze sequences in the whole genomic scale. However, on-memory algorithms such as suffix tree and suffix array are not applicable to the analysis of whole genome sequence set, since the size of individual whole genome ranges from several million base pairs to hundreds billion base pairs. In order to effectively manipulate the huge sequence data, it is necessary to use the indexed data structure for external memory. In this paper, we introduce a workbench called SequeX for the analysis and visualization of whole genome sequences using SSB-tree (Static SB-tree). It consists of two parts: the analysis query subsystem and the visualization subsystem. The query subsystem supports various transactions such as pattern matching, k-occurrence, and k-mer analysis. The visualization subsystem helps biologists to easily understand whole genome structure and feature by sequence viewer, annotation viewer, CGR (Chaos Game Representation) viewer, and k-mer viewer. The system also supports a user-friendly programming interface based on Java script for batch processing and the extension for a specific purpose of a user. SequeX can be used to identify conserved genes or sequences by the analysis of the common k-mers and annotation. We analyze the common k-mer for 72 microbial genomes announced by Entrez, and find an interesting biological fact that the longest common k-mer for 72 sequences is 11-mer, and only 11 such sequences exist. Finally we note that many common k-mers occur in conserved region such as CDS, rRNA, and tRNA.
ALGORIGENE Bioinformatics Lab., Department of Computer Science, Pusan National University, Kum-Jung-Ku, Pusan 609-735, Korea. jhchoi@pusan.ac.kr
Jeong-Hyeon
JH
Choi
Hwan-Gue
HG
Cho
D001105:Archaea / Q000235:genetics; D001419:Bacteria / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D016678:Genome; D009841:Oligonucleotides / Q000235:genetics; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software
D009841:Oligonucleotides