ABSTRACT
The extremely radioresistant bacteria of the genus Deinococcus and the extremely thermophilic bacteria of the genus Thermus belong to a common taxonomic group. Considering the distinct living environments of Deinococcus and Thermus, different genes would have been acquired through horizontal gene transfer after their divergence from a common ancestor. Their guanine-cytosine (GC) contents are similar; however, we hypothesized that their genomic signatures would be different. Our findings indicated that the genomes of Deinococcus radiodurans and Thermus thermophilus have different tetranucleotide frequencies. This analysis showed that the genome signature of D. radiodurans is most similar to that of Pseudomonas aeruginosa, whereas the genome signature of T. thermophilus is most similar to that of Thermanaerovibrio acidaminovorans. This difference in genome signatures may be related to the different evolutionary backgrounds of the 2 genera after their divergence from a common ancestor.
ABSTRACT
About 1 million expressed sequence tag (EST) sequences comprising 125.3 Mb nucleotides were accreted from 51 cDNA libraries constructed from a variety of tissues and organs under a range of conditions, including abiotic stresses and pathogen challenges in common wheat (Triticum aestivum). Expressed sequence tags were assembled with stringent parameters after processing with inbuild scripts, resulting in 37,138 contigs and 215,199 singlets. In the assembled sequences, 10.6% presented no matches with existing sequences in public databases. Functional characterization of wheat unigenes by gene ontology annotation, mining transcription factors, full-length cDNA, and miRNA targeting sites were carried out. A bioinformatics strategy was developed to discover single-nucleotide polymorphisms (SNPs) within our large EST resource and reported the SNPs between and within (homoeologous) cultivars. Digital gene expression was performed to find the tissue-specific gene expression, and correspondence analysis was executed to identify common and specific gene expression by selecting four biotic stress-related libraries. The assembly and associated information cater a framework for future investigation in functional genomics.
Subject(s)
Expressed Sequence Tags , Gene Expression Profiling/methods , Genes, Plant , Triticum/genetics , Computational Biology/methods , DNA, Complementary/isolation & purification , Databases, Genetic , Gene Expression Regulation, Plant , Gene Library , Molecular Sequence Annotation , Polymorphism, Single Nucleotide , Sequence Analysis, DNA/methods , Triticum/growth & developmentABSTRACT
Similarity of gene expression profiles provides important clues for understanding the biological functions of genes, biological processes and metabolic pathways related to genes. A gene expression network (GEN) is an ideal choice to grasp such expression profile similarities among genes simultaneously. For GEN construction, the Pearson correlation coefficient (PCC) has been widely used as an index to evaluate the similarities of expression profiles for gene pairs. However, calculation of PCCs for all gene pairs requires large amounts of both time and computer resources. Based on correspondence analysis, we developed a new method for GEN construction, which takes minimal time even for large-scale expression data with general computational circumstances. Moreover, our method requires no prior parameters to remove sample redundancies in the data set. Using the new method, we constructed rice GENs from large-scale microarray data stored in a public database. We then collected and integrated various principal rice omics annotations in public and distinct databases. The integrated information contains annotations of genome, transcriptome and metabolic pathways. We thus developed the integrated database OryzaExpress for browsing GENs with an interactive and graphical viewer and principal omics annotations (http://riceball.lab.nig.ac.jp/oryzaexpress/). With integration of Arabidopsis GEN data from ATTED-II, OryzaExpress also allows us to compare GENs between rice and Arabidopsis. Thus, OryzaExpress is a comprehensive rice database that exploits powerful omics approaches from all perspectives in plant science and leads to systems biology.
