Many clone-based physical maps have been built with the FingerPrinted Contig (FPC) software, which is written in C and runs locally for fast and flexible analysis. If the maps were viewable only from FPC, they would not be as useful to the whole community since FPC must be installed on the user machine and the database downloaded. Hence, we have created a set of Web tools so users can easily view the FPC data and perform salient queries with standard browsers. This set includes the following four programs: WebFPC, a view of the contigs; WebChrom, the location of the contigs and genetic markers along the chromosome; WebBSS, locating user-supplied sequence on the map; and WebFCmp, comparing fingerprints. For additional FPC support, we have developed an FPC module for BioPerl and an FPC browser using the Generic Model Organism Project (GMOD) genome browser (GBrowse), where the FPC BioPerl module generates the data files for input into GBrowse. This provides an alternative to the WebChrom/WebFPC view. These tools are available to download along with documentation. The tools have been implemented for both the rice (Oryza sativa) and maize (Zea mays) FPC maps, which both contain the locations of clones, markers, genetic markers, and sequenced clone (along with links to sites that contain additional information).
Rice blast disease, caused by the fungal pathogen Magnaporthe grisea, is an excellent model system to study plant-fungal interactions and host defense responses. In this study, comprehensive analysis of the rice (Oryza sativa) transcriptome after M. grisea infection was conducted using robust-long serial analysis of gene expression. A total of 83,382 distinct 21-bp robust-long serial analysis of gene expression tags were identified from 627,262 individual tags isolated from the resistant (R), susceptible (S), and control (C) libraries. Sequence analysis revealed that the tags in the R and S libraries had a significant reduced matching rate to the rice genomic and expressed sequences in comparison to the C library. The high level of one-nucleotide mismatches of the R and S library tags was due to nucleotide conversions. The A-to-G and U-to-C nucleotide conversions were the most predominant types, which were induced in the M. grisea-infected plants. Reverse transcription-polymerase chain reaction analysis showed that expression of the adenine deaminase and cytidine deaminase genes was highly induced after inoculation. In addition, many antisense transcripts were induced in infected plants and expression of four antisense transcripts was confirmed by strand-specific reverse transcription-polymerase chain reaction. These results demonstrate that there is a series of dynamic and complex transcript modifications and changes in the rice transcriptome at the M. grisea early infection stages.
The MGOS (Magnaporthe grisea Oryza sativa) web-based database contains data from Oryza sativa and Magnaporthe grisea interaction experiments in which M. grisea is the fungal pathogen that causes the rice blast disease. In order to study the interactions, a consortium of fungal and rice geneticists was formed to construct a comprehensive set of experiments that would elucidate information about the gene expression of both rice and M. grisea during the infection cycle. These experiments included constructing and sequencing cDNA and robust long-serial analysis gene expression libraries from both host and pathogen during different stages of infection in both resistant and susceptible interactions, generating >50,000 M. grisea mutants and applying them to susceptible rice strains to test for pathogenicity, and constructing a dual O. sativa-M. grisea microarray. MGOS was developed as a central web-based repository for all the experimental data along with the rice and M. grisea genomic sequence. Community-based annotation is available for the M. grisea genes to aid in the study of the interactions.
Identification of important transcripts from fungal pathogens and host plants is indispensable for full understanding the molecular events occurring during fungal-plant interactions. Recently, we developed an improved LongSAGE method called robust-long serial analysis of gene expression (RL-SAGE) for deep transcriptome analysis of fungal and plant genomes. Using this method, we made 10 RL-SAGE libraries from two plant species (Oryza sativa and Zea maize) and one fungal pathogen (Magnaporthe grisea). Many of the transcripts identified from these libraries were novel in comparison with their corresponding EST collections. Bioinformatic tools and databases for analyzing the RL-SAGE data were developed. Our results demonstrate that RL-SAGE is an effective approach for large-scale identification of expressed genes in fungal and plant genomes.
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