The Sol Genomics Network (solgenomics.net): growing tomatoes using Perl

The Sol Genomics Network (SGN; http://solgenomics.net/) is a clade-oriented database (COD) containing biological data for species in the Solanaceae and their close relatives, with data types ranging from chromosomes and genes to phenotypes and accessions. SGN hosts several genome maps and sequences, including a pre-release of the tomato (Solanum lycopersicum cv Heinz 1706) reference genome. A new transcriptome component has been added to store RNA-seq and microarray data. SGN is also an open source software project, continuously developing and improving a complex system for storing, integrating and analyzing data. All code and development work is publicly visible on GitHub (http://github.com). The database architecture combines SGN-specific schemas and the community-developed Chado schema (http://gmod.org/wiki/Chado) for compatibility with other genome databases. The SGN curation model is community-driven, allowing researchers to add and edit information using simple web tools. Currently, over a hundred community annotators help curate the database. SGN can be accessed at http://solgenomics.net/

Rice Stress-Resistant SNP Database.

BACKGROUND:Rice (Oryza sativa L.) yield is limited inherently by environmental stresses, including biotic and abiotic stresses. Thus, it is of great importance to perform in-depth explorations on the genes that are closely associated with the stress-resistant traits in rice. The existing rice SNP databases have made considerable contributions to rice genomic variation information but none of them have a particular focus on integrating stress-resistant variation and related phenotype data into one web resource. RESULTS:Rice Stress-Resistant SNP database (http://bioinformatics.fafu.edu.cn/RSRS) mainly focuses on SNPs specific to biotic and abiotic stress-resistant ability in rice, and presents them in a unified web resource platform. The Rice Stress-Resistant SNP (RSRS) database contains over 9.5 million stress-resistant SNPs and 797 stress-resistant candidate genes in rice, which were detected from more than 400 stress-resistant rice varieties. We incorporated the SNPs function, genome annotation and phenotype information into this database. Besides, the database has a user-friendly web interface for users to query, browse and visualize a specific SNP efficiently. RSRS database allows users to query the SNP information and their relevant annotations for individual variety or more varieties. The search results can be visualized graphically in a genome browser or displayed in formatted tables. Users can also align SNPs between two or more rice accessions. CONCLUSION:RSRS database shows great utility for scientists to further characterize the function of variants related to environmental stress-resistant ability in rice

Choosing the Best Gene Predictions with GeneValidator.

GeneValidator is a tool for determining whether the characteristics of newly predicted protein-coding genes are consistent with those of similar sequences in public databases. For this, it runs up to seven comparisons per gene. Results are shown in an HTML report containing summary statistics and graphical visualizations that aim to be useful for curators. Results are also presented in CSV and JSON formats for automated follow-up analysis.Here, we describe common usage scenarios of GeneValidator that use the JSON output results together with standard UNIX tools. We demonstrate how GeneValidator's textual output can be used to filter and subset large gene sets effectively. First, we explain how low-scoring gene models can be identified and extracted for manual curation-for example, as input for genome browsers or gene annotation tools. Second, we show how GeneValidator's HTML report can be regenerated from a filtered subset of GeneValidator's JSON output. Subsequently, we demonstrate how GeneValidator's GUI can be used to complement manual curation efforts. Additionally, we explain how GeneValidator can be used to merge information from multiple annotations by automatically selecting the higher-scoring gene model at each common gene locus. Finally, we show how GeneValidator analyses can be optimized when using large BLAST databases