GermOnline 4.0 is a portal that cross-species database which focuses on high-throughput expression data relevant to the development of the germline, meiotic cell cycle and mitosis in healthy compared to the malignant cells. Thus the source of information for life scientists and physicians who are interested in gene expression and regulatory networks. The GermOnline gateway provides unlimited access to the information generated by high-density oligonucleotide microarrays (3′-UTR GeneChips), protein-DNA binding test genome-wide and the study of protein-protein interactions in the context of the explanation Ensembl genome.
The sample used to produce high-throughput expression data and to perform genome-wide in vivo DNA binding test described by MIAME-compliant Multiomics Information Management and Annotation System (Mimas 3.0). Furthermore, Saccharomyces Genomics Viewer (SGV) was developed and integrated into the gateway. SGV is a visualization tool that outputs explanation stranded DNA genome and specific expression data produced by high-density oligonucleotide tiling microarrays (Sc_tlg GeneChips) covering the complete genome of yeast starters on both strands of DNA. This facilitates the interpretation of the expression levels and transcript structures are determined for different types of cells were cultured in growth and differentiation under different conditions.
Most scientific journals published microarray experiments require to meet the Minimum Information About Microarray Experiment (MIAME) standards. Ensuring that other researchers have the information needed to interpret the results or reproduce them. MIAME the necessary information including the experimental raw data, processed data, and data processing procedures.
However, the normalization method frequently reported inaccurately or not at all. Perhaps the scale factor is not even known except for experienced users of software normalization. We propose that using seed clustering algorithm, researchers can identify or verify the normalization information previously unknown or in doubt. For that, we produce descriptive statistics (mean, variance, quantiles and moments) for normalized expression data from gene chip experiments available in the data base ArrayExpress chip and clusters based on these statistics.
To verify clustering grouped chip with normalization method, we normalize the raw data to the chips selected from an experiment in ArrayExpress using several methods. We then produce the same descriptive statistics for the data is normalized and chip cluster using these statistics. We use this dataset lineage known as seeding data to identify the normalization method used in situations of unknown or doubtful.
GermOnline 4.0 is a genomics gateway for germline development, meiosis and the mitotic cell cycle.
MAGIC database and interfaces: an integrated package for gene discovery and expression.
Rapidly increasing the rate at which the biological data produced requires a corresponding growth in the relational database and related tools that can help laboratories to compete with that data.
With these needs in mind, we describe here for Genomic Approach Modular, Integrated and Comprehensive (MAGIC) Database. Oracle 9i database or obtained from an initial focus in our laboratory on gene discovery through the production and analysis of expressed sequence tag (EST), and then the expression of genes that are considered good by grouping EST and microarray.
Description: Rat Rena-strip Kit could be used both as qualitative or quantitative (with reader) for a detection of KIM-1 in urine samples from rats. The procedure takes about 25 minutes.
Description: Human Rena-strip Kit could be used both as qualitative or quantitative (with reader) for a detection of KIM-1 in urine samples from human. The procedure takes about 25 minutes.
Gene discovery database MAGIC part focuses on the information obtained from the DNA sequence and its biological relevance. Besides MAGIC SEQ-LIMS, which are designed to support the activities in the laboratory, it contains some additional subschemas.
No Comment