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—Databases  
 
—Tools
NextGen tools are next-generation software that offer perceived superior features, algorithmic properties or data sets. Because they are cutting edge, these tools are often command line-based and may require more complex installations. More
Genomes
Tool for tiling array, ChIP-seq, genome and cis-regulatory element analysis CisGenome browser      
Genes, proteins & sequence analysis 
Easy to use next-generation tools for molecular biology CLC Bio Combined Workbench for molbio        
Expression & function 
Novel approach for classifying microarray gene expression based on the time dimension StepMiner temporal microarray gene expression      
Literature & biodata mining 
Easy to use search engine that provides highly summarized bioresearch data NextBio search engine  [click for Enterprise-level trial access]        

Bioresearch Notifications

Solution biotool: MeV

Need to analyze microarray data? If so, have look at the MultiExperiment Viewer (MeV) from TIGR - by far the easiest way compare and visualize results generated by multiple algorithms.

So What? Success Tip #5

So What? Tip Computing efficiency tip: Get a bigger monitor! Why? Studies have shown that a larger monitor is the principal important factor in increasing user productivity on a computer, once it has enough memory and a decent processor. If you're analyzing lots of data on a computer, do yourself a favor and get a larger monitor.

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Search BioWareDB BiowareDB is a search engine for biosoftware and databases

  Browse Biodatabases:
  NAR database survey NAR provides a yearly listing of molecular biology databases, organized by subject area   Browse Bio web services:
  NAR web services survey NAR provides a yearly listing of molecular biology web services, organized by subject area

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PharmGKB is a major repository of pharmacogenomic data for Homo sapiens. Its principal investigator is Stanford Professor of Bioengineering Russ B. Altman. The GO Ontologies are the principal ontologies describing the properties of genes and proteins of many organisms. One of its architects is Stanford Genetics Professor J. Michael Cherry. SMD is a major repository of microarray gene expression. The database is a joint project in the Departments of Biochemistry and Genetics at the School of Medicine, Stanford University. SOURCE is a data retrieval tool provided by the Department of Genetics at the School of Medicine, Stanford University.
[more] SGD is the main repository of genome data from Saccharomyces cerevisiae. Its principal investigator is Stanford Genetics Professor J. Michael Cherry.
[more] TGD is the main repository of genome data from Tetrahymena thermophila. Its principal investigator is Stanford Genetics Professor J. Michael Cherry. TGD is the main repository of genome data from Candida albicans. Its principal investigator is Stanford Genetics Assistant Professor Gavin Sherlock. SAM is a powerful tool for evaluating the statistical significance microarray gene expression data. The tool is provided by Stanford Professor of Statistics Robert Tibshirani. TGD is the main repository of genome data from Tetrahymena thermophila. Its principal investigator is Stanford Genetics Professor J. Michael Cherry. TAIR is the main repository of genome data from Arabidopsis thaliana. It is curated by personnel form Stanford's Department of Plant Biology. Using microarray data, BioProspector examines the upstream region of genes in the same gene expression pattern group, looking for regulatory sequence motifs. It was developed by Stanford Biochemistry Professor Douglas L. Brutlag. A powerful tool for microarray and genome data analysis, BioProspector examines the upstream region of genes in the same gene expression pattern group, looking for regulatory sequence motifs. It was developed by a team that includes a number of Stanford researchers. Using combined tiling array and comparative genomics data, CisGenome was developed by a team that includes Stanford Professor of Statistics Wing Wong. This resource is restriced to holders of a SUNet ID. StepMiner was developed by a team that includes Stanford Professor of Statistics Robert Tibshirani, Stanford Associate Professor of Radiology Sylvia K. Plevritis, and Stanford Professor of Computer Science David L. Dill.
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