* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Zeeberg - Gene Ontology Consortium
Survey
Document related concepts
Secreted frizzled-related protein 1 wikipedia , lookup
Gene desert wikipedia , lookup
Community fingerprinting wikipedia , lookup
Genomic imprinting wikipedia , lookup
Gene expression wikipedia , lookup
Ridge (biology) wikipedia , lookup
Promoter (genetics) wikipedia , lookup
Molecular evolution wikipedia , lookup
Gene regulatory network wikipedia , lookup
Silencer (genetics) wikipedia , lookup
Genome evolution wikipedia , lookup
Artificial gene synthesis wikipedia , lookup
Alternative splicing wikipedia , lookup
Exome sequencing wikipedia , lookup
Endogenous retrovirus wikipedia , lookup
Transcript
GUI GoMiner and High-Throughput GoMiner Analysis of Alternative Splice Variants Barry Zeeberg, Ari Kahn, Michael Ryan, David Kane, Curtis Jamison, Hongfang Liu, Alessandro Ferrucci, William Reinhold, and John Weinstein plus a lot of help from Rich Einstein and Mike Brenner of ExonHit The World According to a Microarray: • Genes are not Genes • Genes are a Mixture of Splice Variants Patterns of alternative splicing The Ostrich Effect • Tend to hide our head in the sand • Treat microarray data as if a gene did not have multiple alternative splice forms • But altered expression of one splice variant can be more important than altered expression of the “gene” > i.e., lumping together all splice forms in one monolithic measurement is bad to do Motivation: The Problem • In many disease states, differential expression of individual splice variants may be more relevant than differential expression of genes • Traditional microarrays are not designed to permit elucidation of individual splice variants • State-of-the-art microarrays are being developed to permit elucidation of individual splice variants • A major limitation is that software tools are not available to exploit the potential information content of the state-of-the-art microarrays Our Solution: Three Components • Develop a database (EVDB) and web application (SpliceMiner) that maps probe sequences to known splice variants • Enhance GoMiner with a mechanism to process splice variants • Connect these two “ends” with the appropriate integration approach Our Solution: Three Components • Develop a database (EVDB) and web application (SpliceMiner) that maps probe sequences to known splice variants • Enhance GoMiner with a mechanism to process splice variants • Connect these two “ends” with the appropriate integration approach SpliceMiner Home Page Remember these: used later in GoMiner “Tilde” mechanism!! HGNC symbol chromosomal coordinates “Batch” is key to analysis of microarray results Our Solution: Three Components • Develop a database (EVDB) and web application (SpliceMiner) that maps probe sequences to known splice variants • Enhance GoMiner with a mechanism to process splice variants • Connect these two “ends” with the appropriate integration approach GoMiner and High-Throughput GoMiner • GoMiner organizes lists of 'interesting' genes (for example, under- and overexpressed genes from a microarray experiment) for biological interpretation in the context of the Gene Ontology • High-Throughput GoMiner is an enhancement of GoMiner which efficiently performs the computationally-challenging task of automated batch processing of an arbitrary number of microarray experiments GoMiner “Tilde” (“~”) Mechanism • GoMiner traditionally dereplicates input files so that only one instance of a gene name is processed • When multiple alternatively spliced forms are to be analyzed, however, dereplication would result in a loss of relevant information • Consequently, we have added a new feature to GoMiner to retain full information about the alternative splice variants by replicating the input of each gene according to the number of alternative exons Example of Tilde Mechanism • As a specific example, suppose that a microarray platform contained probes that were unique for two different splice variants of BRCA1 • Then the two splice variants would be designated as 'BRCA1~1' and 'BRCA1~2' • The '~' tells GoMiner to treat these as different entries, rather than to de-replicate them, but to ignore the suffix when querying the GO database • By this mechanism, all splice variants are counted when computing the Fisher exact p value A Publication using Tilde Mechanism • Study of “exon expression” regulated by Nova, a key neuronal splicing factor • Reference: Nova regulates brainspecific splicing to shape the synapse, Ule et al., Nature Genetics 37, 844 852 (2005) GoMiner Detected Differences in Neurologically-Important GO Categories between Wild Type and Nova Knockouts Significance of Nova paper • First description of a regulatory module operating at the level of information content mediated by RNA exon usage • Levels of Nova-regulated RNAs are unchanged in knockout versus wild-type brains: alternative exon usage as a means of modulating the quality of synaptic protein interactions • Regulation of quality, not quantity Our Solution: Three Components • Develop a database (EVDB) and web application (SpliceMiner) that maps probe sequences to known splice variants • Enhance GoMiner with a mechanism to process splice variants • Connect these two “ends” with the appropriate integration approach Generalization of the Tilde Mechanism • A Previous slide noted that two splice variants could be designated as ‘BRCA1~1’ and ‘BRCA1~2’ • But the suffix can be an arbitrary string that carries biological information, not just used as an ordinal index • So we can use the output of SpliceMiner (HGNC symbol, GenBank accession, chromosomal coordinates) to construct a string of the correct form, with a suffix that is highly informative • Using the output from SpliceMiner as the input to GoMiner will connect the two “ends” and permit splice variant-based GO categorization Conclusions • The new era of microarray research will demand analysis of differential expression of exons and transcripts, rather than genes • We are developing resources to map probe sequences to exons and transcripts • GoMiner can integrate this information with GOA to allow the molecular biologist to leverage both knowledgebases for enhanced analysis and interpretation of microarray data Collaborators GBG: Ari Kahn Michael Ryan David Kane Hongfang Liu William Reinhold John Weinstein GMU: Curtis Jamison UMBC: Alessandro Ferrucci ExonHit: Rich Einstein Mike Brenner