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 HGNC symbol chromosomal coordinates Remember these: used later in GoMiner “Tilde” mechanism!!

“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 brain- specific splicing to shape the synapse, Ule et al., Nature Genetics 37, (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