GAMMA is a previously developed algorithm (14) to identify correlated transcripts across microarray experiments. After the most correlated transcripts are identified, an entity-based literature-mining approach (38) is used to analyze their literature commonalities and predict ncRNA function. We considered the top 40 most highly correlated coding transcripts with the ncRNAs under scrutiny, and we analyzed their literature commonalities. These commonalities include terms such as diseases, chemicals, phenotypes, other genes, cellular functions, cellular structures, and biological processes, similar to GO associations. Thus, when function is not known, these transcriptional correlation networks can be used to infer function, tissue specificity, cellular localization, and phenotype and help prioritize experimentation. The use of GAMMA to successfully predict mRNA function has been previously reported in several other instances, but this is the first successful test of the approach to predict ncRNA functions. GAMMA was used to search for uncharacterized noncoding transcripts associated with the literature annotations: “Brain development,” “Synaptic vesicle,” “Synaptic vesicle exocytosis,” “Synaptic vesicle endocytosis,” “Regulation of synaptic plasticity,” “Synaptogenesis,” “Neurotransmission,” “Dendritic spine,” and “Neuronal protein.” These terms were used to define a “Guilt by association score” (data S1A) that was used during the first selection round. The filters used for scoring the transcripts were (i) low coding potential (as previously defined) (39); (ii) conservation across species, defined as percentage homology; (iii) distance from the TSS of a known annotated protein-coding gene; and (iv) size (sequences longer than 5 kb were excluded for obvious practical constraints).

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