Poeschl, Y.; Delker, C.; Trenner, J.; Ullrich, K.; Quint, M. & Grosse, I. Optimized Probe Masking for Comparative Transcriptomics of Closely Related Species PLOS ONE 8, e78497, (2013) DOI: 10.1371/journal.pone.0078497
Microarrays are commonly applied to study the transcriptome of specific species. However, many available microarrays arerestricted to model organisms, and the design of custom microarrays for other species is often not feasible. Hence,transcriptomics approaches of non-model organisms as well as comparative transcriptomics studies among two or morespecies often make use of cost-intensive RNAseq studies or, alternatively, by hybridizing transcripts of a query species to amicroarray of a closely related species. When analyzing these cross-species microarray expression data, differences in thetranscriptome of the query species can cause problems, such as the following: (i) lower hybridization accuracy of probes dueto mismatches or deletions, (ii) probes binding multiple transcripts of different genes, and (iii) probes binding transcripts ofnon-orthologous genes. So far, methods for (i) exist, but these neglect (ii) and (iii). Here, we propose an approach forcomparative transcriptomics addressing problems (i) to (iii), which retains only transcript-specific probes binding transcriptsof orthologous genes. We apply this approach to an Arabidopsis lyrata expression data set measured on a microarraydesigned for Arabidopsis thaliana, and compare it to two alternative approaches, a sequence-based approach and a genomicDNA hybridization-based approach. We investigate the number of retained probe sets, and we validate the resultingexpression responses by qRT-PCR. We find that the proposed approach combines the benefit of sequence-based stringencyand accuracy while allowing the expression analysis of much more genes than the alternative sequence-based approach. Asan added benefit, the proposed approach requires probes to detect transcripts of orthologous genes only, which provides asuperior base for biological interpretation of the measured expression responses.