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Appendix S1: Supplementary Methods
Microarray Development:
Assembled sequences from the A. palmata transcriptome
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(Polato, Vera et al. 2011) were used to generate a NimbleGen 12 plex 135K feature Custom
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Gene Expression Array (Roche Diagnostics, IN). Two 60-mer probes were designed for each con-
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tig (n = 85,260), and a single probe was designed for each singleton sequence (n = 45,390). Sin-
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gletons were included on the array because a large proportion (46%) had significant BLAST hits
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to known sequences, and evidence suggests that singletons are likely to represent low abun-
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dance transcripts rather than artifacts or contaminants (Meyer, Aglyamova et al. 2009). Two
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additional probes each were developed for sequences associated with annotation information
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relating to calcium metabolism and stress response (n = 4,798). Replicate probes for individual
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sequences from the assembled transcriptome were not identical; rather they represented mul-
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tiple different 60-mer sequences from the original template. Microarray probe sequences and
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annotation data is available at https://homes.bio.psu.edu/people/faculty/baums/Links.htm.
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Information on how to order this array from Roche-NimbleGen is available from the authors.
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Microarray Hybridization:
To prepare samples for microarray hybridization, one cycle
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of amplification was performed on 1ug of each RNA sample using the Amino Allyl MessageAmp
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II aRNA Amplification Kit (Ambion Life Technologies, AM1753) following the manufacturer’s
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protocol. Dye coupling of 15 ug of aRNA was performed with Cy3 or Cy5 (GE Health Care,
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RPN5661), and subsequently purified according to the Ambion Kit instructions. For each pair of
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samples that were to be hybridized to the same array, 2µg each of the Cy3 and Cy5 labeled
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sample were combined and fragmented using RNA Fragmentation Reagents (Ambion, AM8740)
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according the manufacturer’s instructions, then dried down completely in a speed-vac. Samples
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were resuspended in the appropriate tracking control and hybridization solution master mix
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was added following manufacturer’s instructions (Roche NimbleGen). Following two 5-minute
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incubations at 95°C and 42°C, the mixer was attached to the array and hybridization solutions
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were added according to the manufacturer’s instructions. Hybridization was performed while
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mixing overnight at 42°C in a MAUI hybridization instrument (BioMicro Systems, UT). Hybrid-
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ized slides were washed according to the manufacturer’s instructions (Roche NimbleGen), and
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spun at 1000 RPM for 3 minutes in a 50 ml conical tube filled with N2 gas. Dried slides were
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placed in fresh tubes with 2.5 ml of DyeSaver (Genisphere Inc.) and rotated for 45 seconds to
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coat the slide. A final spin at 700 RPM for 3 seconds removed excess DyeSaver, before air drying
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briefly and scanning with Axon GenePix 4000B.
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Functional Analysis: Analysis of the functions associated with differentially expressed
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probes was performed in two ways to overcome challenges inherent to functional analysis of
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data from non-model species obtained via electronic annotation. First, single enrichment analy-
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sis was run using the program GOEAST (Zheng and Wang 2008). This method compares lists of
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GO terms associated with DEGs, to a list of all GO terms associated with the array. It then tests
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if the number of occurrences of any specific GO term is present in the DEG list more often than
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would be expected by chance using a hypergeometric test. The resulting enrichment P-values
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are corrected for multiple testing via the false discovery rate method of Benjamini and Yekutieli
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(2001). This approach has the benefit of being highly customizable as any list of GO terms can
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be loaded into the program for comparison to all GO terms on the array, thus enabling analysis
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of the specific subsets of DEGs associated with the factors of interest described above. This in-
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cludes GO terms associated with genes identified in non-model organisms. Shortcomings of
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this approach however, include highly redundant output (a consequence of the directed acyclic
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graph based structure of GO) and no clear way to collapse expression values from multiple
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probes from the same sequence into a single expression value. The problem was dealt with by
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processing the output lists with the program REVIGO (Supek, Bošnjak et al. 2011) which re-
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moved redundant GO terms based on semantic similarity scores. These reduced lists were then
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visually inspected for accuracy and terms were replaced or further reduced as appropriate.
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The second approach taken to characterize important functions in the differen-
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tially expressed genes made use of the Ingenuity Pathway Analysis (IPA) software (Ingenuity
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Systems, CA). IPA is a web-based application that performs functional enrichment analyses to
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determine the probability that a given gene set is associated with pre-defined functions beyond
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what would be expected by chance. Gene function data in IPA is based on information in the
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Ingenuity Knowledge Base, a manually reviewed database of pathways and relationships taken
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from primary literature and public databses including GO, KEGG and Entrez. The statistical re-
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sults are based on enrichment P-values computed with a Fisher’s Exact Test corrected for mul-
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tiple tests (Benjamini and Hochberg 1995). This technique is comparable to other well-known
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enrichment analysis methods (Huang, Sherman et al. 2009), and experimental evidence sug-
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gests that it performs well on large datasets (Hong, Pawitan et al. 2009). The benefit of this
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method was that redundancy in the input and output was minimized by considering expression
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levels from only the probes with the highest observed log fold change, when multiple probes to
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a single sequence were present on the array. Furthermore, it enabled analysis of transcription
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factor activation states based on differential regulation of downstream target molecules (a use-
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ful technique considering the fact that a very slight change in transcriptions factor expression
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may not pass significance thresholds in traditional microarray analysis but can be responsible
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for strong differential expression of downstream targets). The primary limitation of this method
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however was that the IPA database is based on findings primarily from model vertebrates, thus
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differentially expressed genes without homologs in mice, rats, or humans are not considered in
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the analysis, and information on the functions and interactions of some genes may not be ap-
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propriate for taxa as evolutionarily distant as the Cnidaria.
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References:
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Benjamini, Y. and Y. Hochberg (1995). "Controlling the false discovery rate: a practical and powerful
approach to multiple testing." Journal of the Royal Statistical Society. Series B (Methodological)
57(1): 289-300.
Benjamini, Y. and D. Yekutieli (2001). "The control of the false discovery rate in multiple testing under
dependency." Annals of statistics: 1165-1188.
Hong, M. G., Y. Pawitan, et al. (2009). "Strategies and issues in the detection of pathway enrichment in
genome-wide association studies." Human genetics 126(2): 289-301.
Huang, D. W., B. T. Sherman, et al. (2009). "Bioinformatics enrichment tools: paths toward the
comprehensive functional analysis of large gene lists." Nucleic acids research 37(1): 1.
Supek, F., M. Bošnjak, et al. (2011). "REVIGO summarizes and visualizes long lists of Gene Ontology
terms." PloS one 6(7): e21800.
Zheng, Q. and X. J. Wang (2008). "GOEAST: a web-based software toolkit for Gene Ontology enrichment
analysis." Nucleic acids research 36(suppl 2): W358-W363.
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