Gonzalez-Dominguez and Schmidt, 2016 Gonzalez-Dominguez J.

Schmidt B. ParDRe: faster parallel duplicated reads removal tool for sequencing studies.

Martin, 2011 Martin M. Cutadapt Removes Adapter Sequences From High-Throughput Sequencing Reads.

Bolger et al., 2014 Bolger A.M.

Lohse M.

Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data.

Pimentel et al., 2017 Pimentel H.

Bray N.L.

Puente S.

Melsted P.

Pachter L. Differential analysis of RNA-seq incorporating quantification uncertainty.

Goodarzi et al., 2009 Goodarzi H.

Elemento O.

Tavazoie S. Revealing global regulatory perturbations across human cancers.

Goodarzi et al., 2009 Goodarzi H.

Elemento O.

Tavazoie S. Revealing global regulatory perturbations across human cancers.

Goodarzi et al., 2009 Goodarzi H.

Elemento O.

Tavazoie S. Revealing global regulatory perturbations across human cancers.

Because we observed high rates of likely PCR duplicates among the reads for most samples, the raw reads were de-duplicated using ParDRe (), allowing one mismatch and using an 18 bp prefix. Testing on internal controls using the ERCC spike-in mix showed that de-duplication improved the correlation of transcript abundances with known relative values (data not shown). Surviving reads that had any recognizable fragment of the Nugen sequencing adaptor were removed using cutadapt 1.8.1 () and low quality ends were removed using Trimmomatic 0.22 () to remove all terminal bases with quality scores below three, and then requiring that for surviving bases, their average quality score over a 4 bp window was at least 15. Reads with fewer than 20 surviving bases were subsequently dropped. Preprocessed reads were aligned to the Drosophila melanogaster Flybase release 6.08 transcriptome, augmented with Gal4 and EGFP transcript sequences, using kallisto 0.43.0 () with a k-mer size of 21 and 200 bootstrap replicates. We used sleuth () for further postprocessing of the RNA-seq data; in particular, all significance tests for differential expression on RNA-seq data use p values or q-values (as noted) from sleuth for a Wald test on the coefficient distinguishing the groups in question. While we initially obtained three biological replicates for each of the CD and SD cases, we noted that one replicate from each condition was a substantial outlier from all other points (across both conditions) based on the Jensen-Shannon divergence between samples; we excluded that outlier pair from all described analysis. Similar pruning was applied to other sample sets. The final numbers of biological replicates for analyzed sequencing data are given in Table S1 . For the pathway analysis in Figure S7, we used iPAGE () to find gene ontology (GO) terms showing significant mutual information with the profile of fitted gene-level effect sizes from sleuth. Note that due to the several tests incorporated into the iPAGE pipeline (many of which are not shown), the overall false discovery rate of the procedure on expression profiles has been empirically been shown to be less than 0.05 (). To classify genes for the Venn diagram in Figure 7 D, we first selected only the set of genes showing significant changes in expression between CD and SD (q < 0.1), and then conditioning on membership in that set, calculated FDR-corrected p values for the significance of changes in transcript level for the same genes between the Gr5a-GAL4/UAS-OGT RNAi flies and the corresponding Gr5a-GAL4/+ controls, (using a threshold of an FDR-corrected p value < 0.2). Genes showing significant expression changes in both experiments were classified based on the signs of the observed log fold changes. For the pathway analysis shown in Figure 7 E, we used iPAGE () to identify GO terms showing significant mutual information with the status of genes as being in any of the oppositely-regulated categories of Figure 7 D, or among the set of all other genes (a ‘background’ set that is not shown). iPAGE calculations used GO term annotations from the dmel_r6.08 Flybase release. Data was uploaded to GEO as submission # GSE113159.