Warden and Wu Preprint: v1 In general, this primarily focuses on the following types of comparisons: Cell line experiments with over-expression or knock-down to define a known causal gene, with processing starting with public reads. Processed TCGA (The Cancer Genome Atlas) data for breast cancer (BRCA) to compare gene expression by immunohistochemistry status (ER/ESR1, PR/PGR, or HER2/ERBB2). Differential expression methods include the following: edgeR (GLM) edgeR-robust (GLM) edgeR (QL) edgeR-robust (QL) DESeq1 DESeq2 limma-voom limma-trend (CPM) limma-trend (FPKM/RPKM) ANOVA (log2 FRPKM/RPKM) The most common preprocessing strategies include STAR, TopHat2, and Salmon. However, a limited amount of additional processing with HISAT2, kallisto, Bowtie2 (+eXpress), and Bowtie1 (+RSEM) is also provided. Most STAR and TopHat2 alignments use htseq-count for quantification, as well as running cuffdiff (for single variable 2-group comparisons). However, a limited amount of additional processing with featureCounts is also provided. Most STAR and TopHat2 alignments start with the public forward reads, even if paired-end data was available.

For an earlier version of the templates for RNA-Seq analysis, acknowledgements were listed for labs in a subfolder of the GitHub repository. So, we would like to provide similar acknowledgements for labs supported since that time (in a similar way). Namely, we would also like to thank the following users that gave us permission to acknowledge these templates (or scripts similar to these templates) were used for analysis of their data: Ke Ma (with lab members Xuekai Xiong, Weini Li, and Tali Kiperman), Saul Priceman (with lab members Yukiko Yamaguchi, Hee Jun/Eric Lee, Anthony Park, and John Murad), Jose Enrique Montero Casimiro / Bart Roep (with Veronica Lifshitz and David Arribas-Layton), Rebecca Deegan (UNMC, with lab member Elizabeth Kosmacek), James Figarola (with lab member Jyotsana Singhal and collaborator Josh Tompkins), Linda Malkas (with lab member Robert Lingeman), and Stephen Forman / Christine Brown (with lab member Dongrui Wang). There are also 5 internal collaborators with whom there was not clear confirmation for approval; however, this acknowledgement can be updated in the future.

While the analysis has to come as a secondary priority to other responsibilities, this data set can serve at least two purposes: 1) Provide an experimental public lab notebook / log for an on-going experiment: https://sourceforge.net/projects/rnaseq-deg-methodlimit/files/LOG.txt 2) Provided an updated citation for RNA-Seq methods used for core support (at least for Charles Warden). In the meantime, the best citation to use is probably the GitHub acknowledgements: https://github.com/cwarden45/RNAseq_templates/blob/master/TopHat_Workflow/README.md