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The two main sources of information we are using to investigate Portulaca's C4+CAM system are 1) the P. amilis genome and 2) transcripts from a CAM induction experiment. These data will be used separately to understand the genomic context of genes and patterns of transcript abundance, but the genome and transcripts will also pass information to each other to produce gene models and a reference transcriptome. In this wiki I'll mostly stick to

1. creating reference transcriptomes
2. differential transcript abundance analyses (which I'll often refer to as 'differential expression')
3. co-expression network analysis
4. cis-element analysis

but there may be some crosstalk between this page and the genome annotation.

I've found this review by Babarinde et al. (2019) to be very helpful in understanding the mechanics and design of RNAseq projects. References within that review, as well as this paper by Wilhelmsson et al. (2019) highlight that there are trade offs between different methods, and that employing a few different methods or a consensus of results is best if possible. During the evolution of this paper I worked through many transcriptome assembly processes with varying filtering and trimming stringencies, multiple read quantification tools (RSEM, STAR, Kallisto), multiple clustering techniques (maSigPro, WGCNA, and custom methods), and multiple quantification programs (EdgeR, DESeq2, and sleuth). There is way too much to show here and many paths hit dead ends, so I'll try to stick to the final analyses here.