Quantseq Results Summary

[DimmestP]

Weronika and I discussed preliminary results and possible conclusions from the quantseq analysis on 16/08/21.

Objectives from the Quantseq Analysis

1. Show that the most common isoform length for each terminator construct is 27nt (size of three motifs) longer than WT isoform
2. Confirm RNAseq results correlate with qPCR results
3. Select 2 or 3 major major isoforms that appear across any of the constructs (Separate analysis for tTSA1 and tRPS3 terminators)
4. Compare normalised counts for each major isoform. Check if they:
   • exhibit differential usage across terminators
   • exhibit differential usage across promoters
   • contain all or some of the motifs

We also should select some control genes to run the same analysis with the expectation that no differential usages should be found. This should check we have reproducible results across all samples.

We will continue to use the chimera-quantseq repo to hold analysis code.

Tasks Sam

• Re-run Nextflow pipeline on 5pseq
• Search for an algorithm for polyA site calling
• Check qPCR results and RNAseq results are there

Weronika

• Compare most common isoform length for each terminator construct to WT
• Add a materials and methods section for quantseq
• Upload plotting code (re-written in R) to repo

[DimmestP]

I have plotted the qPCR vs quantseq mRNA abundance for pRPS3-tRPS3, pPGK1-tRPS3 and pTSA1-tTSA1 constructs. Only mod_NNN, WT, mod_NTN, mod_HTH and mod_NAA are available from quantseq. The quantseq data is normalised to URA3 counts before mod_NNN. There is high correlation between the two measurements for pRPS3-tRPS3 and pTSA1-tTSA1 but not pPGK1-tRPS3.

I am not entirely sure why the pPGK1-tRPS3 constructs behave differently. As you can see from the RNAseq abundance plotted on its own (below), it appears the mod_NNN construct is less abundant that the any of the constructs with decay motifs inserted which is worrying.