Dr. Brad Weiler’s new publication on diel transcriptomics of Pseudodiploria strigosa was just published on bioRxiv here, and his pipeline is available now on GitHub here.

I want to go through his steps and follow his code since he constructed a de novo transcriptome for P. strigosa.

The sequencing he performed was “Poly-A capture/enrichment methodology by Novogene (Beijing, China) on the 158 Illumina NovaSeq PE 150bp. The methodology follows a standard workflow of fragmentation, reverse 159 transcription, cDNA synthesis, end-repair and A-tailing.”

3’ RNA-Seq doesn’t necessarily equate to Poly-A selection/capture/enrichment. 3’ RNA-Seq can be done with no selection, meaning that it would just sequence the 3’ends of RNA molecules but isn’t targeting molecules with polyA tails. Poly-A selection/capture/enrichment is performed to enrich mRNA molecules over other types or RNA (typically rRNA) that dominate total RNA content.

The question is, did my reciprocal transplant reads get any sort of selection? Looking back through my notes, I don’t see anything about Poly-A selection.

His pipeline:

1. FastQC
2. Adapter trimming using CutAdapt wrapper script TrimGalore
3. Ribosomal RNA partitioned from total RNA using SortMeRNA and default fasta reference database
4. Remaining RNA mapped to index of genomes (Ostreobium quekettii, Symbiodinium sp. 57, Breviolum sp. 58, Cladocopium sp. 57, Durusdinium sp. 59 183 , Eimeria tenella, Emitis 184 houghton, Sarcocystis neurona, Toxoplasma gondii, Malassezia restricta, Nitzschia putrida, Gracilaria gracilis, and Uronemita sp.) to isolate non-coral RNA reads using BBsplit
5. Unmapped reads concaenated for de novo transcriptome assembly using Trinity
6. Decontamination using Standard PlusPF reference database in Kraken2
7. Reads aligned to transcriptome using Salmon
8. STAR used to align Breviolum binned reads
9. Gene transcripts were first predicted into longest open reading frames (ORFs) using transDecoder
10. Annotations using EggNOG-mapper
11. Gene ontology (GO) annotations supplemented using GoPredSim ProtT5 protein language model 65 195 derived topGO terms using FANTASIA

Using SortMeRNA for removing rRNA

• Looking online, there seems to be some thoughts that removing rRNA reads is not required when Poly-A selection is performed during sequencing, as preferentially selecting polyA tails automatically creates a bias towards mRNA (see this article and this article).
• However, some suggest that removing rRNA would save computation time and lead to a cleaner assembly (see here).
• This tutorial/paper is a good resource - it says that if GC content is abnormal, it could be a sign of high rRNA content (GC content of over 50% is typical for rRNA) and then SortMeRNA should be used. They also said “if there is any doubt, this step should be performed.”

Based on the multiQC report of my trimmed reads, it looks like the average GC content is not normally distributed and is higher for some samples. To be safe, I’ll follow Brad’s code and run SortMeRNA on it.