TITLE: RNA-Seq protocols for non-model species 
AUTHORS: Chase Donnelly

[DESCRIPTION]
This is a protocol under development for RNA seq analysis on non model plant species. 

[STEPS]
SECTION: RNA Extraction
1. RNA Extraction followed a modified protocols from RNeasy plant mini kit (Qiagen), below we share only the modifications used to extract high qualilty RNA from difficult plant species. 
https://www.qiagen.com/us/products/discovery-and-translational-research/dna-rna-purification/rna-purification/total-rna/rneasy-plant-mini-kit
SECTION: RNA Extraction
2. Note: Amount of Starting Material: the amount of starting material to use can vary between species, we found that for more troublesome species containing a lot of secondary metabolites, such as Plantago, using less starting (~30-50mg) material resulted in higher quality RNA output. 
SECTION: RNA Extraction
3. Note: Grind with mortar and pestle: while instruments to disrupt tissue can work, we found that grinding for an extended period by hand in liquid nitrogen produced higher quality results for RNA extractions on non model plants 
SECTION: RNA Extraction
4. Note: When adding 450 ul of RLT Buffer perform a 3 min incubation before vortexing, proper mixing here is key to a successful extraction 
SECTION: RNA Extraction
5. Modification: The wash step with RW1 was performed twice on difficult samples, this helped remove excess metabolites that were still present in plant samples 
SECTION: RNA Extraction
6. Modification: The wash step with RPE was performed a minimum of three times but more washes were performed if color of the sample was still green or brown. The final sample should be a clear liquid. 
SECTION: Software Setup
7. After sequencing the first step is to set up a clean compute space to analyze the sequencing data to avoid issues with mixing versions of software. 
SECTION: Software Setup
8. There are a few different ways to manage a compute environment, here conda/mamba was used to create a clean environment as it works with all of the downstream programs and has an extensive manual. Mamba is a newer alternative to conda that can install programs with greater speed. 

conda doc: https://conda.io/projects/conda/en/latest/user-guide/getting-started.html

mamba doc: https://mamba.readthedocs.io/en/latest/

mamba and conda can both be used in the same way, just replace mamba with conda in the codes below. 

SECTION: Software Setup
9. To set up an environment with conda or mamba you can use the following code to create an environment and install the required programs: 
 


SECTION: Data Quality Assessment
10. When obtaining data from a sequencing facility, it is essential to check the quality of data received for each sample and remove any unwanted (see adaptors) or low-quality reads. FastQC is the standard QC tool for sequencing experiments and other options are mainly command-line-only options that require
multiple steps or tools (Lo and Chain 2014). 

Fast qc can be ran through command line or by downloading their GUI interface here: 
https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ 

Read quality tends to decrease as it progresses
to tail ends of the reads, and FastQC should be used to check the quality at
the read tail. If the read quality is low or adapters are still present, then
the reads should be removed through one of multiple available trimming tools

SECTION: Transcriptome Assembly
12. When no reference genome is available, or the
available genome is of low quality, RNA-Seq reads can be assembled into a de
novo transcriptome using a variety of tools. A few of the available tools are listed below.   

 
No tool is perfect for every organism, with specific tools performing better or worse depending on the ploidy level and repetitiveness of the reads and so a comprehensive comparison should be done before starting assembly (Madritsch, Burg, and Sehr 2021; Hölzer and Marz 2019).

SECTION: Data Quality Assessment
11. Low quality reads should be removed by one of the many available read tools (Table 2), below is an example of how this is done with Trimmomatic (A. M. Bolger, Lohse, and Usadel 2014).     


 



 

SECTION: Transcript Quality Assessment
15. Once the de novo assembly is completed, the next step is to check assembly quality. The quality of an assembly will affect multiple downstream analysis such as differential expression analysis, gene structure prediction, and isoform identification (Raghavan et al. 2022). Multiple tools and methods were researched to find the best practices for quality assessment, but no universal protocol is currently used to allow for comparison across studies (Haas et al. 2013; Xie et al. 2014). 

A large percentage (70-90%) of a high-quality transcriptome will also map back to the reads, and this is an important step before proceeding with downstream analysis. For this protocol multiple ways to characterize the quality assembly were tested, starting with aligning reads to the assembly to count the number of proper and improper pairs. 

 

The code for QA can be found below 


 


SECTION: Transcriptome Assembly
13. For this protocol we will be using Trinity due to its high performance and its use in
previous publications for similar species to be used in this study (Ponnuchamy et al. 2020; Boehm et al., 2017).

SECTION: Transcriptome Assembly
14. 
 

SECTION: Trascriptome Annotation
16. After transcriptome assembly and quality assessment, the putative functionality of the contained sequences can be elucidated. Most tools follow a similar procedure comprised of three steps: homology transfer and assignment via a search algorithm, sequence feature annotation, and gene ontology assignment. 


 

This protocol will use trinotate as it works directly from trinity, used above for de novo transcriptome assembly, the code to run trinotate is below. 


 


EnTAP was also performed to compare annotation results across platforms, the code for this is below. 


 

SECTION: Transcript Quantification
17. As with the protocols described above, multiple tools are currently available to estimate transcript abundance in the absence of a genome. Recent comparisons of these tools can be found by (Wu et al. 2018; Corchete et al. 2020). These studies found multiple tools perform well and the choice of tool comes down to multiple factors that will differ between experiments. 


 

Kallisto was used for this protocol due to speed and low computational requirments with high accuracy. 


 

SECTION: If reference is available
18. Some plant species already have a reference genome available, if this is the case, mapping can be done directly to the reference. This can be done with multiple mapping tools (Table 3). A quick example of mapping with STAR is given below. 


 
 
SECTION: Differential Expression and GO Analysis
19. Once the transcripts have been quantified, the next step is to investigate the changes in gene expression caused by the experimental stimuli. There are a variety of tools that can be used for differential gene expression analysis with DeSeq2, EdgeR, and Limma currently being the most used (Costa-Silva, Domingues, and Lopes 2017). This protocol uses DeSeq2, which can be run seperately in R or following the trinity analysis protocol.


 

After differential gene analysis, the next step is to place the change in genes to a biological context. GO enrichment analysis is a technique that is widely used to highlight important biological processes in complex outputs of RNA-Seq experiments (Ashburner et al. 2000).


 

Example code for DEG and GO enrichment analyisis is included below: