TITLE: De - Novo Genome Protocol from only ONT reads
AUTHORS: Chase Donnelly

[DESCRIPTION]
Draft of pipeline and code used for de novo genome assembly of non model plant specis 

[STEPS]
SECTION: DNA Extraction
1. The first challenge to developing this protocol was determining a DNA extraction technique that produced long and high-quality DNA. This protocol used a modified version of the fever tree gdna protocol available via Nanopore's community website. These modifications have since been added to or surpassed in the published protocol and so the latest version on the community page should be used for DNA extraction and Library prep for sequencing before following the rest of this protocol. 
SECTION: Software Setup
2. 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: De Novo Assembly
6. There are multiple assembly options that can work well on plant data (Table 1). As this study uses only long read data, only assemblers optimized for long read assembly, such as Flye, WTDBG2, and Canu were tested. 

 


 


SECTION: Quality Assessment
7. There are many ways to check the quality of an assembly, here we discuss three that will give a good general representation of assembly quality. These tools can be used throughout the protocol to determine the effect of each step of the process. The first tool Nano Pack 2 (v), a toolset designed to perform
multiple long read processing and analysis steps on ONT reads (De Coster and Rademakers 2023), is good for visualization of the data and comes with a large range of tools to deal with ONT reads. 

Next Quast is the standard genome evaluation tool to compare contig number, length, and N50 of each assembly (Gurevich et al. 2013). Quast can provide multiple scores and measurements for genome assemblies with and without a reference genome available. When a reference is available quast can also show contig alignment and gaps in the two assemblies.

BUSCO is the most commonly used tool to determine the completeness and redundancy of the genome and is based on use of universal single copy orthologs, aka highly conserver genes (Manni et al. 2021).  BUSCO looks for the presence or absences of a set of highly conservative genes based on the organism of study, with a score in the high 90% being considered good. Example code for all three tools is provided below. 

 

SECTION: Structural Annotation
12. 
 


SECTION: Functional Annotation
15. Functional annotation was completed with EnTAP. EnTAP was chosen to add functional annotation to our draft genome due to its ability to work across multiple curated datasets, and due to its preexisting setup for the RNA protocols (Hart et al. 2020b).  Using EnTAP allowed for the incorporation of the annotations from closely related species to increase the results of functional annotation. Example code is provided below. 
SECTION: Software Setup
4. 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
3. 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: Quality Trimming
5. After base calling using high accuracy settings with no modifications, the resultant data is then further quality checked with Nano Pack 2 (v), a toolset designed to perform multiple long read processing and analysis steps on ONT reads (De Coster and Rademakers 2023). Short reads <1500 bp in length and reads with a Q score below 10 were filtered out of the sequencing data prior to assembly. 

 
 
 
SECTION: Polishing
8. After initial assembly multiple polishing and correction are performed. The first correction step is polishing and there again are a variety of tools to choose from such as Racon, Pilon, and POLCA. 

 

This protocol uses a combination of Racon and Medaka that is known to produce good results on plant assemblies (Lee et al. 2021). The code used is below, minimap2 is used to map reads for each step of polishing. 


 

SECTION: Remove Haplotigs
9. As discussed, plant genomes contain areas of high heterogeneity, this can cause issues when looking to recognize haplotype homology during genome assembly.  To counteract this issue, Purge Haplotigs was used on the draft genome assembly during multiple steps to improve the genome assembly by labeling haplotigs. Purge Haplotigs uses Minimap2 alignments and mapped read coverage to discover contig pairs that are syntenic and assign one of the contigs to a haplotig “pool” and remove the haplotigs from the primary assembly 

 

SECTION: Scaffolding
10. When high quality genomes of closely related species are available, tools such as RagTag can be used to scaffold assembled contigs to known chromosomes of the related species (Volarić et al. 2022). 


 



SECTION: Structural Annotation
11. After assembly is completed, the draft genome must then go through the process of structural gene annotation. This method finds the positions of genomic features such as protein-coding genes, promoters, and regulatory elements, which can be a challenge for plant genomes as they contain highly repetitive elements (M. E. Bolger, Arsova, and Usadel 2018).  There are several popular protocols available for this process and all go through a similar workflow that use multiple tools and consists of three main stages: masking noncoding regions, predicting gene structure positions, and then finally assigning biological meaning to those predictions (Table 12; Jung et al. 2020). 
SECTION: Structural Annotation
13. For plants, the general process of repeat masking is performed by two main packages RepeatMasker and Repeatmodeler2, both of which soft mask or obscure repeated regions to annotation software (Smit, Hubley, and Green 2015; Flynn et al. 2020). First we use repeatmodeler to create a database and 

Below is example code for both programs. 


 


SECTION: Functional Annotation
16. 
 

SECTION: Structural Annotation
14. Gene prediction on the remaining regions then takes place via evidenced based, evidence free, or a combination of the two methods.  Evidence based methods use RNA-Seq data and protein sequence search algorithms, while evidence free methods rely on gene structure and signal based searches to predict genes (Z. Wang, Chen, and Li 2004). Here we will use BRAKER3, for on protein and RNA seq data to complete to most accurate annotation. BRAKER uses a combination of GenMark-ETP, AUGUSTUS and TESBRA to perform annotations. An example code is provided below.