### Install mafft with apt-get

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/gettingstarted/installation.md

Install the MAFFT aligner using apt-get on compatible Linux systems.

```bash
sudo apt-get install mafft
```

--------------------------------

### Install Panaroo from Git (setup.py)

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/gettingstarted/installation.md

Install Panaroo from a cloned GitHub repository using setup.py.

```bash
git clone https://github.com/gtonkinhill/panaroo
cd panaroo
python3 setup.py install
```

--------------------------------

### Install Panaroo from Git (pip)

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/gettingstarted/installation.md

Install Panaroo directly from its GitHub repository using pip.

```bash
pip3 install git+https://github.com/gtonkinhill/panaroo
```

--------------------------------

### Install Panaroo Locally

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/gettingstarted/installation.md

Install Panaroo from a cloned GitHub repository for local user access using setup.py.

```bash
python3 setup.py install --user
```

--------------------------------

### Install cd-hit with apt-get

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/gettingstarted/installation.md

Install the cd-hit dependency using apt-get on compatible Linux systems.

```bash
sudo apt-get install cd-hit
```

--------------------------------

### Install Mamba Solver

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/gettingstarted/installation.md

Install the Mamba package manager, a faster alternative to Conda.

```bash
conda install mamba -c conda-forge
```

--------------------------------

### Install Panaroo with Mamba

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/gettingstarted/installation.md

Install Panaroo version 1.3 or higher using the Mamba package manager.

```bash
mamba install -c conda-forge -c bioconda -c defaults 'panaroo>=1.3'
```

--------------------------------

### Install Panaroo with Conda

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/gettingstarted/installation.md

Install Panaroo version 1.3 or higher using the Conda package manager.

```bash
conda install -c conda-forge -c bioconda -c defaults 'panaroo>=1.3'
```

--------------------------------

### Create Conda Environment

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/gettingstarted/installation.md

Create a new Conda environment with Python 3.9 for Panaroo installation.

```bash
conda create -n panaroo python=3.9
```

```bash
conda activate panaroo
```

--------------------------------

### Spydrpick Algorithm Help

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/post/coevolution.md

Display help information for the Spydrpick command-line tool, including available arguments and their descriptions.

```bash
panaroo-spydrpick --help
```

--------------------------------

### IMG Model Parameters Help

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/post/pansize.md

This help output lists all available arguments for the panaroo-img command, detailing options for model fitting, rate classes, and initial parameter values.

```bash
Estimate model parameters for either the Infinitely Many Genes Model using
gene frequencies

optional arguments:
  -h, --help            show this help message and exit
  --tree TREE           A dated phylogeny.
  --pa PRESENCE_ABSENCE
                        A presence/absence produced by Panaroo.
  -o OUTPUT_DIR, --out_dir OUTPUT_DIR
                        location of an output directory
  -D {1,2}              Number of separate rate classes to use for the
                        dispensable genome. Can be either 1 or 2.
  --no_essential        Removes essential gene class from model
  --no_constraint       Removes constraint that u/v must equal the genome
                        size.
  --model {coalescent,fixed}
                        Model to fit. Can be one of 'coalescent' or 'fixed'.
  --fit {cp,gf}         Whether to use the gene frequency spectrum or the
                        core/pangenome size for fitting (default=gf)
  --init_u1 U1          Initial value for u1 (default=0.01).
  --init_u2 U2          Initial value for u2 (default=0.01).
  --init_v1 V1          Initial value for v1 (default=0.01).
  --init_v2 V2          Initial value for v2 (default=0.01).
  --init_ess GESS       Initial value for the number of essential genes
                        (default=2000).
  --verbose             print additional output
  --version             show program's version number and exit
```

--------------------------------

### Run Panaroo on initial dataset

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/merge/add_single.md

This command initializes the Panaroo analysis on a set of GFF files. Ensure the output directory and thread count are specified.

```bash
panaroo -i kpII/*.gff -o kp_II_panaroo -t 24 --clean-mode sensitive
```

--------------------------------

### FMG Model Parameters Help

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/post/pansize.md

This help output displays the arguments for the panaroo-fmg command, including options for bootstrap iterations, threads, and output file naming.

```bash
Estimate model parameters for the Finitely Many Genes Model

optional arguments:
  -h, --help            show this help message and exit
  --tree TREE           A dated phylogeny.
  --pa PRESENCE_ABSENCE
                        A presence/absence produced by Panaroo.
  -o OUTPUTFILE, --outfile OUTPUTFILE
                        Name of outputfile.
  --nboot NBOOT         The number of sub-sampling bootstrap iterations to
                        perform.
  -t N_CPU, --threads N_CPU
                        number of threads to use (default=1)
  --verbose             print additional output
  --version             show program's version number and exit
```

--------------------------------

### Basic Panaroo Usage with GFF Files

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/README.md

Run Panaroo with standard GFF files from Prokka. Ensure you have a 'results' directory created.

```bash
mkdir results
panaroo -i *.gff -o results --clean-mode strict
```

--------------------------------

### Configure Docsify

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/index.html

Configure Docsify settings for the project. This includes setting the name, repository link, and enabling the sidebar and cover page.

```javascript
window.$docsify = {
  name: '',
  repo: '',
  loadSidebar: true,
  coverpage: true,
  // onlyCover: true
}
```

--------------------------------

### Panaroo QC Script Parameters

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/quality/quality_control.md

Lists the available command-line arguments for the Panaroo quality control script, including options for threads, graph types, reference databases, and input/output.

```bash
Generate quality control plots prior to a Panaroo run

optional arguments:
  -h, --help            show this help message and exit
  -t N_CPU, --threads N_CPU
                        number of threads to use (default=1)
  --graph_type {contam,ngenes,all,mds,ncontigs}
                        the type of graph to generate (default='all')
  --ref_db REF_DB       reference mash database for contamination
                        quantification.
  --version             show program's version number and exit

Input/output:
  -i INPUT_FILES [INPUT_FILES ...], --input INPUT_FILES [INPUT_FILES ...]
                        input GFF3 files (usually output from running Prokka)
  -o OUTPUT_DIR, --out_dir OUTPUT_DIR
                        location of an output directory
```

--------------------------------

### Run Infinitely Many Genes (IMG) model

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/post/pansize.md

Use this command to estimate IMG model parameters. A dated phylogeny and Panaroo's presence/absence table are required inputs. The output directory will store the results.

```bash
mkdir img_results
panaroo-img --pa gene_presence_absence.Rtab -o img_results -tree dated_phylogeny.newick
```

--------------------------------

### Build Core Gene Phylogeny with IQ-TREE

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/post/pwas.md

Construct a phylogenetic tree from the core gene alignment generated by Panaroo using IQ-TREE. This command is run from within the 'panaroo_output' directory.

```bash
cd panaroo_output
iqtree -s core_gene_alignment.aln -pre core_tree -nt 8 -fast -m GTR
cd ..
```

--------------------------------

### Run Spydrpick Algorithm

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/post/coevolution.md

Execute the Spydrpick algorithm using the gene presence/absence file and cluster information. Ensure the output directory is created beforehand.

```bash
mkdir coevo_results
panaroo-spydrpick -i gene_presence_absence.Rtab -o coevo_results --clusters clusters.csv
```

--------------------------------

### Prodigal for Training File Generation

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/README.md

Generate a training file for Prodigal by running it on a representative genome assembly. This file can then be used with Prokka for consistent gene modeling.

```bash
prodigal -t prodigal_training_file -c -m -g 11 -p single -q -i input_file.fasta > /dev/null
```

--------------------------------

### Run Finitely Many Genes (FMG) model

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/post/pansize.md

Execute this command to estimate FMG model parameters. It requires a dated phylogeny and Panaroo's presence/absence table. The output is saved to a specified file.

```bash
panaroo-fmg --tree dated_phylogeny.newick --pa gene_presence_absence_renamed.Rtab -o fmg_results.txt
```

--------------------------------

### Panaroo Generate GFFs Command Parameters

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/post/output_gffs.md

This section details the command-line arguments for the `generate_gff_files` script, including input/output directories, GFF format options, and thread count.

```bash
usage: generate_gff_files [-h] -o OUTPUT_DIR -i INPUT_FILES [INPUT_FILES ...]
                          [-f {gff3,prokka}] [-t N_CPU]
                          [--verbose]

Generate GFF annotation files for each isolate from the pan-genome graph

optional arguments:
  -h, --help            show this help message and exit
  -t N_CPU, --threads N_CPU
                        number of threads to use (default=1)
  --verbose             print additional output

Input/output:
  -o OUTPUT_DIR, --out_dir OUTPUT_DIR
                        location of the Panaroo output directory
  -i INPUT_FILES [INPUT_FILES ...], --input INPUT_FILES [INPUT_FILES ...]
                        input GFF3 files used to run panaroo. These are
                        required Can also take a file listing each gff file
                        line by line.

GFFs:
  -f {gff3,prokka}, --f {gff3,prokka}
                        Output format for the GFF files. 'prokka' with
                        included FASTA, or strict 'gff3'. Default: 'prokka'
```

--------------------------------

### Run Panaroo Quality Control

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/quality/quality_control.md

Execute the Panaroo QC script with specified input GFF3 files, output directory, and number of threads. A reference mash database is required for contamination checks.

```bash
mkdir results
panaroo-qc -i *.gff -o results -t 3 --graph_type all --ref_db refseq.genomes.k21s1000.msh
```

--------------------------------

### Generate Simplified GFF Files

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/post/output_gffs.md

Run this command using the same output folder from your initial Panaroo run to generate simplified GFF files. The output will be located in `./panaroo_out/panaroo_gffs/`.

```bash
panaroo-generate-gffs -i *.gff -o panaroo_out
```

--------------------------------

### Panaroo Filter PA Usage

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/post/filter.md

This displays the command-line usage for `panaroo-filter-pa`. It shows the required input file (`-i`) and output directory (`-o`), as well as optional arguments like `--type` to specify filtering criteria.

```bash
usage: panaroo_filter_pa [-h] -i INPUT_FILE -o OUTPUT_DIR [--type TYPE]
                         [--version]

Filters the Panaroo gene_presence_absence.csv file to exclude sequences
classed as length outliers, pseudo genes or fragmented.

optional arguments:
  -h, --help            show this help message and exit
  --type TYPE           any combination of 'pseudo', 'length' or 'frag'
                        separated by commas
  --version             show program's version number and exit

Input/output:
  -i INPUT_FILE, --input INPUT_FILE
                        input 'gene_presence_absence.csv' file generated by
                        Panaroo
  -o OUTPUT_DIR, --out_dir OUTPUT_DIR
                        location of an output directory
```

--------------------------------

### Prokka with Custom Prodigal Training File

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/README.md

Generate annotations using Prokka with a custom Prodigal training file (`--prodigaltf`). This ensures consistent gene modeling across all genomes, applying any biases from the training model to all annotations.

```bash
prokka --prodigaltf prodigal_training_file input_file.fasta
```

--------------------------------

### Panaroo QC Pre-processing Script

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/README.md

Utilize the `panaroo-qc` script for preliminary quality checks on input GFF files before running Panaroo. This script requires a reference mash database.

```bash
panaroo-qc -t 3 --graph_type all -i *.gff --ref_db refseq.genomes.k21s1000.msh -o results
```

--------------------------------

### Run Panaroo for Pangenome Analysis

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/post/pwas.md

Execute Panaroo to generate a pangenome from Prokka GFF files. This command processes all GFF files from the 'prokka_output' directory and builds a multiple sequence alignment of core genes using MAFFT.

```bash
mkdir panaroo_output
panaroo -i ./prokka_output/*/*.gff -o panaroo_output -t 8 --verbose -a core
```

--------------------------------

### Run Pyseer for Association Analysis

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/post/pwas.md

Perform association analyses using Pyseer. This script first calculates the phylogenetic distance matrix and then iterates through specified antibiotic resistance phenotypes to find gene associations.

```bash
python ~/pyseer/scripts/phylogeny_distance.py --lmm ./panaroo_output/core_tree.treefile > pyseer_out/phylogeny_K.tsv

for anti in AZM     CRO     CFM     CIP     PEN     SMX     TET
do
python ~/pyseer/pyseer-runner.py --lmm --phenotypes ./metadata/pathogenwatch-neigo-eurogasp2013-amr-profile-1.tab --pres ./panaroo_output/gene_presence_absence.Rtab --similarity ./pyseer_out/phylogeny_K.tsv --phenotype-column $anti --output-patterns ./pyseer_out/gene_patterns_${anti}.txt > ./pyseer_out/${anti}_gwas.txt
done
```

--------------------------------

### Generate Cut Edges with Reference Genome Layout

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/vis/layout.md

Use this script to identify and generate a list of edges that break long-range connections when using a reference genome for layout. The --add_reference_edges flag is recommended to ensure all reference genome connections are preserved.

```bash
python ~/repos/panaroo/scripts/reference_based_layout.py  0 final_graph.gml  capacity_cut_edges.txt --add_reference_edges
```

--------------------------------

### Integrate a single genome into an existing Panaroo graph

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/merge/add_single.md

Use this command to add a new genome to a previously generated Panaroo graph. Specify the directory of the existing graph, the new genome's GFF file, and the output directory for the updated results.

```bash
panaroo-integrate -d kp_II_panaroo/ -i kpIII/5150_1#7.gff -t 24 -o updated_output
```

--------------------------------

### Run Panaroo on separate datasets

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/merge/merge_graphs.md

Execute Panaroo independently on different genome datasets. Use sensitive cleaning mode and ensure all edges are retained in the final graph.

```bash
panaroo -i kpII/*.gff -o kp_II_panaroo -t 24 --clean-mode sensitive --no_clean_edges
panaroo -i kpIII/*.gff -o kp_III_panaroo -t 24 --clean-mode sensitive --no_clean_edges
```

--------------------------------

### Explore gene neighbourhood with custom expansion

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/post/gene_neighbourhood.md

Modify the `panaroo-gene-neighbourhood` behavior to explore a different number of genes upstream and downstream from the target gene by adjusting the `--expand_no` parameter. This allows for a more focused or broader analysis of the gene's neighbourhood.

```bash
panaroo-gene-neighbourhood --gene metG_2 --graph final_graph.gml --out neighbourhood.txt --expand_no 2
```

--------------------------------

### Run Pyseer for Structural Variant Association

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/post/sv.md

This shell script iterates through a list of antibiotics, running Pyseer to find associations between structural variants and phenotypes. It outputs pattern files and WAS results for each antibiotic.

```bash
for anti in AZM     CRO     CFM     CIP     PEN     SMX     TET
do
python ~/pyseer/pyseer-runner.py --lmm --phenotypes ./metadata/pathogenwatch-neigo-eurogasp2013-amr-profile-1.tab --pres ./panaroo_output/struct_presence_absence.Rtab --similarity ./pyseer_out/phylogeny_K.tsv --phenotype-column $anti --output-patterns ./pyseer_out/struct_patterns_${anti}.txt > ./pyseer_out/${anti}_struct_was.txt
done
```

--------------------------------

### Annotate Genomes with Prokka

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/post/pwas.md

Use Prokka to annotate multiple FASTA files of bacterial genomes. This command iterates through each FASTA file in the 'genomes' directory, setting the genus to 'Neisseria' and outputting results to 'prokka_output'.

```bash
mkdir prokka_output
for fasta in ./genomes/*.fasta
do
prefix=$(basename ${fasta} .fasta)
prokka --cpus 5 --genus Neisseria --usegenus --outdir ./prokka_output/${prefix} --prefix $prefix $fasta
done
```

--------------------------------

### Infinitely Many Genes (IMG) Model

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/post/pansize.md

Estimates model parameters for the Infinitely Many Genes model using gene frequencies. Requires a dated phylogeny and a presence/absence file.

```APIDOC
## panaroo-img

### Description
Estimate model parameters for either the Infinitely Many Genes Model using gene frequencies.

### Method
Command-line tool

### Endpoint
`panaroo-img --pa gene_presence_absence.Rtab -o img_results -tree dated_phylogeny.newick`

### Parameters
#### Path Parameters
- `--tree` (string) - Required - A dated phylogeny.
- `--pa` (string) - Required - A presence/absence file produced by Panaroo.
- `-o` or `--out_dir` (string) - Required - Location of an output directory.

#### Optional Arguments
- `-h`, `--help` - show this help message and exit
- `-D` (integer) - Number of separate rate classes to use for the dispensable genome. Can be either 1 or 2.
- `--no_essential` - Removes essential gene class from model.
- `--no_constraint` - Removes constraint that u/v must equal the genome size.
- `--model` (string) - Model to fit. Can be one of 'coalescent' or 'fixed'.
- `--fit` (string) - Whether to use the gene frequency spectrum or the core/pangenome size for fitting (default=gf).
- `--init_u1` (float) - Initial value for u1 (default=0.01).
- `--init_u2` (float) - Initial value for u2 (default=0.01).
- `--init_v1` (float) - Initial value for v1 (default=0.01).
- `--init_v2` (float) - Initial value for v2 (default=0.01).
- `--init_ess` (integer) - Initial value for the number of essential genes (default=2000).
- `--verbose` - print additional output.
- `--version` - show program's version number and exit

### Request Example
```bash
mkdir img_results
panaroo-img --pa gene_presence_absence.Rtab -o img_results -tree dated_phylogeny.newick
```

### Response
Output files containing estimated model parameters will be generated in the specified output directory.
```

--------------------------------

### Run Panaroo with edge filtering disabled

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/post/gene_neighbourhood.md

Execute Panaroo with the `--no_clean_edges` flag to disable edge filtering. This is useful for examining all edges in the graph, including those that might be artifacts of misassemblies, providing a more complete view of the graph structure.

```bash
panaroo -i *.gff -o output_panaroo/ --clean-mode strict -t 24 --no_clean_edges
```

--------------------------------

### Panaroo in Sensitive Mode

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/README.md

Run Panaroo in sensitive mode to retain a higher proportion of genes, including rare plasmids, at the potential expense of increased contamination. This mode is less conservative than the default.

```bash
panaroo -i *.gff -o results --clean-mode sensitive
```

--------------------------------

### Finitely Many Genes (FMG) Model

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/post/pansize.md

Estimates model parameters for the Finitely Many Genes model. Requires a dated phylogeny and a presence/absence file.

```APIDOC
## panaroo-fmg

### Description
Estimate model parameters for the Finitely Many Genes Model.

### Method
Command-line tool

### Endpoint
`panaroo-fmg --tree dated_phylogeny.newick --pa gene_presence_absence_renamed.Rtab -o fmg_results.txt`

### Parameters
#### Path Parameters
- `--tree` (string) - Required - A dated phylogeny.
- `--pa` (string) - Required - A presence/absence file produced by Panaroo.
- `-o` or `--outfile` (string) - Required - Name of the output file.

#### Optional Arguments
- `-h`, `--help` - show this help message and exit
- `--nboot` (integer) - The number of sub-sampling bootstrap iterations to perform.
- `-t` or `--threads` (integer) - Number of threads to use (default=1).
- `--verbose` - print additional output.
- `--version` - show program's version number and exit

### Request Example
```bash
panaroo-fmg --tree dated_phylogeny.newick --pa gene_presence_absence_renamed.Rtab -o fmg_results.txt
```

### Response
Output file containing estimated model parameters will be generated with the specified name.
```

--------------------------------

### Find all paths through a gene

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/post/gene_neighbourhood.md

Use `panaroo-gene-neighbourhood` to find all unique paths that traverse a specified gene in the genome graph. This helps in understanding structural variations and supported paths.

```bash
panaroo-gene-neighbourhood --gene metG_2 --graph final_graph.gml --out neighbourhood.txt
```

--------------------------------

### Align Core Genome with Clustal

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/gettingstarted/params.md

Aligns core genes using Clustal, specifying the core genome and number of CPUs. Ensure input GFF files are provided.

```bash
panaroo -i *.gff -o ./results/ --clean-mode strict -a core --aligner clustal --core_threshold 0.98 -t 10
```

--------------------------------

### Count Patterns for False Discovery Analysis

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/post/sv.md

This Python script counts the number of patterns found by Pyseer, which is necessary for calculating the false discovery rate threshold.

```python
python ~/pyseer/scripts/count_patterns.py ./pyseer_out/struct_patterns_AZM.txt
```

--------------------------------

### Merge Panaroo graphs

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/merge/merge_graphs.md

Merge the pangenome graphs generated from independent Panaroo runs. This command takes the output directories of previous Panaroo runs as input.

```bash
mkdir kp_merge
panaroo-merge -d kp_II_panaroo/ kp_III_panaroo/ -o kp_merge -t 24
```

--------------------------------

### Merge Paralogs

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/gettingstarted/params.md

Enables the merging of paralogs into separate clusters. Use this option when paralogs should be treated as a single gene family.

```bash
panaroo -i *.gff -o ./results/  --clean-mode strict --merge_paralogs
```

--------------------------------

### Adjust Gene Family Collapse Threshold

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/gettingstarted/params.md

Runs Panaroo with a relaxed family sequence identity threshold of 50%. Adjusts the level at which genes are collapsed into putative families.

```bash
panaroo -i *.gff -o ./results/ --clean-mode strict -f 0.5
```

--------------------------------

### Summarize Rearrangement Results in R

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/post/sv.md

This R code aggregates structural variant association results across multiple antibiotics, filters for significant p-values, removes duplicates, and saves the annotated top results to a CSV file.

```r
gono_struct_was <- do.call(rbind, map(antibiotics, function(ant){
  tbl <- fread(paste(c("./gono_harris_gwas/",ant,"_struct_was.txt"), collapse = ""), data.table = FALSE)
  tbl$antibiotic <- ant
  return(tbl)
}))

gono_struct_was <- gono_struct_was[order(gono_struct_was$`lrt-pvalue`),]
gono_struct_was <- gono_struct_was[!grepl("bad-chisq", gono_struct_was$notes),]

# threshold form running count_patterns in pyseer
sig_threshold <- 0.05/(3239*length(antibiotics))

sum(gono_struct_was$`lrt-pvalue`<sig_threshold)
top_results <- gono_struct_was[gono_struct_was$`lrt-pvalue`<sig_threshold,]
top_results <- top_results[!duplicated(top_results$variant),]

write.csv(top_results, file="./gono_harris_gwas/gono_struct_was_annotated.csv", quote = FALSE)
```

--------------------------------

### Summarize GWAS Results in R

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/post/pwas.md

Summarize and filter significant results from the Pyseer GWAS analysis using R. This script reads the GWAS output files, applies a Bonferroni correction threshold, and writes the significant hits to a CSV file.

```r
antibiotics <- c('AZM', 'CRO', 'CFM', 'CIP', 'PEN', 'SMX', 'TET')

gono_gwas <- do.call(rbind, map(antibiotics, function(ant){
  tbl <- fread(paste(c("./gono_harris_gwas/",ant,"_gwas.txt"), collapse = ""), data.table = FALSE)
  tbl$antibiotic <- ant
  return(tbl)
}))

gono_gwas <- gono_gwas[order(gono_gwas$`lrt-pvalue`),]
gono_gwas <- gono_gwas[!grepl("bad-chisq", gono_gwas$notes),]

# threshold form running count_patterns in pyseer
sig_threshold <- 0.05/(529*length(antibiotics))

sum(gono_gwas$`lrt-pvalue`<sig_threshold)
sig_hits <- gono_gwas[gono_gwas$`lrt-pvalue`<sig_threshold,]

write.csv(sig_hits, file = "pan_was_results.csv", quote = FALSE, row.names = FALSE)
```

--------------------------------

### Filter Gene Presence/Absence Matrix

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/post/filter.md

Use this command to filter the `gene_presence_absence.csv` file. Specify the input file and output directory. You can filter by 'pseudo' genes, genes of unusual 'length', or 'frag'mented genes by providing a comma-separated list to the --type argument.

```bash
panaroo-filter-pa -i ./gene_presence_absence.csv -o ./ --type pseudo,length
```

--------------------------------

### Refind Genes with Custom Radius and Match Proportion

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/gettingstarted/params.md

Refinds genes using a search radius of 1000 nucleotides and requires at least 50% of the missing gene sequence to match. Useful for identifying genes missed by initial annotation.

```bash
panaroo -i *.gff -o ./results/ --clean-mode strict --refind_prop_match 0.5 --search_radius 1000
```

--------------------------------

### Count Patterns for Multiple Testing Correction

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/post/pwas.md

Count the number of unique gene patterns identified by Pyseer. This is a necessary step for controlling multiple testing in association studies.

```bash
python ~/pyseer/scripts/count_patterns.py ./pyseer_out/gene_patterns_AZM.txt
```

--------------------------------

### Panaroo with Invalid Gene Removal

Source: https://github.com/gtonkinhill/panaroo/blob/master/docs/README.md

Use the `--remove-invalid-genes` flag to ignore annotations that do not conform to expected standards, such as those with premature stop codons or invalid lengths. This is useful when dealing with GFFs from sources like RefSeq.

```bash
panaroo -i *.gff -o results --clean-mode strict --remove-invalid-genes
```

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