---
title: Extracting allele tables using pmotools-python
---
```{r setup, echo=F}
source("../common.R")
```
# pmotools-python extract_allele_table
To extract allele table information from a PMO the command line interactive script with `pmotools-python extract_allele_table` can be used
* pmotools-python extract_allele_table
* **Required arguments**
* **\-\-file** - the PMO file to extract from
* **\-\-output** - the output stub of the files to be created
* **Optional arguments**
By default only 3 fields are extracted by this extractor, 1) sampleID (library_sample_sample_name), 2) locus (target_name), and 3) allele (microhaplotype_id) with those default column names. This can be controlled by **\-\-default_base_col_names** and if you supply 3 comma separated values you can change the default header.
You can also add to the table any values from the other portions of the PMO file by using the following arguments
* adding fields arguments
* **\-\-specimen_info_meta_fields** - Meta Fields if any to include from the specimen table
* **\-\-library_sample_info_meta_fields** - Meta Fields if any to include from the library_sample table
* **\-\-microhap_fields** - additional optional fields from the detected microhaplotype object to include
* **\-\-representative_haps_fields** - additional optional fields from the detected representative object to include
Other optional arguments have to do with the ouput file over writing and delimiter being used, use `-h` to see all arguments
```{bash}
pmotools-python extract_allele_table -h
```
The python code for `extract_allele_table` script is below
```{python}
#| echo: true
#| eval: false
#| code-fold: true
#| code-line-numbers: true
#| filename: pmotools-python/src/pmotools/scripts/pmo_to_tables/extract_allele_table.py
#| file: ../pmotools-python/src/pmotools/scripts/pmo_to_tables/extract_allele_table.py
```
Can download example PMOs here
```{bash, eval = F}
wget https://plasmogenepi.github.io/PMO_Docs/format/moz2018_PMO.json.gz
wget https://plasmogenepi.github.io/PMO_Docs/format/PathWeaverHeome1_PMO.json.gz
```
```{bash}
mkdir -p example
cd example
# default
pmotools-python extract_allele_table --file ../../format/moz2018_PMO.json.gz --output extraction --overwrite
```
```{bash}
cd example
# changing default column names
pmotools-python extract_allele_table --file ../../format/moz2018_PMO.json.gz --output extraction --overwrite --default_base_col_names sample,target,hapid
```
Changing the output file delimiter
```{bash}
cd example
pmotools-python extract_allele_table --file ../../format/moz2018_PMO.json.gz --output extraction --overwrite --delim ,
```
Adding on additional columns from the specimen_infos section
```{bash, eval = F}
cd example
# adding other PMO fields
pmotools-python extract_allele_table --file ../../format/moz2018_PMO.json.gz --output STDOUT --specimen_info_meta_fields collection_country,collection_date | head
```
```{bash, echo = F}
cd example
# adding other PMO fields
pmotools-python extract_allele_table --file ../../format/moz2018_PMO.json.gz --output STDOUT --specimen_info_meta_fields collection_country,collection_date 2>/dev/null | head
```
Can continue to add on more columns from other sections
```{bash, eval = F}
cd example
# adding other PMO fields including reads field
pmotools-python extract_allele_table --file ../../format/moz2018_PMO.json.gz --output STDOUT --specimen_info_meta_fields collection_country,collection_date --microhap_fields reads | head
```
```{bash, echo = F}
cd example
# adding other PMO fields including seq field
pmotools-python extract_allele_table --file ../../format/moz2018_PMO.json.gz --output STDOUT --specimen_info_meta_fields collection_country,collection_date --microhap_fields reads 2>/dev/null | head
```
## Creating output for MOIRE
MOIRE is a program that can be used to estimate COI and other population estimates from a population. See it's [github](https://github.com/EPPIcenter/moire) for full usage.
```{bash, eval = F}
mkdir -p example
cd example
# default table is all moire needs
pmotools-python extract_allele_table --file ../../format/moz2018_PMO.json.gz --output extraction --overwrite
```
```{r, eval = F}
df <- read.csv("example/extraction_allele_table.tsv", sep = "\t")
data <- load_long_form_data(df)
# With data in appropriate format, run MCMC as follows
mcmc_results <- moire::run_mcmc(data, is_missing = data$is_missing)
```
## Creating output for dcifer
dcifer is a program that can estimate IBD even from mixed infections. See it's [github](https://github.com/EPPIcenter/dcifer) for full usage
```{bash}
mkdir -p example
cd example
# default
pmotools-python extract_allele_table --file ../../format/moz2018_PMO.json.gz --output extraction --overwrite --delim ,
# dcifer can calculate allele frequencies if not provided or you can have extract_allele_table write them as well
pmotools-python extract_allele_table --file ../../format/moz2018_PMO.json.gz --output extraction --overwrite --allele_freqs_output allele_freqs_extraction --delim ,
```
```{r, eval = F}
dsmp <- readDat("example/extraction_allele_table.csv", svar = "sampleID", lvar = "locus", avar = "allele")
lrank <- 2
coi <- getCOI(dsmp, lrank = lrank)
afreq <- calcAfreq(dsmp, coi, tol = 1e-5)
dres0 <- ibdDat(dsmp, coi, afreq, pval = TRUE, confint = TRUE, rnull = 0,
alpha = 0.05, nr = 1e3)
```