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# seaConnect--dataPrep

Scripts to prepare RADSeq data for analysis. 
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- filtering steps for dDocent SNP output
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- outlier detection
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- file conversions, subsetting and renaming 
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## Dependencies
You will need to install the following software:  
- [VCFtools](https://vcftools.github.io)
- [BCFtools](https://samtools.github.io/bcftools/)
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- [PLINK v1.9](https://www.cog-genomics.org/plink/1.9/)
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readme    
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- [PGDSpider](http://www.cmpg.unibe.ch/software/PGDSpider/)
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You will need to have the following R packages:  
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readme    
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adegenet, polysat, pegas, vcfR, hierfstat, coda, radiator, pcadapt
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## 01-SNPfiltering 
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script adapted from [ddocent tutorial](https://www.ddocent.com/filtering/) and additions

### run the script
move to the correct directory and make the output directories
```
cd ~/Documents/project_SEACONNECT/seaConnect--dataPrep/01-SNPfilters/
mkdir 01-Diplodus
mkdir 02-Mullus
```
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set the species-specific arguments for the script to run on  
  $1 = input file (specify path if not in current directory)  
  $2 = output folder  
  $3 = species code  
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```
bash filtering.sh ../00-rawData/01-Diplodus/sar_ddocent.vcf 01-Diplodus dip
bash filtering.sh ../00-rawData/02-Mullus/mullus.vcf 02-Mullus mul
```

### SNP filtering results for Mullus surmuletus					
				
| filtering step | filter for                                        | individuals retained | SNPs retained | run time (sec) | output |
| -------------- | ------------------------------------------------- | :------------------: | :-----------: | :------------: | ------ |
| step 0         | ddocent output data                               | 431                  | 49027         |                | mullus.vcf
| step 1         | call below 50%, mac < 3, min quality score = 30   | 431                  | 49027         | 71.00          | mullus.g5mac3.recode.vcf
| step 2         | min mean depth genotype call = 3                  | 431                  | 49027         | 74.00          | mullus.g5mac3dp3.recode.vcf
| step 3         | individuals > 50% missing data                    | 424                  | 49027         | 70.00          | mullus.g5mac3dplm.recode.vcf
| step 4         | sites > 5% missing data, maf 0.05, min meanDP = 5 | 424                  | 18727         | 14.00          | DP3g95maf05.recode.vcf
| step 5         | filter for allele balance                         |                      | 17965         |                | DP3g95maf05.fil1.vcf
| step 6         | filter out sites with reads from both strands     |                      | **SKIP**      |	             | 
| step 7         | ration mapping qualities ref vs alternate alleles |                      | 17546         |                | DP3g95maf05.fil3.vcf
| step 8         | paired status                                     |                      | 17546         |                | DP3g95maf05.fil4.vcf
| step 9         | remove sites quality score < 1/4 depth            |                      | 17546         |                | DP3g95maf05.fil5.vcf
| step 10        | depth x quality score cutoff	                     | 424                  | 15466         |	             | 
| step 11        | He > 0.6 & Fis > 0.5 & Fix < -0.5                 | 424                  | 15232         | 25 min         | DP3g95maf05.FIL.HFis.recode.vcf
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| step 12        | remove extreme outliers individual O HET          | 413                  | 15232         | 23.00          | DP3g95maf05.FIL.HFis.indHet.recode.vcf
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| step 13        | rename                                            |                      |               |                | mul_all_filtered_origid.vcf
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### SNP filtering results for Diplodus sargus								
				
| filtering step | filter for                                        | individuals retained | SNPs retained | run time (sec) | output |
| -------------- | ------------------------------------------------- | :------------------: | :-----------: | :------------: | ------ |
| step 0         | ddocent output data                               | 297                  | 13362         |                | sar_ddocent_.vcf
| step 1         | call below 50%, mac < 3, min quality score = 30   | 297                  | 13362         | 14.00          | g5mac3.recode.vcf
| step 2         | min mean depth genotype call = 3                  | 297                  | 13362         | 14.00          | g5mac3dp3.recode.vcf
| step 3         | individuals > 50% missing data                    | 297                  | 13362         | 16.00          | g5mac3dplm.recode.vcf
| step 4         | sites > 5% missing data, maf 0.05, min meanDP = 5 | 297                  | 10389         | 11.00          | DP3g95maf05.recode.vcf
| step 5         | filter for allele balance                         | 297                  | 10202         |                | DP3g95maf05.fil1.vcf
| step 6         | filter out sites with reads from both strands     | SKIP                 | **SKIP**      |                | SKIP
| step 7         | ration mapping qualities ref vs alternate alleles | 297                  | 9689          |                | DP3g95maf05.fil3.vcf
| step 8         | paired status                                     | 297                  | 9689          |                | DP3g95maf05.fil4.vcf
| step 9         | remove sites quality score < 1/4 depth            | 297                  | 9688          |                | DP3g95maf05.fil5.vcf
| step 10        | depth x quality score cutoff	                     | 297                  | 8325          | 11.00          | 
| step 11        | He > 0.6 & Fis > 0.5 & Fix < -0.5                 | 297                  | 8206          | 27 min         | DP3g95maf05.FIL.HFis.recode.vcf
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| step 12        | remove extreme outliers individual O HET          | 297                  |               |                | DP3g95maf05.FIL.HFis.indHet.recode.vcf
| step 13        | rename                                            |                      |               |                | dip_all_filtered_origid.vcf 
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### rename individuals for conventional naming system and create population map
this is adapted for our case, review R script or skip according to naming needs
```
bash renaming.sh 01-Diplodus/ dip
bash renaming.sh 02-Mullus/ mul
```
output:
- dip_all_filtered.vcf
- dip_popualtion_map_297ind.txt
- mul_all_filtered.vcf
- mul_population_map_413ind.txt
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## 02-Bayescan

Detect Fst outliers by bayesian inference with the [BayeScan software](http://cmpg.unibe.ch/software/BayeScan/)

### step 1: convert vcf files to Bayescan .txt files

This script will load your vcf file, determine the population identifier for each 
individual, and return a bayescan .txt inputfile
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set arguments :    
  $1 = input file (vcf)  
  $2 = species code  
The script is currently set to detect and assign two populations (K). Run the script 
interactively if you want to determine and assign other values of K.
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#### for mullus :
```
Rscript --vanilla Bayescan_input.R ../01-SNPfilters/02-Mullus/mul_all_filtered.vcf mul
```

#### for diplodus :
```
Rscript --vanilla Bayescan_input.R ../01-SNPfilters/01-Diplodus/dips_all_filtered.vcf dip
```

### step 2: determine outliers with Bayescan

download and compile Bayescan from [here](http://cmpg.unibe.ch/software/BayeScan/download.html) 
and copy the executable to your local bin folder:
```
cp ~/programmes/BayeScan2.1/binaries/BayeScan2.1_macos64bits /usr/local/bin/
```
#### run the BayeScan model for mullus and diplodus data
multiple runs with different parameters and different input data, with two chains per run 
to compare convergence later
```
bash run_bayescan.sh
```
### step 3: verify convergence and extract outliers
Run interactive R script called `Bayescan_evaluation.R`

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The script also extracts outlier lists for the different runs and export loci positions for later subsetting (with run index)
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## 03-PCAdapt

detect outliers with [PCAdapt](https://bcm-uga.github.io/pcadapt/articles/pcadapt.html) in 
R and retrieve loci positions from vcf file

### Run the PCAdapt.R script

This script will load your vcf file, detect outlier loci using the package pcadapt and
output a list of outlier loci positions that can be used to subset your vcf file

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set arguments :  
$1 = input file (vcf)  
$2 = species code  
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#### for mullus :
```
Rscript --vanilla PCAdapt.R ../01-SNPfilters/02-Mullus/mul_all_filtered.vcf mul
```
see how many outliers were detected :
```
wc -l outl_pos_pcadpt_mul.txt
```
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1327 outliers detected for mullus
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#### for diplodus :
```
Rscript --vanilla PCAdapt.R ../01-SNPfilters/01-Diplodus/dip_all_filtered.vcf dip

wc -l outl_pos_pcadpt_dip.txt
```
388 outliers detected for diplodus

## 04-finalPrep

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Final steps to get neutral and adaptive SNP sets and correct file formats for both species.
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This script subsets the filtered vcf file from 01-SNPfilters by outlier positions detected 
in 02-BayeScan and 03-PCAdapt.  
It also subsets the same vcf file for the remaining neutral positions and applies a final 
filter for HWE.
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Finally, the script converts the final adaptive and neutral .vcf files in .bed .bim .fam .raw
and .strct_in format necessary for downstream analyses.

The conversion to genepop format for use of GENODIVE (to calculate kinship) is done with
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the PGDSpider GUI.   
input: neutral.vcf and population map, output: neutral.gen.txt  
outputted .gen.txt file: add information on first line (otherwise genodive won't recognise format)  
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To run the script, set arguments:  
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  $1 = input file (vcf)  
  $2 = species code  
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  $3 = bayescan run index
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#### for diplodus
```
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mkdir 01-Diplodus
cp ../01-SNPfilters/01-Diplodus/dip_population_map_*.txt 01-Diplodus/
cp ../01-SNPfilters/01-Diplodus/dip_all_filtered.vcf 01-Diplodus/

bash outlier_positions.sh 01-Diplodus/dip_all_filtered.vcf dip run1
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```
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In total, 494 outlier loci were detected, with 10 loci detected by both the BayeScan and PCAdapt method.
After HWE filter, 7570 neutral loci were retained.

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#### for Mullus
```
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mkdir 02-Mullus
cp ../01-SNPfilters/02-Mullus/mul_population_map_*.txt 02-Mullus/
cp ../01-SNPfilters/02-Mullus/mul_all_filtered.vcf 02-Mullus/

bash outlier_positions.sh 02-Mullus/mul_all_filtered.vcf mul run1
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```
In total, 2680 adaptive loci were detected, with 10 loci detected by both the BayeScan and PCAdapt method.
After HWE filter, 12432 neutral loci were retained.

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#### clean up files to separate directories
```
mv dip_* 01-Diplodus/
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mv mul_* 02-Mullus/
```
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