README.md 10.1 KB
Newer Older
eboulanger's avatar
eboulanger committed
1
2
3
# seaConnect--dataPrep

Scripts to prepare RADSeq data for analysis. 
eboulanger's avatar
eboulanger committed
4
- filtering steps for dDocent SNP output
eboulanger's avatar
eboulanger committed
5
- outlier detection
eboulanger's avatar
eboulanger committed
6
- file conversions, subsetting and renaming 
eboulanger's avatar
eboulanger committed
7

eboulanger's avatar
eboulanger committed
8
9
10
11
## Dependencies
You will need to install the following software:  
- [VCFtools](https://vcftools.github.io)
- [BCFtools](https://samtools.github.io/bcftools/)
eboulanger's avatar
eboulanger committed
12
- [PLINK v1.9](https://www.cog-genomics.org/plink/1.9/)
eboulanger's avatar
eboulanger committed
13
14
15
16

You will need to have the following R packages:  


eboulanger's avatar
eboulanger committed
17
## 01-SNPfiltering 
eboulanger's avatar
eboulanger committed
18
19
20
21
22
23
24
25
26
27

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
```
eboulanger's avatar
eboulanger committed
28
29
30
31
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  
eboulanger's avatar
eboulanger committed
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
```
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
eboulanger's avatar
eboulanger committed
53
| step 12        | remove extreme outliers individual O HET          | 413                  | 15232         | 23.00          | DP3g95maf05.FIL.HFis.indHet.recode.vcf
eboulanger's avatar
eboulanger committed
54
| step 13        | rename                                            |                      |               |                | mul_all_filtered_origid.vcf
eboulanger's avatar
eboulanger committed
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71

### 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
eboulanger's avatar
eboulanger committed
72
73
| step 12        | remove extreme outliers individual O HET          | 297                  |               |                | DP3g95maf05.FIL.HFis.indHet.recode.vcf
| step 13        | rename                                            |                      |               |                | dip_all_filtered_origid.vcf 
eboulanger's avatar
eboulanger committed
74

eboulanger's avatar
eboulanger committed
75
76
77
78
79
80
81
82
83
84
85
### 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
eboulanger's avatar
eboulanger committed
86
87
88
89
90
91
92
93
94

## 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
eboulanger's avatar
eboulanger committed
95
96
97
98
99
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.
eboulanger's avatar
eboulanger committed
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126

#### 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`

eboulanger's avatar
eboulanger committed
127
The script also extracts outlier lists for the different runs and export loci positions for later subsetting (with run index)
eboulanger's avatar
eboulanger committed
128
129
130
131
132
133
134
135
136
137
138

## 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

eboulanger's avatar
eboulanger committed
139
140
141
set arguments :  
$1 = input file (vcf)  
$2 = species code  
eboulanger's avatar
eboulanger committed
142
143
144
145
146
147
148
149
150

#### 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
```
eboulanger's avatar
eboulanger committed
151
1327 outliers detected for mullus
eboulanger's avatar
eboulanger committed
152
153
154
155
156
157
158
159
160
161
162

#### 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

eboulanger's avatar
eboulanger committed
163
Final steps to get neutral and adaptive SNP sets and correct file formats for both species.
eboulanger's avatar
eboulanger committed
164

eboulanger's avatar
eboulanger committed
165
166
167
168
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.
eboulanger's avatar
eboulanger committed
169

eboulanger's avatar
eboulanger committed
170
171
172
173
174
175
176
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
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)
eboulanger's avatar
eboulanger committed
177

eboulanger's avatar
eboulanger committed
178
To run the script, set arguments:  
eboulanger's avatar
eboulanger committed
179
180
  $1 = input file (vcf)  
  $2 = species code  
eboulanger's avatar
eboulanger committed
181
  $3 = bayescan run index
eboulanger's avatar
eboulanger committed
182
183
184
  
#### for diplodus
```
eboulanger's avatar
eboulanger committed
185
186
187
188
189
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
eboulanger's avatar
eboulanger committed
190
```
eboulanger's avatar
eboulanger committed
191
192
193
194

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.

eboulanger's avatar
eboulanger committed
195
196
#### for Mullus
```
eboulanger's avatar
eboulanger committed
197
198
199
200
201
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
eboulanger's avatar
eboulanger committed
202
203
204
205
```
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.

eboulanger's avatar
eboulanger committed
206

eboulanger's avatar
eboulanger committed
207
208
209
#### clean up files to separate directories
```
mv dip_* 01-Diplodus/
eboulanger's avatar
eboulanger committed
210
211
mv mul_* 02-Mullus/
```
eboulanger's avatar
eboulanger committed
212