tar xfvz stacks-2.xx.tar.gz
cd stacks-2.xx
./configure
make
(become root)
make install
(or, use sudo)
sudo make install

五，详细过程
5.1.Clean the data
process_radtags 样本拆分和过滤低质量序列
样本是否需要拆分，以及barcode是什么，需要和实验人员确定。
参考手册4.1.1　指定 --inline_index参数
https://catchenlab.life.illinois.edu/stacks/manual/#install
指定barcode列表

cat barcodes_lane3
CGATA<tab>sample_01
CGGCG     sample_02
GAAGC     sample_03
GAGAT     sample_04
TAATG     sample_05
TAGCA     sample_06
AAGGG     sample_07
ACACG     sample_08
ACGTA     sample_09

process_radtags -p ./raw/ -o ./samples/ -b ./barcodes/barcodes_lane3 \
                  -e sbfI -r -c -q
process_radtags -P -p ./raw  -b ./barcodes/barcodes -o ./samples/ \
                  -c -q -r --inline_index --renz_1 nlaIII --renz_2 mluCI

-p 待处理fataq文件所在的目录
-o 输出结果目录
-b barcode列表文件
-e restrction enzyme类型
5.2 和参考序列进行比对
可以使用常用的比对软件　GSnap, BWA, or Bowtie2.
可以用samtools或者picard查看比对的质量。
建议使用bwa mem进行比对。
烟草有参考基因组吗？
5.3
无参流程denovo_map.pl 或者单独执行命令
有参流程ref_map.pl或者单独执行命令
大部分stack2的组件需要一个population map文件，这个文件其实是根据样本特征进行分组，打标签
population map

cat popmap_both
sample_01<tab>red<tab>high
sample_02     red      high
sample_03     red      high
sample_04     red      high
sample_05     yellow   high
sample_06     yellow   high
sample_07     yellow   high
sample_08     yellow   high
sample_09     blue     low
sample_10     blue     low
sample_11     blue     low
sample_12     blue     low
sample_13     orange   low
sample_14     orange   low
sample_15     orange   low
sample_16     orange   low

denovo_map.pl -T 8 -M 6 -o ./stacks/ --samples ./samples --popmap ./popmaps/popmap --paired

或者单独执行命令：

#!/bin/bash

src=$HOME/research/project

files=”sample_01
sample_02
sample_03”

#
# Build loci de novo in each sample for the single-end reads only. If paired-end reads are available, 
# they will be integrated in a later stage (tsv2bam stage).
# This loop will run ustacks on each sample, e.g.
#   ustacks -f ./samples/sample_01.1.fq.gz -o ./stacks -i 1 --name sample_01 -M 4 -p 8
#
id=1
for sample in $files
do
    ustacks -f $src/samples/${sample}.1.fq.gz -o $src/stacks -i $id --name $sample -M 4 -p 8
    let "id+=1"
done

# 
# Build the catalog of loci available in the metapopulation from the samples contained
# in the population map. To build the catalog from a subset of individuals, supply
# a separate population map only containing those samples.
#
cstacks -n 6 -P $src/stacks/ -M $src/popmaps/popmap -p 8

#
# Run sstacks. Match all samples supplied in the population map against the catalog.
#
sstacks -P $src/stacks/ -M $src/popmaps/popmap -p 8

#
# Run tsv2bam to transpose the data so it is stored by locus, instead of by sample. We will include
# paired-end reads using tsv2bam. tsv2bam expects the paired read files to be in the samples
# directory and they should be named consistently with the single-end reads,
# e.g. sample_01.1.fq.gz and sample_01.2.fq.gz, which is how process_radtags will output them.
#
tsv2bam -P $src/stacks/ -M $src/popmaps/popmap --pe-reads-dir $src/samples -t 8

#
# Run gstacks: build a paired-end contig from the metapopulation data (if paired-reads provided),
# align reads per sample, call variant sites in the population, genotypes in each individual.
#
gstacks -P $src/stacks/ -M $src/popmaps/popmap -t 8

#
# Run populations. Calculate Hardy-Weinberg deviation, population statistics, f-statistics
# export several output files.
#
populations -P $src/stacks/ -M $src/popmaps/popmap -r 0.65 --vcf --genepop --structure --fstats --hwe -t 8

ref_map.pl -T 8 --popmap ./popmaps/popmap -o ./stacks/ --samples ./aligned

或者单独执行命令：

#!/bin/bash

src=$HOME/research/project
bwa_db=$src/bwa_db/my_bwa_db_prefix
    
files=”sample_01
sample_02
sample_03”

#
# Align paired-end data with BWA, convert to BAM and SORT.
#
for sample in $files
do 
    bwa mem -t 8 $bwa_db $src/samples/${sample}.1.fq.gz $src/samples/${sample}.2.fq.gz |
      samtools view -b |
      samtools sort --threads 4 > $src/aligned/${sample}.bam
done

#
# Run gstacks to build loci from the aligned paired-end data. We have instructed
# gstacks to remove any PCR duplicates that it finds.
#
gstacks -I $src/aligned/ -M $src/popmaps/popmap --rm-pcr-duplicates -O $src/stacks/ -t 8

#
# Run populations. Calculate Hardy-Weinberg deviation, population statistics, f-statistics and 
# smooth the statistics across the genome. Export several output files.
#
populations -P $src/stacks/ -M $src/popmaps/popmap -r 0.65 --vcf --genepop --fstats --smooth --hwe -t 8

5.4 结果数据分析
genetic map
population analysis
STRUCTURE
Adegenet
coverage