使用blast对其reads 然后根据密码子转换成氨基酸

Data preprocessing and predictors
After sequencing the quasispecies, we collected the point mutation data for 457 positions including the positions from 1 to 61 and 2820 to 3215 in and close to the preS region. We counted the frequencies of the nucleotides in each position. To describe the mutation complexity in each position, we transformed the frequency data to Shannon entropy, which is defined as H=−∑ipilogpi, ∑ipi=1 where i∈{A,C,G,T} and pi is its frequency, xlog(x)=0 when x = 0. Entropy of all the 457 nucleotide positions of preS region were used as predictors for HCC diagnosis.

docker run -v /home/bioinfo/hbv_pipeline/data:/data anneng01:8090/app/fqc fqc qc hbv s1 /data/SRR6378032_1.fastq.gz --r2 /data/SRR6378032_2.fastq.gz -o /data/qc/

2.cutadapt
具体算法见
https://cutadapt.readthedocs.io/en/stable/guide.html?highlight=max-n#dealing-with-n-bases

docker run -v /home/bioinfo/hbv_pipeline/:/workplace pegi3s/cutadapt -q 1 --max-n 0 --minimum-length 10 -o /workplace/data/SRR6378032_1.cleaned.fastq.gz -p /workplace/data/SRR6378032_2.cleaned.fastq.gz /workplace/data/SRR6378032_1.fastq.gz /workplace/data/SRR6378032_2.fastq.gz

-q 按照质量值进行过滤
--max-n 按N碱基数量进行过滤
--minimum-length 按长度进行过滤
-o R1的输出
-p R2的输出

3.qap的环状参考基因组修复过程涉及了几个自己的perl和R脚本 算法质量情况不明确 我们采用下面的软件来替代：
https://github.com/apeltzer/CircularMapper
java -jar generator-1.93.5.jar CircularGenerator -e 20 -i ../data/demo.fasta -s "AB033556.1"
这个软件有个bug fasta中的序列id不能包括空格 有空格的话就找不到这条序列 导致没有进行处理 处理的算法很简单 就是从头部取一定的碱基数量加到尾部
本步骤要做成一个cwl的选择项 只有环状基因组需要执行这个步骤
参考序列处理完毕后就可以使用新生成的参考序列进行比对（BWA、Bowtie2等） 但是比对完毕后要继续使用另外一个模块(RealignSAMFile.jar)进行重新对齐

4.序列比对
docker run -v /home/bioinfo/hbv_pipeline/data/:/data anneng01:8090/library/angs_bwa:1.0.0 bwa mem -M /data/HBV_C_AB033556_150.fasta /data/SRR6377924_1.fastq.gz /data/SRR6377924_2.fastq.gz -o /data/SRR6377924.sam

//下面的命令不支持bwa mem 的结果 先不执行
java -jar RealignSAMFile.jar -e 500 -i SRR6377924.sam -r HBV_C_AB033556.fasta

过滤没有比对上的序列
docker run -v /home/bioinfo/hbv_pipeline/:/work jweinstk/samtools samtools view -bF 4 /work/mapping/SRR6377924.sam -o /work/mapping/SRR6377924.bam

5.去除PCR重复

docker run -v /home/bioinfo/hbv_pipeline/:/work quay.io/biocontainers/samtools:1.15.1--h1170115_0 samtools collate -o /work/mapping/SRR6377924.namecollate.bam /work/mapping/SRR6377924.bam 

docker run -v /home/bioinfo/hbv_pipeline/:/work quay.io/biocontainers/samtools:1.15.1--h1170115_0 samtools fixmate -m /work/mapping/SRR6377924.namecollate.bam  /work/mapping/SRR6377924.fixmate.bam

docker run -v /home/bioinfo/hbv_pipeline/:/work quay.io/biocontainers/samtools:1.15.1--h1170115_0 samtools sort -o /work/mapping/SRR6377924.sorted.bam /work/mapping/SRR6377924.fixmate.bam

docker run -v /home/bioinfo/hbv_pipeline/:/work quay.io/biocontainers/samtools:1.15.1--h1170115_0 samtools markdup -r  /work/mapping/SRR6377924.sorted.bam /work/mapping/SRR6377924.sorted.rmdup.bam

6.call snp

docker run -v /home/bioinfo/hbv_pipeline/:/workplace quay.io/biocontainers/lofreq:broken---2.5.1--py38h1bd3507_2 lofreq call -f /workplace/data/HBV_C_AB033556_150.fasta -o /workplace/calling/SRR6377924.vcf /workplace/mapping/SRR6377924.sorted.rmdup.bam

7.merge R1 R2

docker run -v /home/bioinfo/hbv_pipeline/:/workhome quay.io/biocontainers/samtools:1.15.1--h1170115_0 samtools view -h -o /workhome/mapping/SRR6377924.sorted.rmdup.sam /workhome/mapping/SRR6377924.sorted.rmdup.bam

docker run -v /home/bioinfo/hbv_pipeline/:/workhome quay.io/biocontainers/samtools:1.15.1--h1170115_0 samtools fasta -1 /workhome/merging/SRR6377924_R1.fasta -2 /workhome/merging/SRR6377924_R2.fasta  -0 /dev/null -s /dev/null -n  /workhome/mapping/SRR6377924.sorted.rmdup.sam

docker run -v /home/bioinfo/hbv_pipeline/:/workplace staphb/bbtools bbmerge.sh in1=/workplace/merging/SRR6377924_R1.fasta in2=/workplace/merging/SRR6377924_R2.fasta out=/workplace/merging/SRR6377924_QS.fasta

//======有异常 先不用这个软件====

wget https://github.91chi.fun//https://github.com//neufeld/pandaseq/archive/refs/tags/v2.11.tar.gz
tar xvfz v2.11.tar.gz
sudo apt-get install build-essential libtool automake zlib1g-dev libbz2-dev pkg-config
./autogen.sh && ./configure && make && sudo make install

wget https://github.91chi.fun//https://github.com//neufeld/pandaseq-sam/archive/refs/tags/v1.4.tar.gz
./autogen.sh && ./configure && make && sudo make install

//==========================

对突变频率 香农熵做了详细分析

香农熵计算器

这个表格的格式可以作为分析的模板
行是样本 列是突变的位置或者重要图标的代号

从蛋白序列预测血清型

https://sci-hub.st/10.1159/000080872
对HBV进行进化树分析 里面也提到血清型和基因型之间的复杂的对应关系。
涉及的软件：
DNADIST and NEIGHBOR from the Phylip program package version 3.53

PUZZLE

Bootstrap on 1,000 replicas was performed with SEQBOOT, DNADIST, NEIGHBOR, and CONSENSE from the Phylip package.

HIV-GRADE HBV

文章提到了一些工具 用于分型、耐药、免疫逃逸的分析

/home/bioinfo/miniconda2/envs/assembly/bin/spades.py      -1      /ceph_disk3/hbv/HBV_illumina/106/106_S46_L001_R1_001.fastq      -2      /ceph_disk3/hbv/HBV_illumina/106/106_S46_L001_R2_001.fastq      -o      /ceph_disk3/hbv/HBV_illumina/106/spades

find ../all_data/*_L001_R1_001.fastq.gz | sed 's/_L001_R1_001.fastq.gz$//' | parallel 'bbmerge.sh in1=../all_data/{}_L001_R1_001.fastq.gz in2=../all_data/{}_L001_R2_001.fastq.gz out={}.fastq  outu1={}.R1.umerged outu2={}.R2.unmerged'

发现327个样本中 有几个样本 R1 和 R2 的数量不一致 针对这些样本 使用spades进行组装 取最长的序列进行第二步
因为涉及到组装 无法进行混合样品的分析 把这些样本当作单样本处理
将所有的fastq转成fasta(blast只识别fasta)

parallel 'seqtk seq -a {}> {.}.fasta' ::: *.fastq

2.使用blast 对样本中的序列进行分型 得到每个样本中各种分型的序列数量
构建blast数据库
从hbvdb下载的参考序列 有一个类别是RF 例如 https://www.ncbi.nlm.nih.gov/nucleotide/EU871985.1?report=genbank&log$=nuclalign&blast_rank=1&RID=Z8DW1MY8016 这个序列 NCBI没有标识类型 hbvdb将其注释为了BC重组型 我们当前先把这种RF的去掉

makeblastdb -in all_hbvdb_Genomes.fas -dbtype nucl

blastn -task blastn -max_target_seqs 1 -query ../0-merging-pe/100_S42.fasta -db ../hbvdb/all_hbvdb_Genomes.fas -num_threads 10 -out 100_S42.m8 -outfmt 6

nohup bash -c "find ../0-merging-pe/*.fasta | sed 's/.fasta$//' |  parallel --joblog ./logs -j40 blastn -task blastn -max_target_seqs 1 -query ../0-merging-pe/{}.fasta -db ../hbvdb/A-H/HBV_A_H.fas -out {/}.m8 -outfmt 6 " &

3.比对

nohup bash -c "find ../all_data/*_L001_R1_001.fastq.gz | sed 's/_L001_R1_001.fastq.gz$//' | parallel 'bwa mem -M AB033556_hbc_type_C.fasta {}_L001_R1_001.fastq.gz {}_L001_R2_001.fastq.gz > {/}.sam' " &

nohup parallel "samtools view -bF 4 {} > {/.}.bam" ::: ./sam/*.sam &
parallel samtools sort {} -o {.}.sorted.bam ::: *.bam

4.call

nohup parallel "lofreq indelqual {} --dindel -f ../3-mapping/AB033556_hbc_type_C.fasta -o {/.}.sorted.dindel.bam " ::: ../3-mapping/bam/*.sorted.bam &

nohup parallel "lofreq call {} --call-indels -f ../3-mapping/AB033556_hbc_type_C.fasta -o {/.}.vcf " ::: *.bam &

5.分析单倍型

find /ceph_disk2/siyida_327_sample/3-mapping/sam/ -name "*.sam" -exec basename \{} .sam \; | sed 's/.sam$//' |parallel 'java -jar clique-snv.jar -m snv-illumina -in /ceph_disk2/siyida_327_sample/3-mapping/sam/{}.sam'

直接用reads 在进化树上进行分型 并且能进行混合样本的分型