基因组重测序的unmapped reads assembly探究 【直播】我的基因组86

在前面的直播基因组系列，我们讲解过那些比对不少我们人类的参考基因组序列的数据，其实可以细致的进行探究。

这里主要参考这篇文章的图4：http://www.nature.com/ng/journal/v42/n11/figtab/ng.691F4.html

组装的contig注释到物种

这是2010年发表于nature genetics杂志的Whole-genome sequencing and comprehensive variant analysis of a Japanese individual using massively parallel sequencing 虽然文章选择的是SOAPdenovo,ABySS,Velvet这3款软件来进行组装，但毕竟是2010年的文章了，现在其实有更好的选择，比如Minia

选择Minia工具来组装

Minia软件也是基于de Bruijn图原理的短序列组装工具，优于以前的ABySS和SOAPdenovo，关键是速度非常快，十几分钟就OK了，不消耗计算机资源，所以这里就选择它啦。

下载安装Minia

安装官网的指导说明书下载二进制版本即可，代码如下：

1. ## Download and install Minia

2. # http://minia.genouest.org/

3. cd ~/biosoft

4. mkdir Minia &&  cd Minia

5. wget https://github.com/GATB/minia/releases/download/v2.0.7/minia-v2.0.7-bin-Linux.tar.gz

6. tar -zxvf minia-v2.0.7-bin-Linux.tar.gz

7. ~/biosoft/Minia/minia-v2.0.7-bin-Linux/bin/minia --help

8. ## eg: ./minia -in reads.fa -kmer-size 31 -abundance-min 3 -out output_prefix

软件使用方法也非常简单，就一行命令，其中最佳 -kmer-size需要用KmerGenie来确定。

使用

step1:提取比对失败的reads

1. samtools view -f4 jmzeng_recal.bam |perl -alne '{print "\@$F[0]\n$F[9]\n+\n$F[10]" }' >unmapped.fq

2. perl ~/biosoft/PRINSEQ/prinseq-lite-0.20.4/prinseq-lite.pl -verbose -fastq unmapped.fq -graph_data unmapped.gd -out_good null -out_bad null

3. perl ~/biosoft/PRINSEQ/prinseq-lite-0.20.4/prinseq-graphs.pl -i unmapped.gd -png_all -o unmapped

4. perl ~/biosoft/PRINSEQ/prinseq-lite-0.20.4/prinseq-graphs.pl -i unmapped.gd -html_all -o unmapped

5. cd ~/data/project/myGenome/gatk/jmzeng/unmapped

共31481084/4=7870271，仅仅是7.8M的reads

Input Information

Input file(s):	unmapped.fq
Input format(s):	FASTQ
# Sequences:	7,870,271
Total bases:	1,180,540,650

step2: 用KmerGenie确定kmer值

KmerGenie estimates the best k-mer length for genome de novo assembly.

KmerGenie predictions can be applied to single-k genome assemblers (e.g. Velvet, SOAPdenovo 2, ABySS, Minia).

1. ## http://kmergenie.bx.psu.edu/

2. cd ~/biosoft

3. mkdir KmerGenie &&  cd KmerGenie

4. wget http://kmergenie.bx.psu.edu/kmergenie-1.7044.tar.gz

5. tar zxvf kmergenie-1.7044.tar.gz

6. cd kmergenie-1.7044

7. make

8. python setup.py install --user

9. ~/.local/bin/kmergenie --help

10. cd ~/data/project/myGenome/gatk/jmzeng/unmapped

11. ~/.local/bin/kmergenie unmapped.fq

step3: 运行Minia

1. cd ~/data/project/myGenome/gatk/jmzeng/unmapped

2. ~/biosoft/Minia/minia-v2.0.7-bin-Linux/bin/minia  -in unmapped.fq -kmer-size 31 -abundance-min 3 -out output_prefix

7.8M的reads组装之后有272007条contigs

组装之后：

Prinseq v0.20.4 was used to calculate assembly statistics, including N50 contig size, GC content

1. cd ~/data/project/myGenome/gatk/jmzeng/unmapped

2. perl ~/biosoft/PRINSEQ/prinseq-lite-0.20.4/prinseq-lite.pl -verbose -fasta output_prefix.contigs.fa  -graph_data contigs.gd -out_good null -out_bad null

3. perl ~/biosoft/PRINSEQ/prinseq-lite-0.20.4/prinseq-graphs.pl -i contigs.gd -png_all -o contigs

4. perl ~/biosoft/PRINSEQ/prinseq-lite-0.20.4/prinseq-graphs.pl -i contigs.gd -html_all -o contigs

5. perl ~/biosoft/PRINSEQ/prinseq-lite-0.20.4/prinseq-lite.pl -verbose -fasta output_prefix.contigs.fa  -stats_assembly

就是给出一些指标，如下；

1. stats_assembly    N50 176

2. stats_assembly    N75 113

3. stats_assembly    N90 78

4. stats_assembly    N95 70

Input Information

Input file(s):	output_prefix.contigs.fa
Input format(s):	FASTA
# Sequences:	272,007
Total bases:	44,868,011

Length Distribution

Mean sequence length:	164.95 ± 204.44 bp
Minimum length:	63 bp
Maximum length:	10,187 bp
Length range:	10,125 bp
Mode length:	150 bp with 16,461 sequences

然后用RNA-SEQ数据来比对验证！ 以后再讲

把组装好的contigs拿去NCBI做blast看看物种分布,Distribution of top nucleotide BLAST hits by species from the NCBI nr database for 1000 random contigs in the assembly！其实上面的prinseq软件也简单的给出了一个污染物种分布情况表，但是这个原理不一样。以后再讲

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