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	<title>生信菜鸟团 &#187; 软件安装</title>
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		<title>自学CHIP-seq分析第四讲~必要软件安装以及文章结果下载</title>
		<link>http://www.bio-info-trainee.com/1740.html</link>
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		<pubDate>Tue, 05 Jul 2016 00:34:53 +0000</pubDate>
		<dc:creator><![CDATA[ulwvfje]]></dc:creator>
				<category><![CDATA[tutorial]]></category>
		<category><![CDATA[CHIP-seq]]></category>
		<category><![CDATA[fastqc]]></category>
		<category><![CDATA[软件安装]]></category>

		<guid isPermaLink="false">http://www.bio-info-trainee.com/?p=1740</guid>
		<description><![CDATA[博文的顺序有点乱，因为怕读到前面的公共测序数据下载这篇文章的朋友搞不清楚，我如何 &#8230; <a href="http://www.bio-info-trainee.com/1740.html">Continue reading <span class="meta-nav">&#8594;</span></a>]]></description>
				<content:encoded><![CDATA[<p>博文的顺序有点乱，因为怕读到前面的公共测序数据下载这篇文章的朋友搞不清楚，我如何调用各种软件的，所以我这里强势插入一篇博客来描述这件事，当然也只是略过，我所有的软件理论上都是安装在我的home目录下的biosoft文件夹，所以你看到我一般安装程序都是:</p>
<p>cd ~/biosoft<br />
mkdir macs2 &amp;&amp; cd macs2 ##指定的软件安装在指定文件夹里面<span id="more-1740"></span></p>
<p>这只是我个人的安装习惯，因为我不是root，所以不能在linux系统下做太多事，我这里贴出我所有的软件安装代码：</p>
<blockquote><p>## pre-step: download <span style="color: #ff0000;">sratoolkit /fastx_toolkit_0.0.13/fastqc/bowtie2/bwa/MACS2/HOMER/QuEST/mm9/hg19/bedtools</span><br />
## http://www.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?view=software<br />
## http://www.ncbi.nlm.nih.gov/books/NBK158900/</p>
<p>## Download and install sratoolkit<br />
cd ~/biosoft<br />
mkdir sratoolkit &amp;&amp; cd sratoolkit<br />
wget http://ftp-trace.ncbi.nlm.nih.gov/sra/sdk/2.6.3/sratoolkit.2.6.3-centos_linux64.tar.gz<br />
##<br />
## Length: 63453761 (61M) [application/x-gzip]<br />
## Saving to: "sratoolkit.2.6.3-centos_linux64.tar.gz"<br />
tar zxvf sratoolkit.2.6.3-centos_linux64.tar.gz</p>
<p>## Download and install bedtools<br />
cd ~/biosoft<br />
mkdir bedtools &amp;&amp; cd bedtools<br />
wget https://github.com/arq5x/bedtools2/releases/download/v2.25.0/bedtools-2.25.0.tar.gz<br />
## Length: 19581105 (19M) [application/octet-stream]<br />
tar -zxvf bedtools-2.25.0.tar.gz<br />
cd bedtools2<br />
make</p>
<p>## Download and install PeakRanger<br />
cd ~/biosoft<br />
mkdir PeakRanger &amp;&amp; cd PeakRanger<br />
wget https://sourceforge.net/projects/ranger/files/PeakRanger-1.18-Linux-x86_64.zip/<br />
## Length: 1517587 (1.4M) [application/octet-stream]<br />
unzip PeakRanger-1.18-Linux-x86_64.zip<br />
~/biosoft/PeakRanger/bin/peakranger -h</p>
<p>## Download and install bowtie<br />
cd ~/biosoft<br />
mkdir bowtie &amp;&amp; cd bowtie<br />
wget https://sourceforge.net/projects/bowtie-bio/files/bowtie2/2.2.9/bowtie2-2.2.9-linux-x86_64.zip/download<br />
#Length: 27073243 (26M) [application/octet-stream]<br />
#Saving to: "download" ## I made a mistake here for downloading the bowtie2<br />
mv download bowtie2-2.2.9-linux-x86_64.zip<br />
unzip bowtie2-2.2.9-linux-x86_64.zip</p>
<p>mkdir -p ~/biosoft/bowtie/hg19_index<br />
cd ~/biosoft/bowtie/hg19_index</p>
<p># download hg19 chromosome fasta files<br />
wget http://hgdownload.cse.ucsc.edu/goldenPath/hg19/bigZips/chromFa.tar.gz<br />
# unzip and concatenate chromosome and contig fasta files<br />
tar zvfx chromFa.tar.gz<br />
cat *.fa &gt; hg19.fa<br />
rm chr*.fa<br />
## ~/biosoft/bowtie/bowtie2-2.2.9/bowtie2-build ~/biosoft/bowtie/hg19_index/hg19.fa ~/biosoft/bowtie/hg19_index/hg19<br />
## Download and install BWA<br />
cd ~/biosoft<br />
mkdir bwa &amp;&amp; cd bwa</p>
<p>http://sourceforge.net/projects/bio-bwa/files/</p>
<p>tar xvfj bwa-0.7.12.tar.bz2 # x extracts, v is verbose (details of what it is doing), f skips prompting for each individual file, and j tells it to unzip .bz2 files<br />
cd bwa-0.7.12<br />
make<br />
export PATH=$PATH:/path/to/bwa-0.7.12 # Add bwa to your PATH by editing ~/.bashrc file (or .bash_profile or .profile file)<br />
# /path/to/ is an placeholder. Replace with real path to BWA on your machine<br />
source ~/.bashrc<br />
# bwa index [-a bwtsw|is] index_prefix reference.fasta<br />
bwa index -p hg19bwaidx -a bwtsw ~/biosoft/bowtie/hg19_index/hg19.fa<br />
# -p index name (change this to whatever you want)<br />
# -a index algorithm (bwtsw for long genomes and is for short genomes)<br />
## Download and install macs2<br />
## // https://pypi.python.org/pypi/MACS2/<br />
cd ~/biosoft<br />
mkdir macs2 &amp;&amp; cd macs2<br />
wget ~~~~~~~~~~~~~~~~~~~~~~MACS2-2.1.1.20160309.tar.gz<br />
tar zxvf MACS2-2.1.1.20160309.tar.gz<br />
cd MACS2-2.1.1.20160309<br />
python setup.py install --user</p>
<p>#################### The log for installing MACS2:<br />
Creating ~/.local/lib/python2.7/site-packages/site.py<br />
Processing MACS2-2.1.1.20160309-py2.7-linux-x86_64.egg<br />
Copying MACS2-2.1.1.20160309-py2.7-linux-x86_64.egg to ~/.local/lib/python2.7/site-packages<br />
Adding MACS2 2.1.1.20160309 to easy-install.pth file<br />
Installing macs2 script to ~/.local/bin<br />
Finished processing dependencies for MACS2==2.1.1.20160309<br />
############################################################<br />
~/.local/bin/macs2 --help</p>
<p>Example for regular peak calling:<br />
macs2 callpeak -t ChIP.bam -c Control.bam -f BAM -g hs -n test -B -q 0.01<br />
Example for broad peak calling:<br />
macs2 callpeak -t ChIP.bam -c Control.bam --broad -g hs --broad-cutoff 0.1</p>
<p>## Download and install homer (Hypergeometric Optimization of Motif EnRichment)<br />
## // http://homer.salk.edu/homer/<br />
## // http://blog.qiubio.com:8080/archives/3024<br />
## pre-install: Ghostscript，seqlogo,blat<br />
cd ~/biosoft<br />
mkdir homer &amp;&amp; cd homer<br />
wget http://homer.salk.edu/homer/configureHomer.pl<br />
perl configureHomer.pl -install<br />
perl configureHomer.pl -install hg19</p></blockquote>
<p>一般来说，对我这样水平的人来说，软件安装就跟家常便饭一样，没有什么问题了，但如果你是初学者呢，肯定没那么轻松，所以请加强学习，我无法在这里讲解太具体的知识了。</p>
<p>所有软件安装完毕后就可以下载文章对这些CHIP-seq的处理结果了，这个很重要，检验我们是否重复了人家的数据分析过程：</p>
<blockquote><p>## step3 : download the results from paper<br />
## http://www.bio-info-trainee.com/1571.html<br />
mkdir paper_results &amp;&amp; cd paper_results<br />
wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE52nnn/GSE52964/suppl/GSE52964_RAW.tar<br />
tar xvf GSE52964_RAW.tar</p>
<p>ls *gz |xargs gunzip</p>
<p>## step4 : run FastQC to check the sequencing quality.</p>
<p>##这里可以看到我们下载的原始数据已经被作者处理好了，去了接头，去了低质量序列</p>
<p>ls *.fastq | while read id ; do ~/biosoft/fastqc/FastQC/fastqc $id;done<br />
## Sequence length 51<br />
## %GC 39<br />
## Adapter Content passed</p>
<p>The quality of the reads is pretty good, we don't need to do any filter or trim</p>
<p>mkdir QC_results<br />
mv *zip *html QC_results/</p></blockquote>
<p>所以我们可以直接拿这些数据去做比对了！！！</p>
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