OrthoMCL能做什么

Orthologs are homologs separated by speciation events. Paralogs are homologs separated by duplication events. Detection of orthologs is becoming much more important with the rapid progress in genome sequencing.

OrthoMCL is a genome-scale algorithm for grouping orthologous protein sequences. It provides not only groups shared by two or more species/genomes, but also groups representing species-specific gene expansion families. So it serves as an important utility for automated eukaryotic genome annotation. OrthoMCL starts with reciprocal best hits within each genome as potential in-paralog/recent paralog pairs and reciprocal best hits across any two genomes as potential ortholog pairs. Related proteins are interlinked in a similarity graph. Then MCL (Markov Clustering algorithm, Van Dongen 2000; www.micans.org/mcl) is invoked to split mega-clusters. This process is analogous to the manual review in COG construction. MCL clustering is based on weights between each pair of proteins, so to correct for differences in evolutionary distance the weights are normalized before running MCL.

OrthoMCL is similar to the INPARANOID algorithm (Remm, Storm et al. 2001), but is extended to cluster orthologs from multiple species. OrthoMCL clusters are coherent with groups identified by EGO (Lee, Sultana et al. 2002), and an analysis using EC number suggests a high degree of reliability (Li, Stoeckert et al. 2003).

In a recent assessment (Chen, et al. 2007), the performance of seven widely used orthology detection algorithms, representing three kinds of strategies (phylogeny-based, evolutionary distance-based and BLAST-based), are evaluated using the statistical technique Latent Class Analysis (LCA). LCA is useful when there are large data sets available but no gold standard. The results show an overall trade-off between sensitivity and specificity among these algorithms, with INPARANOID and OrthoMCL as the two best methods having both False Positive (FP) and False Negative (FN) error rates lower than 20%.

安装和使用

• 统一配置环境变量，一劳永逸

  • 环境变量配置：在系统中新建目录 ~/bin，将其完整路径加入到环境变量。
    • 把export PATH=${PATH}:~/bin 加到 ~/.bashrc中
  • PERL5LIB配置：在系统中新建目录 ~/perl5lib，将其完整路径加入到环境变量。
    • 把export PERL5LIB=${PERL5LIB}:~/perl5lib/加到~/.bashrc中
  • 更新环境变量配置 source ~/.bashrc

• mcl安装

  wget http://www.micans.org/mcl/src/mcl-latest.tar.gz
  tar xvzf mcl-latest.tar.gz
  cd mcl-latest
  ./configure --prefix=`pwd`/../mcl_bin
  make
  make install
  ln -s `pwd`/../mcl_bin/bin/* ~/bin/
  

• orthoMCL安装

  wget http://orthomcl.org/common/downloads/software/v2.0/orthomclSoftware-v2.0.9.tar.gz
  tar xvzf orthomclSoftware-v2.0.9.tar.gz
  cd orthomclSoftware-v2.0.9
  ln -s `pwd`/bin/* ~/bin/
  ln -s `pwd`/lib/perl/* ~/perl5lib
  

• 配置Mysql数据库

  • 安装mysql数据库
    • yum install mysql mysql-server
  • 设置mysql根用户的密码
    • mysql -uroot登录mysql数据库

    • 在mysql操作界面依次输入sql语句

      SET PASSWORD=PASSWORD("passwd");

      FLUSH PRIVILEGES;

  • 因为OrthoMCL运行时需要较大的存储空间，而我的根目录下空间不够， 因此需要更换数据库目录；如果根目录下空间足够，则不需要这部分操 作。

    • 查看mysql服务 service mysqld status

    • 关掉mysql服务 service mysqld stop

    • 移动数据库目录到目标位置

      • mkdir ~/mysql; chown mysql:mysql ~/mysql
      • mv /var/lib/mysql/* ~/mysql/
      • 在/etc/my.cnf文件中修改datadir为~/mysql

  • 修改/etc/my.cnf配置文件

    [mysqld]
    datadir=~/mysql
    #[OPTIMIZATION]
    ##Set this value to 50% of available RAM if your environment permits.
    myisam_sort_buffer_size=60G
    
    ##[OPTIMIZATION]
    ##This value should be at least 50% of free hard drive space. Use
    #caution if setting it to 100% of free space however. Your hard disk
    #may fill up!
    myisam_max_sort_file_size=200G
    
    ##[OPTIMIZATION]
    ##Our default of 2G is probably fine for this value. Change this value
    #only if you are using a machine with little resources available. 
    read_buffer_size=2G
    

  • 启动mysql服务 service mysqld start

    • 若启动失败，查看log文件 /var/log/mysqld.log中的错误信息。
    • /usr/libexec/mysqld: Can't change dir to [Error code 13]
      • 确保datadir的所有上层目录有x属性
      • 若依然启动不了，在终端运行setenforce 0 关闭SELINUX
  • 新建用户和数据库
    • 新建名字为orthomcl的数据库 CREATE DATABASE orthomcl;

    • 新建用户orthomcl，密码为152108, 该用户对数据库orthomcl有完全操作 权限

      GRANT SELECT,INSERT,UPDATE,DELETE,CREATE VIEW,
      CREATE,INDEX,DROP on orthomcl.* TO 'orthomcl'@'localhost' 
      IDENTIFIED BY '152108';
      		  
      FLUSH PRIVILEGES;
      

  • centos7中使用mariadb取代了mysql, 但所有命令的执行相同 (忽略掉这一段)

    yum install mariadb mariadb-server
    systemctl start mariadb ==> 启动mariadb
    systemctl enable mariadb ==> 开机自启动
    mysql_secure_installation ==> 设置 root密码等相关
    mysql -uroot -pPASSWD ==> 测试登录！
    

• 配置OrthoMCL工作文件

  • 建一个目录 (~/orthmcl_work)，存储OrthoMCL配置文件
  • 拷贝orthomclSoftware-v2.0.9/doc/OrthoMCLEngine/Main/orthomcl.config.template 到~/orthmcl_work，重命名为orthomcl.config

  • 修改内容为：

    # this config assumes a mysql database named 'orthomcl'. 
    # Adjust according to your situation.
    dbVendor=mysql 
    #Databsename: orthmcl
    dbConnectString=dbi:mysql:orthomcl
    #Database username
    dbLogin=orthomcl
    #Database password
    dbPassword=152108
    # Change strings as you like
    similarSequencesTable=SimilarSequences
    orthologTable=Ortholog
    inParalogTable=InParalog
    coOrthologTable=CoOrtholog
    #Standards
    interTaxonMatchView=InterTaxonMatch
    percentMatchCutoff=50
    evalueExponentCutoff=-5
    oracleIndexTblSpc=NONE
    

  • 生成数据表：
    • orthomclInstallSchema orthomcl.config inst_schema.log species

• 创建OrthoMCL输入文件

  • OrthoMCL的输入文件为fasta格式文件，其中fasta序列的名字格式为 >taxoncode|unique_prot_id。序列名称为空格或下划线分开的两列， 第一列为3到4个字母的物种代码，第二列为蛋白序列的唯一ID。
  • 通常一个基因选择一条代表性蛋白序列。
  • 这些文件使用统一后缀.fasta，并存储于同一文件夹orthlMCL下 (这个文件夹下只能存储fasta格式序列，不然运行 orthomclBlastParser时会报错)。
  • 序列过滤，允许最短的蛋白长度为10，stop codons最大比例为20%, 默认得到goodProteins.fasta。
    • orthomclFilterFasta orthlMCL 10 20
  • 将得到的goodProteins.fasta与 orthoMCL的数据合并， 得到orthoMCL.fa。
  • 通常我们需要准备研究物种及其多个近缘或者有代表性物种的蛋白质序列 ，因此可不与orthoMCL数据库中的蛋白质序列合并，直接用我们的 goodProteins.fasta作为orthoMCL.fa。

• 序列BLAST

  makeblastdb -in orthoMCL.fa -dbtype prot -title orthomcl \
  	-out orthomcl -logfile orthomcl.log`
  blastp -db orthomcl -query goodProteins.fasta -seg yes \
  	-out orthomcl.blastout -evalue 1e-5 -outfmt 7 -num_threads 70`
  

• 略却其它步骤，都整合到一个bash脚本中。

• 整合的分析脚本orthoMcl.sh

  Usage:
    
  /MPATHB/self/NGS/orthoMcl.sh options
    
  Function:
    
  This script is used to perform orthoMcl analysis using MySql, MCL and
  orthomcl.
    
  Before running this script, one must have one mysql database and a
  mysql user which can perform operation on this database.
    
    
  OPTIONS:
  	-d	Mysql database name (using user_name as prefix to avoid
  		duplication) [Necessary]
  	-u	Mysql database username [Necessary]
  	-p	Mysql database password [Necessary]
  	-s	Target species of this analysis 
  		(Any representing string is OK, the shorter the better)
  		[Necessary]
  	-D	A directory containing FASTA files for all proteins.
  		[Necessary]
  	-S	Sequences downloaded from orthMCL website.
  		[Optional, not used anymore]
  	-t	Number of threads for blast. [Default 50]
  

• parseOrthoMclResult.py解析orthoMCL的输出结果，主要是groups.xls文件

  • 获得每个物种各个基因簇中基因数目的矩阵。
  • 提取在所有物种中 都只有一个拷贝的基因，提交给工具 orthoMclPhyloGenetic.py用于做进化分析。
  • 提取特定物种特有的基因簇。
  • 提取多个物种共有相对于其它物种特异的基因簇。

  • 提取某物种特异扩增或缺失的基因家族。

  • parseOrthoMclResult.py

    Program description:
    	This is designed to parse orthmcl results.
    
    	Input file format:
          	
    	cluster_name<colon><any blank>spe1<vertical_line>prot1<any blank>spe2<verticial_line>prot2<any blank>.....
    
    	C10000: Aco|Aco000153.1 Aco|Aco004369.1 Aco|Aco010005.1
    	C10001: Aco|Aco000153.1 Cla|Cla004369.1 Dec|Dec010005.1
    
    	Tasks:
    
    	1. Get a matrix showing the number of proteins in each cluster.
    	2. Extract single gene clusters and their sequences in all given
    	species. In the output nucleotide file, ending stop codon (TAA,
    	TAG, TGA) will be removed for compatible with
    	`translatorx_vLocal.pl` and `trimal`.
    	3. Extract species specific clusters for given species.
    	4. Extract gene-expansion clusters for given species.
    	5. Extract multiple-species specific clusters.
    
    Usage: parseOrthoMclResult.py -i file
    
    Options:
      -h, --help            show this help message and exit
      -i FILEIN, --input-file=FILEIN
    						Output of `orthomclMclToGroups`.
      -t MAIN_SPE, --target-species=MAIN_SPE
    						Specify the `species` name used for extracting species
    						specific clusters or specially expanded clusters.
      -E EXCLUDE_WHEN_READING, --exclude-all=EXCLUDE_WHEN_READING
    						Comma or blank separated strings representing species
    						excluded when reading in the result. It will affect
    						all tasks. Default including all species.
      -e EXCLUDE_SINGLE_CONSERVE, --exclude-2=EXCLUDE_SINGLE_CONSERVE
    						Comma or blank separated strings representing species
    						should not be considered when performing task <2>.
    						Default including all species.
      -s SPECIFIC_MULTIPLE, --specific-multiple-5=SPECIFIC_MULTIPLE
    						Comma or blank separated strings representing multiple
    						species used for task <5>. Default muting task 5.
      -P DIR_PROT, --directory-prot=DIR_PROT
    						Directory containing all protein sequences used for
    						`orthoMcl.sh`. All sequences have a suffix `.fasta`.
      -N DIR_NUCL, --directory-nucl=DIR_NUCL
    						Directory containing all nucleotide sequences used for
    						`orthoMcl.sh`. All sequences have a suffix `.fasta`.
      -o OUTP, --output-prefix=OUTP
    						Prefix for output files.
      -v, --verbose         Show process information
      -d, --debug           Debug the program