Gene Prediction

• 先至GeneMark的網站

 http://exon.biology.gatech.edu/

• 選擇想要預測的物種類別(以下的選擇是以predict bacteria為主)

 > Gene Prediction in Bacteria, Archaea and Metagenomes --->  GeneMark-P* and GeneMark.hmm-P

• 進去網頁之後，需要輸入的資訊如下所示

  1. Sequence Text / Sequence code upload：將要預測的序列剪貼/上傳於此網站

  2. Species：基因預測是與一個已知的物種與此物種相比較，找出相對應exon所在，故在此選擇欲比較的物種名稱
     ( note：選擇不一樣的物種可能會導致其預測出來的結果不相同 )

  3. Output Options：依照個人需要選擇想要output的格式。
     ( 由於需要預測出來的序列，所以Sequences of predicted genes這個選項需要勾選 )

• 先至Augustus的網站

 http://augustus.gobics.de/

• 依照個人所需做選擇

 *若是想要使用網頁板的基因預測，則選擇web interface
 * 若是想要使用單機版的基因預測，則選擇download AUGUSTUS

• 使用網頁板的Augustus，需要輸入的資訊如下所示

 1.Paste your sequence(s) here / or upload a file in (multiple) FASTA format：將要預測的序列剪貼/上傳於此網站
 2. Organism：基因預測是與一個已知的物種與此物種相比較，找出相對應exon所在，故在此選擇欲比較的物種名稱
    ( note：選擇不一樣的物種可能會導致其預測出來的結果不相同 )
 3. 點選Run AUGUSTUS

• 要判斷基因預測是否有順利執行完成，將其網頁拉至最下方，若是出現類似以下的指令，表示執行成功

 /var/www/augustus.gobics.de/augustus/bin/augustus --species=arabidopsis --strand=both --singlestrand=false --genemodel=partial --codingseq=on --sample=100 --keep_viterbi=true --alternatives-from-sampling=true --minexonintronprob=0.2 --minmeanexonintronprob=0.5 --maxtracks=2 /var/tmp/augustus/AUG-1666923489/input.fa --exonnames=on

• 若是輸入序列或是上傳檔案的序列出現以下的情況，則會使其基因預測執行失敗

 >contig 1
 NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN
 亦即其序列裡面除了N之外再也沒有其他ATCG的字母

• 若是想要得到predicted coding sequences，則在執行結果的那個頁面選擇graphical and text results這個選項，選擇predicted coding sequences，如此一來就可以得到predicted coding sequences了。

  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  %                                                                                %
  %     Pipeline for training and running the gene finder AUGUSTUS automatically   %
  %                                                                                %
  %     Xin Xing, Ralph Krimmel, Mario Stanke                                      %
  %     University of Goettingen                                                   %
  %     Contact: Mario Stanke, mario@gobics.de                                     %
  %                                                                                %
  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  
   1. Introduction
   2. System Overview
   3. Software Components
   4. Installation and Configuration
   5. Typical Usage
   6. Command Line Arguments
   7. Component Scripts
      a. autoAugPred.pl
      b. autoAugTrain.pl
  
  		1. Introduction
  		---------------
  
  The purpose of this pipeline script is to run the AUGUSTUS training process and gene
  prediction algorithm automatically on a given eukaryotic genome with
  available cDNA evidence. The complete process contains the following steps.
  These are encapsulated in this automatic training pipeline.
  
  -- Construct an initial training set of genes (protein coding regions thereof), e.g. using PASA
  -- Train AUGUSTUS to predict coding regions (i.e. without Untranslated Regions (UTR)) using the initial training set. 
  -- Predict coding regions of genes in your genome (both ab initio and using hints from cDNA)
  -- Train the UTR model of AUGUSTUS using EST-supported genes and EST alignments.
  -- Predict genes including UTRs in your genome using hints from cDNA.
  
  The first step is optional and can be replaced by preparing your own training set of genes. For a schema of the
  pipeline look at the poster in the doc folder. 
  
  TIP: Please manually/visually check the results of the pipeline, e.g. by loading them into a browser.
       The pipeline outputs files for tracks on a GBrowse. 
  
  WARNING: Please do not assume that you get the best possible results from a fully automatic pipeline.
           Eykaryotic gene prediction is still a complex craft and some beasts are just different.
  
  		2. System Overview
  		------------------
  
  This program runs on a UNIX/LINUX-based architecture and involves 
  components written in Perl and C++. Results of the training-set generating step (PASA) are stored 
  primarily within a MySQL database. The whole pipeline can take in the order of 10 CPU days on a
  medium complete genome, so please be patient and consider using a compute cluster.
  If you happen to have a Sun Grid Engine available you can configure the pipeline to run in parallel
  and completely automatically. If you are using another cluster, the job scripts will be created and you 
  can then submit them manuall using your batch job submission system.
  
  		3. Software Components
  		-----------------------------------
  
  AUGUSTUS: Download from http://augustus.gobics.de/
  
  BLAT:     You get Jim Kent's alignment program at http://users.soe.ucsc.edu/~kent/src/
            Also install pslCDnaFilter which is part of the Jim Kent src tree
            (http://hgwdev.cse.ucsc.edu/~kent/src/unzipped/hg/pslCDnaFilter/).
  
  PASA:     Download the Program to Assemble Spliced Alignments from 
            Sourceforge: http://sourceforge.net/projects/pasa
            You do not need PASA if you already have a training set of genes for your species.
  
  SCIPIO:   Install SCIPIO if you have training genes in the form of protein sequences.
  	  SCIPIO can then find their gene structures in the genome. Most users will not need this.
  
  http://www.webscipio.org/
  
  		4. Installation and Configuration
  		---------------------------------
  
  Install the required components according to their documentation.
  Make sure you have set the environment variables AUGUSTUS_CONFIG_PATH and those for
  PASA. That means for example lines such as
  export AUGUSTUS_CONFIG_PATH=/home/mario/augustus/config
  export PASAHOME=/home/mario/PASA
  shall be written in your startup script (like ~/.bashrc). Furthermore, 
  all scripts of AUGUSTUS and PASA shall be executable. For 
  details see the documentations of AUGUSTUS and PASA.
  
  If you use PASA with GMAP instead of BLAT then please note that PASA is curently not fully compatible
  with GMAP versions after the version from 2007-09-28. If you use a newer GMAP version then remove
  the "-S" option in the PASA script scripts/process_GMAP_alignments.pl so it says:
  gmap_setup -D gmap_db_dir -d $genome_db $genome_db
  
  Furthermore, if you are using a cluster, AUGUSTUS should be installed there as well. The 
  path to "augustus" should also be concated to the "PATH" in your cluster.  
  
  In case you are using a Sun Grid Engine and you want to use the option "noninteractive" (see section 6.),
  configure your ssh so that you can login on the cluster without having to type a password.
  
  Make sure to include the augustus/scripts directory in your PATH environment variable!
  There is test data in augustus/examples/autoAug for testing this pipeline. I recommend you
  test the installation on this small set first to quickly detect possible installation problems.
  
  		5. Typical Usage
  		----------------
  
  First create a new directory to store all files created by the pipeline.
  
  Situation 1:
  
  You already have a set of training genes (e.g. constructed from ESTs using PASA).
  The way you run the pipeline depends on whether you want to use a compute cluster
  to run the prediction jobs in parallel or whether you want to run it on a single PC.
  The easiest but slower way is to run everything sequentially in one command, e.g.
  
  >autoAug.pl -g genome.fa -t traingenes.gff --species=yourSpecies --cdna=cdna.fa -v  --singleCPU --useexisting
  
  traingenes.gff is the set of training gene structures in GFF format (e.g. as produced by PASA).
  Alternatively you can use -t traingenes.gb if you have a training set
  in Genbank format. This has the advantage that the training genes can be based
  on a different DNA sequence than genome.fa.
  cdna.fa is a fasta format file with all available cDNA sequences (ESTs, 454 transcripts, mRNA).
  In a first test run we recommend to use the option --optrounds=0 to skip the
  optimization, which usually takes most time. 
  
  If you have a compute cluster you can remove the option --singleCPU from above
  command. In that case the program flow is interrupted at certain times so you
  can submit the jobs on your cluster. Simply follow the output prompts. If your
  SGE cluster happens to be set as in section 4 you can also use conveniently
  
  >autoAug.pl -g genome.fa -t traingenes.gff --species=yourSpecies --cdna=cdna.fa -v --noninteractive
  
  Note: For this pipeline you only need a training set with coding regions, even
  if you want AUGUSTUS to predict the UTRs. It will construct a training set of
  UTRs from EST alignments.
  
  Situation 2:
  
  You can just input a cDNA sequence file in FASTA format, and let PASA extract
  a training set of genes. You need to install PASA for this separately.
  
  >autoAug.pl -g genome.fa --species=yourSpecies -c cdna.fa -v -v --pasa --useexisting
  
  Then follow the output prompts. If your cluster is set as in section 4 you can also use the option --noninteractive.
  
  		6. Command Line Arguments
  		-------------------------
  
  The program supports file name arguments with absolute path (i.e. file name begin with "/") as well as with
  relative path (beginning with "..", "~" etc.).
  The keywords (such as genome) can be abbreviated (e.g. "ge") to the extend that they are still unique.
  
  --genome=genomeFile 
    genomeFile is a file in Fasta format with the genome assembly
  
  --trainingset=trainingfile    
    trainingfile is a file with training genes in Genbank format, GFF format or protein FASTA format
  
  --species=sname 
    sname is species name. This is used to identify the parameter set later. The program will check if a
    directory 'sname' already exists under $AUGUSTUS_CONFIG_PATH/species/.
    If there is one, the program will be aborted unless you specify --useexisting.
  
  --cdna=cdna.fa
    cdna.fa is a cdna sequence file in the FASTA format (typically EST sequences or 454)
  
  --pasa
    This argument swithes the PASA function on for creating a training set of genes from the cDNA, default: off.
  
  --hints=hintsfile
    hintsfile is a file with extrinsic evicence about the location and structure of genes, see documentation of AUGUSTUS.
  
  --estali=estfile
    cDNA alignments in PSL format (as generated by BLAT and GMAP) are used to construct UTRs
  
  --noutr
    use this if you do not want predictions of untranslated regions and want to safe a bunch of time
    However, note that usually the predictions for the coding regions are better with UTRs turned on, because
    typical EST alignments at the transcript ends can be better "interpreted" by AUGUSTUS.
  
  --verbose=n
    verbosity level 0, 1, 2, 3. Default: 2. You can also use a shortcut: "-v", "-v -v" or "-v -v -v" 
  
  --workingdir=/path/to/wd/
    In the working directory results and temporary files are stored. Default: current working directory
  
  --noninteractive
    switch it on to avoid manual operation in cluster, it works only if you use a SUN GRID ENGINE cluster
  
  --clustername=cname
    set you SUN GRID ENGINE cluster name by using --noninteractive. Default: fe
  
                  7. Component Scripts
                  ------------------------------------
  
  autoAug.pl uses two the two scripts autoAugTrain.pl and autoAugPred.pl for training and prediction subtasks that
  are important steps of their own.
  
  a. autoAugPred.pl predict genes with AUGUSTUS on genomes
  
  Once parameters for a species have been trained, you can use this script to (re)predict genes on your genome,
  e.g. when you have new data.
  
  Usage:
  
  autoAugPred.pl [OPTIONS] --genome=genome.fa --species=sname
  autoAugPred.pl [OPTIONS] --genome=genome.fa --species=sname --hints=hintsfile 
  
  --genome=genome.fa             
    FASTA file with DNA sequences for training
  
  --species=sname                
    species name as used by AUGUSTUS
  
  --hints=hintsfile             
    run AUGUSTUS using extrinsic information from hintsfile
  
  --noninteractive               
    for SGE users, who have configurated openssh key
    with this option one can run AUGUSTUS automatically
  
  --utr                          
    switch on prediction of untranslated regions. Off by default.
  
  --hints=hintsfile              
  run AUGUSTUS with hintsfile
  
  --verbose                  
    the verbosity level
  
  --remote=clustername           
    works only by using --noninteractive, specify the SGE cluster name for noninteractive, default by "fe"
  
  b. autoAugTrain.pl: train AUGUSTUS automatically
  
  Usage:
  
  autoAugTrain.pl [OPTIONS] --species=sname --trainingset=training.gb
  autoAugTrain.pl [OPTIONS] --species=sname --genome=genome.fa --trainingset=genes.gff
  autoAugTrain.pl [OPTIONS] --species=sname --genome=genome.fa --trainingset=proteins.fa
  autoAugTrain.pl [OPTIONS] --species=sname --genome=genome.fa --species=sname --utr --estali=cdna.psl --aug=augustus.gff
  
  --species=sname                
    species name as used by AUGUSTUS
  
  --trainingset=genes.gb      
    genes.gb is a file with training genes a Genbank format. For examples for the exact format look at 
    the files in http://augustus.gobics.de/datasets/
  
  --trainingset=genes.gff     
    genes.gff is a file with training genes in GFF format
  
  --trainingset=proteins.fa
    proteins.fa is a file with protein sequences from the target species or a very close relative
    You can use this when the structures 'got lost' or you changed the assembly.
  
  --genome=genome.fa             
    fasta file with DNA sequences for training
    genome.fa should include the corresponding
    sequences in this case
  
  --est=cdna.f.psl               
    EST alignments are used to guess the UTR and its end point.
  
  --utr                          
    Switch it on to train AUGUSTUS with untranslated regions. Off by default
  
  --flanking_DNA=length          
    flanking_DNA length, default:4000
  
  --verbose                      
    verbosity level of the program,  default 
    level: 1, max level: 3