MAKER Genome Annotation using cc-tools and Jetstream (WQ-MAKER)

Rationale and background:

MAKER is a flexible and scalable genome annotation pipeline that automates the many steps necessary for the detection of protein coding genes (Campbell et al. 2013). MAKER identifies repeats, aligns ESTs and proteins to a genome, produces ab initio gene predictions, and automatically synthesizes these data into gene annotations having evidence-based quality indices.  MAKER was developed by the Yandell Lab and is described in several publications (Cantarel et al. 2008; Holt & Yandell 2011).  Additional background is available at the MAKER Tutorial at GMOD and is highly recommended reading

MAKER with CCTools (aka WQ-MAKER) is a modified MAKER annotation tools capable of running MAKER on distributed computing resources such as Jetstream cloud (Thrasher et al., 2012). Using the work-queue platform, users can now run MAKER across multiple virtual machines to achieve a several fold reduction in the duration of the MAKER run. 

This tutorial will take users through steps of:

  1. Running WQ-MAKER on Jetstream cloud
  2. Running WQ-MAKER on an example genome assembly data

Considerations

Sounds great, what do I need to get started?

  1. XSEDE account
  2. XSEDE allocation: Users can fill this Jetstream cloud allocation through CyVerse form for running WQ-MAKER (quick-start). Later on, they can request a startup XSEDE allocation
  3. Your data (or you can run example data)

What kind of data do I need?

  1. Mandatory requirements
    1. Genome assembly (fasta file)
    2. Organism type
      1. Eukaryotic (default, set as: organism_type=eukaryotic)
      2. Prokaryotic (set as: organism_type=prokaryotic)
  2. Additional data that can be used to improve the annotation (Highly recommended)
    1. RNA evidence (at least one of them is needed)
      1. Assembled mRNA-seq transcriptome (fasta file)
      2. Expressed sequence tags (ESTs) data (fasta file)
      3. Aligned EST or transcriptome GFF3 from your organism
      4. Aligned EST or transcriptome GFF3 from a closely related organism
    2. Protein evidence
      1. protein sequence file in fasta format (i.e. from multiple organisms)

      2. protein gff (aligned protein homology evidence from an external GFF3 file)

What kind of resources will I need for my project?

  1. Enough storage space on the WQ-MAKER Jetstream instance for both input and output files
    1. Creating and mounting an external volume to the running WQ-MAKER MASTER instance would be recommended
  2. One Master and several workers needed for running your computation
    1. Benchmarking results for data sets can help you estimate the number of workers need for running your annotation
  3. Enough AUs to run your computation

Part 1: Connect to an instance of an WQ-MAKER Jetstream Image (virtual machine)

Step 1. Go to https://use.jetstream-cloud.org/application and log in with your XSEDE credentials.

 

Step 2. Click on the "Create New Project"  in the Project tab on the top and enter the name of the project and a brief description 


Step 3. Launch an instance from the selected image and name it as MASTER 

After the project has been created and entered inside it, click the "New" button, select "MAKER 2.31.9 with CCTools" image and then click Launch instance. In the next window (Basic Info),

Step 4. As the instance is launched behind the scenes, you will get an update as it goes through each step.

Status updates of Instance launch (both MASTER and WORKER) include Build-requesting launch, Build-networking, Build-spawning, Active-networking, Active-deploying. Depending on the usage load on Jetstream, it can take anywhere from 2-5 mins for an instance to become active. You can force check updates by using the refresh button in the Instance launch page or the refresh button on your browser. Once the instance becomes active a virtual machine with the ip address provided will become available for you to connect to. This virtual machine will have all the necessary components to run WQ-MAKER and test files to run a MAKER demo. 

Step 5: Create a volume

Since the m1 medium instance size (60GB disk space) selected for running MASTER instance of WQ-MAKER may not be sufficient for most of the MAKER runs, it is recommended to run it on volumes

5.1 Click the "New" button in the project and select "Create Volume". Enter the name of the volume, volume size (GB) needed and the provider (TACC or Indiana) and finally click "Create Volume" 

Attach the created volume to the MASTER instance

5.2 Click on the MASTER instance now

Jetstream provides web-shell, a web-based terminal, for accessing your VM at the command line level once it has been deployed.

However, you might find that you wish to access your VM via SSH if you’ve provisioned it with a routable IP number. For SSH access, you can create (or copy) SSH public-keys for your non-Jetstream computer that will allow it to access Jetstream then deposit those keys in your Atmosphere settings. More instructions can be found here 

$ ssh <username>@<ip.address>

Step 6. Add public SSH key of MASTER to Jetstream 

If you do not already have a ~/.ssh/id_rsa.pub file, then run this command to create it. Use all the defaults..

$ ssh-keygen
Generating public/private rsa key pair.
Enter file in which to save the key (/home/upendra/.ssh/id_rsa):
Enter passphrase (empty for no passphrase):
Enter same passphrase again:
Your identification has been saved in /home/upendra/.ssh/id_rsa.
Your public key has been saved in /home/upendra/.ssh/id_rsa.pub.
The key fingerprint is:
SHA256:fPvsPyvtcWKl9kQotmJskFljlHQNCNP2pITigdLS3mM upendra@js-157-180.jetstream-cloud.org
The key's randomart image is:
+---[RSA 2048]----+
|   o .  o=oooo   |
|  o + o .o*.. .  |
|   + o o o++     |
|    . E. =...  . |
|     . .S . o . o|
|         + o o + |
|          * ..* o|
|         o +.oo* |
|           .+++o.|
+----[SHA256]-----+

Copy the public SSH key from your id_rsa.pub file and paste it to the https://use.jetstream-cloud.org/application/settings, give a name (MASTER) to it and click confirm.

cat ~/.ssh/id_rsa.pub

Step 7. Launch WORKER instances from MAKER 2.31.9 with CCTools image 

Launch one to several instances from the MAKER 2.31.9 with CCTools image and name them as WORKER-1, WORKER-2 etc.,

How many workers to launch? It depends on the number of sequences. Be default WQ-MAKER generates files each containing 10 sequences

Part 3: Set up a MAKER run using the Terminal window. Instead IF you want to run the WQ-MAKER using Jupyter notebook please see the section below 

Step 1 Navigate to the mounted volume. 

Once you have logged in to your instance using webshell or ssh of your MASTER instance, you must change the directory permissions as below
# Change the ownership and group permission on the mount location
$ sudo chown -hR $USER /vol_b
$ sudo chgrp -hR $USER /vol_b

# cd into the /vol_b and then run WQ-MAKER in there
$ cd /vol_b

Step 2. Get oriented. You will find staged example data in "/opt/WQ-MAKER_example_data/" within the MASTER instance.  List its contents with the ls command:

$ ls /opt/WQ-MAKER_example_data/
maker_bopts.ctl  maker_exe.ctl  maker-hosts  maker_opts.ctl  test_data  worker-launch.yml
 
$ ls /opt/WQ-MAKER_example_data/test_data
mRNA.fasta  msu-irgsp-proteins.fasta  Os-rRNA.fa  plant_repeats.fasta  test_genome_chr1.fasta  test_genome.fasta

Executables for running MAKER are located in /opt/maker/bin and /opt/maker/exe:

$ ls /opt/maker/bin/
cegma2zff       fasta_tool         maker           maker_functional        map_fasta_ids
chado2gff3      genemark_gtf2gff3  maker2chado     maker_functional_fasta  map_gff_ids
compare         gff3_merge         maker2eval_gtf  maker_functional_gff    mpi_evaluator
cufflinks2gff3  iprscan2gff3       maker2jbrowse   maker_map_ids           mpi_iprscan
evaluator       iprscan_wrap       maker2wap       map2assembly            tophat2gff3
fasta_merge     ipr_update_gff     maker2zff       map_data_ids  

As the names suggest the "/opt/maker/bin" directory includes many useful auxiliary scripts.  For example cufflinks2gff3 will convert output from an RNA-seq analysis into a GFF3 file that can be used for input as evidence for WQ-MAKER. RepeatMasker, augustus, blast, exonerate, and snap are programs that MAKER uses in its pipeline.  We recommend reading MAKER Tutorial at GMOD for more information about these.

Step 3. Set up a WQ-MAKER run.  Create a working directory called "maker_run" on your home directory using the mkdir command and use cd to move into that directory:

# Navigate to the mounted volume for creating test directory
$ cd /vol_b
$ mkdir wq_maker_run
$ cd wq_maker_run

Step 4. Copy the contents of  "WQ-MAKER_example_data" into the current directory using cp -r command.  Verify using the ls command.  Change the permissions on that directory

$ sudo cp -r /opt/WQ-MAKER_example_data/test_data .

Step 5. Run the maker command with the --help flag to get a usage statement and list of options:

$ maker -h
Argument "2.53_01" isn't numeric in numeric ge (>=) at /usr/local/lib/x86_64-linux-gnu/perl/5.22.1/forks.pm line 1570.
MAKER version 2.31.9
Usage:
     maker [options] <maker_opts> <maker_bopts> <maker_exe>
Description:
     MAKER is a program that produces gene annotations in GFF3 format using
     evidence such as EST alignments and protein homology. MAKER can be used to
     produce gene annotations for new genomes as well as update annotations
     from existing genome databases.
     The three input arguments are control files that specify how MAKER should
     behave. All options for MAKER should be set in the control files, but a
     few can also be set on the command line. Command line options provide a
     convenient machanism to override commonly altered control file values.
     MAKER will automatically search for the control files in the current
     working directory if they are not specified on the command line.
     Input files listed in the control options files must be in fasta format
     unless otherwise specified. Please see MAKER documentation to learn more
     about control file  configuration.  MAKER will automatically try and
     locate the user control files in the current working directory if these
     arguments are not supplied when initializing MAKER.
     It is important to note that MAKER does not try and recalculated data that
     it has already calculated.  For example, if you run an analysis twice on
     the same dataset you will notice that MAKER does not rerun any of the
     BLAST analyses, but instead uses the blast analyses stored from the
     previous run. To force MAKER to rerun all analyses, use the -f flag.
     MAKER also supports parallelization via MPI on computer clusters. Just
     launch MAKER via mpiexec (i.e. mpiexec -n 40 maker). MPI support must be
     configured during the MAKER installation process for this to work though
Options:
     -genome|g <file>    Overrides the genome file path in the control files
     -RM_off|R           Turns all repeat masking options off.
     -datastore/         Forcably turn on/off MAKER's two deep directory
      nodatastore        structure for output.  Always on by default.
     -old_struct         Use the old directory styles (MAKER 2.26 and lower)
     -base    <string>   Set the base name MAKER uses to save output files.
                         MAKER uses the input genome file name by default.
     -tries|t <integer>  Run contigs up to the specified number of tries.
     -cpus|c  <integer>  Tells how many cpus to use for BLAST analysis.
                         Note: this is for BLAST and not for MPI!
     -force|f            Forces MAKER to delete old files before running again.
                         This will require all blast analyses to be rerun.
     -again|a            recaculate all annotations and output files even if no
                         settings have changed. Does not delete old analyses.
     -quiet|q            Regular quiet. Only a handlful of status messages.
     -qq                 Even more quiet. There are no status messages.
     -dsindex            Quickly generate datastore index file. Note that this
                         will not check if run settings have changed on contigs
     -nolock             Turn off file locks. May be usful on some file systems,
                         but can cause race conditions if running in parallel.
     -TMP                Specify temporary directory to use.
     -CTL                Generate empty control files in the current directory.
     -OPTS               Generates just the maker_opts.ctl file.
     -BOPTS              Generates just the maker_bopts.ctl file.
     -EXE                Generates just the maker_exe.ctl file.
     -MWAS    <option>   Easy way to control mwas_server for web-based GUI
                              options:  STOP
                                        START
                                        RESTART
     -version            Prints the MAKER version.
     -help|?             Prints this usage statement.

Step 6.  Create control files that tell MAKER what to do. Three files are required:

To create these files run the maker command with the -CTL flag. Verify with ls:

$ maker -CTL
$ ls
maker_bopts.ctl  maker_exe.ctl  maker_opts.ctl  test_data

If pressed for time a pre-edited version of the "maker_opts.ctl" file is staged in /opt/WQ-MAKER_example_data. Delete the current file and copy the staged version here. Make the following changes and then skip to Step 6.

$ rm maker_opts.ctl
$ cp /opt/WQ-MAKER_example_data/maker_opts.ctl .
#-----Gene Prediction
snaphmm= #SNAP HMM file
gmhmm= #GeneMark HMM file
augustus_species= #Augustus gene prediction species model
fgenesh_par_file= #FGENESH parameter file
pred_gff= #ab-initio predictions from an external GFF3 file
model_gff= #annotated gene models from an external GFF3 file (annotation pass-through)
est2genome=1 #infer gene predictions directly from ESTs, 1 = yes, 0 = no # Change 1 to 0
protein2genome=1 #infer predictions from protein homology, 1 = yes, 0 = no # Change 1 to 0
unmask=0 #also run ab-initio prediction programs on unmasked sequence, 1 = yes, 0 = no

Otherwise open the maker_opts.ctl in a text editor of choice ) 

$ nano maker_opts.ctl

Here are the sections of the "maker_opts.ctl" file you need to edit.  For more information about the this please check this The_MAKER_control_files_explained - Add path information to files as shown.

Do not allow any spaces after the equal sign or anywhere else

The files can be present in same the directory as the "maker_opts.ctl" or make sure you use the relative path if the files are present in other directories

This section pertains to specifying the genome assembly to be annotated and setting organism type:

#-----Genome (these are always required)
genome=./test_data/test_genome.fasta #genome sequence (fasta file or fasta embeded in GFF3 file)
organism_type=eukaryotic #eukaryotic or prokaryotic. Default is eukaryotic

The following section pertains to EST and other mRNA expression evidence.  Here we are only using same species data, but one could specify data from a related species using the "altest" parameter. With RNA-seq data aligned to your genome by Cufflinks or Tophat one could use maker auxiliary scripts (cufflinks2gff3 and tophat2gff3) to generate GFF3 files and specify these using the est_gff parameter:

#-----EST Evidence (for best results provide a file for at least one)
est=./test_data/mRNA.fasta #set of ESTs or assembled mRNA-seq in fasta format
altest= #EST/cDNA sequence file in fasta format from an alternate organism
est_gff= #aligned ESTs or mRNA-seq from an external GFF3 file
altest_gff= #aligned ESTs from a closely relate species in GFF3 format

The following section pertains to protein sequence evidence.  Here we are using previously annotated protein sequences.  Another option would be to use SwissProt or other database:

#-----Protein Homology Evidence (for best results provide a file for at least one)
protein=./test_data/msu-irgsp-proteins.fasta  #protein sequence file in fasta format (i.e. from mutiple oransisms)
protein_gff=  #aligned protein homology evidence from an external GFF3 file

This next section pertains to repeat identification:

#-----Repeat Masking (leave values blank to skip repeat masking)
model_org= #select a model organism for RepBase masking in RepeatMasker
rmlib=./test_data/plant_repeats.fasta #provide an organism specific repeat library in fasta format for RepeatMasker
repeat_protein= #provide a fasta file of transposable element proteins for RepeatRunner
rm_gff= #pre-identified repeat elements from an external GFF3 file
prok_rm=0 #forces MAKER to repeatmask prokaryotes (no reason to change this), 1 = yes, 0 = no
softmask=1 #use soft-masking rather than hard-masking in BLAST (i.e. seg and dust filtering)

Step 7.  Run WQ-MAKER 

Before running MAKER, check to make sure all worker instances have become active. 

On the MASTER instance, make sure you are in the "maker_run" directory and all of your files are in place and then run:

$ nohup wq_maker -contigs-per-split 1 -cores 1 -memory 2048 -disk 4096 -N wq_test_${USER} -d all -o master.dbg -debug_size_limit=0 -stats test_out_stats.txt > log_file.txt 2>&1 &

Unless otherwise you have a complete genome containing chromosomes or very few scaffolds, it is not recommended to use this option. For example if you have a genome that contains 10,000 sequences, then this option will create 10,000 files on your working directory which is not ideal of navigation purposes. Check to see how many contigs/scaffolds/chromosomes you have in your genome using `grep ">" -c <genome fasta file>` and if the number is too high, then avoid this option

Wait for the MASTER to advertise master status to the catalog server before your run WQ-MAKER on the WORKERS (see below). 

 $ tail log_file.txt

Mon Sep 11 15:08:22 2017 :: Submitting file ./test_data/test_genome.fasta_000008 for processing.
Mon Sep 11 15:08:22 2017 :: Submitted task 11 for annotating ./test_data/test_genome.fasta_000008 with command: mpiexec -n 1 maker -g ./test_data/test_genome.fasta_000008 -base test_genome -debug_size_limit=0
Mon Sep 11 15:08:22 2017 :: Submitting file ./test_data/test_genome.fasta_000006 for processing.
Mon Sep 11 15:08:22 2017 :: Submitted task 12 for annotating ./test_data/test_genome.fasta_000006 with command: mpiexec -n 1 maker -g ./test_data/test_genome.fasta_000006 -base test_genome -debug_size_limit=0
warning: this work queue master is visible to the public.
warning: you should set a password with the --password option.

Once the log_file show the above output and once your WORKERS are in active state, then either ssh or use webshell into each of the WORKERS and then run

$ nohup work_queue_worker -N wq_test_${USER} --cores all --debug-rotate-max=0 -d all -o worker.dbg > log_file_2.txt 2>&1 &

You can use Anisble method to launch jobs without ssh'ing into WORKERS from the MASTER itself. Once the maker run is started on the master, and once your WORKERS are in active state 

Step 1: Copy ansible.cfg file into your home directory which will help you to avoid host verification

$ cp /opt/WQ-MAKER_example_data/.ansible.cfg ~

Step 2: Copy maker-hosts file into your working directory and populate it with ip addresses of the WORKERS

$ cp /opt/WQ-MAKER_example_data/maker-hosts .
$ echo "149.165.169.21" >> maker-hosts # This ip address of the WORKER is specific to my account. This will not work for you
$ echo "149.165.169.78" >> maker-hosts # This ip address of the WORKER is specific to my account. This will not work for you

Step 3: Copy the Ansible playbook to your working directory

$ cp /opt/WQ-MAKER_example_data/worker-launch.yml .
$ cat worker-launch.yml

---
- hosts : workers
  environment:
    PATH: "{{ ansible_env.PATH }}:/home/${USER}/bin:/home/${USER}/.local/bin:/opt/icommands:/opt/icommands:/opt/exonerate-2.2.0-x86_64/bin/:/opt/cctools/bin:/opt/ncbi-blast-2.6.0+/bin/:/opt/snoscan-0.9.1/:/opt/tRNAscan-SE-1.3.1/:/opt/snap/:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin:/opt/augustus-3.2.2/bin:/opt/maker/bin:/opt/RepeatMasker:/opt/snap"
    PERL5LIB: "/opt/tRNAscan-SE-1.3.1::/opt/cctools/lib/perl5/site_perl"
  tasks :
  - name : Execute the script
    shell : /opt/cctools/bin/work_queue_worker -N wq_test_${USER} -s /home/${USER} --cores all --debug-rotate-max=0 -d all -o /home/${USER}/worker.dbg
  • - hosts is the name of the hosts (workers in this case. It can be anything)
  • tasks is the task that need to be performed by the Ansible (In this case run work_queue_worker)
  • name is just name of the task (It can be anything) 
  • -N maker_run_test sets the project name to maker_run_test. This is mandatory if we need to run WQ-MAKER
  • -s /home/upendra/ Set the location for creating the working directory of the worker

  • --debug-rotate-max=0 Set the maximum size of the debug log (default 10M, 0 disables)

  • -d all Sets the debug flag for Work Queue. For all debugging output, try 'all'
  • -o worker.dbg Sets the debug file for Work Queue

Step 4: Run WQ-MAKER on the WORKERS

 $ nohup ansible-playbook -u ${USER} -i maker-hosts worker-launch.yml > log_file_2.txt 2>&1 &

To check the status of the WQ-MAKER job, run the following. 

$ work_queue_status -M wq_test_${USER}
PROJECT            HOST                   PORT WAITING RUNNING COMPLETE WORKERS 
maker_run_test     js-157-131.jetstream-  9155       8       4        0       4 

Step 8. Stats output from MASTER instance 

The log_file.txt will tell you if the job has been finished or not. 

$ tail log_file.txt 

WQ-MAKER Start_time:    1505157588000000
WQ-MAKER End_time:      1505157849000000
WQ-MAKER Elapsed:       0d 0:04:21.000000
Work Queue Wall Time:   0d 0:04:00.427755
Cumulative Task Wall Time:      0d 0:36:25.377304
Cumulative Task Good Execute Time:      0d 0:36:25.377304
Work Queue Send Time:   0d 0:00:01.437632
Work Queue Receive Time:        0d 0:00:03.863163
-----------------------------------------------------------------
Mon Sep 11 15:24:09 2017 :: MPI used :: Cores 1 :: Memory 1024 :: Disk 2048
-----------------------------------------------------------------

The following are the output files from WQ-MAKER 

$ ls test_genome.maker.output/test_genome.maker.output
maker_bopts.log  maker_exe.log  maker_opts.log  mpi_blastdb  test_genome_datastore  test_genome_master_datastore_index.log

Check the test_genome_master_datastore_index.log and task_outputs.txt to see if there were any failures:

$ cat test_genome.maker.output/test_genome_master_datastore_index.log
Chr1    test_genome_datastore/41/30/Chr1/       STARTED
Chr10   test_genome_datastore/7C/72/Chr10/      STARTED
Chr11   test_genome_datastore/1E/AA/Chr11/      STARTED
Chr12   test_genome_datastore/1B/FA/Chr12/      STARTED
Chr2    test_genome_datastore/E9/36/Chr2/       STARTED
Chr3    test_genome_datastore/CC/EF/Chr3/       STARTED
Chr4    test_genome_datastore/A3/11/Chr4/       STARTED
Chr5    test_genome_datastore/8A/9B/Chr5/       STARTED
Chr6    test_genome_datastore/13/44/Chr6/       STARTED
Chr7    test_genome_datastore/91/B7/Chr7/       STARTED
Chr8    test_genome_datastore/9A/9E/Chr8/       STARTED
Chr9    test_genome_datastore/87/90/Chr9/       STARTED
Chr1    test_genome_datastore/41/30/Chr1/       FINISHED
Chr10   test_genome_datastore/7C/72/Chr10/      FINISHED
Chr11   test_genome_datastore/1E/AA/Chr11/      FINISHED
Chr12   test_genome_datastore/1B/FA/Chr12/      FINISHED
Chr2    test_genome_datastore/E9/36/Chr2/       FINISHED
Chr3    test_genome_datastore/CC/EF/Chr3/       FINISHED
Chr4    test_genome_datastore/A3/11/Chr4/       FINISHED
Chr5    test_genome_datastore/8A/9B/Chr5/       FINISHED
Chr6    test_genome_datastore/13/44/Chr6/       FINISHED
Chr7    test_genome_datastore/91/B7/Chr7/       FINISHED
Chr8    test_genome_datastore/9A/9E/Chr8/       FINISHED
Chr9    test_genome_datastore/87/90/Chr9/       FINISHED

All completed.  Other possible status entries include:

The actual output data is stored in in nested set of directories under* test_genome_datastore* in a nested directory structure.

A typical set of outputs for a contig looks like this:

$ ls test_genome.maker.output/test_genome_datastore/*/*/*
test_genome_datastore/13/44/Chr6:
Chr6.gff                                                Chr6.maker.proteins.fasta                 Chr6.maker.transcripts.fasta
Chr6.maker.non_overlapping_ab_initio.proteins.fasta     Chr6.maker.snap_masked.proteins.fasta     run.log
Chr6.maker.non_overlapping_ab_initio.transcripts.fasta  Chr6.maker.snap_masked.transcripts.fasta  theVoid.Chr6

The output directory theVoid.Chr1 contains raw output data from all of the pipeline steps.  One useful file found here is the repeat-masked version of the contig, query.masked.fasta.

Step 9: Merge the gff files

$ gff3_merge -n -d test_genome.maker.output/test_genome_master_datastore_index.log

By default, the output of the gff3_merge is test_genome.all.gff, but you can have an alternate base name for the output files using "-o" option

If you want to perform abinition gene predictions then you should skip `-n` option. Run

$ gff3_merge -d test_genome_master_datastore_index.log

And follow the rest of the steps of abinitio gene predictions in here

The final output from gff3_merge is "test_genome.all.gff"

##gff-version 3
Chr6    maker   gene    43764   46139   .   -   .   ID=maker-Chr6-snap-gene-0.3;Name=maker-Chr6-snap-gene-0.3
Chr6    maker   mRNA    43764   46139   .   -   .   ID=maker-Chr6-snap-gene-0.3-mRNA-1;Parent=maker-Chr6-snap-gene-0.3;Name=maker-Chr6-snap-gene-0.3-mRNA-1;_AED=0.12;_eAED=0.50;_QI=64|0|0|1|0|0.33|3|0|76
Chr6    maker   exon    43764   43846   .   -   .   ID=maker-Chr6-snap-gene-0.3-mRNA-1:exon:2;Parent=maker-Chr6-snap-gene-0.3-mRNA-1
Chr6    maker   exon    44833   44896   .   -   .   ID=maker-Chr6-snap-gene-0.3-mRNA-1:exon:1;Parent=maker-Chr6-snap-gene-0.3-mRNA-1
Chr6    maker   exon    45992   46139   .   -   .   ID=maker-Chr6-snap-gene-0.3-mRNA-1:exon:0;Parent=maker-Chr6-snap-gene-0.3-mRNA-1
Chr6    maker   five_prime_UTR  46076   46139   .   -   .   ID=maker-Chr6-snap-gene-0.3-mRNA-1:five_prime_utr;Parent=maker-Chr6-snap-gene-0.3-mRNA-1
Chr6    maker   CDS 45992   46075   .   -   0   ID=maker-Chr6-snap-gene-0.3-mRNA-1:cds;Parent=maker-Chr6-snap-gene-0.3-mRNA-1
Chr6    maker   CDS 44833   44896   .   -   0   ID=maker-Chr6-snap-gene-0.3-mRNA-1:cds;Parent=maker-Chr6-snap-gene-0.3-mRNA-1
Chr6    maker   CDS 43764   43846   .   -   2   ID=maker-Chr6-snap-gene-0.3-mRNA-1:cds;Parent=maker-Chr6-snap-gene-0.3-mRNA-1

Moving data from CyVerse Datastore using iCommands

iCommands is a collection of commands for Linux and Mac OS operating systems that are used in the iRODS system to interact with the CyVerse Data Store. Many commands are very similar to Unix utilities. For example, to list files and directories, in Linux you use ls, but in iCommands you use ils.

While iCommands are great for all transfers and for automating tasks via scripts, they are the best choice for large files (2-100 GB each) and for bulk file transfers (many small files). For a comparison of the different methods of uploading and downloading data items, see Downloading and Uploading Data.

iCommands can be used by CyVerse account users to download files that have been shared by other users and to upload files to the Data Store, as well as add metadata, change permissions, and more. Commonly used iCommands are listed below. Follow the instructions on Setting Up iCommands for how to download and configure iCommands for your operating system.

A CyVerse account is not required to download a public data file via iCommands. To see instructions just for public data download with iCommands, see the iCommands section on Downloading Data Files Without a User Account.

For configuring icommands and the different commands that can be used to move the data in and out of datastore, please refer this link. You may want to watch a CyVerse video about iCommands.

Part 4: Set up a MAKER run using the Jupyter notebook


Step1: Running "tmux" command to run the anlaysis in the background

$ cd ~
$ tmux

Step2: Copy the example Jupyter notebook onto your home directory

$ cp /opt/WQ-MAKER_example_data/WQ-MAKER-Jupyter-notebook.ipynb .

Step3: Launch jupyter notebook in the background

$ python /opt/anaconda2/bin/jupyter-notebook --no-browser --ip=0.0.0.0 2>&1 | sed s/0.0.0.0/$(curl -s ipinfo.io/ip)/g
[I 16:54:10.440 NotebookApp] Writing notebook server cookie secret to /run/user/1000/jupyter/notebook_cookie_secret
[I 16:54:10.650 NotebookApp] Serving notebooks from local directory: /home/upendra
[I 16:54:10.650 NotebookApp] 0 active kernels 
[I 16:54:10.650 NotebookApp] The Jupyter Notebook is running at: http://129.114.104.169:8888/?token=483a27cd0387ccd04133570999ba8ce8072cf0f45663289f
[I 16:54:10.650 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[C 16:54:10.650 NotebookApp] 
    
    Copy/paste this URL into your browser when you connect for the first time,
    to login with a token:
        http://129.114.104.169:8888/?token=483a27cd0387ccd04133570999ba8ce8072cf0f45663289f ## Copy and paste this link in the browser

Step 4: Now click open "WQ-MAKER-Jupyter-notebook.ipynb" and run the jupyter notebooks.

Here is the example Jupyter notebook that was ran before

http://nbviewer.jupyter.org/github/upendrak/WQ-MAKER_example_data/blob/master/WQ-MAKER-Jupyter-notebook-demo.ipynb

If you want to exit out of tmux shell without killing it. Press ctrl + b and d. If you want to kill it. Then press ctrl +c