Anacapa is a toolkit designed to construct reference databases and assign taxonomy, from eDNA sequences.
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This guide is designed to step you though running your eDNA sequence data through the anacapa toolkit on QUTs HPC, as the published anacapa documentation on Github can be a bit hard to follow and needs some modification to work on the HPC.
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An overview of HPC commands and usage, as well as a link for requesting access to the HPC (if you don’t currently have a HPC account) is here:
There are plenty of online guides that teach basic Linux command line usage, for example:
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https://www.youtube.com/watch?v=s3ii48qYBxA
Step 1: initial setup
You will be running various processes on the HPC that require quite a lot of processing power. Do not run these command on the 'head node' (which is the node you enter when you log on). Instead, either submit these command via a PBS script or an interactive PBS session, which runs your processes on another node.
The details of creating and submitting a PBS script can be found here:
If you’re testing several tools or running multiple separate commands then an interactive PBS session may be preferable. Below is the command to create an interactive PBS session with 8 CPUs and 64GB memory.
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How to use this guide
In this guide, commands to be entered by the user will be in grey boxes like the one below. Most commands can simply be cut and paste ‘as-is’ into your command line. Some need to be modified due to variations in your data (e.g. target species) or location.
You can hover your mouse over the code box to see a ‘copy’ button on the right. Just click this to copy all the code in the box.
Try this with the code box below (this will show the directory paths defined by your profile).
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echo $PATH |
Step 1: initial setup
You will be running various processes on the HPC that require quite a lot of processing power. Do not run these command on the 'head node' (which is the node you enter when you log on). Instead, either submit these commands via a PBS script or an interactive PBS session, which runs your processes on another node.
The details of creating and submitting a PBS script can be found here:
If you’re testing several tools or running multiple separate commands then an interactive PBS session may be preferable. Below is the command to create an interactive PBS session with 8 CPUs, 64GB memory and a maximum running time of 11 hours (12 hours is the absolute maximum that can be requested for an interactive session).
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qsub -I -S /bin/bash -l walltime=11:00:00 -l select=1:ncpus=8:mem=64gb |
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From your home directory, create a subdirectory called ‘anacapa’ and enter this subdirectory. This guide has commands that assume your
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cd ~ mkdir anacapa cd anacapa |
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mkdir ~/anacapa/crux_db/EMBL cd ~/anacapa/crux_db/EMBL wget https://www.funet.fi/pub/sci/molbio/embl_release/std/rel_std_mam_01_r143.dat.gz wget https://www.funet.fi/pub/sci/molbio/embl_release/std/rel_std_mam_02_r143.dat.gz gzip -d rel_std_mam_01_r143.dat.gz gzip -d rel_std_mam_02_r143.dat.gz |
Again, in this guide we’re just looking at mammal sequences. If you’re looking at another taxonomic group, you’ll need to download the appropriate databases. below are the codes for the available EMBL taxonomic groups.
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Division Code
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#alternatively download all EMBL files
wget https://www.funet.fi/pub/sci/molbio/embl_release/std/rel*
#uncompress all downloaded files
for i in `ls rel*`; do echo $i; gzip -d $i; done |
Again, in this guide we’re just looking at mammal sequences. If you’re looking at another taxonomic group, you’ll need to download the appropriate databases. below are the codes for the available EMBL taxonomic groups.
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Division Code ---------------- ------------------ Bacteriophage PHG - common Environmental Sample ENV - common Fungal FUN - map to PLN (plants + fungal) Human HUM - map to PRI (primates) Invertebrate INV - common Other Mammal MAM - common Other Vertebrate VRT - common Mus musculus MUS - map to ROD (rodent) Plant PLN - common Prokaryote PRO - map to BCT (poor name) Other Rodent ROD - common Synthetic SYN - common Transgenic TGN - ??? map to SYN ??? Unclassified UNC - map to UNK Viral VRL - common |
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First create directories to output these databases:
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mkdir ~/anacapa/crux_db/Obitools_databases mkdir-p ~/anacapa/crux_db/Obitools_databases/OB_dat_EMBL_std |
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Run the obiconvert command from the anacapa Singularity image.
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Important: You need to change every instance of /home/your_home_directory in the below command to your actual home directory (this is because obiconvert requires absolute paths). To find your home directory path, type cd ~ and then pwd. Use the path that this displays to replace the /home/your_home_directory.
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singularity exec /home/your_home_directory/anacapa/anacapa-1.5.0.img obiconvert -t /home/your_home_directory/anacapa/crux_db/EMBL/*.datTAXO --skipembl --on-error |
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ecopcrdb-output=/home/your_home_directory/anacapa/crux_db |
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/ |
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Obitools_databases/OB_dat_EMBL_std/ |
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OB_dat_EMBL_std |
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If you have downloaded and extracted all the databases in the correct directories you should now see obiconvert running with the following messages:
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Reading taxonomy dump file...
List all taxonomy rank...
Indexing taxonomy...
Indexing parent and rank...
Adding scientific name...
Adding taxid alias...
Adding deleted taxid...
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During initial testing on the mammal EMBL databases, this took about 8 hours to complete. Note that a PBS interactive session has a maximum time limit of 12 hours (and we requested 11 hours when we started our session). If you are working with a larger dataset - e.g. vertebrates or invertebrates - this process may take much longer, and in fact longer than an interactive session will run, requiring you to submit the above obiconvert command as a PBS script (again, see HPC for instructions on how to do this).
Step 4: Running CRUX
Once you have downloaded and converted the required databases (section above), you can run CRUX.
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/home/your_home_directory/anacapa/crux_db/EMBL/*.dat --skip-on-error |
MAKE SURE THE OUTPUT DIRECTORY IS EMPTY (--ecopcrdb-output= ...). If you’ve previously run this obiconvert command (as a test, or if it failed) using this same output directory, there may be some leftover files in there, in which case obiconvert won’t overwrite them, but will sequentially add to the database.
The above obiconvert command uses the NCBI taxonomy database (downloaded to ~/anacapa/crux_db/TAXO) and the EMBL database (downloaded to ~/anacapa/crux_db/EMBL/*.dat) and it outputs the ecoPCR converted database to /Obitools_databases/OB_dat_EMBL_std/ and prepends the generated ecoPCR database files with OB_dat_EMBL_std....
If you have downloaded and extracted all the databases in the correct directories you should now see obiconvert running with the following messages:
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Reading taxonomy dump file...
List all taxonomy rank...
Indexing taxonomy...
Indexing parent and rank...
Adding scientific name...
Adding taxid alias...
Adding deleted taxid...
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During initial testing on the mammal EMBL databases, this took about 8 hours to complete. Note that a PBS interactive session has a maximum time limit of 12 hours (and we requested 11 hours when we started our session). If you are working with a larger dataset - e.g. vertebrates or invertebrates - this process may take much longer, and in fact longer than an interactive session will run, requiring you to submit the above obiconvert command as a PBS script (again, see Start using the HPC for instructions on how to do this).
An example PBS script for running obitools can be seen below.
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#!/bin/bash -l
#PBS -N ObiRun
#PBS -l select=1:ncpus=2:mem=64gb
#PBS -l walltime=96:00:00
cd $PBS_O_WORKDIR
singularity exec /home/your_home_directory/anacapa/anacapa-1.5.0.img \
obiconvert \
-t /home/your_home_directory/anacapa/crux_db/TAXO \
--embl \
--ecopcrdb-output=/home/your_home_directory/anacapa/crux_db/Obitools_databases/OB_dat_EMBL_std/OB_dat_EMBL_std \
--skip-on-error \
/home/your_home_directory/anacapa/crux_db/EMBL/*.dat |
As before, you’ll need to change the above directory locations to match where your singularity image is, your taxonomy database, your output directory and your EMBL database.
To create this script you can use a text editor like nano. In your HPC command line, type:
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module load nano |
To load nano, then type:
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nano launch.pbs |
This will create an empty PBS script file called ‘launch.pbs’. Copy and paste the PBS script text from the code block above into nano, then press control and o to save the file, then control and x to exit nano.
Now you can launch this as a PBS job by typing:
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qsub launch.pbs |
Your job will be added to the queue, so may take some time to start if there are many jobs queued. You can check the status of your jobs by typing:
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qstat -u <username> |
Change <username> to your own user (logon) name.
Step 4: Running CRUX
Once you have downloaded and converted the required databases (section above), you can run CRUX.
CRUX generates taxonomic reference libraries by querying your primers against an ecoPCR database you generated in Step 3. Anacapa then uses these libraries for taxonomic assignment of your sequences.
Example command:
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/bin/bash ~/Crux/crux_db/crux.sh -n 12S -f GTCGGTAAAACTCGTGCCAGC -r CATAGTGGGGTATCTAATCCCAGTTTG -s 80 -m 280 -o ~/Crux/crux_db/12S -d ~/Crux/crux_db -l |
The -s and -m parameters indicate the shortest and longest expected amplicons respectively. -n is the name of the primer set. -f and -r are the forward and reverse primers. -o is the output directory and -d is the directory location containing subdirectories of the CRUX databases you generated previously (NCBI taxonomy, obiconvert results, NCBI accession2taxonomy) .
See here for more details on what tools and steps are run in this section:
GitHub - limey-bean/CRUX_Creating-Reference-libraries-Using-eXisting-tools
Create a subdirectory, under your main anacapa directory, to output the Anacapa results. In this example we’re running the test 16S mammal primers/databases, so we’ll call the output directory ‘16mam’. Change this to a name suitable for your dataset (and also change 'your_home_directory' to your actual home directory).
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cd /home/your_home_directory/anacapa
mkdir 16Smam |
Modifying your CRUX config script
You’ll need to change some lines in your ‘crux_config.sh’ file, so that this config file points to the correct locations of tools and databases.
Your ‘crux_config.sh’ file should be in: /home/your_home_directoryAnacapa/CRUX_Creating-Reference-libraries-Using-eXisting-tools/crux_db/scripts
I have attached a copy of a working crux_config.sh file below.
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First, back up your current crux_config.sh file like so (make sure you’re in the directory where the ‘crux_config.sh’ file is):
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mv crux_config.sh crux_config.sh_bak |
Now copy the above attached crux_config.sh file to that directory.
There is one line you’ll still need to manually modify. Open crux_config.sh in nano:
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nano crux_config.sh |
And change the BLAST_DB="/home/whatmorp/nextflow/pia_eDNAFlow/db/nt" line to where you downloaded your NCBI nt library (see the ‘Download the NCBI nt library’ section in this guide).
Control-o to save the file and control-x to exit nano.
Running CRUX
We have added all the required tools to a singularity image, so run the CRUX command using this singularity image.
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singularity exec docker://ghcr.io/eresearchqut/anacapa-image:0.0.3 /bin/bash /home/your_home_directory/anacapa/crux_db/crux.sh -n 16Smam -f CGGTTGGGGTGACCTCGGA -r GCTGTTATCCCTAGGGTAACT -s 40 -m 240 -o /home/your_home_directory/anacapa/16Smam -d /home/your_home_directory/anacapa/crux_db/ -l |
As before, change all instances of 'your_home_directory' to your actual home directory.
Change the name (-n) to your primer set name. This should be the same name as your output directory.
Change your -f and -r to your primer sequences.
Change -s and -m to Find the expected length of your amplicons (should be in the literature associated with your primer set) and make -s 100bp smaller and -m 100bp longer than this length. E.g. our 16Smam test primers amplify a product approx. 140bp in length, so we use -s 40 -m 240
Change the -o output directory to the directory location you created at the start of this section.
The -d should point to where your CRUX databases are. Check in this directory. You should see subdirectories containing NCBI taxonomy, obiconvert results, NCBI accession2taxonomy databases (see ‘Step 3: Create reference libraries using CRUX’ to see where you created these databases).
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#!/bin/bash -l
#PBS -N CRUX
#PBS -l select=1:ncpus=2:mem=64gb
#PBS -l walltime=24:00:00
cd $PBS_O_WORKDIR
singularity exec docker://ghcr.io/eresearchqut/anacapa-image:0.0.3 /bin/bash \
/home/your_home_directory/anacapa/crux_db/crux.sh \
-n 16Smam -f CGGTTGGGGTGACCTCGGA -r GCTGTTATCCCTAGGGTAACT \
-s 40 -m 240 \
-o /home/your_home_directory/anacapa/16Smam \
-d /home/your_home_directory/anacapa/crux_db/ -l |
Step 5: Running anacapa
Now that the CRUX databases have been constructed, we can run anacapa itself on these databases.
This constitutes 2 steps (steps 5 and 6 in this guide).
First (this section, section 5) we run:
..sequence QC and generate amplicon sequence variants (ASV) from Illumina data using dada2 (Callahan et al. 2016). ASVs are a novel solution to identifying biologically informative unique sequences in metabarcoding samples that replaces the operational taxonomic unit (OTU) framework. Unlike OTUs, which cluster sequences using an arbitrary sequence similarity (ex 97%), ASVs are unique sequence reads determined using Bayesian probabilities of known sequencing error. These unique sequences can be as little as 2 bp different, providing improved taxonomic resolution and an increase in observed diversity. Please see (Callahan et al. 2016, Amir et al. 2017) for further discussion.
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Example anacapa script:
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/bin/bash ~/Anacapa_db/anacapa_QC_dada2.sh -i <input_dir> -o <out_dir> -d <database_directory> -a <adapter type (nextera or truseq)> -t <illumina run type HiSeq or MiSeq> -l |
Required arguments:
-i path to .fastq.gz files, if files are already uncompressed use -g
-o path to output directory
-d path to the CRUX database you generated in the previous section..
-a Illumina adapter type: nextera, truseq, or NEBnext
-t Illumina Platform: HiSeq (2 x 150) or MiSeq (>= 2 x 250)
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singularity exec /home/whatmorp/nextflow/pia_eDNAFlow/Anacapa/anacapa-1.5.0.img /bin/bash /home/whatmorp/nextflow/pia_eDNAFlow/Anacapa/anacapa/Anacapa_db/anacapa_QC_dada2.sh -i /home/whatmorp/nextflow/pia_eDNAFlow/fastq -o /home/whatmorp/nextflow/pia_eDNAFlow/Anacapa/16Smam_anacapa_output -d /home/whatmorp/nextflow/pia_eDNAFlow/Anacapa/anacapa/Anacapa_db -a nextera -t MiSeq -g -l |
Step 6: Running anacapa classifier
Example:
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/bin/bash ~/Anacapa_db/anacapa_classifier.sh -o <out_dir_for_anacapa_QC_run> -d <database_directory> -u <hoffman_account_user_name> -l |
Required Arguments:
-o path to output directory generated in the Sequence QC and ASV Parsing script
-d path to Anacapa_db
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singularity exec /home/whatmorp/nextflow/pia_eDNAFlow/Anacapa/anacapa-1.5.0.img /bin/bash /home/whatmorp/nextflow/pia_eDNAFlow/Anacapa/anacapa/Anacapa_db/anacapa_classifier.sh -o /home/whatmorp/nextflow/pia_eDNAFlow/Anacapa/16Smam_anacapa_output -d /home/whatmorp/nextflow/pia_eDNAFlow/Anacapa/anacapa/Anacapa_db -l |
chmod 777 /home/whatmorp/nextflow/pia_eDNAFlow/Anacapa/16Smam_anacapa_output/Run_info/run_scripts/16Smam_bowtie2_blca_job.sh
Cleanup
Running the anacapa workflow involves downloading and generating various large databases. These will just take up space on the HPC unless removed.
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