Pando is a bioinformatics tool for for exploring and characterising bacterial genome data. Input is paired-end reads and assemblies. Output is an NJ tree inferred from the assemblies (using Andi and Quicktree) and a table of results (species ID, mlst, antimicrobial resistance genes, plasmid replicon types, Virulence genes, read and assembly QC metrics). Github repo and issue tracker: https://github.com/schultzm/Pando/. Pypi repository: https://pypi.python.org/pypi/Pando_
To get help:
Pando is a command-line tool for for exploring and characterising bacterial whole genome DNA sequence data. It is a computational pipeline written in python and is scalable via implementation using the ruffus pipeline library. Ruffus handles task-scheduling and task-parallelisation during the run. Pando is particularly useful for pathogenic species as it performs AMR and MLST profiling but in theory could be used for any bacterial species against any sequence database. It was originally written to characterise and QC assemblies and reads of bacterial isolates at the Microbiological Diagnostic Unit Public Health Laboratory, Victoria, Australia. Input is a tab-delimited text file that points the software to the assembly and paired-end read files for each isolate. Specifically, the file has no header, one line per isolate, and four columns per line in the following column order:
+-------------+------------------------------------------------+----------------------------+---------------------------+ |isolate_name | full_path_to_assembly (i.e., a 'contigs' file) | full_path_to_paired_reads1 | full_path_to_paired_reads2| +-------------+------------------------------------------------+----------------------------+---------------------------+
An example isolates.tab file looks like this:
isolate-1 /path/to/isolate-1/contigs.fa /path/to/isolate-1/reads-1.fq.gz /path/to/isolate-1/reads-2.fq.gz isolate-2 /path/to/isolate-2/contigs.fa /path/to/isolate-2/reads-1.fq.gz /path/to/isolate-2/reads-2.fq.gz isolate-3 /path/to/isolate-3/contigs.fa /path/to/isolate-3/reads-1.fq.gz /path/to/isolate-3/reads-2.fq.gz isolate-4 /path/to/isolate-4/contigs.fa /path/to/isolate-4/reads-1.fq.gz /path/to/isolate-4/reads-2.fq.gz isolate-5 /path/to/isolate-5/contigs.fa /path/to/isolate-5/reads-1.fq.gz /path/to/isolate-5/reads-2.fq.gz
The following packages need to be installed before pip3 installing Pando. To install dependencies, do:
cpan -i Moo cpan -i List::MoreUtils cpan -i Bio::Perl brew tap homebrew/science brew tap tseemann/homebrew-bioinformatics-linux brew update brew install mlst --HEAD brew install abricate --HEAD brew install quicktree brew install seqtk brew install andi brew install mummer brew install bowtie2 brew install cd-hit brew install ariba brew install kraken
You will then need to install a kraken database:
Choose a folder (say $HOME) to put it in, you need ~4 GB free:
tar -C $HOME minikraken.tgz
Then add the following to your $HOME/.bashrc:
To perform any of these install steps for all users, remove ‘–user’. The final symlink step is not required if installing for all users. pando is written for python3 and installation requires pip3 and setuptools. To install the latest ‘stable’ version of pando for the current user only, do:
pip3 install --user --no-binary :all: Pando
pip3 install --user --upgrade Pando
To install the latest, potentially unstable, bleeding-edge version:
pip3 install --user https://github.com/schultzm/Pando/zipball/master
If installing via the ‘–user’ option Check where the executable is:
which pando # ~/.local/bin/pando
Check where the site-packages are:
python3 -m site --user-site # ~/.local/lib/python3.6/site-packages
Now, symlink the packaged databases in site-packages above to the folder containing the executable shown above:
ln -s ~/.local/lib/python3.6/site-packages/Pando/CARD/ ~/.local/bin ln -s ~/.local/lib/python3.6/site-packages/Pando/VFDB/ ~/.local/bin ln -s ~/.local/lib/python3.6/site-packages/Pando/VFDB_core/ ~/.local/bin ln -s ~/.local/lib/python3.6/site-packages/Pando/plasmidfinder/ ~/.local/bin
To uninstall, do:
pip3 uninstall Pando # Remove the symlinks rm -r ~/.local/bin/CARD rm -r ~/.local/bin/VFDB* rm -r ~/.local/bin/plasmidfinder
Now, check that the dependencies are in the path using:
For large jobs, run in screen mode, otherwise skip this step:
screen -SL screenname
All screen-output within the screen started above, will be saved to:
screenlog.%n # Where %n is the number of the screen.
After exiting the screen, the screenlog.0 can be viewed as the run progresses using standard command line actions:
tail screenlog.0 less screenlog.0 watch "tail screenlog.0"
To get help for pando, just do:
Output looks like:
usage: pando <command> <options> Run summary analyses. optional arguments: -h, --help show this help message and exit -v, --version Print version and quit. Available commands:: check Check non-pip3-installable dependencies input Generate input table run Run the pando pipeline merge Runs the module to read in the metadata table and merge it with Pando output.csv files.
Notice, above, three modules: one each for input, run and merge. Each can be run independently.
This module is used to generate the isolates.tab file. Final output from this command is sent to standard out (stdout). To capture the information from stdout, redirect it to a file using ‘> isolates.tab’:
pando input -h pando input -i isolates.txt > isolates.tab
This module is used to run the analysis pipeline. In the example below, output will be a folder called results and we have selected to use the tree option with -t using a JC model of evolution (default for the -t option, but user can also choose from Raw or Kimura):
pando run -h pando run -i isolates.tab -o results -t
In the results folder there is a sub-folder for each isolate containing the results for each isolate. Also within the results folder there are three files:
results/isolates_metadataAll.csv results/isolates_metadataAll_simplified.csv results/isolates_andi_JCdist_nj.tre
This module is used to join existing metadata tables (e.g., LIMS table) with one of the output tables from the run module. In this example, an excel file (AMR_ongoing_20170307.xlsx, with a header on line 5 and first column containing isolate names that were expanded with a wildcard search during the pando input step above) is joined with the results/isolates_metadataAll_simplified.csv table. Again, final output is sent to stdout, which in this example is redirected to file using ‘> results/AMR_ongoing_20170307_join_isolates_metadataAll_simplified.csv’:
pando merge -h pando merge -l AMR_ongoing_20170307.xlsx -r results/isolates_metadataAll_simplified.csv > results/AMR_ongoing_20170307_join_isolates_metadataAll_simplified.csv
Why use Pando?
Pando provides a single table of metadata and a tree that the metadata can be plotted next to using phandango
The single NJ tree for the whole isolate set is inferred using the program quicktree from a distance matrix computed by andi using any of the evolutionary models JC, Raw or Kimura. Andi infers the distance matrix from the assemblies (typically contigs.fa files). The tree will not include isolates for which the assembly file is missing.
The summary table (csv) for all isolates combines the results from:
- inferred species from running kraken on the reads
- inferred species call from running kraken on the contigs (assemblies)
- Inferred consensus species call from a consensus of the best hit from kraken on the reads and kraken on the contigs
- Based on the species call, runs mlst using the appropriate scheme (if available) or autodetects scheme
- Gene content profiles using unlimited number of user databases of resistance genes, plasmid rep genes, virulence genes, etc., using abricate (contigs, BLAST contigs against database, assembly based) and ariba (reads, MiniMapping to database, mapping based)
- QC metrics using seqtk for reads and contigs
- Reports software versions and paths to databases used in the analysis (for repeatability)
- The pipeline is modular in that the user can choose not to perform the tree inference step with andi plus quicktree and/or the can choose not to perform the read mapping step using ariba
- A flowchart is produced for the run (however, if the total path length of the results folders combined exceeds 16384 characters then the flowchart cannot be drawn)
- The user can supply reads and/or contigs for each file. The final tree will only include taxa for which contigs have been supplied
First you need to download assemblies or perform the assemblies yourself from readsets (using e.g., unicycler https://github.com/rrwick/Unicycler, which uses SPAdes (http://bioinf.spbau.ru/spades) or MegaHit (https://github.com/voutcn/megahit)). It doesn’t necessarily make sense to supply reads for an isolate but contigs that have been assembled using a readset other than the one supplied. If you don’t have reads, leave the columns blank for that isolate (for example, if you just want to characterise assemblies downloaded from NCBI GenBank). If you don’t have contigs and only have reads, leave the column blank for contigs (but without contigs there will be no tree).
This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details. You should have received a copy of the GNU Affero General Public License along with this program. If not, see <http://www.gnu.org/licenses/>.