This toolkit assemblies organelle genome from genomic skimming data.
Please denote the version of GetOrganelle as well as the dependencies in your manuscript for reproducible science.
Citation: Jian-Jun Jin*, Wen-Bin Yu*, Jun-Bo Yang, Yu Song, Claude W. dePamphilis, Ting-Shuang Yi, De-Zhu Li. GetOrganelle: a fast and versatile toolkit for accurate de novo assembly of organelle genomes. Genome Biology 21, 241 (2020). https://doi.org/10.1186/s13059-020-02154-5
License: GPL https://www.gnu.org/licenses/gpl-3.0.html
Please also cite the dependencies if used:
GetOrganelle is currently maintained under Python 3.7.0, but designed to be compatible with versions higher than 3.5.1 and 2.7.11. It was built for Linux and macOS.
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The easiest way to install GetOrganelle and its dependencies is using conda:
conda install -c bioconda getorganelleYou have to install Anaconda or Miniconda before using the above command. If you don't like conda, or want to follow the latest versions, you can find more installation options here.
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Since v1.7.0, the default databases are isolated as a new repository GetOrganelleDB. After installation of GetOrganelle v1.7.0+, please download and initialize the database of your preferred organelle type (embplant_pt, embplant_mt, embplant_nr, fungus_mt, animal_mt, and/or other_pt). Supposing you are assembling chloroplast genomes:
get_organelle_config.py --add embplant_pt,embplant_mtCheck Initialization from local files if connection keeps failing.
Download a simulated Arabidopsis thaliana WGS dataset:
wget https://github.com/Kinggerm/GetOrganelleGallery/raw/master/Test/reads/Arabidopsis_simulated.1.fq.gz
wget https://github.com/Kinggerm/GetOrganelleGallery/raw/master/Test/reads/Arabidopsis_simulated.2.fq.gz
then do the fast plastome assembly (memory: ~600MB, CPU time: ~60s):
get_organelle_from_reads.py -1 Arabidopsis_simulated.1.fq.gz -2 Arabidopsis_simulated.2.fq.gz -t 1 -o Arabidopsis_simulated.plastome -F embplant_pt -R 10
You are going to get a similar running log as here and the same result as here.
Find more real data examples at GetOrganelle wiki, GetOrganelleGallery and GetOrganelleComparison.
What you actually need to do is just typing in one simple command as suggested in Recipes. But you are still recommended to read the following introductions:
Preparing Data
Currently, get_organelle_from_reads.py was written for illumina pair-end/single-end data (fastq or fastq.gz). Usually, >1G per end is enough for plastome for most normal angiosperm samples, and >5G per end is enough for mitochondria genome assembly. Since v1.6.2, get_organelle_from_reads.py will automatically estimate the read data it needs, without user assignment nor data reducing (see flags --reduce-reads-for-coverage and --max-reads for more options).
Filtering and Assembly
Take your input seed (fasta; the default is GetOrganelleLib/SeedDatabase/*.fasta) as probe, the script would recruit target reads in successive rounds (extending process). The default seed works for most samples, but using a complete organelle genome sequence of a related species as the seed would help rescue data of bad sequence quality (e.g. degraded DNA samples). The value word size (followed with "-w"), like the kmer in assembly, is crucial to the feasibility and efficiency of this process. The best word size changes upon data and will be affected by read length, read quality, base coverage, organ DNA percent and other factors. Since version 1.4.0, if there is no user assigned word size value, GetOrganelle would automatically estimate a proper word size based on the data characters. Although the automatically-estimated word size value does not ensure the best performance nor the best result, you do not need to adjust the value if a complete/circular organelle result is produced, because the circular result by GetOrganelle is highly consistent under different options and seeds. After extending, this script will automatically call SPAdes to assembly the target reads produced by the former step. The best kmer depends on a wide variety of factors too.
Producing Result
By default, SPAdes is automatically called to produce the assembly graph file filtered_spades/assembly_graph.fastg. Then, Utilities/slim_graph.py is called to modify the filtered_spades/assembly_graph.fastg file and produce a new fastg file (would be assembly_graph.fastg.extend_embplant_pt-embplant_mt.fastg if "-F embplant_pt" been used by get_organelle_from_reads.py) along with a tab-format annotation file (assembly_graph.fastg.extend_embplant_pt-embplant_mt.csv).
The assembly_graph.fastg.extend_embplant_pt-embplant_mt.fastg file along with the assembly_graph.fastg.extend_embplant_pt-embplant_mt.csv file would be further parsed by disentangle_organelle_assembly.py, and your target sequence file(s) *complete*path_sequence.fasta would be produced as the final result, if disentangle_organelle_assembly.py successfully solve the path.
Otherwise, if GetOrganelle failed to solve the path (produce *scaffolds*path_sequence.fasta), you could use the incomplete sequence to conduct downstream analysis or manually view assembly_graph.fastg.extend_embplant_pt-embplant_mt.fastg and load the assembly_graph.fastg.extend_embplant_pt-embplant_mt.csv in Bandage, choose the best path(s) as the final result. You could execute slim_graph.py -F embplant_pt -E embplant_mt assembly_graph.fastg.extend_embplant_pt-embplant_mt.fastg to further remove mitogenome contigs for this easier visualization and manual completion.
Here (or here) is a short video showing a standard way to manually extract the plastome from the assembly graph with Bandage. See here or here for more examples.
GetOrganelle flowchart
To assembly Embryophyta plant plastome (e.g. using 2G raw data of 150 bp paired reads), typically I use:
get_organelle_from_reads.py -1 forward.fq -2 reverse.fq -o plastome_output -R 15 -k 21,45,65,85,105 -F embplant_pt
or in a draft way:
get_organelle_from_reads.py -1 forward.fq -2 reverse.fq -o plastome_output --fast -k 21,65,105 -w 0.68 -F embplant_pt
or in a slow and memory-economic way:
get_organelle_from_reads.py -1 forward.fq -2 reverse.fq -o plastome_output -R 30 -k 21,45,65,85,105 -F embplant_pt --memory-save
To assembly Embryophyta plant mitochondria (usually you need more than 5G raw data):
get_organelle_from_reads.py -1 forward.fq -2 reverse.fq -o mitochondria_output -R 50 -k 21,45,65,85,105 -P 1000000 -F embplant_mt
To assembly Embryophyta plant nuclear ribosomal RNA (18S-ITS1-5.8S-ITS2-26S):
get_organelle_from_reads.py -1 forward.fq -2 reverse.fq -o nr_output -R 7 -k 35,85,115 -F embplant_nr
To assembly fungus mitochondria:
get_organelle_from_reads.py -1 forward.fq -2 reverse.fq -R 10 -k 21,45,65,85,105 -F fungus_mt -o fungus_mt_out # if you fails with the default database, use your own seed database and label database with "-s" and "--genes"
To assembly animal mitochondria:
get_organelle_from_reads.py -1 forward.fq -2 reverse.fq -R 10 -k 21,45,65,85,105 -F animal_mt -o animal_mt_out # if you fails with the default database, use your own seed database and label database with "-s" and "--genes"
See a brief illustrations of those arguments by typing in:
get_organelle_from_reads.py -h
or see the detailed illustrations:
get_organelle_from_reads.py --help
To extract the plastome from an existing assembly graph (*.fastg/*.gfa):
get_organelle_from_assembly.py -F embplant_pt -g ONT_assembly_graph.gfa
If your question is running specific, please attach the get_org.log.txt file and the post-slimming assembly graph (assembly_graph.fastg.extend_*.fastg, could be Bandage-visualized *.png format to protect your data privacy).
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Report bugs & Open issues here.
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Send email to us ([email protected]/[email protected], [email protected])
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Join the QQ group (ID: 859158590)