A tool for detecting Bubbles and Superbubble chains in graphs. Takes as input a GFA file and output a JSON file with information about bubble chains and nested chains inside superbubbles.
The tools has function also to compact the graph, extract certain chains and neighborhoods in the graph for downstream analysis of the bubbles.
BubbleGun can be installed using the setup scrip
python3 setup.py install
This will also download the needed dependencies and creat a callable entry point for the tool and it should be added to the path, so BubbleGun can be called anywhere to get the CLI and then be used.
Also pip -e . can be used for the setups. However, for some reason it doesn't always install the dependencies but the previous method always install dependencies. Anyway, there are only two dependencies that can easily be install with pip too.
The graph above represents a bluntified (overlaps removed) of a de Bruijn graph with k-mer size of 9. These 4 sequences are traced with the different dotted lines in the graph, and they construct a bubble chains of 3 bubbles. 2 simple bubbles and 1 superbubble with a nested simple bubble inside.
The following sections will demonstrate some examples of using this tool.
The tool has several subcommans and -h or --help can be used to print this message.
usage: main.py [-h] [-e] [-g GRAPH_PATH] [-k K] [--with_coverage]
[--log LOG_LEVEL]
{bchains,compact,biggestcomp,bfs,gamdigest,chainout} ...
Find Bubble Chains.
Subcommands:
{bchains,compact,biggestcomp,bfs,gamdigest,chainout}
Available subcommands
bchains Command for detecting bubble chains
compact Command for compacting graphs
biggestcomp Command for separating biggest component
bfs Command for separating neighborhood
gamdigest Command for digesting a gam file
chainout Outputs certain chain(s) given by their id as a GFA
file
Global Arguments:
-h, --help show this help message and exit
-e, --examples prints out example commands to use the tool
-g GRAPH_PATH, --in_graph GRAPH_PATH
graph file path (GFA or VG)
-k K, --k_mer K K value of as integer
--with_coverage If this option given, mean coverage is taken from the
GFA file
--log LOG_LEVEL The logging level [DEBUG, INFO, WARNING, ERROR,
CRITICAL]As shown, it takes some Global arguments then specific subcommands. The global arguments -g and -k are required
This subcommand is for detecting bubble and superbubble chains. The following help page is available for bchains
usage: main.py bchains [-h] [--bubble_json OUT_JSON] [--only_simple]
[--save_memory] [--chains_gfa CHAINS_GFA]
[--fasta FASTA] [--out_haplos]
optional arguments:
-h, --help show this help message and exit
--bubble_json OUT_JSON
Outputs Bubbles, Superbubbles, and Chains as a JSON
file
--only_simple If used then only simple bubbles are detected
--save_memory Identifies bubble chain with less memory. No
statistics outputted
--chains_gfa CHAINS_GFA
Output only bubble chains as a GFA file
--fasta FASTA Outputs the bubble branches as fasta file (doesn't
work with memory saving)
--out_haplos output randomly two haplotypes for each chain (doesn't
work with memory saving)Examples:
-
A user wants to detect chains and output JSON file with information about the bubbles and low memory usage. . With saving memory, only the graph topology will be stored in memory and the sequences will not be read from the file. Command:
./main.py -g test_graph.gfa -k 9 bchains --bubble_json test_graph_chains.json -
A user wants to detect chains and output a new GFA graph with only the bubble chains. Command:
./main.py -g test_graph.gfa -k 51 bchains --chains_gfa chains_output.gfa -
A user wants to detect chains and output a FASTA file with only bubble branches sequences (currently only works for simple bubbles), where the sequence name indicate from which chains and which bubble they come from. Command:
./main.py -g test_graph.gfa -k 51 bchains --fasta test_output.fasta
For the previous example, the JSON and FASTA output can be seen in example Of course, all the previous commands can be combined in one to output a JSON, a GFA and a FASTA.
This subcommand separates a chain or several chains by their ids according to the JSON file. The following help page is available for the chainout subcommand:
usage: main.py chainout [-h] [--json_file JSON_FILE]
[--chain_ids CHAIN_IDS [CHAIN_IDS ...]]
[--output_chain OUTPUT]
optional arguments:
-h, --help show this help message and exit
--json_file JSON_FILE
The JSON file wtih bubble chains information
--chain_ids CHAIN_IDS [CHAIN_IDS ...]
Give the chain Id(s) to be outputted
--output_chain OUTPUT
Output path for the chains chosenExample comman:
./main.py -g test_graph.gfa -k 41 chainout --json_file test_graph_chains.json --chain_ids 10 13 30 --output_chain test_output.gfa
The ids given should be present in the JSON file and the graph given should be the same graph used to generate the JSON file.
This subcommand outputs a compacted GFA file. Example:
./main.py -g test_graph.gfa -k 41 compact compacted_test.gfa
This subcommand separates the biggest component in the graph and outputs it. Example:
./main.py -g test_graph.gfa -k 41 biggestcomp biggest_comp.gfa
This subcommand can be used to extract a neighborhood using BFS around a start node or several start nodes (takes the node id), these neighborhood will be outputted as a GFA file. Examples:
-
Extracting a neighborhood of size 100 nodes around the node with id 540
./main.py -g test_graph.gfa -k 51 bfs --start 540 --neighborhood_size 100 --output_neighborhood output.gfa -
Extracting the neighborhoods of size 100 nodes around nodes 500, 540, and 1509. Regardless if these neighborhood are connected or not, they all will be in the same output file.
./main.py -g test_graph.gfa -k 51 bfs --start 500 540 1509 --neighborhood_size 100 --output_neighborhood output.gfa
This subcommand filters an alignemnt GAM file. The following help is available for this subcommand:
usage: main.py gamdigest [-h] [--json_file JSON_FILE] [--alignment_file GAM]
[--min_cutoff MIN_CUTOFF] [--out_dict PICKLE_OUT]
optional arguments:
-h, --help show this help message and exit
--json_file JSON_FILE
The JSON file wtih bubble chains information
--alignment_file GAM Take GAM file and output pickled dict
--min_cutoff MIN_CUTOFF
The minimum cutoff of a mapping length.
--out_dict PICKLE_OUT
A pickled dictionary output path. contains
read_id:[nodes]This command is used to "filter" a GAM file which is an alignment file of reads aligned to the graph. This mainly works on the output from GraphAligner after aligning long reads to the graph.
GraphAligner outputs a GAM files which this commands takes along with the bubble chain graph aligned to and a minimum length cutoff for mappings. Each read would have several mappings, first, all mappings that are smaller than the cutoff are dicarded, then if the same read mapped to the same chain more than once, the longest mapping is kept. The output is a pickled dictionary with keys as read names and values as a list of nodes the read have mapped to. This pickled dictionary along with the graph can be given then to Whatshap phaseb command to output phased bubbles according to how the long reads mapped to these bubbles, but this is still under construction.
Example command:
./main.py gamdigest --json_file test_graph_chains.json --alignment_file test_alignment.gam --min_cutoff 200 --out_dict alignment_dictionary.pickle