TwoPaCo 0.9.4
It is an implementation of the algorithm described in the paper “TwoPaCo: An efficient algorithm to build the compacted de Bruijn graph from many complete genomes”.
This distribution contains two programs:
Links to the data used for bencharmking in the paper: https://github.com/medvedevgroup/TwoPaCo/blob/master/data.txt
To compile the code, you need the following (Linux only):
Once you’ve got all the things above, do the following:
This will make two targets: twopaco and graphdump. Compilation under other platforms is possible, portable makefiles are in progress.
To construct the graph (assuming you are in dir with “twopaco”), type:
./twopaco -f <filter_size> -k <value_of_k> <input_files>This will constuct the compressed graph for the vertex size of <value_of_k> using 2^<filter_size> bits in the Bloom filter. The output file is a binary that can be either converted to a text file or read directly using an API (will be available soon).
The filter size -f is a very important parameter that affects both the memory usage and the speed. TwoPaCo will use at least 2^<filter_size> / 8 bytes of memory, but setting it too low can massively increase the size of the memory used and slow down the program. We recommend the user to set -f to to the value so that 2^<filter_size> / 8 is the maximum memory in bytes they wish to allocate to the algorithm. If the memory usage then exceeds the value above, then the number of rounds should be increased until the memory usage falls below the desired value (see the section “Number of rounds”).
If the memory usage is not a concern, then as a rule of thumb for the fastest speed, set the parameter -f as large as possible. Here are the recommended settings given the memory size of a machine:
| Machine RAM | Recommended -f value | Corresponding Bloom fitler size |
|---|---|---|
| 4 GB | 34 | 2.1 GB |
| 8 GB | 35 | 4.3 GB |
| 16 GB | 36 | 8.6 GB |
| 32 GB | 37 | 17.2 GB |
| 64 GB | 38 | 34.4 GB |
| 128 GB | 39 | 68.7 GB |
| 256 GB | 40 | 137.4 GB |
For a memory size in between, go up a value, i.e. for 12GB RAM use 36, not 35. For more details on how the Bloom filter size affects performance, please see the paper. Below is description of the other parameters.
Alternatively, you can specify the memory used by the filter using the “filtermemory” option:
--filtermemory <memory amount in GB>Note that the filter will be of size 2^n bits with n being as large as possible such that the filter fits the memory size specified. So if you use 20 as the filtermemory, TwoPaCo will allocate 17.2 GBs for the Bloom filter.
Number of computational rounds. For the fastest performance, use 1 round (the default). Increasing the number of rounds will decrease the memory usage at the expense of longer runtime. Prior to increasing the number of rounds, please make sure to set the Bloom filter size correctly as described above. To set the rounds parameter, use:
-r <number> or --rounds <number>This value sets the size of a vertex in the de Bruijn graph. Default is 25, to change, use:
-k <number> or --kvalue <number>Note that: 1) TwoPaCo uses k as the size of the vertex and (k + 1) as the size of the edge 2) k must be odd
The maximum value of K supported by TwoPaCo is determined at the compile time. To increase the max value of K, increase the value “MAX_CAPACITY” defined in the header “vertexenumerator.h” and recompile. The value of “MAX_CAPACITY” should be at least (K + 4) / 32 + 1. Note that increasing the parameter will slow down the compilation.
The number of hash functions used for the Bloom filter. The default is five. To change, use:
-q <number> or --hashfnumber <number>More hash functions increases the running time. At the same time, more hash functions may decrease the number of false positives and the memory usage.
twopaco can be run with multiple threads. The default is 1. To change, use:
-t <number> or --threads <number>The directory for temporary files. The default is the current working directory. To change, use (the directory must exist):
--tmpdir <path_to_the_directory>The name of the output file. The default is “de_bruijn.bin”. To change, use:
--o <file_name> or --outfile <file_name>If the flag is set, TwoPaCo will run a set of internal tests instead of processing the input file:
--testThis utility turns the binary file a text one. There are several output formats available. The folder “example” contains an example described in details.
In the next release I will add an option to output coordinates of all occurrences of the junctions in GFF format.
This format is used for visualization. The resulting DOT file can be converted into an image using Graphviz package:
http://www.graphviz.org/To get the DOT file, use:
graphdump <twopaco_output_file> -f dot -k <value_of_k>Note that the graph is a union of graphs built from both strands, with blue edges coming from the main strand and red ones from reverse one. The labels of the edges will indicate its position on a chromosome.
GFA is the most handy option. It explicitly represents the graph as a list of edges (non-branching paths in the non-compacted de Bruijn graph) graph and adjacencies between them. The file also contains all occurrences of the strings spelled by the paths in the input genomes.
In other words, it describes a colored de Bruijn graph where each path is mapped to several locations in the input (“colored”). TwoPaCo supports both GFA1 and GFA2. They are described here:
https://github.com/GFA-spec/GFA-specTo get GFA output, run:
graphdummp <twopaco_output_file> -f gfa[version] -k <value_of_k> -s <input_genomes>In the resulting file compacted non-branching paths are “segments” with “links” (GFA1) or “edges” (GFA2) containing them. “Containment” (GFA1) or “Fragment” (GFA2) records desrcibe the mapping between the non-branching paths in the graph and the input genomes. For GFA1, each input chromosome is also a “segment” described in the very beginning of the GFA file.
GFA1 only: each segment representing an input chromosome has the name of the corresponding header of the sequence in input FASTA file. In case if there are duplicate headers, one can add a prefix to segment names:
"s<number>_" + header of the sequence in input FASTA fileTo do so, use the switch:
--prefixFor an example of GFA output and more detailed explanation, see the “example” folder.
In this format the output file only contains positions of junctions in the input genomes. As described in the paper, you can trivially restore information about edges from this junctions list. Note that junctions are mapped to genomes, i.e. one can reconstruct a colored graph from it. To get the junctions list, run:
graphdump -f seq -k <value_of_k>This command will output a text file to the standard output. Each line will contain a triple indicating an occurence of junction:
<seq_id_i> <pos_i> <junction_id_i> The first number is the index number of the sequence, the second one is the position, and the third one is the junction id. The index number of the sequence is the order of the sequence in the input file(s). All positions/orders count from 0. Positions appear in the file in the same order they appear in the input genomes. The <junction_id> is a signed integer, the id of the junction that appears on the positive strand strand. A positive number indicates “direct” version of the junction, while a negative one shows the reverse complimentary version of the same junction. For example +1 and -1 are different versions of the same junction. This way, one can obtain all multi-edges of the graph with a linear scan, as described in the paper. For example, a sequence of of junctions ids:
a_1
a_2
a_3Generates edges a_1 -> a_2, a_2 -> a_3 in the graph corresponding to the positive strand. To obtain the edges from the positive strand, one has to traverse them in the backwards order and negate signs, e.g. for the example above the sequence will be -a_3 -> -a_2 -> -a_1. One can also output junctions grouped by ids, it is useful for comparison between different graphs:
graphdump -f group -k <value_of_k>In this format the i-th line line corresponds to the i-th junction and is of format:
<seq_id_0> <pos_0>; <seq_id_1> <pos_1>; ....Where each pair “seq_id_i pos_j” corresponds to an occurence of the junction in sequence “seq_id_i” at position “pos_j”. Sequence ids are just the numbers of sequences in the order they appear in the input. All positions count from 0.
This is the most parsimonious option in terms of involved resources. One can read junctions and/or edges from the output file using a very simple C++ API. I will add the description in the future release. For now, one can use the sources of graphdump as a reference, it is relatively straightforward.
See LICENSE.txt
Please e-mail your feedback at ivminkin@gmail.com.
You also can report bugs or suggest features using issue tracker at GitHub https://github.com/medvedevgroup/TwoPaCo
If you use TwoPaCo, please cite:
Ilia Minkin, Son Pham, and Paul Medvedev
"TwoPaCo: An efficient algorithm to build the compacted de Bruijn graph from many complete genomes"
Bioinformatics, 2016 doi:10.1093/bioinformatics/btw609This project has been supported in part by NSF awards DBI-1356529, CCF-1439057, IIS-1453527, and IIS-1421908.