- Ilia Minkin (Pennsylvania State University)
- Paul Medvedev (Pennsylvania State University)
SibeliaZ is a whole-genome alignment and locally-coliinear blocks construction pipeline. The blocks coordinates are output in GFF format and the alignment is in MAF.
SibeliaZ was designed for the inputs consisting of multiple similar genomes, like different strains of the same species. The tool works best for the datasets with the distance from a leaf to the most recent common ancestor not exceeding 0.09 substitutions per site, or 9 PAM units.
Currently SibeliaZ does not support chromosomes in the input longer than 4294967296 bp, this will be fixed in the future releases.
The easiset way to install SibeliaZ, is to use bioconda. Once you have bioconda environment installed, install package sibeliaz:
conda install sibeliaz
To compile the code yourself, you will need recent installations of the following software (Linux only):
- Git
- CMake
- A GCC compiler supporting C++11
- Intel TBB library properly installed on your system. In other words, G++ should be able to find TBB libs (future releases will not depend on TBB)
SibeliaZ was tested with CMake 3.5.1, gcc 5.4.0 and TBB 4.4. The easiest way to install the dependencies is to use a package management system. For APT on Debian systems they can be installed by the following command, which should take only a couple of minutes:
sudo apt-get install git cmake g++ libtbb-dev
Once you installed the things above, do the following:
Clone the repository by running:
git clone https://github.com/medvedevgroup/SibeliaZ
Go to the root directory of the project and create the "build" folder by executing:
cd SibeliaZ
mkdir build
Initialize dependencies by executing:
git submodule update --init --recursive
Go to the "build" directory and compile and install the project by running:
cd build
cmake .. -DCMAKE_INSTALL_PREFIX=<path to install the binaries>
make install
The make run will produce and install the executables of twopaco, sibeliaz-lcb, spoa and a wrapper script sibeliaz which implements the pipeline.
SibeliaZ takes a collection of FASTA file as an input. The simplest way to run SibeliaZ is to run the following command:
sibeliaz <input FASTA files>
For example:
sibeliaz genome1.fa genome2.fa
The alignment will be reported relative to the sequence ids, so all the input sequences should have a unique id in the fasta header. By default, the output will be written in the directory "sibeliaz_out" in the current working directory. It will contain a GFF file "blocks_coords.gff" containing coordinates of the found blocks, and file "alignment.maf" with the actual alignment. The subdirectory "examples" contains an example of running SibeliaZ and the output it produces. Running SibeliaZ on this example should require less than 5 minutes on a typical machine. SibeliaZ has several parameters that affect the accuracy and performance, they are described below.
You can also construct longer synteny blocks required for certain analyses. By default, sibeliaz also installs program maf2synteny written by Mikhail Kolmogorov. If you already have output of SibeliaZ, you can run maf2synteny on it:
maf2synteny <GFF or MAF file created by SibeliaZ>
Otherwise, it is best to run sibeliaz without the alignment step saving time and memory:
sibeliaz -n <output_directory>
maf2synteny <output_directory>/blocks_coords.gff
For further information on synteny blocks construction, please refer to the documentation of maf2synteny.
The output directory will contain:
- A GFF file with coordinates of the locally-collinear blocks. Lines that have identical id fields correspond to different copies of the same block. The file name is "blocks_coords.gff"
- A MAF file with the whole-genome alignment of the input. The file name is "alignment.maf".
Note: the actual alignment is produced by globally aliging the locally-colliner blocks, which is memory-hungry. It could be impossible to align certain blocks, especially if they have a lot of copies and/or long due to the aligner running out of memory, even on machines with large RAM. The output directory will have a subdirectory "blocks" that will contain FASTA files with blocks that were impossible to align. Each file correspond to a block and contains its copies. FASTA headers contain the coordinates of all copies of the block in the same format as MAF records, except that fields are separated by a semicolon.
It is possible to skip the alignment (use the -n switch) step and produce only coordinates of the blocks if the alignment is not needed for downstream analysis. In this case SibeliaZ will not produce the "alignment.maf" file and "blocks" subdirectory.
This parameter defines the order of the de Bruijn graph being used and controls the tradeoff between the sensitivity on one hand, and speed and memory usage on the other. The parameter is set by the key
-k <an odd integer>
In general the lower the k, the slower and more sensitive the alignment is. For small datasets, like bacteria, we recommend k=15, and for mammalian-sized genomes k=25. The default is 25.
Input genomes often contain repeated elements that make the graph large and convoluted. To deal with this issue, SibeliaZ removes all k-mers with frequency more than a threshold, which is controlled by the option:
-a <integer>
We recommend setting it to twice the maximum number of copies of a homologous block the underlying input genome collection has. For example, assume a single input genome in the collection of the input genomes has a genomic element that is duplicated D times and the user is interested in this element being properly aligned across the input. This element could be a gene that was duplicated multiple times or a transposon; we assume that D is the maximum frequency of occurrence across all such genomic elements of interest in a single genome.
If there are N input genomes containing the genomic element, then the parameter -a should be set to at least D * N * 2, otherwise, the genomic element will likely be missing in the resulting alignment. If the resources allow, this parameter could be set to a higher value, but setting it too high could significantly delay the computation. For example, suppose we align 10 genomes, and each genome has a gene that is duplicated 4 times. Hence, the parameter -a should be set to at least 10 * 4 * 2 = 80 or higher.
The default parameter value is 150 and is optimized for a collection of mammalian genomes. This parameter must be adjusted accordingly in case the input contains hundreds of short genomes like bacteria or viruses.
SibeliaZ analyzes the graph by looking for long chains of bubbles in it. A bubble is a pair of paths having the same endpoints. A long chain of bubbles is likely to be generated by a pair of homologous sequences, which SibeliaZ looks for. However, if the paths between endpoints of a bubble is too long, it may arise through the spurious similarity. To avoid this, SibeliaZ discards bubbles with paths longer than the threshold -b, which can be set by:
-b <integer>
The default value of -b is 200. Increasing value may increase recall of divergent sequences, but if -b is too high, it will decrease accuracy as well.
SibelaZ only output blocks longer than a specified threshold, which is set by
-m <integer>
The default value is 50. Warning: increasing this parameter may significantly slow down the computation.
To skip the alignment and only output coordinates of the blocks, use the switch
-n
The maximum number of thread for SibeliaZ to use. This parameter is set by
-t <integer>
By default SibeliaZ tries to use as much threads as possible. You can limit this number by using the above switch. Note that different stages of the pipeline have different scalabilities. TwoPaCo will not use more than 16 threads, while graph analyzer sibeliaz-lcb and the global aligner will use as much as possible.
The graph constructor TwoPaCo preallocates memory for Bloom filter. By default, the Bloom filter size is thrice of the size of the input files. The Bloom filter size can be set manually with the option:
-f <memory amount in GB>
The directory for the output files can be set by the argument
-o <directory>
The default is "sibeliaz_out" in the current working directory.
SibeliaZ and TwoPaCo currently do not recognize soft-masked characters (i.e. using lowercase characters), so please convert soft-masked repeats to hard-maksed ones (with Ns) if you would like to mask the repeats explicitly. However, it is not necessary as SibeliaZ uses the abundance parameter -a to filter out high-copy repeats.
SibeliaZ is the future developement of synteny-finder Sibelia. The key difference is that old Sibelia was designed to produce long synteny blocks, while SibeliaZ produces shorter locally-collinear blocks or LCBs. Output of SibeliaZ is very similar to Sibelia's when it is run in a single stage. At the same time, SibeliaZ is much faster and can handle longer genomes.
The script located at Sibeliaz-LCB/maf_to_gfa1.py lets you convert a MAF file produced by SibeliaZ to a GFA1 file representing a graph induced by the alignment. The GFA1 file then can be imported into vg or visualized. Usage:
python maf_to_gfa1.py <MAF alignment file> <input FASTA files>
The script located at Sibeliaz-LCB/maf_to_gfa1.py lets you convert a MAF file to XMFA format. Requires BioPython of version >= 1.6.9. Usage:
python maf_to_xmfa.py < <MAF alignment file>
It could be that SibeliaZ runs out of memory on large inputs. Possible reasons include:
-
TwoPaCo having the Bloom filter too small. To increase its size, use the -f switch
-
SibeliaZ-LCB running out of memory. You can try to reduce the abundance parameter -a to prune the internal data structure and reduce its size
If you use SibeliaZ in your research, please cite:
Minkin, I., Medvedev, P. Scalable multiple whole-genome alignment and locally collinear block construction with SibeliaZ.
Nat Commun 11, 6327 (2020). https://doi.org/10.1038/s41467-020-19777-8
If you also used maf2synteny, please cite the Ragout paper.
See LICENSE.txt
E-mail your feedback at [email protected].
See: https://github.com/medvedevgroup/SibeliaZ/blob/master/DATA.txt
