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BubbZ 1.1.1

Release date: 8th August 2020

Authors

  • Ilia Minkin (Pennsylvania State University)
  • Paul Medvedev (Pennsylvania State University)

Introduction

BubbZ is a whole-genome homology mapping pipeline. BubbZ works best in the case when the user needs to find all (possibly overalpping) pairwise mappings in a collection of genomes. The mappings are output in GFF format.

Compilation and installation

To compile the code, you 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)

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:

sudo apt-get install git cmake g++ libtbb-dev

Once you installed the things above, do the following:

Clone the repository https://github.com/medvedevgroup/BubbZ by running:

git clone https://github.com/medvedevgroup/BubbZ 

Go to the root directory of the project and create the "build" folder by executing:

cd BubbZ
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, BubbZ-lcb, spoa and a wrapper script BubbZ which implements the pipeline.

BubbZ usage

BubbZ takes a collection of FASTA file as an input. The simplest way to run BubbZ is to run the following command:

bubbz <input FASTA files>

For example:

bubbz genome1.fa genome2.fa

By default, the output will be written in the directory "bubbZ_out" in the current working directory. It will contain a GFF file "blocks_coords.gff" containing coordinates of the mappings. BubbZ has several parameters that affect the accuracy and performance, they are described below.

Output description

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"

Parameters affecting accuracy

The value of k

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=21. The default is 21.

Vertices frequency threshold

Mammalian genomes contain many repeated elements that make the graph large and convoluted. To deal with this issue, BubbZ removes all k-mers with frequency more than a threshold, which is controlled by the option:

-a <integer>

We recommend to set it to the twice of the maximum number of copies a homologous block in the input genomes has. For example, if the largest gene family of the input genomes has N members, set -a to at least N * 2. However, increasing this value may significantly slow down the computation. The default value is 150.

Gap size threshold

BubbZ analyzes the graph by looking for long chains of common vertices in it. The gap size in a chain is limited by a parameter measured in the number of basepairs, which can be set using:

-b <integer>

The default value of -b is 200. Increasing value may improve recall of divergent sequences, but if -b is too high, it will decrease accuracy as well.

Mapping block size

BubbZ only output blocks longer than a specified threshold in basepairs, which is set by:

-m <integer>

The default value is 200.

Technical parameters

Threads number

The maximum number of thread for BubbZ to use. This parameter is set by

-t <integer>

By default BubbZ 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 BubbZ-lcb will use as much as possible.

Memory allocation

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>

Output directory

The directory for the output files can be set by the argument

-o <directory>

The default is "BubbZ_out" in the current working directory.

A note about the repeat masking

BubbZ 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 BubbZ uses the abundance parameter -a to filter out high-copy repeats.

Input length

Currently BubbZ does not support chromosomes in the input longer than 4294967296 bp, this will be fixed in the future releases. The total length of the input is not limited.

Citation

If you use SibeliaZ in your research, please cite:

Scalable pairwise whole-genome homology mapping of large genomes with BubbZ
Ilia Minkin, Paul Medvedev
iScience, 2020: https://doi.org/10.1016/j.isci.2020.101224

License

See LICENSE.txt

Contact

E-mail your feedback at ivminkin@gmail.com.

Datasets used of analyses in the paper

Mice and simulated genomes: https://github.com/medvedevgroup/SibeliaZ/blob/master/DATA.txt

Salmonella RefSeq genomes accession numbers: https://github.com/medvedevgroup/BubbZ/blob/master/salmonella_refseq.txt

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