pyYAMB 0.1.1

Yet Another Metagenome Binner

pyYAMB

pyYAMB is an implementation of YAMB (Yet another metagenome binner) on Python (>=3.8). Originally YAMB was described in the preprint https://www.biorxiv.org/content/10.1101/521286.abstract and it's main point is the use of tSNE and HDBSCAN to process tetramer frequencies and coverage depth of metagenome fragments. pyYAMB strives for parallel computing wherever possible, currently coverage depth extraction is single threaded and takes the most time.

pyYAMB data processing includes

• contig filtering and fragmentation
• read mapping with minimap2
• mapping files processing and coverage depth extraction with pysam
• k-mer (by default tetramer) frequency calculation
• data dimensions reduction with tSNE
• data clustering with HDBSCAN
• writing bins to FASTA

Possible features in far future

• read processing
• metagenome assembly
• bin QC

How to start

Warning! pyYAMB now is in alpha-testing and may be unstable, use it at Your own risk.

Installation

Conda

pyYAMB is available at Anaconda and may be installed with all dependencies:

conda install -c laxeye pyyamb

PyPI

pyYAMB is available at PyPI and may be installed with:

pip install pyYAMB

Also yo need to install dependencies

GitHub

Another way (not recommended) is to clone the repository

git clone https://github.com/laxeye/pyYAMB.git or gh repo clone laxeye/pyYAMB

and run

python setup.py install

It installs pyYAMB and python libraries. Problems may appear with hdbscan module and cython. Just reinstall hdbscan using pip install hdbscan and try again python setup.py install.

Dependencies

If you installed pyYAMB from PyPI or GitHub, you need to install dependencies: minimap2 and samtools (e.g. using conda).

conda install -c bioconda minimap2 "samtools>=1.10"

Usage

pyYAMB entry point is the all-in-one command pyyamb. pyYAMB has two dozens of arguments, their description is available after running pyyamb -h

You may start from metagenome assembly and processed (quality trimmed etc.) reads, e.g.:

pyyamb --task all -1 Sample_1.R1.fastq.gz Sample_2.R1.fastq.gz -2 Sample_1.R2.fastq.gz Sample_2.R2.fastq.gz -i assembly.fasta -o results/will/be/here --threads 8

After completion bins could be found in bins subfolder in output folder. "-1" bin collects unbinned sequences.

Results and benchmarks

pyYAMB will be tested on CAMI dataset soon. YAMB showed quality compared with CONCOCT binner (see the preprint for details).

References

Van Der Maaten, L. (2014). Accelerating t-SNE using tree-based algorithms. The Journal of Machine Learning Research, 15(1), 3221-3245.

Campello, R. J., Moulavi, D., & Sander, J. (2013, April). Density-based clustering based on hierarchical density estimates. In Pacific-Asia conference on knowledge discovery and data mining (pp. 160-172). Springer, Berlin, Heidelberg.

Li, H. (2018). Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics, 34:3094-3100. https://dx.doi.org/10.1093/bioinformatics/bty191

Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P., & Tyson, G. W. (2015). CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome research, 25(7), 1043-1055. https://dx.doi.org/10.1101/gr.186072.114

Cock, P. J., Antao, T., Chang, J. T., Chapman, B. A., Cox, C. J., Dalke, A., ... & De Hoon, M. J. (2009). Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics, 25(11), 1422-1423. https://doi.org/10.1093/bioinformatics/btp163