metabinner_cami2b

GitHub repository for binning CAMI2b challenge dataset using MetaBinner

Getting Started

Conda

We recommend using conda to run MetaBinner.

Obtain codes and create an environment

After installing Anaconda (or miniconda), fisrt obtain MetaBinner:

git clone https://github.com/ziyewang/metabinner_cami2b

Then simply create a metabinner_cami2b environment

cd metabinner_cami2b
conda env create -f metabinner_cami2b_env.yaml
conda activate metabinner_cami2b_env

Install checkM (python3 version) like this

(please make sure you have installed openssl)

cd CheckM-1.0.18
python setup.py install

Install checkM database:

CheckM relies on a number of precalculated data files which can be downloaded from https://data.ace.uq.edu.au/public/CheckM_databases/. (More details are available at https://github.com/Ecogenomics/CheckM/wiki/Installation#how-to-install-checkm):

mkdir <checkm_data_dir>
cd <checkm_data_dir>
wget https://data.ace.uq.edu.au/public/CheckM_databases/checkm_data_2015_01_16.tar.gz
tar xzf checkm_data_2015_01_16.tar.gz 
checkm data setRoot .

CheckM requires the following programs to be added to your system path:

HMMER (>=3.1b1)

prodigal (2.60 or >=2.6.1) executable must be named prodigal and not prodigal.linux

pplacer (>=1.1) ;guppy, which is part of the pplacer package, must also be on your system path; pplacer binaries can be found on the pplacer GitHub page

or you can install the dependencies as follows:

conda install -c bioconda prodigal
conda install -c bioconda hmmer 
conda install -c bioconda pplacer

An example:

#Filter short contigs and generate kmer profiles:
python scripts/filter_tooshort_for_contig_file.py test_data/final_contigs.fa 999
python scripts/gen_kmer.py test_data/final_contigs.fa 999 4

#path to the input files for metabinner and the output dir:
contig_file=test_data/final_contigs_999.fa
kmer_files=test_data/kmer_4_f999.csv
coverage_profiles=test_data/Coverage_f1k.tsv
output_dir=test_data/output

mkdir ${output_dir}/metabinner_res

bash ${metabinner_path}/code_for_cami2b/metabinner_cami2b_pipeline_v1.2.sh ${contig_file} ${output_dir} ${coverage_profiles} ${kmer_profile} ${metabinner_path}


#The file "final_result_combo_greedy_combo2_mypipeline.tsv" in the "${output_dir}/metabinner_res" is the final output.

Contacts and bug reports

Please send bug reports or questions (such as the appropriate modes for your datasets) to Ziye Wang: [email protected] and Dr. Shanfeng Zhu: [email protected]

References

[1] Lu, Yang Young, et al. "COCACOLA: binning metagenomic contigs using sequence COmposition, read CoverAge, CO-alignment and paired-end read LinkAge." Bioinformatics 33.6 (2017): 791-798.

[2] https://github.com/dparks1134/UniteM.

[3] Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. 2015. "CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes." Genome Research, 25: 1043–1055.

[4] Graham ED, Heidelberg JF, Tully BJ. (2017) "BinSanity: unsupervised clustering of environmental microbial assemblies using coverage and affinity propagation." PeerJ 5:e3035