MetaBGC | metagenomic strategy for harnessing the chemical repertoire | Genomics library

These instructions will get you setup to run MetaBGC on your local Linux or Apple environment.
MetaBGC can be installed using Bioconda too. The following commands install the dependencies and then install PyPI metabgc from PyPI:.
Docker container files are provided for releases > 2.0.0. Go to the latest release on GitHub and download the source code tar ball and uncompress it.
Change to the source code directory and build the container. Then you can run the container from commandline to view metabgc help.
The quick start guide provides 2 sample datasets for building a new database and searching an existing database. The example commands are provided to run the processes and can be used as a template.
To run a toy build example, please download a file from here. This should take about 1 hour using 8 threads on a 2.5 GHz Ivybridge Intel processor. The directory also has a SLURM job script runBuildTest.sh, that can be submitted to a cluster after changing the paths.
To build and evaluate spHMMs for the protein family of interest, the metabgc build command has to be executed with required input files. To select high performance spHMMs, a synthetic metagenomic dataset must be generated with reads from true positive genes spiked in to test the performance of each spHMM. To generate synthetic metagenomes for the build process use the metagbc synthesize module.
To build and evaluate spHMMs for the protein family of interest, the metabgc build command has to be executed with required input files. To select high performance spHMMs, a synthetic metagenomic dataset must be generated with reads from true positive genes spiked in to test the performance of each spHMM. To generate synthetic metagenomes for the build process use the metagbc synthesize module. 1. --prot_alignment, required=True: Alignment of homologs from the protein family of interest in FASTA format. 2. --prot_family_name, required=True: Name of the protein family. This is used as prefix for spHMM files. 3. --cohort_name, required=True: Name of the cohort of synthetic metagenomic samples used for evaluation. 4. --nucl_seq_directory, required=True: Directory of reads for the synthetic metagenomic samples. The filenames are used as sample names. 5. --prot_seq_directory, required=False: Directory with translated synthetic read files of the cohort. Computed if not provided. 6. --seq_fmt, required=True: {fasta, fastq} Sequence file format and extension. 7. --pair_fmt, required=True: {single, split, interleaved} Paired-end information. 8. --R1_file_suffix, required=False: Suffix including extension of the file name specifying the forward reads. Not specified for single or interleaved reads. Example: .R1.fastq 9. --R2_file_suffix, required=False: Suffix including extension of the file name specifying the reverse reads. Not specified for single or interleaved reads. Example: .R2.fastq 10. --tp_genes_nucl, required=True: Nucleotide sequence of the full-length true-positive genes in the synthetic dataset in multi-FASTA format. This can be generated using the "metabgc findtp" module. 11. --F1_Thresh, required=False: Threshold of the F1 score for selection of high performance spHMMs (Def.=0.5). 12. --blastn_search_directory, required=False: Directory with BLAST search of the synthetic read files against the TP genes. Computed if not provided. To compute seperately, please see job_scripts in development. 13. --hmm_search_directory, required=False: Directory with HMM searches of the synthetic read files against all the spHMMs. Computed if not provided. To compute seperately, please see job_scripts in development. 14. --output_directory, required=True: Directory to save results. 15. --cpu, required=False: Number of CPU threads to use (Def.=4).
The high-performance spHMMs will be saved in the HiPer_spHMMs folder in the output directory specified. The HiPer_spHMMs folder should have the following files: 1. F1_Plot.png : F1 score plot of all the spHMMs and the F1 cutoff threshold. 2. *.hmm : A set of spHMMs that perform above the F1 cutoff threshold. 3. <prot_family_name>_F1_Cutoff.tsv: HMM search cutoff scores to be used for each high-performance spHMM interval. 4. <prot_family_name>_Scores.tsv: FP, TP and FN read counts of the the HMM search for all the spHMMs. 5. <prot_family_name>_FP_Reads.tsv: The false positive reads. These are reads identified in the spHMM search but are derived from the TP genes in the BLAST search. Because synthetic datasets do not fully represent real data, please be aware that some spHMM cutoffs may need to be further tuned after running MetaBGC on a real metagenomic dataset, as was done with the Type II polyketide cyclase cutoffs in the original MetaBGC publication.