Authors: Valerie de Anda (1), Cesar Poot-Hernandez (2), Bruno Contreras-Moreira (3)
- Instituto de Ecologia, UNAM, Mexico
- Instituto de Investigaciones Matematicas Aplicadas y en Sistemas, UNAM, Mexico
- Fundacion ARAID & EEAD-CSIC, Zaragoza, Spain
The main goal of MEBS is capture with a single value the importance of complex metabolic pathways or biogeochemical cycles in a large omic datasets (either genomes or metagenomes). The algortithm has been thoroughly tested with the sulfur cycle, but currently other cycles are also supported. The script mebsv1.pl allows you to score your own genome/metagenome in terms of biogeochemical cycles. All that is required is a directory containing peptide FASTA files of encoded proteins/fragments with .faa extension.
The MEBS software is available as an open-source package distributed from a GitHub repository. Thus, the natural way of installing it is by cloning the repository via the following commands:
git clone https://github.com/eead-csic-compbio/metagenome_Pfam_score
#Alternatively, a ZIP file can be downloaded and then unpacked:
unzip metagenome_Pfam_score-master.zip
Before you start, make sure you have hmmserch installed, v3.1b1 or greater, otherwise the program will generate errors, see issue 1
Instructions and full documentation of MEBS are available in HTML and PDF
Have a look at the options of the main script mebs.pl
perl mebs.pl
Program to compute MEBS for a set of genomic/metagenomic FASTA files in input folder.
Version: v1.0
usage: mebs.pl [options]
-help Brief help message
-input Folder containing FASTA peptide files (.faa) (required)
-type Nature of input sequences, either 'genomic' or 'metagenomic' (required)
-fdr Score cycles with False Discovery Rate 0.1 0.01 0.001 0.0001 (optional, default=0.01)
-cycles Show currently supported biogeochemical cycles
-comp Compute the metabolic completeness (optional)
The following biogeochemical cycles are ready to use with MEBS:
- sulfur: Includes the mobilization of inorganic and inorganic sulfur compounds
- carbon:Usage of CH4 compounds by methanotrophs, methanogens, and methylotrophs
- oxygen: Represented by oxygenic photosynthesis
- iron: The Fe reduction and oxidation including also siderophores uptake
- nitrogen: We included the pathways involved in the reduction and oxidation of both inorganic (nitrate(+5) to ammonia(-3) ) and organic nitrogen compounds (i.e taurine, urea, and choline degradation)
perl mebs.pl -cycles
# Available cycles:
sulfur
carbon
oxygen
iron
nitrogen
# Available files to compute completeness:
cycles/sulfur/pfam2kegg.tab
cycles/carbon/pfam2kegg.tab
To run MEBS you only need to specifyt the input folder and the type of data (either genomic or metagenomic). The latter is required for MEBS to allocate the pre-computed entropies for each type of data considering the fragmentary nature of the metagenomic sequences.
perl mebs.pl -input test_genomes/ -type genomic
sulfur carbon oxygen iron nitrogen
Enterococus_durans.faa -0.063 0.284 0.883 0.214 3.044
Archaeoglobus_profundus_DSM_5631.faa 11.434* 24.834* 1.493 0.765 6.873
The scores that meets the criteria of specific FDR are shown in asterisc, yet the score will be the same regardless of the FDR that is used. If the Score if greater or equal to the FDR, then an asterisc will be shown in the output. In the case of using the default FDR (0.01), more false positive will be obtained, for example the genome Archaeoglobus profundus a well known microorgnism involved in the S-cycle, could seem to have a CH4 metabolism by using a default FDR,however if we increase to FDR 0.001, the C cycle asterisc is gone and only the S-cycle ramain. Therefore, we recomend a more restrictive FDR in order to eliminate false positives.
perl mebs.pl -input test_genomes/ -type genomic -fdr 0.001
sulfur carbon oxygen iron nitrogen
Enterococcus_durans.faa -0.063 0.284 0.883 0.214 3.044
Archaeoglobus_profundus_DSM_5631.faa 11.434 24.834* 1.493 0.765 6.873
If you attempt to benchmark your own metabolism, we recomend to add your own FDR values in this config file at the end of this file. In a 16.04 Ubuntu system, 16Gb RAM, intel Inside i7 the time to run the scritpt in the example folder is less than 20 seconds.
real 0m14.183s
user 0m22.961s
sys 0m0.865s
To compare your data with the maximum scores that you can obtain from the entropy data, have a look at the following data If you are computing MEBS in genomes compare your results with the row "Genomic data". In the case that you are computing MEBS in metagenomes see the corresponding MSL and MSLbin to you compare your results.
| sulfur | methane | oxygen | iron | nitrogen | |
|---|---|---|---|---|---|
| Genomic data | 16.018 | 85.332 | 10.703 | 10.464 | 22.079 |
| 30 | 13.676 | 84.503 | 10.438 | 8.843 | 20.642 |
| 60 | 16.818 | 85.347 | 11.253 | 9.567 | 22.148 |
| 100 | 15.566 | 85.221 | 9.965 | 10.676 | 21.43 |
| 150 | 15.848 | 84.81 | 10.152 | 10.316 | 21.379 |
| 200 | 15.887 | 84.765 | 10.463 | 9.832 | 21.938 |
| 250 | 16.031 | 85.057 | 10.387 | 10.215 | 21.853 |
| 300 | 15.929 | 84.942 | 10.569 | 10.284 | 21.968 |
Table containing metadata of 900 publicly available metagenomes from MG-RAST
Planned feature improvements are publicly catalogued at the main MEBS development site on github. Bug reports and problems using MEBS are welcome on the issues tracker. We prefer posting to the issue tracker over email as these posts are searchable by other users who may experience the same problems.
If you find this software usefull please cite us as:
- De Anda V, Zapata-Penasco I, Poot Hernandez AC, Fruns LE, Contreras Moreira B, Souza V (2017) MEBS, a software platform to evaluate large (meta)genomic collections according to their metabolic machinery: unraveling the sulfur cycle. doi:10.1093/gigascience/gigascience/gix096/4561660