Perform a topological and evolutionary analysis of a metabolic network: creates a directed reaction graph from a metabolic model (MATLAB format).
For running in Ubuntu you need to install:
- Python 2.7.12 (Packages: cobrapy, networkx, pandas)
- R 3.4.1 (Packages: biomaRt, seqinr, ggplot2)
- awk
- gzip
- bedtools intersectBed http://bedtools.readthedocs.io/en/latest/index.html
- CodonW https://sourceforge.net/projects/codonw/
Paths to binary programs and input/output directories are set in config.mk
Give the example running with Erythrocyte model
Create a directed reaction graph from a metabolic model. If the products of a node (REACTION) are the reactants of any other node, it creates a directed link between them. It removes currency metabolites and allows self-loops.
Returns a series of files with information about the graph:
- List of nodes (REACTIONS)
- List of edges (DIRECTED)
- List of genes (EntrezGene IDs)
- List of subsystems (METABOLIC PATHWAYS)
- Link between genes IDs and reactions
- Gene coordinates in GRCh37 (requires internet connection to connect to Ensembl).
Calculate topology measures by connected components. It calculates: in-degree, out-degree, degree, closeness, betweenness, ratio in/out-degree, source/sink, predecessors, successors.
Format hierarchical boosting scores files already calculated in 1000GP data to use with bedtools intersect.
Added file of threshold of significance based on simulations: thresholds_of_significance.POP.txt
Downloaded from: http://hsb.upf.edu/hsb_data/hierarchical_boosting/selection_scan/
Calculate maximum and average value for each boosting score per gene (+10kb up/downstream). If at least one window reaches the threshold of significance classifies the gene as being under positive selection.
Retrieve CDS (coding sequence) in FASTA format for all genes.
make parseBoosting # already run
make model2DRG
make topology
make boostStats
make getSequences
Alternatively make all run all the previous steps.
- Begona Dobon