Install & Use

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Installation Prerequisities

Our framework is written in Python programming language. By default, Python is available on any Linux platform. The optimal assignment of Inferred Scaffolding to Reference Scafolding is performed by using IBM CPLEX optimizer.

We use the following scripts/libraries:

• Fastaq script suite from (Hunt et al., Genome Biology, 2014). For convenience, the source of Fastaq is included in the repository
• Biopython library
• Python API of CPLEX
• MUMmer (rapid alignment of very large DNA and amino acid sequences)

Install Biopython

To install Biopython, type the following commands in your prompt:

pip install numpy
pip install biopython

Install Python API of CPLEX

IBM CPLEX optimizer is a very powerful tool used for solving a wide range of optimization problems (Linear Programming, etc). A free CPLEX academic license can be obtained here. As per CPLEX documentation:

To install the CPLEX-Python modules on your system, use the script setuy.py located in yourCplexhome/python/PLATFORM. If you want to install the CPLEX-Python modules in a nondefault location, use the option –home to identify the installation directory. For example, to install the CPLEX-Python modules in the default location, use the following command from the command line:

python setup.py install

To install in the directory yourPythonPackageshome/cplex, use the following command from the command line:

python setup.py install --home yourPythonPackageshome/cplex

Both of those commands (default and home-specified) invoke the Python package distutils. For other options available with that package, consult the documentation of Python distutils.

Install MUMmer

The current MUMmer release can be downloaded from SourceForge. To install MUMmer, type the following commands:

tar -xvzf MUMmer3.0.tar.gz
make check
make install

Our framework uses nucmer, delta-filter, and show-coords scripts from MUMmer package.

Framework Usage

Our framework takes as input the following:

• Reference genome fasta file
• Assembly contigs i.e., contigs obtained by running an assembly program. For example:
  • Velvet
  • ALLPATHS-LG
  • SOAPdenovo2
• Scaffolding output fasta file i.e. scaffolds obtained by running a scaffolding tool. For example:
  • ScaffMatch
  • OPERA-LG
  • BESST

We first have to prepare Reference Scaffolding dataset which is based on real assembly contigs. Correction of misassemblies has to be done and the output scaffolding is written to the ref_scaf file in the $output directory:

python3 build_ref_scaf.py $assembly_contigs $reference "$outdir/ref_scaf"

ref_scaf is a file of .scaf format as used by OPERA tool.

Now, having the output scaffolding, we have to create the out_scaf .scaf file which corresponds to the Inferred Scaffolding. In our framework, we align the assembly contigs to the output scaffolding using nucmer. Run the following command to get the out_scaf file:

python3 build_out_scaf.py $scaffolds "$outdir/ref_scaf.fa" "$outdir/out_scaf"

Finally, to evaluate a scaffolding tool we have to find the assignment of the Inferred Scaffolding contigs to the Reference Scaffolding contigs. The following command peforms the optimal assignment i.e the assignment which keeps the maximum number of contig links:

python validation.py $outdir/out_scaf.scaf $outdir/ref_scaf.scaf

The output is the number of correct contig links and two measures - sensitivity and PPV (positive predictive value).