GenPhylo 0.1.1

GenPhylo generates synthetic alignments on a phylogenetic tree with given branch lengths.

GenPhylo: Generating Data on Phylogenetic trees

Authors: Marta Casanellas, Martí Cortada, Adrià Diéguez

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GenPhylo generates synthetic alignments on a phylogenetic tree with given branch lengths.

The user has two options depending on which arguments are chosen. Both of them need to take as input a tree in the Newick format (with nodes of any degree) with annotated branch lengths.

▶️ Option 1: Generate $N$ FASTA files with alignments of length $L$ given a Newick tree

The program generates a set of transition matrices according to the input tree <tree.txt> and its branch lengths. From these matrices, $N$ different alignments of length $L$ are simulated A root distribution input parameter also needs to be passed: we can impose a random distribution or a specific one. The outputs (a .txt file with the transition matrices and a .tar with the .FASTA files corresponding to the simulated alignments) can be named using the <experiment_name> input parameter and are saved in the output_files directory

An example with $N = 5$ and $L=1000$:

Using a random root distribution:

python3 GenPhylo.py <tree.txt> 5 1000 random <experiment_name>

Using a specific root distribution (note that A,G,C,T must be values that sum up to 1)

python3 GenPhylo.py <tree.txt> 5 1000 "[A,G,C,T]" <experiment_name>

▶️ Option 2: Generate FASTA files with alignments of given lengths $L_1,...,L_d$ given a Newick tree

Working very similarly as the first option, the second one allows to generate aligments of different length. The program generates a set of transition matrices according to the input tree <tree.txt> and its branch lengths and from these matrices, alignments of length $L_1,...,L_d$ are generated (we can add as many lengths as alignments we want to generate. ). The outputs are saved the same way as described for Option 1.

An example, for $L_1 = 500$, $L_2 = 1000$ and $L_3 = 10000$

Using a random root distribution:

python3 GenPhylo.py <tree.txt> L500 L1000 L10000 random <experiment_name>

Using a specific root distribution (note that A,G,C,T must be values that sum up to 1)

python3 GenPhylo.py <tree.txt> L500 L1000 L10000 "[A,G,C,T]" <experiment_name>

⚠️ Note that in this case, sequence lengths are preceeded by an $L$.

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This repository includes the file IQ-TREE_analysis.pdf that describes how we verified the precision of our alignments simulator by using the phylogenetic software IQ-TREE.

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⚙️ This software is developped under de GNU General Public License v3