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usage: nexus2fasta_dirty usage: nexus2fasta_dirty

This directory contains a bunch of scripts to automate common (or not so common) tasks, mostly targeted at bioinformatics and phylogenetic systematic research. To install the scripts, clone the git repo into a local directory and add that directory to your PATH and PYTHONPATH environment variables. E.g.:

# clone the scripts repo
cd ~/ && git clone https://github.com/chinchliff/physcripts

# add it to paths
echo "export PATH=$(echo ~)/physcripts:\$PATH" >> ~/.bash_profile
echo "export PYTHONPATH=$(echo ~)/physcripts:\$PYTHONPATH" >> ~/.bash_profile

# load new environment variables
source ~/.bash_profile

The descriptions below include a variety of example calls to the scripts. The input files used in these examples are included in the test_files directory. The example calls are intended to be run from within the physcripts directory, but you can run the elsewhere by if you modify the calls to indicate the correct path to the example input files.

Below is a (complete) list of the available Python scripts, along with (highly incomplete) descriptive documentation about each one. Links to scripts with more complete documentation are provided below, and the documentation for these scripts precedes the docs for those with less information.

More details can usually be found in the form of comments within the scripts (in addition, of course, to my optimistically somewhat self-documenting code).

Add tips to a chronogram

Get information about names from a PHLAWD database

Filter a phylip alignment

Create a sampling matrix from a set of FASTA files

Root a tree using a reference tree

Estimate quartet jackknife ICA support on a tree

script: add_species_to_chronogram.py

Adds names from an input file into to a designated tree, while maintaining consistent depths for all affected internal nodes, and ensuring that newly added tips will have depths equal to the depth of their sister clades. In other words, this will maintain the time-relations in the chronogram as new tips are added.

./add_species_to_chronogram.py -t test_files/tree_with_50_tips.tre -n test_files/names_50.txt -s -b 0.1 

script: extract_names_and_ids_from_phlawd_db.py

Given a set of names, extract some minimal information about those names from a specified PHLAWD database.

./extract_names_and_ids_from_phlawd_db.py -d ~/phylo/data/gbpln_2014_04_25.db -n test_files/names_and_synonyms.txt 

script: filter_phylip.py

Filters a phylip alignment; can accept either a set of names to accepted or a set to be excluded, and saves the output to another file

script: make_sampling_matrix_from_fastas.py

This script accesses a directory, and traverses all FASTA files in it, recording the names of all taxa present in each file. Then it creates a tab-delimited file containing a matrix where the rows represent the taxa and the columns the FASTA files. The intended use is for a directory containing a set of FASTA files each corresponding to a single locus, and containing homologous sequences of that locus for different taxa. The script will record a 1 in the resulting matrix if a taxon is present in a locus file, or a 0 if not.

Key point: the script does not intelligently differentiate FASTA files from other types, and it will attempt to parse any file in the directory. For this reason, you should remove all other files before you run the script.

It will create (or overwrite!) a file in the passed directory called 'sampling_matrix.txt' that may be opened in any conventional spreadsheet or text-editor app. This file should be in the proper format for use in the Decisivator application.

This script requires BioPython.

script: root_tree_against_master.py

Place the root of a target tree in a position determined by the relationships present in a given master tree. The following conditions must be met:

1. The master tree contains all the taxa in the target tree.
2. Some bipartition X = A|B exists in the master tree such that for some bipartition Y = C|D in the target, C is a subset of A and D is a subset of B

If this case is satisfied, the target tree will be rooted at Y.

script: subsample_edge_quartets.py

Consider each node N in the rooted tree to identify a bipartition X, which is represented in the tree as the outgoing edge connecting N to its parent M. In a fully bifurcating tree, the nodes N and M will each have two other connected edges, which connect the nodes A and B to N, and C and D to M. If we unroot the tree, then the bipartition X can be written as X = {A,B}|{C,D}, and the branch represented by X in the rooted tree can be represented as the internal branch in a four-tip unrooted topology with tips T = {A, B, C, D}, with an internal branch representing X, and each tip t_i in T corresponding to a set of leaves S_i in the original tree.

We may then perform a number of replicated topology searches consisting of randomly drawing one leaf s_j from each S_i, reconstructing the topology for the four random tips using the sequence data from the original alignment, and recording the topology for each replicate. Each such topology replicate must either contain an internal branch that is consistent with X (thus the tips from A and B are on one side of the internal branch, and those from C and D are on the other), or else contain an internal branch that is in conflict with X (A and B are on different sides of the internal branch). To assess the support for bipartition X in the original tree, we summarize the quartet topology replicates, and calculate the ICA score for the internal branch that is consistent with X.

script: clean_gb_names_simple_fasta.py

script: clean_phlawd_spp_tip_names.py

script: excise_knuckles.py

script: extract_names_from_roguenarok_output.py

script: figtree_blocks.py

script: fill_tips_from_taxonomy.py

script: filter_fasta.py

Input files are expected to be in fasta format. The script will traverse all files in the input dir, so the input dir should contain only fasta files. The taxon list should be a line-delimited text file containing the names of tips as they correspond to those in the fasta alignments.

script: filter_list.py

script: find_names_missing_from_tree.py

script: fix_gb_names_in_fasta.py

Cleans the namestrings in fasta files downloaded from ncbi, saving only minimal name information with no special characters. Optionally will use an interactive prompt to allow the user to rename things with names in unrecognized formats

script: get_nested_taxa_for_names.py

uses a phlawd sqlite3 taxonomy database to retrieve all the names associated with each taxon in the provided list. specifies the rank of the taxa to be returned.

script: make_bootstraps.py

script: make_constraint_tree.py

script: make_partitions_from_pb_settings.py

script: make_random_tree.py

script: make_readme.py

Generate simple markdown documentation for the scripts in this directory

script: make_sampling_matrix_from_alignment.py

script: make_tip_list_by_genus.py

Reads a newick tree with phlawd-style tip names -- <ncbi_id> -- and creates a line-delimited list of the generic names in the tree and their constituent species that is written to outfile. Names are just extracted and parsed with regex search, does not use the tree structure at all.

script: match_taxa_in_fasta.py

script: merge_lists.py

script: newick3.py

Classes and methods for performing basic operations on Newick trees.

script: paint_branches.py

script: phylip2fasta.py

script: phylo3.py

Classes and methods for performing basic operations on phylogenetic trees.

script: print_tip_names.py

script: prune_long_tips.py

script: prune_tips.py

script: root_trees.py

Will root a set of newick trees using a line-delimited list of taxon names to be included in the outgroup.

script: strip_node_labels.py

script: test_monophyly_against_tree.py

script: transpose_fastas.py

transpose_fastas.py version 0.1 cody hinchliff 2012.7.21

This script accesses a directory, and traverses all FASTA files in it, recording them into a dict object. It then writes new fasta files to a directory named 'inverted' within the provided dir. One inverted fasta file is created for each sequence id found in the original set of fastas; each of these files contains the sequences associated with this sequence id, labeled with ids representing the original fasta files whence they came.

For example, if the script is passed a directory containing fasta files corresponding to loci, containing sequences labeled with taxon names, the inverted directory will contain fasta files corresponding to taxon names, containing sequences labeled with locus names (drawn from the filenames of the original fastas).