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This repository includes the scripts which were used to simulate the matching success of several UCE probe sets among 400 genomes of different descents.
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configs
README.md
fetch_genomes.sh
head_script.sh
head_slicer.sh
match_probes.sh
phyluce_count_probes_chopper.py
slicer.sh

README.md

UCE match counter

This repository includes the scripts which were used to simulate the matching success of several UCE probe sets among 400 genomes of different descents.

Getting Started

The repository includes the entire code used in a modeling study by Bossert & Danforth, and consists of 5 different shell scripts and one Python script. The general workflow is outlined below.

Prerequisites

Programs

The scripts require the following programs:

Folder architecture

The scripts further require two folders:

  • (1) Bait set folder. The scripts need to have access to a "probesets" folder, which has to be nested in the main folder (i.e., the folder in which the following scripts are executed). The "probesets" folder contains the probe sets of interest in .fasta format (e.g., hymenoptera.fasta). The probe sets used in the above mentioned study can be found in Faircloth's (2017) Dryad repository. The Hymenoptera probe set is from Branstetter et al. (2017).
  • (2) Configuration file folder. The scripts need to have access to a "configs" folder. The folder contains the configuration files that are needed to slice out the genomic sequence data from the utilized genomes. The configuration files can be found in this repository, however, they have to be altered if different taxa are used.
main_folder
      ├── probesets
      │       ├── hymenoptera.fasta
      │       ├── coleoptera.fasta
      │       ├── diptera.fasta
      │       └── ...
      │
      ├── configs
      │       ├── run_1.conf
      │       ├── run_2.conf
      │       ├── run_3.conf    
      │       ├── run_4.conf  
      │       └── ...
      │
      ├── fetch_genomes.sh
      ├── head_script.sh
      ├── match_probes.sh
      ├── head_slicer.sh
      ├── slicer.sh
      └── here_comes_python.py

Workflow

  1. fetch_genomes.sh – (e.g., call as bash -x fetch_genomes.sh 2>&1 | tee output_fetch.log). This script downloads the 400 genomes used in the above mentioned study from NCBI. It will further create the folder architecture that the other scripts of the repository rely on. Keep in mind that these are very large downloads.

  2. head_script.sh – (e.g., call as bash -x head_script.sh hymenoptera 2>&1 | tee match_probes_master_hymenoptera.log). This script runs the actual matching process, wrapped around the phyluce_probe_run_multiple_lastzs_sqlite script from PHYLUCE. The first argument represents the name of the taxon group (in this case hymenoptera); this name has to correspond to a probe set file in the probesets-folder (i.e., hymenoptera.fasta). The script calls the helper script match_probes.sh, which has to be in the same folder. The first argument represents the name of the taxon group (in this case hymenoptera); this name has to correspond to a probe set file in the probesets-folder (i.e., hymenoptera.fasta).

  3. head_slicer.sh – (e.g., call as bash -x head_script.sh hymenoptera 2>&1 | tee match_probes_master_hymenoptera.log). This script slices out the matches from the previously created sqlite databases, using the phyluce_probe_slice_sequence_from_genomes program from PHYLUCE. The first argument represents the name of the taxon group (in this case hymenoptera) that was previously matched. I.e., it has to correspond to the folder name with /results_$(taxon) (in this case it is results_hymenoptera). It makes use of two additional helper scripts (slicer.sh; phyluce_count_probes_chopper.py) to quantify the successfull matches.

Literature

  1. Branstetter, M. G., Longino, J. T., Ward, P. S. & Faircloth, B. C. Enriching the ant tree of life: enhanced UCE bait set for genome-scale phylogenetics of ants and other Hymenoptera. Methods Ecol. Evol. doi:10.1111/2041-210X.12742 (2017).

  2. Faircloth, B. C. PHYLUCE is a software package for the analysis of conserved genomic loci. Bioinformatics 32, 786-788, doi:10.1093/bioinformatics/btv646 (2016).

  3. Faircloth, B. C. Identifying conserved genomic elements and designing universal bait sets to enrich them. Methods Ecol. Evol. 10.1111/2041-210X.12754 (2017).

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