/cpDNA

Chloroplast Tree Of Life Pipeline

Primary LanguageJupyter Notebook

Plastid Dataset and Tree building Pipeline

Here we describe the pipeline used to produce the plastid dataset and tree published in Leempoel et al. (preprint).

The validated dataset is available on Dryad (link) and the annotated plastid genomes and contigs are available on INSDC under our umbrella project PRJEB35285

Pipeline

Input

A list of samples must be provided in a space delimited file, which should have the following format [Project_Name]_labels.txt and have two columns (without header): Sample_Name Label.

Plastid contigs are expected to be located in the Data/ folder and Files should have the following format [Sample_Name]_pt.fasta

Parameters

  • Reference=References/cpDNA_RefCDS.fasta
  • min_cov=50 Minimum coverage of the matching reference gene to keep a blast match
  • min_len_r=0.2 minimum proportion of sum of genes length to keep a sample
  • min_sample_r=0.5 minimum proportion of samples to keep a gene
  • slurmThrottle=80 Number of parallel slurm jobs

Running modes

The script has 3 running modes. In all cases, a label file must be provided

  1. filter, to launch the gene recovery, followed by a filtering based on the minimum proportion of sum of genes.
  • Samples' contigs are blasted against the Reference. A custom python script scripts/targets_from_blast.py processes the blastn output by selecting the highest bitscore by gene and discarding matches below the minimum reference coverage min_cov. An attempt is made a recovering all exons for chloroplast genes with multiple exons.
  • All recovered gene sequences are concatenated into a single fasta file. The latter is then processed with scripts/Transpose_s2g.py, which writes fasta files per gene with all accepted samples. Here thresholds min_len_r & min_sample_r define the proportion of samples and genes kept.
  1. tree, to build the plastid tree from recovered genes
  • Genes are aligned with mafft --adjustdirection --auto and trimmed with trimal -gappyout
  • The tree is built with iqtree -s Aligned_genes_trimmed/ -m GTR+G -B 1000 and labelled.
  1. distrib, to assemble the validated dataset into a single compressed archive

Examples

cpDNA_pipeline.sh Araceae_labels.txt "tree" 0.1 #where Araceae_labels is the label file and 0.1 is the min_len_r, corresponding to 10% of the sum of genes length
cpDNA_pipeline.sh cpTree_v7_tree_DataDistribution.txt "distrib" 0

Dependencies

- blastn
- iqtree/2.1.3
- mafft/7.487
- trimal/1.3
- newick-utils 1.6 
- python3.6
  - Pandas
  - Numpy
  - Bio