MAST4 Population Genomics

This repository contains the scripts and code used to analyze population genomic variation in MAST-4 species as described in:

Latorre F., Jaillon O., Sieracki M. E., Cruaud C., Massana R., and Logares R.

Global population structure of a unicellular marine predator

Manuscript under review / preprint available at: https://doi.org/10.1101/2024.11.27.625609

The analyses are based on single-cell–derived reference genomes and surface ocean metagenomes from the Tara Oceans expedition, and focus on genetic diversity, population structure, and signatures of selection across global MAST-4 populations.

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Repository scope

This repository is intended to:

• Document the main computational steps used in the study
• Provide transparency on how population genomic metrics were computed
• Allow reuse or adaptation of specific analysis scripts by other researchers

It is not intended to be a fully automated or plug-and-play pipeline. Some scripts are tailored to the structure of the datasets used in this study.

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Contents

tutorials/

Contains small receips/tutorials following the Methods section in the Manuscript, including:

• Mapping - Mapping of Metagenomic reads against the reference genomes
• Relative Abundance - Computation of Relative Abundance values for the genomes across metagenomic samples
• Population Genomics analyses - Variant Calling and the computation of population genomics parameters
• Variant effects - Computation of the effects of the Variants predicted
• dNdS analysis - Computation of the dN/dS ratios for positive selection assessment

scripts/

Contains custom scripts used for population genomic analyses and data processing, including:

• dNdS.py - Python script used to compute gene-level dN/dS ratios across stations and populations.
• idfilter.pl - Perl script used to filter BAM files by read identity and coverage.
• FST_density_histogram.R - R script to process FST data into density histograms (Figure 1).
• FST_dend_map.R - R script to generate the FST dendrograms to infer populations along with the relative abundance per sample (Figure 2).
• dNdS_gclust_funcpies.R - R script to plot and compute gene cluster based on dN/dS ratios across samples (Figure 3) and the Pie Charts with eggNOG functional data (Figure S2).

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Data availability

Due to their size, raw metagenomic and genomic datasets are not included in this repository, but are available at:

• DNA sequences from Tara Oceans are stored at ENA with the accession numbers PRJEB6603 for the SAGs, and PRJEB4352 for the metagenomes.
• Genome coassemblies, coding sequence predictions, and amino acid predictions are available in FigShare (DOI: https://doi.org/10.6084/m9.figshare.13072322).

All other study data are included in the article, supporting information, or this GitHub Repository.

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Software and dependencies

Analyses were performed using a combination of custom scripts and external software, including (but not limited to):

• Python
• Perl
• Samtools
• BWA
• CoverM
• FreeBayes
• SnpEff
• POGENOM
• R (for statistical analyses and visualization)

Exact versions and parameters are reported in the Methods section of the manuscript.

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Reproducibility notes

Because the analyses rely on:

• Large metagenomic datasets
• Coverage-based filtering
• Dataset-specific preprocessing steps

Full reproduction of the results requires access to the original data and following the workflow described in the manuscript. Nevertheless, the scripts provided here capture the core analytical logic used in the study.

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Contact

For questions regarding the code or analyses, please contact:

Francisco Latorre 📧 [latorre@icm.csic.es]