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. 2021 Apr 17;14:143. doi: 10.1186/s13104-021-05553-4

Broadly sampled orthologous groups of eukaryotic proteins for the phylogenetic study of plastid-bearing lineages

Mick Van Vlierberghe 1, Hervé Philippe 2,3, Denis Baurain 1,✉
PMCID: PMC8052839  PMID: 33865444

Abstract

Objectives

Identifying orthology relationships among sequences is essential to understand evolution, diversity of life and ancestry among organisms. To build alignments of orthologous sequences, phylogenomic pipelines often start with all-vs-all similarity searches, followed by a clustering step. For the protein clusters (orthogroups) to be as accurate as possible, proteomes of good quality are needed. Here, our objective is to assemble a data set especially suited for the phylogenomic study of algae and formerly photosynthetic eukaryotes, which implies the proper integration of organellar data, to enable distinguishing between several copies of one gene (paralogs), taking into account their cellular compartment, if necessary.

Data description

We submitted 73 top-quality and taxonomically diverse proteomes to OrthoFinder. We obtained 47,266 orthogroups and identified 11,775 orthogroups with at least two algae. Whenever possible, sequences were functionally annotated with eggNOG and tagged after their genomic and target compartment(s). Then we aligned and computed phylogenetic trees for the orthogroups with IQ-TREE. Finally, these trees were further processed by identifying and pruning the subtrees exclusively composed of plastid-bearing organisms to yield a set of 31,784 clans suitable for studying photosynthetic organism genome evolution.

Keywords: Orthology, Phylogenomics, Algae, CASH, Proteomes, Eukaryotic evolution, Contamination, Organelles, Endosymbiotic gene transfer (EGT), Horizontal or lateral gene transfer (HGT/LGT), Kleptoplasty

Objective

Our main objective is to analyse the phylogenetic origin of plastid-targeted genes in complex algae [1–3] in a fully automated fashion. To do so, we designed and developed a series of strategies and tools around a large-scale single-gene tree analysis pipeline. The first step was to build alignments of orthologous sequences with OrthoFinder, a high accuracy orthogroup inference algorithm [4]. We focused on top-quality proteomes, especially with high completeness, which is essential to obtain the most complete and balanced OGs possible [5, 6]. In order to maximize completeness and to facilitate the phylogenetic analysis, we complemented beforehand the proteomes having no or only incomplete plastid and/or nucleomorph sequences. Then we processed the resulting OGs, first by isolating the OGs containing photosynthetic organisms, and second by sorting out gene copies shared by plastid-bearing algae from their paralogs. To this end, we built trees using IQ-TREE [7] and used our own tool (tree-clan-splitter.pl) to detect and prune the subtree(s) of interest.

Data description

We collected 73 top-quality eukaryotic proteomes (i.e., conceptually translated genomes; Data file 1, Data set 1, Data set 2) with high completeness (Data file 2) [5, 6] and low contamination levels (Data set 3) [8, 9] (Table 1). Those were selected to be taxonomically diverse, covering all photosynthetic phyla [10, 11], along with some non-photosynthetic organisms to be used as beacons by our clan-identifying algorithm. Those proteomes were complemented with organellar (i.e., plastid and nucleomorph) proteins if they were partly or fully missing in the original source. Hence, 16 were complemented with plastid proteomes whereas two were complemented with nucleomorph proteomes. All proteomes (complemented or not) were dereplicated with CD-HIT [12]. In addition, we used tag-loc-ids.pl, a custom tool designed to tag sequence identifiers according to their encoding genome and cellular localization, such as nuclear-encoded-and-plastid-targeted (nucpt#), nuclear-encoded-periplastid-compartment-targeted (nuppct#), plastid-encoded-plastid-targeted (cpcpt#), nucleomorph-encoded (nm#), and mitochondrion-encoded (mt#), to facilitate subsequent phylogenetic analyses. Then, we used OrthoFinder [4] for orthology inference, which resulted in 47,266 OGs (Data file 3, Data set 4), composed of two or more sequences belonging to eleven main taxonomic groups (according to NCBI Taxonomy [13]), either classified as “primary algae” (Glaucocystophyceae, Rhodophyta, Viridiplantae) or “complex algae” (Apicomplexa, Colpodellida, Dinophyceae, Cryptophyceae, Euglenozoa, Ochrophyta (including Pelagophyceae), Haptophyta, and Chlorarachniophyceae). Hence, OGs were tabulated into three different categories: “two-algae” (at least one complex alga from two different groups or at least one complex alga and one primary alga, n = 11,775), “one-alga” (at least one alga, n = 18,844) and “zero-algae” (no algae, n = 16,647) using the script classify-mcl-out.pl. In order to address the issue of multiple-copy genes (paralogs), we developed a strategy to isolate subtrees (“clans”) of interest, i.e., including only plastid-bearing organisms. Briefly, we computed trees for the 11,775 “two-algae” OGs when possible (i.e., ≥ 3 sequences, n = 11,499) with IQ-TREE [7] and developed a tool for identifying and pruning subtrees fulfilling user-specified taxonomic filters (tree-clan-splitter.pl). This way, we obtained 31,784 “photosynthetic” clans (Data set 5) only composed of plastid-bearing organisms (including species with a non-photosynthetic plastid, such as Plasmodium falciparum). Additionally, we provide detailed annotation reports obtained with eggNOG [14].

Table 1.

Overview of data files/data sets

Label Name of data file/data set File types (file extension) Data repository and identifier (DOI or accession number)
Additional file 1 Methods PDF file (.pdf) Figshare https://doi.org/10.6084/m9.figshare.13604102.v3 [18]
Data file 1 Taxonomic sampling Image file (.png) Figshare https://doi.org/10.6084/m9.figshare.13603511.v1 [19]
Data set 1 Proteome set description Text files (.csv,.html) Figshare https://doi.org/10.6084/m9.figshare.13113893.v1 [20]
Data set 2 Proteome files FASTA files (.tar.gz) Figshare https://doi.org/10.6084/m9.figshare.13573424.v2 [21]
Data file 2 BUSCO report Text file (.csv) Figshare https://doi.org/10.6084/m9.figshare.13235045.v1 [22]
Data set 3 Forty-Two reports and configuration files Text files (.tsv,.csv,.yaml) Figshare https://doi.org/10.6084/m9.figshare.13235063.v3 [23]
Data file 3 Orthogroup properties Image file (.pdf) Figshare https://doi.org/10.6084/m9.figshare.13312622.v1 [24]
Data set 4 Orthogroups FASTA files, YAML configuration file (.tar.gz) Figshare https://doi.org/10.6084/m9.figshare.13573658.v3 [25]
Data set 5 Clans FASTA files (.tar.gz) Figshare https://doi.org/10.6084/m9.figshare.13573415.v1 [26]
Data file 4 Organelle database Text file (.tsv) Figshare https://doi.org/10.6084/m9.figshare.13246841.v1 [27]
Data file 5 Plastid-targeted proteins Spreadsheet (.xlsx) Figshare https://doi.org/10.6084/m9.figshare.13246784.v1 [28]
Data file 6 eggNOG OG annotations Text file (.tsv) Figshare https://doi.org/10.6084/m9.figshare.13415048.v1 [29]
Data file 7 eggNOG clan annotations Text file (.tsv) Figshare https://doi.org/10.6084/m9.figshare.13415060.v1 [30]
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Limitations

  • Occasionally, organellar genome sequences are from a different strain than the nucleus data; it could be an issue if we were trying to resolve relationships between close relatives of the same lineage. Nonetheless, it is not the case here, since the major endosymbiotic-like events we are tracking occurred most certainly between distinct lineages.

  • The way we handle the tagging overwrites the information about potential NUMTs, NUNMs and NUPTs; this means that if a gene existed in both genomic compartments (nucleus and organelle) we always retained the organellar counterpart.

  • Only a few of the nucleus-encoded-and-plastid-targeted proteins (nucpt#) were identified by proteomics (e.g., in P. falciparum) [17]; the remaining are the results of in silico predictions [15, 16], which are less reliable than proteomic experiments.

Acknowledgements

We are grateful to Ugo Cenci for sharing the protein sequences of Goniomonas avonlea (Cenci U, Sibbald SJ, Curtis BA, Kamikawa R, Eme L, Moog D, et al. Nuclear genome sequence of the plastid-lacking cryptomonad Goniomonas avonlea provides insights into the evolution of secondary plastids. BMC Biology (2018) 16:137. https://doi.org/10.1186/s12915-018-0593-5).

Abbreviations

OGs

Orthologous groups or orthogroups

NUMTs

Nuclear mitochondrial DNAs

NUNMs

Nucleomorph-derived DNAs

NUPTs

Nuclear plastid DNAs

Authors’ contributions

MVV and DB designed the experiments, MVV performed all the computational analyses and drew the figures, MVV and DB wrote the manuscript. HP substantively revised the work. All authors read and approved the final manuscript.

Funding

Mick Van Vlierberghe was a FRIA fellow of the FRS-FNRS (National Fund for Scientific Research of Belgium). Computational resources were provided through two grants to Denis Baurain (University of Liège “Crédit de démarrage 2012” SFRD-12/04; FRS-FNRS “Crédit de recherche 2014” CDR J.0080.15).

Availability of data and materials

All data generated or analysed during this study are publicly available in the figshare repository (https://doi.org/10.6084/m9.figshare.13604102.v3, https://doi.org/10.6084/m9.figshare.13603511.v1, https://doi.org/10.6084/m9.figshare.13113893.v1, https://doi.org/10.6084/m9.figshare.13573424.v2, https://doi.org/10.6084/m9.figshare.13235045.v1, https://doi.org/10.6084/m9.figshare.13235063.v3, https://doi.org/10.6084/m9.figshare.13312622.v1, https://doi.org/10.6084/m9.figshare.13573658.v3, https://doi.org/10.6084/m9.figshare.13573415.v1, https://doi.org/10.6084/m9.figshare.13246841.v1, https://doi.org/10.6084/m9.figshare.13246784.v1, https://doi.org/10.6084/m9.figshare.13415048.v1, https://doi.org/10.6084/m9.figshare.13415060.v1). Please see Table 1 and reference list [18–30] for details and links to the data.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Mick Van Vlierberghe, Email: mvanvlierberghe@doct.uliege.be.

Hervé Philippe, Email: herve.philippe@sete.cnrs.fr.

Denis Baurain, Email: denis.baurain@uliege.be.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Citations

  1. Van Vlierberghe M, Philippe H, Baurain D. 2021. Supplementary file 1-Methods. Figshare. [DOI]
  2. Van Vlierberghe M, Philippe H, Baurain D. 2021. Data file 1-Taxonomic sampling. Figshare. [DOI]
  3. Van Vlierberghe M, Philippe H, Baurain D. 2021. Data set 1-Proteome set description. Figshare. [DOI]
  4. Van Vlierberghe M, Philippe H, Baurain D. 2021. Data set 2-Proteome set. Figshare. [DOI]
  5. Van Vlierberghe M, Philippe H, Baurain D. 2021. Data file 2-BUSCO report. Figshare. [DOI]
  6. Van Vlierberghe M, Philippe H, Baurain D. 2021. Data set 3-Forty-two reports and configuration files. Figshare. [DOI]
  7. Van Vlierberghe M, Philippe H, Baurain D. 2021. Data file 3-Orthogroup properties. Figshare. [DOI]
  8. Van Vlierberghe M, Philippe H, Baurain D. 2021. Data set 4-Orthogroups. Figshare. [DOI]
  9. Van Vlierberghe M, Philippe H, Baurain D. 2021. Data set 5-Clans. Figshare. [DOI]
  10. Van Vlierberghe M, Philippe H, Baurain D. 2021. Data file 4-Organelle database. Figshare. [DOI]
  11. Van Vlierberghe M, Philippe H, Baurain D. 2021. Data file 5-Plastid-targeted proteins. Figshare. [DOI]
  12. Van Vlierberghe M, Philippe H, Baurain D. 2021. Data file 6-eggNOG OG annotations. Figshare. [DOI]
  13. Van Vlierberghe M, Philippe H, Baurain D. 2021. Data file 7-eggNOG clan annotations. Figshare. [DOI]

Data Availability Statement

All data generated or analysed during this study are publicly available in the figshare repository (https://doi.org/10.6084/m9.figshare.13604102.v3, https://doi.org/10.6084/m9.figshare.13603511.v1, https://doi.org/10.6084/m9.figshare.13113893.v1, https://doi.org/10.6084/m9.figshare.13573424.v2, https://doi.org/10.6084/m9.figshare.13235045.v1, https://doi.org/10.6084/m9.figshare.13235063.v3, https://doi.org/10.6084/m9.figshare.13312622.v1, https://doi.org/10.6084/m9.figshare.13573658.v3, https://doi.org/10.6084/m9.figshare.13573415.v1, https://doi.org/10.6084/m9.figshare.13246841.v1, https://doi.org/10.6084/m9.figshare.13246784.v1, https://doi.org/10.6084/m9.figshare.13415048.v1, https://doi.org/10.6084/m9.figshare.13415060.v1). Please see Table 1 and reference list [18–30] for details and links to the data.

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