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



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.


Our main objective is to analyse the phylogenetic origin of plastid-targeted genes in complex algae [1,2,3] 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 ( 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, 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 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 ( 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


  • 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.

Availability of data and materials

All data generated or analysed during this study are publicly available in the figshare repository (,,,,,,,,,,,, Please see Table 1 and reference list [18,19,20,21,22,23,24,25,26,27,28,29,30] for details and links to the data.



Orthologous groups or orthogroups


Nuclear mitochondrial DNAs


Nucleomorph-derived DNAs


Nuclear plastid DNAs


  1. Petersen J, Teich R, Brinkmann H, Cerff R. A “green” phosphoribulokinase in complex algae with red plastids: evidence for a single secondary endosymbiosis leading to haptophytes, cryptophytes, heterokonts, and dinoflagellates. J Mol Evol. 2006;62:143–57.

    Article  CAS  Google Scholar 

  2. Teich R, Zauner S, Baurain D, Brinkmann H, Petersen J. Origin and distribution of Calvin cycle fructose and sedoheptulose bisphosphatases in plantae and complex algae: a single secondary origin of complex red plastids and subsequent propagation via tertiary endosymbioses. Protist. 2007;158:263–76.

    Article  CAS  PubMed  Google Scholar 

  3. Sibbald SJ, Archibald JM. Genomic Insights into Plastid Evolution. Genome Biol Evol. 2020;12:978–90.

    Article  CAS  Google Scholar 

  4. Emms DM, Kelly S. OrthoFinder: solving fundamental biases in whole genome comparisons dramatically improves orthogroup inference accuracy. Genome Biol. 2015;16:157.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Simão FA, Waterhouse RM, Ioannidis P, Kriventseva EV, Zdobnov EM. BUSCO: Assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics. 2015;31:3210–2.

    Article  Google Scholar 

  6. Waterhouse RM, Seppey M, Simao FA, Manni M, Ioannidis P, Klioutchnikov G, et al. BUSCO applications from quality assessments to gene prediction and phylogenomics. Mol Biol Evol. 2018;35:543–8.

    Article  CAS  Google Scholar 

  7. Nguyen LT, Schmidt HA, Von Haeseler A, Minh BQ. IQ-TREE: A fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015;32:268–74.

    Article  CAS  Google Scholar 

  8. Simion P, Philippe H, Baurain D, Jager M, Richter DJ, Di Franco A, et al. A large and consistent phylogenomic dataset supports sponges as the sister group to all other animals. Curr Biol. 2017;27:958–67.

    Article  CAS  PubMed  Google Scholar 

  9. Irisarri I, Baurain D, Brinkmann H, Delsuc F, Sire JY, Kupfer A, et al. Phylotranscriptomic consolidation of the jawed vertebrate timetree. Nat Ecol Evol. 2017;1:1370–8.

    Article  Google Scholar 

  10. Blaby-Haas CE, Merchant SS. Comparative and functional algal genomics. Annu Rev Plant Biol. 2019;70:605–38.

    Article  CAS  PubMed  Google Scholar 

  11. Hanschen ER, Starkenburg SR. The state of algal genome quality and diversity. Algal Res. 2020;50:101968.

    Article  Google Scholar 

  12. Li W, Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006;22(13):1658–9.

    Article  CAS  Google Scholar 

  13. Schoch CL, Ciufo S, Domrachev M, Hotton CL, Kannan S, Khovanskaya R, et al. NCBI Taxonomy: a comprehensive update on curation, resources and tools. Database (Oxford). 2020;2020:1–21.

    Article  Google Scholar 

  14. Huerta-Cepas J, Forslund K, Coelho LP, Szklarczyk D, Jensen LJ, Von Mering C, et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol Biol Evol. 2017;34:2115–22.

    Article  CAS  Google Scholar 

  15. Dorrell RG, Gile G, McCallum G, Méheust R, Bapteste EP, Klinger CM, et al. Chimeric origins of ochrophytes and haptophytes revealed through an ancient plastid proteome. Elife. 2017;6:1–45.

    Article  Google Scholar 

  16. Novák AMG, Orcid V, Füssy Z, Ebenezer TE, Dobáková EL, Eliáš M. Metabolic quirks and the colourful history of the Euglena gracilis secondary plastid. 2019;44:0–2.

  17. Boucher MJ, Ghosh S, Zhang L, Lal A, Jang SW, Ju A, et al. Integrative proteomics and bioinformatic prediction enable a high-confidence apicoplast proteome in malaria parasites. PLOS Biol. 2018;16:e2005895.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Van Vlierberghe M, Philippe H, Baurain D. Supplementary file 1-Methods. 2021. Figshare.

  19. Van Vlierberghe M, Philippe H, Baurain D. Data file 1-Taxonomic sampling. 2021. Figshare.

  20. Van Vlierberghe M, Philippe H, Baurain D. Data set 1-Proteome set description. 2021. Figshare.

  21. Van Vlierberghe M, Philippe H, Baurain D. Data set 2-Proteome set. 2021. Figshare.

  22. Van Vlierberghe M, Philippe H, Baurain D. Data file 2-BUSCO report. 2021. Figshare.

  23. Van Vlierberghe M, Philippe H, Baurain D. Data set 3-Forty-two reports and configuration files. 2021. Figshare.

  24. Van Vlierberghe M, Philippe H, Baurain D. Data file 3-Orthogroup properties. 2021. Figshare.

  25. Van Vlierberghe M, Philippe H, Baurain D. Data set 4-Orthogroups. 2021. Figshare.

  26. Van Vlierberghe M, Philippe H, Baurain D. Data set 5-Clans. 2021. Figshare.

  27. Van Vlierberghe M, Philippe H, Baurain D. Data file 4-Organelle database. 2021. Figshare.

  28. Van Vlierberghe M, Philippe H, Baurain D. Data file 5-Plastid-targeted proteins. 2021. Figshare.

  29. Van Vlierberghe M, Philippe H, Baurain D. Data file 6-eggNOG OG annotations. 2021. Figshare.

  30. Van Vlierberghe M, Philippe H, Baurain D. Data file 7-eggNOG clan annotations. 2021. Figshare.

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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.


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).

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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.

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Correspondence to Denis Baurain.

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Van Vlierberghe, M., Philippe, H. & Baurain, D. Broadly sampled orthologous groups of eukaryotic proteins for the phylogenetic study of plastid-bearing lineages. BMC Res Notes 14, 143 (2021).

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