Field, C. B., Behrenfeld, M. J., Randerson, J. T. & Falkowski, P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science 281, 237–240 (1998).
Google Scholar
Boyce, D. G., Lewis, M. R. & Worm, B. Global phytoplankton decline over the past century. Nature 466, 591–596 (2010).
Google Scholar
Henson, S. A., Cael, B. B., Allen, S. R. & Dutkiewicz, S. Future phytoplankton diversity in a changing climate. Nat. Commun. 12, 5372 (2021).
Google Scholar
Vaulot, D., Eikrem, W., Viprey, M. & Moreau, H. The diversity of small eukaryotic phytoplankton (≤3 μm) in marine ecosystems. FEMS Microbiol. Rev. 32, 795–820 (2008).
Google Scholar
Agawin, N. S. R., Duarte, C. M. & Agustí, S. Nutrient and temperature control of the contribution of picoplankton to phytoplankton biomass and production. Limnol. Oceanogr. 45, 591–600 (2000).
Google Scholar
Morán, X. A. G., López-Urrutia, Á., Calvo-Díaz, A. & Li, W. K. W. Increasing importance of small phytoplankton in a warmer ocean. Glob. Change Biol. 16, 1137–1144 (2010).
Google Scholar
Li, W. K. W., McLaughlin, F. A., Lovejoy, C. & Carmack, E. C. Smallest algae thrive as the arctic ocean freshens. Science 326 https://doi.org/10.1126/science.1179798 (2009).
Benner, I., Irwin, A. J. & Finkel, Z. V. Capacity of the common Arctic picoeukaryote Micromonas to adapt to a warming ocean. Limnol. Oceanography Lett. 5, 221–227 (2020).
Sunda, W. G. & Huntsman, S. A. Iron uptake and growth limitation in oceanic and coastal phytoplankton. Mar. Chem. 50, 189–206 (1995).
Google Scholar
Raven, J. A. The twelfth Tansley Lecture. Small is beautiful: the picophytoplankton. Funct. Ecol. 12, 503–513 (1998).
Google Scholar
Morel, F. M. M. & Price, N. M. The biogeochemical cycles of trace metals in the oceans. Science 300, 944–947 (2003).
Google Scholar
Gao, X., Bowler, C. & Kazamia, E. Iron metabolism strategies in diatoms. J. Exp. Bot. 72, 2165–2180 (2021).
Google Scholar
Caputi, L. et al. Community-level responses to iron availability in open ocean plankton ecosystems. Glob. Biogeochemical Cycles 33, 391–419 (2019).
Google Scholar
Carradec, Q. et al. A global ocean atlas of eukaryotic genes. Nat. Commun. 9, 373 (2018).
Google Scholar
Morrissey, J. et al. A novel protein, ubiquitous in marine phytoplankton, concentrates iron at the cell surface and facilitates uptake. Curr. Biol. 25, 364–371 (2015).
Google Scholar
Moore, C. M. et al. Processes and patterns of oceanic nutrient limitation. Nat. Geosci. 6, 701–710 (2013).
Google Scholar
Kumar, A. & Bera, S. Revisiting nitrogen utilization in algae: a review on the process of regulation and assimilation. Bioresour. Technol. Rep. 12, 100584 (2020).
Google Scholar
Smith, S. R. et al. Evolution and regulation of nitrogen flux through compartmentalized metabolic networks in a marine diatom. Nat. Commun. 10, 4552 (2019).
Google Scholar
Berg, G. M., Glibert, P. M., Lomas, M. W. & Burford, M. A. Organic nitrogen uptake and growth by the chrysophyte Aureococcus anophagefferens during a brown tide event. Mar. Biol. 129, 377–387 (1997).
Google Scholar
Andersen, R. A., Saunders, G. W., Paskind, M. P. & Sexton, J. P. Ultrastructure and 18s rRNA gene sequence for Pelagomonas calceolata gen. et sp. nov. and the description of a new algal class, the pelagophyceae classis nov. J. Phycol. 29, 701–715 (1993).
Google Scholar
Choi, C. J. et al. Seasonal and geographical transitions in eukaryotic phytoplankton community structure in the Atlantic and Pacific Oceans. Front. Microbiol. 11, 542372 (2020).
Google Scholar
Duerschlag, J. et al. Niche partitioning by photosynthetic plankton as a driver of CO2-fixation across the oligotrophic South Pacific Subtropical Ocean. ISME J 1–12 https://doi.org/10.1038/s41396-021-01072-z (2021).
Worden, A. Z. et al. Global distribution of a wild alga revealed by targeted metagenomics. Curr. Biol. 22, R675–R677 (2012).
Google Scholar
Dimier, C. é, Brunet, C., Geider, R. & Raven, J. Growth and photoregulation dynamics of the picoeukaryote Pelagomonas calceolata in fluctuating light. Limnol. Oceanogr. 54, 823–836 (2009).
Google Scholar
Dupont, C. L. et al. Genomes and gene expression across light and productivity gradients in eastern subtropical Pacific microbial communities. ISME J. 9, 1076–1092 (2015).
Google Scholar
Kang, Y. et al. Transcriptomic responses of four pelagophytes to nutrient (N, P) and light stress. Front. Mar. Sci. 8, 636699 (2021).
Huff, J. T., Zilberman, D. & Roy, S. W. Mechanism for DNA transposons to generate introns on genomic scales. Nature 538, 533–536 (2016).
Google Scholar
Waterhouse, R. M. et al. BUSCO applications from quality assessments to gene prediction and phylogenomics. Mol. Biol. Evol. 35, 543–548 (2018).
Google Scholar
Nambiar, M. & Smith, G. R. Repression of harmful meiotic recombination in centromeric regions. Semin Cell Dev. Biol. 54, 188–197 (2016).
Google Scholar
Pessia, E. et al. Evidence for widespread GC-biased gene conversion in eukaryotes. Genome Biol. Evol. 4, 675–682 (2012).
Google Scholar
Chi, J., Mahé, F., Loidl, J., Logsdon, J. & Dunthorn, M. Meiosis gene inventory of four ciliates reveals the prevalence of a synaptonemal complex-independent crossover pathway. Mol. Biol. Evol. 31, 660–672 (2014).
Google Scholar
Ramesh, M. A., Malik, S.-B. & Logsdon, J. M. A phylogenomic inventory of meiotic genes; evidence for sex in Giardia and an early eukaryotic origin of meiosis. Curr. Biol. 15, 185–191 (2005).
Google Scholar
Schurko, A. M. & Logsdon, J. M. Using a meiosis detection toolkit to investigate ancient asexual ‘scandals’ and the evolution of sex. Bioessays 30, 579–589 (2008).
Google Scholar
Ibarbalz, F. M. et al. Global trends in marine plankton diversity across kingdoms of life. Cell 179, 1084–1097.e21 (2019).
Google Scholar
Frémont, P. et al. Restructuring of plankton genomic biogeography in the surface ocean under climate change. Nat. Clim. Chang. 12, 393–401 (2022).
Google Scholar
Ward, D. M. & Kaplan, J. Ferroportin-mediated iron transport: expression and regulation. Biochim Biophys. Acta 1823, 1426–1433 (2012).
Google Scholar
Gobler, C. J., Lonsdale, D. J. & Boyer, G. L. A review of the causes, effects, and potential management of harmful brown tide blooms caused by Aureococcus anophagefferens (Hargraves et sieburth). Estuaries 28, 726–749 (2005).
Google Scholar
Agusti, S., Lubián, L. M., Moreno-Ostos, E., Estrada, M. & Duarte, C. M. Projected changes in photosynthetic picoplankton in a warmer subtropical ocean. Front. Mar. Sci. 5, 506 (2019).
Google Scholar
Anderson, S. I., Barton, A. D., Clayton, S., Dutkiewicz, S. & Rynearson, T. A. Marine phytoplankton functional types exhibit diverse responses to thermal change. Nat. Commun. 12, 6413 (2021).
Google Scholar
Martin, J. H. et al. Testing the iron hypothesis in ecosystems of the equatorial Pacific Ocean. Nature 371, 123–129 (1994).
Google Scholar
Shi, D., Xu, Y., Hopkinson, B. M. & Morel, F. M. M. Effect of ocean acidification on iron availability to marine phytoplankton. Science 327, 676–679 (2010).
Google Scholar
McQuaid, J. B. et al. Carbonate-sensitive phytotransferrin controls high-affinity iron uptake in diatoms. Nature 555, 534–537 (2018).
Google Scholar
Turnšek, J. et al. Proximity proteomics in a marine diatom reveals a putative cell surface-to-chloroplast iron trafficking pathway. eLife 10, e52770 (2021).
Google Scholar
Urzica, E. I. et al. Systems and trans-system level analysis identifies conserved iron deficiency responses in the plant lineage[W][OA]. Plant Cell 24, 3921–3948 (2012).
Google Scholar
Mao, X. et al. Diversity, prevalence, and expression of cyanase genes (cynS) in planktonic marine microorganisms. ISME J. 16, 602–605 (2022).
Google Scholar
Ou, L., Cai, Y., Jin, W., Wang, Z. & Lu, S. Understanding the nitrogen uptake and assimilation of the Chinese strain of Aureococcus anophagefferens (Pelagophyceae). Algal Res. 34, 182–190 (2018).
Google Scholar
Shu, C. J., Ulrich, L. E. & Zhulin, I. B. The NIT domain: a predicted nitrate-responsive module in bacterial sensory receptors. Trends Biochem Sci. 28, 121–124 (2003).
Google Scholar
Wu, S. Q., Chai, W., Lin, J. T. & Stewart, V. General nitrogen regulation of nitrate assimilation regulatory gene nasR expression in Klebsiella oxytoca M5al. J. Bacteriol. 181, 7274–7284 (1999).
Google Scholar
Durand, N. C. et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst. 3, 95–98 (2016).
Google Scholar
Li, R., Li, Y., Kristiansen, K. & Wang, J. SOAP: short oligonucleotide alignment program. Bioinformatics 24, 713–714 (2008).
Google Scholar
Alberti, A. et al. Viral to metazoan marine plankton nucleotide sequences from the Tara Oceans expedition. Sci. Data 4, 170093 (2017).
Google Scholar
Kopylova, E., Noé, L. & Touzet, H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217 (2012).
Google Scholar
Kim, D., Song, L., Breitwieser, F. P. & Salzberg, S. L. Centrifuge: rapid and sensitive classification of metagenomic sequences. Genome Res. https://doi.org/10.1101/gr.210641.116 (2016).
Vurture, G. W. et al. GenomeScope: fast reference-free genome profiling from short reads. Bioinformatics 33, 2202–2204 (2017).
Google Scholar
Vaser, R. & Šikić, M. Yet another de novo genome assembler. BioRxiv. https://doi.org/10.1101/656306 (2019).
Liu, H. et al. SMARTdenovo: a de novo assembler using long noisy reads. Gigabyte 2021, 1–9 (2021).
Google Scholar
Kolmogorov, M., Yuan, J., Lin, Y. & Pevzner, P. A. Assembly of long, error-prone reads using repeat graphs. Nat. Biotechnol. 37, 540–546 (2019).
Google Scholar
Ruan, J. & Li, H. Fast and accurate long-read assembly with wtdbg2. Nat. Methods 17, 155–158 (2020).
Google Scholar
Wick, R. R., Schultz, M. B., Zobel, J. & Holt, K. E. Bandage: interactive visualization of de novo genome assemblies. Bioinformatics 31, 3350–3352 (2015).
Google Scholar
Vaser, R., Sović, I., Nagarajan, N. & Šikić, M. Fast and accurate de novo genome assembly from long uncorrected reads. Genome Res 27, 737–746 (2017).
Google Scholar
Aury, J.-M. & Istace, B. Hapo-G, haplotype-aware polishing of genome assemblies with accurate reads. NAR Genomics Bioinform. 3, lqab034 (2021).
Benson, G. Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res. 27, 573–580 (1999).
Google Scholar
Morgulis, A., Gertz, E. M., Schäffer, A. A. & Agarwala, R. A fast and symmetric DUST implementation to mask low-complexity DNA sequences. J. Comput Biol. 13, 1028–1040 (2006).
Google Scholar
Smit, A. F. A., Hubley, R. & Green, P. RepeatMasker. http://repeatmasker.org/ (2013).
Price, A. L., Jones, N. C. & Pevzner, P. A. De novo identification of repeat families in large genomes. Bioinformatics 21, i351–i358 (2005).
Google Scholar
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
Google Scholar
Pedersen, B. S. & Quinlan, A. R. Mosdepth: quick coverage calculation for genomes and exomes. Bioinformatics 34, 867–868 (2018).
Google Scholar
Schulz, M. H., Zerbino, D. R., Vingron, M. & Birney, E. Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels. Bioinformatics 28, 1086–1092 (2012).
Google Scholar
Zerbino, D. R. & Birney, E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 18, 821–829 (2008).
Google Scholar
Li, H. et al. The sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Google Scholar
Marchler-Bauer, A. et al. CDD: NCBI’s conserved domain database. Nucleic Acids Res. 43, D222–D226 (2015).
Google Scholar
Niang, G. et al. METdb: A genomic reference database for marine species. F1000Research, https://doi.org/10.7490/f1000research.1118000.1 (2020).
Kent, W. J. BLAT–the BLAST-like alignment tool. Genome Res. 12, 656–664 (2002).
Google Scholar
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).
Google Scholar
Birney, E., Clamp, M. & Durbin, R. GeneWise and genomewise. Genome Res. 14, 988–995 (2004).
Google Scholar
Stanke, M. et al. AUGUSTUS: ab initio prediction of alternative transcripts. Nucleic Acids Res. 34, W435–W439 (2006).
Google Scholar
Dubarry, M. et al. Gmove a tool for eukaryotic gene predictions using various evidences. F1000Research, https://doi.org/10.7490/f1000research.1111735.1 (2016).
Sibbald, S. J., Lawton, M. & Archibald, J. M. Mitochondrial genome evolution in pelagophyte algae. Genome Biol. Evol. 13, evab018 (2021).
Quevillon, E. et al. InterProScan: protein domains identifier. Nucleic Acids Res. 33, W116–W120 (2005).
Google Scholar
Buchfink, B., Reuter, K. & Drost, H.-G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat. Methods 18, 366–368 (2021).
Google Scholar
Aramaki, T. et al. KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics 36, 2251–2252 (2020).
Google Scholar
Delmont, T. O. et al. Functional repertoire convergence of distantly related eukaryotic plankton lineages abundant in the sunlit ocean. Cell Genomics 2, 100123 (2022).
Google Scholar
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
Google Scholar
Pesant, S. et al. Open science resources for the discovery and analysis of Tara Oceans data. Sci. Data 2, 150023 (2015).
Google Scholar
Aumont, O., Ethé, C., Tagliabue, A., Bopp, L. & Gehlen, M. PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies. Geoscientific Model Dev. 8, 2465–2513 (2015).
Google Scholar
Clayton, S. et al. Biogeochemical versus ecological consequences of modeled ocean physics. Biogeosciences 14, 2877–2889 (2017).
Google Scholar
Ravindra, K., Rattan, P., Mor, S. & Aggarwal, A. N. Generalized additive models: building evidence of air pollution, climate change and human health. Environ. Int. 132, 104987 (2019).
Google Scholar
Günther, F. & Fritsch, S. neuralnet: training of neural networks. R. J. 2, 30–38 (2010).
Google Scholar
Gobler, C. J. et al. Niche of harmful alga Aureococcus anophagefferens revealed through ecogenomics. Proc. Natl Acad. Sci. USA 108, 4352–4357 (2011).
Google Scholar
Guo, L. et al. Genome assembly of Nannochloropsis oceanica provides evidence of host nucleus overthrow by the symbiont nucleus during speciation. Commun. Biol. 2, 1–12 (2019).
Google Scholar
Bowler, C. et al. The Phaeodactylum genome reveals the evolutionary history of diatom genomes. Nature 456, 239–244 (2008).
Google Scholar
Armbrust, E. V. et al. The genome of the diatom thalassiosira pseudonana: ecology, evolution, and metabolism. Science 306, 79–86 (2004).
Google Scholar
Worden, A. Z. et al. Green evolution and dynamic adaptations revealed by genomes of the marine picoeukaryotes micromonas. Science 324, 268–272 (2009).
Google Scholar
Palenik, B. et al. The tiny eukaryote Ostreococcus provides genomic insights into the paradox of plankton speciation. PNAS 104, 7705–7710 (2007).
Google Scholar
Moreau, H. et al. Gene functionalities and genome structure in Bathycoccus prasinos reflect cellular specializations at the base of the green lineage. Genome Biol. 13, R74 (2012).
Google Scholar
Read, B. A. et al. Pan genome of the phytoplankton Emiliania underpins its global distribution. Nature 499, 209–213 (2013).
Google Scholar
Source: Ecology - nature.com
