Heliorhodopsin Evolution Is Driven by Photosensory Promiscuity in Monoderms
Jazyk angličtina Země Spojené státy americké Médium print-electronic
Typ dokumentu časopisecké články, práce podpořená grantem
PubMed
34817235
PubMed Central
PMC8612252
DOI
10.1128/msphere.00661-21
Knihovny.cz E-zdroje
- Klíčová slova
- heliorhodopsin, metagenomics, oxidative stress, rhodopsins,
- MeSH
- konformace proteinů MeSH
- metagenomika * MeSH
- molekulární modely MeSH
- oxidační stres MeSH
- rhodopsiny mikrobiální chemie genetika MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Názvy látek
- heliorhodopsin MeSH Prohlížeč
- rhodopsiny mikrobiální MeSH
Rhodopsins are light-activated proteins displaying an enormous versatility of function as cation/anion pumps or sensing environmental stimuli and are widely distributed across all domains of life. Even with wide sequence divergence and uncertain evolutionary linkages between microbial (type 1) and animal (type 2) rhodopsins, the membrane orientation of the core structural scaffold of both was presumed universal. This was recently amended through the discovery of heliorhodopsins (HeRs; type 3), that, in contrast to known rhodopsins, display an inverted membrane topology and yet retain similarities in sequence, structure, and the light-activated response. While no ion-pumping activity has been demonstrated for HeRs and multiple crystal structures are available, fundamental questions regarding their cellular and ecological function or even their taxonomic distribution remain unresolved. Here, we investigated HeR function and distribution using genomic/metagenomic data with protein domain fusions, contextual genomic information, and gene coexpression analysis with strand-specific metatranscriptomics. We bring to resolution the debated monoderm/diderm occurrence patterns and show that HeRs are restricted to monoderms. Moreover, we provide compelling evidence that HeRs are a novel type of sensory rhodopsins linked to histidine kinases and other two-component system genes across phyla. In addition, we also describe two novel putative signal-transducing domains fused to some HeRs. We posit that HeRs likely function as generalized light-dependent switches involved in the mitigation of light-induced oxidative stress and metabolic circuitry regulation. Their role as sensory rhodopsins is corroborated by their photocycle dynamics and their presence/function in monoderms is likely connected to the higher sensitivity of these organisms to light-induced damage. IMPORTANCE Heliorhodopsins are enigmatic, novel rhodopsins with a membrane orientation that is opposite to all known rhodopsins. However, their cellular and ecological functions are unknown, and even their taxonomic distribution remains a subject of debate. We provide evidence that HeRs are a novel type of sensory rhodopsins linked to histidine kinases and other two-component system genes across phyla boundaries. In support of this, we also identify two novel putative signal transducing domains in HeRs that are fused with them. We also observe linkages of HeRs to genes involved in mitigation of light-induced oxidative stress and increased carbon and nitrogen metabolism. Finally, we synthesize these findings into a framework that connects HeRs with the cellular response to light in monoderms, activating light-induced oxidative stress defenses along with carbon/nitrogen metabolic circuitries. These findings are consistent with the evolutionary, taxonomic, structural, and genomic data available so far.
European Translational Oncology Prevention and Screening Institute Tirol Austria
OptoBioTechnology Research Center Nagoya Institute of Technologygrid 47716 33 Showa Nagoya Japan
Research Institute for Biomedical Aging Research University of Innsbruck Innsbruck Austria
The Institute for Solid State Physics The University of Tokyo Kashiwa Japan
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Pushkarev A, Inoue K, Larom S, Flores-Uribe J, Singh M, Konno M, Tomida S, Ito S, Nakamura R, Tsunoda SP, Philosof A, Sharon I, Yutin N, Koonin EV, Kandori H, Béjà O. 2018. A distinct abundant group of microbial rhodopsins discovered using functional metagenomics. Nature 558:595–599. doi:10.1038/s41586-018-0225-9. PubMed DOI PMC
Kovalev K, Volkov D, Astashkin R, Alekseev A, Gushchin I, Haro-Moreno JM, Chizhov I, Siletsky S, Mamedov M, Rogachev A, Balandin T, Borshchevskiy V, Popov A, Bourenkov G, Bamberg E, Rodriguez-Valera F, Büldt G, Gordeliy V. 2020. High-resolution structural insights into the heliorhodopsin family. Proc Natl Acad Sci USA 117:4131–4141. doi:10.1073/pnas.1915888117. PubMed DOI PMC
Flores-Uribe J, Hevroni G, Ghai R. 2019. Heliorhodopsins are absent in diderm (Gram-negative) bacteria: some thoughts and possible implications for activity. Environ Microbiol Rep 11:419–424. PubMed
Tanaka T, Singh M, Shihoya W, Yamashita K, Kandori H, Nureki O. 2020. Structural basis for unique color tuning mechanism in heliorhodopsin. Biochem Biophys Res Commun 533:262–267. doi:10.1016/j.bbrc.2020.06.124. PubMed DOI
Shihoya W, Inoue K, Singh M, Konno M, Hososhima S, Yamashita K, Ikeda K, Higuchi A, Izume T, Okazaki S, Hashimoto M, Mizutori R, Tomida S, Yamauchi Y, Abe-Yoshizumi R, Katayama K, Tsunoda SP, Shibata M, Furutani Y, Pushkarev A, Béjà O, Uchihashi T, Kandori H, Nureki O. 2019. Crystal structure of heliorhodopsin. Nature 574:132–136. doi:10.1038/s41586-019-1604-6. PubMed DOI
Aravind L. 2000. Guilt by association: contextual information in genome analysis. Genome Res 10:1074–1077. doi:10.1101/gr.10.8.1074. PubMed DOI
Huynen M, Snel B, Lathe W, III, Bork P. 2000. Predicting protein function by genomic context: quantitative evaluation and qualitative inferences. Genome Res 10:1204–1210. doi:10.1101/gr.10.8.1204. PubMed DOI PMC
Parks DH, Chuvochina M, Chaumeil P-A, Rinke C, Mussig AJ, Hugenholtz P. 2020. A complete domain-to-species taxonomy for Bacteria and Archaea. Nat Biotechnol 38:1079–1086. doi:10.1038/s41587-020-0501-8. PubMed DOI
Taib N, Megrian D, Witwinowski J, Adam P, Poppleton D, Borrel G, Beloin C, Gribaldo S. 2020. Genome-wide analysis of the Firmicutes illuminates the diderm/monoderm transition. Nat Ecol Evol 4:1661–1672. doi:10.1038/s41559-020-01299-7. PubMed DOI
Megrian D, Taib N, Witwinowski J, Beloin C, Gribaldo S. 2020. One or two membranes? Diderm Firmicutes challenge the Gram-positive/Gram-negative divide. Mol Microbiol 113:659–671. doi:10.1111/mmi.14469. PubMed DOI
Saiki T, Kobayashi Y, Kawagoe K, Beppu T. 1985. Dictyoglomus thermophilum gen. nov., sp. nov., a chemoorganotrophic, anaerobic, thermophilic bacterium. Int J Syst Evol Microbiol Microbiol Soc 35:253–259.
Ikuta T, Shihoya W, Sugiura M, Yoshida K, Watari M, Tokano T, Yamashita K, Katayama K, Tsunoda SP, Uchihashi T, Kandori H, Nureki O. 2020. Structural insights into the mechanism of rhodopsin phosphodiesterase. Nat Commun 11:5605. doi:10.1038/s41467-020-19376-7. PubMed DOI PMC
Timmers PHA, Vavourakis CD, Kleerebezem R, Damsté JSS, Muyzer G, Stams AJM, Sorokin DY, Plugge CM. 2018. Metabolism and occurrence of methanogenic and sulfate-reducing syntrophic acetate oxidizing communities in haloalkaline environments. Front Microbiol 9:3039. doi:10.3389/fmicb.2018.03039. PubMed DOI PMC
Vavourakis CD, Andrei A-S, Mehrshad M, Ghai R, Sorokin DY, Muyzer G. 2018. A metagenomics roadmap to the uncultured genome diversity in hypersaline soda lake sediments. Microbiome 6:168. doi:10.1186/s40168-018-0548-7. PubMed DOI PMC
Vavourakis CD, Mehrshad M, Balkema C, van Hall R, Andrei A-Ş, Ghai R, Sorokin DY, Muyzer G. 2019. Metagenomes and metatranscriptomes shed new light on the microbial-mediated sulfur cycle in a Siberian soda lake. BMC Biol 17:69. doi:10.1186/s12915-019-0688-7. PubMed DOI PMC
Takeshima H, Komazaki S, Nishi M, Iino M, Kangawa K. 2000. Junctophilins: a novel family of junctional membrane complex proteins. Mol Cell 6:11–22. PubMed
El-Gebali S, Mistry J, Bateman A, Eddy SR, Luciani A, Potter SC, Qureshi M, Richardson LJ, Salazar GA, Smart A, Sonnhammer ELL, Hirsh L, Paladin L, Piovesan D, Tosatto SCE, Finn RD. 2019. The Pfam protein families database in 2019. Nucleic Acids Res 47:D427–D432. doi:10.1093/nar/gky995. PubMed DOI PMC
Im YJ, Davis AJ, Perera IY, Johannes E, Allen NS, Boss WF. 2007. The N-terminal membrane occupation and recognition nexus domain of Arabidopsis phosphatidylinositol phosphate kinase 1 regulates enzyme activity. J Biol Chem 282:5443–5452. doi:10.1074/jbc.M611342200. PubMed DOI
Ma H, Lou Y, Lin WH, Xue HW. 2006. MORN motifs in plant PIPKs are involved in the regulation of subcellular localization and phospholipid binding. Cell Res 16:466–478. doi:10.1038/sj.cr.7310058. PubMed DOI
Sajko S, Grishkovskaya I, Kostan J, Graewert M. 2020. Structures of three MORN repeat proteins and a re-evaluation of the proposed lipid-binding properties of MORN repeats. bioRxiv https://www.biorxiv.org/content/10.1101/826180v2.abstract. PubMed DOI PMC
Kanno M, Tamaki H, Mitani Y, Kimura N, Hanada S, Kamagata Y. 2015. pH-induced change in cell susceptibility to butanol in a high butanol-tolerant bacterium, Enterococcus faecalis strain CM4A. Biotechnol Biofuels 8:69. doi:10.1186/s13068-015-0251-x. PubMed DOI PMC
Shibata M, Inoue K, Ikeda K, Konno M, Singh M, Kataoka C, Abe-Yoshizumi R, Kandori H, Uchihashi T. 2018. Oligomeric states of microbial rhodopsins determined by high-speed atomic force microscopy and circular dichroic spectroscopy. Sci Rep 8:8262. doi:10.1038/s41598-018-26606-y. PubMed DOI PMC
Sefah E, Mertz B. 2021. Bacterial analogs to cholesterol affect dimerization of proteorhodopsin and modulates preferred dimer interface. J Chem Theory Comput 17:2502–2512. doi:10.1021/acs.jctc.0c01174. PubMed DOI
Kajava AV. 2012. Tandem repeats in proteins: from sequence to structure. J Struct Biol 179:279–288. doi:10.1016/j.jsb.2011.08.009. PubMed DOI
Aravind L, Iyer LM, Anantharaman V. 2010. Natural history of sensor domains in bacterial signaling systems, p 1–38. In Stephen RD (ed), Sensory mechanisms in bacteria: molecular aspects of signal recognition. Caister Academic Press, Norfolk, UK.
Zimmermann L, Stephens A, Nam S-Z, Rau D, Kübler J, Lozajic M, Gabler F, Söding J, Lupas AN, Alva V. 2018. A completely reimplemented MPI bioinformatics toolkit with a new HHpred server at its core. J Mol Biol 430:2237–2243. doi:10.1016/j.jmb.2017.12.007. PubMed DOI
Krishna SS, Majumdar I, Grishin NV. 2003. Structural classification of zinc fingers: survey and summary. Nucleic Acids Res 31:532–550. doi:10.1093/nar/gkg161. PubMed DOI PMC
Rastogi RP, Richa Kumar A, Tyagi MB, Sinha RP. 2010. Molecular mechanisms of ultraviolet radiation-induced DNA damage and repair. J Nucleic Acids 2010:592980. doi:10.4061/2010/592980. PubMed DOI PMC
Yatsunami R, Ando A, Yang Y, Takaichi S, Kohno M, Matsumura Y, et al.. 2014. Identification of carotenoids from the extremely halophilic archaeon Haloarcula japonica. Front Microbiol 5:100. PubMed PMC
Hashimoto M, Katayama K, Furutani Y, Kandori H. 2020. Zinc binding to heliorhodopsin. J Phys Chem Lett 11:8604–8609. doi:10.1021/acs.jpclett.0c02383. PubMed DOI
Lillig CH, Berndt C, Holmgren A. 2008. Glutaredoxin systems. Biochim Biophys Acta 1780:1304–1317. doi:10.1016/j.bbagen.2008.06.003. PubMed DOI
Rouhier N, Couturier J, Johnson MK, Jacquot J-P. 2010. Glutaredoxins: roles in iron homeostasis. Trends Biochem Sci 35:43–52. doi:10.1016/j.tibs.2009.08.005. PubMed DOI PMC
Bhabak KP, Mugesh G. 2010. Functional mimics of glutathione peroxidase: bioinspired synthetic antioxidants. Acc Chem Res 43:1408–1419. doi:10.1021/ar100059g. PubMed DOI
Ferguson GP, Tötemeyer S, MacLean MJ, Booth IR. 1998. Methylglyoxal production in bacteria: suicide or survival? Arch Microbiol 170:209–218. doi:10.1007/s002030050635. PubMed DOI
Zhao S, Zhang Y, Gordon W, Quan J, Xi H, Du S, von Schack D, Zhang B. 2015. Comparison of stranded and non-stranded RNA-seq transcriptome profiling and investigation of gene overlap. BMC Genomics 16:675. doi:10.1186/s12864-015-1876-7. PubMed DOI PMC
Kavagutti VS, Andrei A-Ş, Mehrshad M, Salcher MM, Ghai R. 2019. Phage-centric ecological interactions in aquatic ecosystems revealed through ultra-deep metagenomics. Microbiome 7:135. doi:10.1186/s40168-019-0752-0. PubMed DOI PMC
Neuenschwander SM, Ghai R, Pernthaler J, Salcher MM. 2018. Microdiversification in genome-streamlined ubiquitous freshwater Actinobacteria. ISME J 12:185–198. doi:10.1038/ismej.2017.156. PubMed DOI PMC
Resto M, Yaffe J, Gerratana B. 2009. An ancestral glutamine-dependent NAD(+) synthetase revealed by poor kinetic synergism. Biochim Biophys Acta 1794:1648–1653. doi:10.1016/j.bbapap.2009.07.014. PubMed DOI
García-Domínguez M, Reyes JC, Florencio FJ. 1999. Glutamine synthetase inactivation by protein-protein interaction. Proc Natl Acad Sci USA 96:7161–7166. doi:10.1073/pnas.96.13.7161. PubMed DOI PMC
Oubrie A, Rozeboom HJ, Kalk KH, Olsthoorn AJ, Duine JA, Dijkstra BW. 1999. Structure and mechanism of soluble quinoprotein glucose dehydrogenase. EMBO J 18:5187–5194. doi:10.1093/emboj/18.19.5187. PubMed DOI PMC
Maresca JA, Keffer JL, Hempel PP, Polson SW, Shevchenko O, Bhavsar J, Powell D, Miller KJ, Singh A, Hahn MW. 2019. Light modulates the physiology of nonphototrophic actinobacteria. J Bacteriol 201 doi:10.1128/JB.00740-18. PubMed DOI PMC
Shmakov SA, Makarova KS, Wolf YI, Severinov KV, Koonin EV. 2018. Systematic prediction of genes functionally linked to CRISPR-Cas systems by gene neighborhood analysis. Proc Natl Acad Sci USA 115:E5307–E5316. doi:10.1073/pnas.1803440115. PubMed DOI PMC
Coleman GA, Davín AA, Mahendrarajah T, Spang A, Hugenholtz P, Szöllősi GJ, et al.. 2020. A rooted phylogeny resolves early bacterial evolution. Cold Spring Harb Lab https://www.biorxiv.org/content/10.1101/2020.07.15.205187v1. PubMed DOI
Kandori H. 2020. Biophysics of rhodopsins and optogenetics. Biophys Rev 12:355–361. doi:10.1007/s12551-020-00645-0. PubMed DOI PMC
Ghai R, McMahon KD, Rodriguez-Valera F. 2012. Breaking a paradigm: cosmopolitan and abundant freshwater actinobacteria are low GC. Environ Microbiol Rep 4:29–35. doi:10.1111/j.1758-2229.2011.00274.x. PubMed DOI
Kim S, Kang I, Seo J-H, Cho J-C. 2019. Culturing the ubiquitous freshwater actinobacterial acI lineage by supplying a biochemical “helper” catalase. ISME J 13:2252–2263. doi:10.1038/s41396-019-0432-x. PubMed DOI PMC
Imlay JA. 2013. The molecular mechanisms and physiological consequences of oxidative stress: lessons from a model bacterium. Nat Rev Microbiol 11:443–454. doi:10.1038/nrmicro3032. PubMed DOI PMC
Ezraty B, Gennaris A, Barras F, Collet J-F. 2017. Oxidative stress, protein damage and repair in bacteria. Nat Rev Microbiol 15:385–396. doi:10.1038/nrmicro.2017.26. PubMed DOI
Feuerstein O, Ginsburg I, Dayan E, Veler D, Weiss EI. 2005. Mechanism of visible light phototoxicity on Porphyromonas gingivalis and Fusobacterium nucleatum. Photochem Photobiol 81:1186–1189. doi:10.1562/2005-04-06-RA-477. PubMed DOI
Hamblin MR, Hasan T. 2004. Photodynamic therapy: a new antimicrobial approach to infectious disease? Photochem Photobiol Sci 3:436–450. doi:10.1039/b311900a. PubMed DOI PMC
Wainwright M. 1998. Photodynamic antimicrobial chemotherapy (PACT). J Antimicrob Chemother 42:13–28. doi:10.1093/jac/42.1.13. PubMed DOI
Maclean M, MacGregor SJ, Anderson JG, Woolsey G. 2009. Inactivation of bacterial pathogens following exposure to light from a 405-nanometer light-emitting diode array. Appl Environ Microbiol 75:1932–1937. doi:10.1128/AEM.01892-08. PubMed DOI PMC
Andrei A-S, Salcher MM, Mehrshad M, Rychtecký P, Znachor P, Ghai R. 2019. Niche-directed evolution modulates genome architecture in freshwater Planctomycetes. ISME J 13:1056–1071. doi:10.1038/s41396-018-0332-5. PubMed DOI PMC
Mehrshad M, Salcher MM, Okazaki Y, Nakano S-I, Šimek K, Andrei A-S, Ghai R. 2018. Hidden in plain sight-highly abundant and diverse planktonic freshwater Chloroflexi. Microbiome 6:176. doi:10.1186/s40168-018-0563-8. PubMed DOI PMC
Bulzu P-A, Andrei A-Ş, Salcher MM, Mehrshad M, Inoue K, Kandori H, Beja O, Ghai R, Banciu HL. 2019. Casting light on Asgardarchaeota metabolism in a sunlit microoxic niche. Nat Microbiol 4:1129–1137. doi:10.1038/s41564-019-0404-y. PubMed DOI
Biller SJ, Berube PM, Dooley K, Williams M, Satinsky BM, Hackl T, Hogle SL, Coe A, Bergauer K, Bouman HA, Browning TJ, De Corte D, Hassler C, Hulston D, Jacquot JE, Maas EW, Reinthaler T, Sintes E, Yokokawa T, Chisholm SW. 2018. Marine microbial metagenomes sampled across space and time. Sci Data 5:180176. doi:10.1038/sdata.2018.176. PubMed DOI PMC
Salazar G, Paoli L, Alberti A, Huerta-Cepas J, Ruscheweyh H-J, Cuenca M, Field CM, Coelho LP, Cruaud C, Engelen S, Gregory AC, Labadie K, Marec C, Pelletier E, Royo-Llonch M, Roux S, Sánchez P, Uehara H, Zayed AA, Zeller G, Carmichael M, Dimier C, Ferland J, Kandels S, Picheral M, Pisarev S, Poulain J, Acinas SG, Babin M, Bork P, Bowler C, de Vargas C, Guidi L, Hingamp P, Iudicone D, Karp-Boss L, Karsenti E, Ogata H, Pesant S, Speich S, Sullivan MB, Wincker P, Sunagawa S, Tara Oceans Coordinators . 2019. Gene expression changes and community turnover differentially shape the global ocean metatranscriptome. Cell 179:1068–1083. doi:10.1016/j.cell.2019.10.014. PubMed DOI PMC
Mitchell AL, Almeida A, Beracochea M, Boland M, Burgin J, Cochrane G, Crusoe MR, Kale V, Potter SC, Richardson LJ, Sakharova E, Scheremetjew M, Korobeynikov A, Shlemov A, Kunyavskaya O, Lapidus A, Finn RD. 2020. MGnify: the microbiome analysis resource in 2020. Nucleic Acids Res 48:D570–D578. PubMed PMC
Li D, Luo R, Liu C-M, Leung C-M, Ting H-F, Sadakane K, Yamashita H, Lam T-W. 2016. MEGAHIT v1.0: a fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods 102:3–11. doi:10.1016/j.ymeth.2016.02.020. PubMed DOI
Hyatt D, Chen G-L, Locascio PF, Land ML, Larimer FW, Hauser LJ. 2010. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11:119. doi:10.1186/1471-2105-11-119. PubMed DOI PMC
Eddy SR. 2011. Accelerated profile HMM searches. PLoS Comput Biol 7:e1002195. doi:10.1371/journal.pcbi.1002195. PubMed DOI PMC
Hauser M, Steinegger M, Söding J. 2016. MMseqs software suite for fast and deep clustering and searching of large protein sequence sets. Bioinformatics 32:1323–1330. doi:10.1093/bioinformatics/btw006. PubMed DOI
Katoh K, Standley DM. 2013. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol 30:772–780. doi:10.1093/molbev/mst010. PubMed DOI PMC
Käll L, Krogh A, Sonnhammer ELL. 2005. An HMM posterior decoder for sequence feature prediction that includes homology information. Bioinformatics 21(Suppl 1):i251–i257. doi:10.1093/bioinformatics/bti1014. PubMed DOI
Li W, Godzik A. 2006. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22:1658–1659. doi:10.1093/bioinformatics/btl158. PubMed DOI
UniProt Consortium. 2019. UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res 47:D506–D515. doi:10.1093/nar/gky1049. PubMed DOI PMC
Haft DH, Selengut JD, White O. 2003. The TIGRFAMs database of protein families. Nucleic Acids Res 31:371–373. doi:10.1093/nar/gkg128. PubMed DOI PMC
Galperin MY, Makarova KS, Wolf YI, Koonin EV. 2015. Expanded microbial genome coverage and improved protein family annotation in the COG database. Nucleic Acids Res 43:D261–D269. doi:10.1093/nar/gku1223. PubMed DOI PMC
Mitchell AL, Attwood TK, Babbitt PC, Blum M, Bork P, Bridge A, Brown SD, Chang H-Y, El-Gebali S, Fraser MI, Gough J, Haft DR, Huang H, Letunic I, Lopez R, Luciani A, Madeira F, Marchler-Bauer A, Mi H, Natale DA, Necci M, Nuka G, Orengo C, Pandurangan AP, Paysan-Lafosse T, Pesseat S, Potter SC, Qureshi MA, Rawlings ND, Redaschi N, Richardson LJ, Rivoire C, Salazar GA, Sangrador-Vegas A, Sigrist CJA, Sillitoe I, Sutton GG, Thanki N, Thomas PD, Tosatto SCE, Yong S-Y, Finn RD. 2019. InterPro in 2019: improving coverage, classification and access to protein sequence annotations. Nucleic Acids Res 47:D351–D360. doi:10.1093/nar/gky1100. PubMed DOI PMC
Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJE. 2015. The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc 10:845–858. doi:10.1038/nprot.2015.053. PubMed DOI PMC
Johnson NL, Kemp AW, Kotz S. 2005. Univariate discrete distributions. John Wiley & Sons, New York, NY.
Benjamini Y, Hochberg Y. 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/j.2517-6161.1995.tb02031.x DOI
Bushmanova E, Antipov D, Lapidus A, Prjibelski AD. 2019. rnaSPAdes: a de novo transcriptome assembler and its application to RNA-Seq data. Gigascience 8:giz100. doi:10.1093/gigascience/giz100. PubMed DOI PMC
Kanehisa M, Sato Y, Morishima K. 2016. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J Mol Biol 428:726–731. doi:10.1016/j.jmb.2015.11.006. PubMed DOI