Bulk Download

Alga-PrAS includes whole protein sequences of 31 algae, such as green algae, red algae, glaucophyte, oomycetes, diatoms, and other microalgae. Additionally, 3 land plants are also provided. Finally, Alga-PrAS houses more than 500,000 protein sequences.
All protein sequences and annotation in Alga-PrAS : Alga-PrAS_Resource.zip (195,898kb)

Tools

  1. CD-HIT [link]
    Fu L., Niu B., Zhu Z., Wu S., Li W. (2012) CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28: 3150–2.
  2. CDD [link]
    Marchler-Bauer, Yu Bo, Lianyi Han, et al.(2017) "CDD/SPARCLE: functional classification of proteins via subfamily domain architectures.", Nucleic Acids Res.45(D):200–3.
  3. DIpro [link]
    Cheng J. L., Saigo H., Baldi P. (2006) Large-scale prediction of disulphide bridges using kernel methods, two-dimensional recursive neural networks, and weighted graph matching. Proteins-Structure Function and Bioinformatics 62: 617–629.
  4. DISOPRED [link]
    Jones D. T., Cozzetto D. (2015) DISOPRED3: precise disordered region predictions with annotated protein-binding activity. Bioinformatics 31: 857–63.
  5. DROP [link]
    Ebina T., Toh H., Kuroda Y. (2011) DROP: an SVM domain linker predictor trained with optimal features selected by random forest. Bioinformatics 27: 487–94.
  6. ePESTfind [link]
    Rice P., Longden I., Bleasby A. (2000) EMBOSS: the European Molecular Biology Open Software Suite. Trends Genet 16: 276–7.
  7. GPI-SOM [link]
    Niklaus F. and Pascal M., (2005) Identification of GPI anchor attachment signals by a Kohonen self-organizing map. Bioinformatics 21:1846–4852
  8. HECTAR [link]
    Gschloessl B., Guermeur Y.and Cock J. M. (2008) HECTAR: A method to predict subcellular targeting in heterokonts. BMC Bioinformatics 9: 393.
  9. InterPro [link]
    Hunter S., Jones P., Mitchell A., Apweiler R., Attwood T. K., Bateman A., Bernard T., Binns D., Bork P., Burge S.,et al. (2012) InterPro in 2011: new developments in the family and domain prediction database. Nucleic Acids Res 40: D306–12.
  10. KOG [link]
    Tatusov R. L., Galperin M. Y., Natale D. A., Koonin E. V. (2000) The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res 28: 33–6.
  11. Musite [link]
    Gao J., Thelen J. J., Dunker A. K., Xu D. (2010) Musite, a tool for global prediction of general and kinase-specific phosphorylation sites. Mol Cell Proteomics 9: 2586–600.
  12. NetNGlyc  [link]
    R. Gupta, E. Jung and S. Brunak. (2004) Prediction of N-glycosylation sites in human proteins. In preparation.
  13. O-glycosylation [link]
    Gomord V., Fitchette A. C., Menu-Bouaouiche L., Saint-Jore-Dupas C., Plasson C., Michaud D., Faye L. (2010) Plant-specific glycosylation patterns in the context of therapeutic protein production. Plant Biotechnol J 8: 564–87.
  14. OrthoMCL [link]
    Li L., Stoeckert C. J., Jr., Roos D. S. (2003) OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res 13: 2178–89.
  15. PASS  [link]
    Kuroda Y., Tani K., Matsuo Y., Yokoyama S. (2000) Automated search of natively folded protein fragments for high-throughput structure determination in structural genomics. Protein Sci 9: 2313–21.
  16. PDB [link]
    Westbrook J., Feng Z., Chen L., Yang H., Berman H. M. (2003) The Protein Data Bank and structural genomics. Nucleic Acids Res 31: 489–91.
  17. RADAR [link]
    Heger A., Holm L. (2000) Rapid automatic detection and alignment of repeats in protein sequences. Proteins 41: 224–37.
  18. SignalP [link]
    Nielsen H., Engelbrecht J., Brunak S., vonHeijne G. (1997) Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites. Protein Engineering 10: 1–6.
  19. SOLpro [link]
    Magnan C. N., Randall A., Baldi P. (2009) SOLpro: accurate sequence-based prediction of protein solubility. Bioinformatics 25: 2200–7.
  20. SSpro [link]
    Cheng J., Randall A. Z., Sweredoski M. J., Baldi P. (2005) SCRATCH: a protein structure and structural feature prediction server. Nucleic Acids Res 33: W72–6.
  21. TMHMM [link]
    Krogh A., Larsson B., von Heijne G., Sonnhammer E. L. (2001) Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 305: 567–80.
  22. Ubpred [link]
    Radivojac P., Vacic V., Haynes C., Cocklin R. R., Mohan A., Heyen J. W., Goebl M. G., Iakoucheva L. M. (2010) Identification, analysis, and prediction of protein ubiquitination sites. Proteins 78: 365–80.
  23. UniProtKB-SwissProt [link]
    Bateman A., Martin M. J., O'Donovan C., Magrane M., Apweiler R., Alpi E. (2015) UniProt: a hub for protein information. Nucleic Acids Research 43:D204–D212.
  24. UniProtKB-TrEMB [link]
    Bateman A., Martin M. J., O'Donovan C., Magrane M., Apweiler R., Alpi E. (2015) UniProt: a hub for protein information. Nucleic Acids Research 43:D204–D212.
  25. Wolf PSORT [link]
    Horton P., Park K. J., Obayashi T., Fujita N., Harada H., Adams-Collier C. J., Nakai K. (2007) WoLF PSORT: protein localization predictor. Nucleic Acids Res 35: W585–7.

Resources

Green algae
  1. Auxenochlorella protothecoides [link]
    Gao C. F., Wang Y., Shen Y., Yan D., He X., Dai J. B.,et al. (2014) Oil accumulation mechanisms of the oleaginous microalga Chlorella protothecoides revealed through its genome, transcriptomes, and proteomes. Bmc Genomics 15.
  2. Bathycoccus prasinos [link]
    Moreau H., Verhelst B., Couloux A., Derelle E., Rombauts S., Grimsley N., et al. (2012) Gene functionalities and genome structure in Bathycoccus prasinos reflect cellular specializations at the base of the green lineage. Genome Biology 13.
  3. Chlamydomonas reinhardtii [link]
    Merchant S. S., Prochnik S. E., Vallon O., Harris E. H., Karpowicz S. J., Witman G. B., et al. (2007) The Chlamydomonas genome reveals the evolution of key animal and plant functions. Science 318: 245–50.
  4. Chlorella variabilis [link]
    Blanc G., Duncan G., Agarkova I., Borodovsky M., Gurnon J., Kuo A., et al. (2010) The Chlorella variabilis NC64A genome reveals adaptation to photosymbiosis, coevolution with viruses, and cryptic sex. Plant Cell 22: 2943–55.
  5. Coccomyxa subellipsoidea [link]
    Blanc G., Agarkova I., Grimwood J., Kuo A., Brueggeman A., Dunigan D. D., et al. (2012) The genome of the polar eukaryotic microalga Coccomyxa subellipsoidea reveals traits of cold adaptation. Genome Biol 13: R39.
  6. Klebsormidium flaccidum [link]
    Hori K., Maruyama F., Fujisawa T., Togashi T., Yamamoto N., Seo M., et al. (2014) Klebsormidium flaccidum genome reveals primary factors for plant terrestrial adaptation. Nature Communications 5: 3978.
  7. Micromonas pusilla [link]
    Worden A. Z., Lee J. H., Mock T., Rouze P., Simmons M. P., Aerts A. L., et al. (2009) Green evolution and dynamic adaptations revealed by genomes of the marine picoeukaryotes Micromonas. Science 324: 268–72.
  8. Micromonas sp RCC299 [link]
    Worden A. Z., Lee J. H., Mock T., Rouze P., Simmons M. P., Aerts A. L., et al. (2009) Green evolution and dynamic adaptations revealed by genomes of the marine picoeukaryotes Micromonas. Science 324: 268–72.
  9. Monoraphidium neglectum [link]
    Bogen C., Al-Dilaimi A., Albersmeier A., Wichmann J., Grundmann M., Rupp O., et al. (2013) Reconstruction of the lipid metabolism for the microalga Monoraphidium neglectum from its genome sequence reveals characteristics suitable for biofuel production. BMC Genomics 14: 926.
  10. Ostreococcus lucimarinus [link]
    Palenik B., Grimwood J., Aerts A., Rouze P., Salamov A., Putnam N., et al. (2007) The tiny eukaryote Ostreococcus provides genomic insights into the paradox of plankton speciation. Proceedings of the National Academy of Sciences of the United States of America 104: 7705–7710.
  11. Ostreococcus tauri [link]
    Blanc-Mathieu R., Verhelst B., Derelle E., Rombauts S., Bouget F. Y., Carre I., et al. (2014) An improved genome of the model marine alga Ostreococcus tauri unfolds by assessing Illumina de novo assemblies. BMC Genomics 15: 1103.
  12. Volvox carteri [link]
    Prochnik S. E., Umen J., Nedelcu A. M., Hallmann A., Miller S. M., Nishii I., et al. (2010) Genomic analysis of organismal complexity in the multicellular green alga Volvox carteri. Science 329: 223–6.
Red algae
  1. Chondrus crispus [link]
    Collen J., Porcel B., Carre W., Ball S. G., Chaparro C., Tonon T., et al. (2013) Genome structure and metabolic features in the red seaweed Chondrus crispus shed light on evolution of the Archaeplastida. Proc Natl Acad Sci USA 110: 5247–52.
  2. Cyanidioschyzon merolae [link]
    Matsuzaki M., Misumi O., Shin I. T., Maruyama S., Takahara M., Miyagishima S. Y., et al. (2004) Genome sequence of the ultrasmall unicellular red alga Cyanidioschyzon merolae 10D. Nature 428: 653–7.
  3. Galdieria sulphuraria [link]
    Schonknecht G., Chen W. H., Ternes C. M., Barbier G. G., Shrestha R. P., Stanke M., et al. (2013) Gene transfer from bacteria and archaea facilitated evolution of an extremophilic eukaryote. Science 339: 1207–10.
  4. Porphyridium purpureum [link]
    Bhattacharya D., Price D. C., Chan C. X., Qiu H., Rose N., Ball S., et al. (2013) Genome of the red alga Porphyridium purpureum. Nature Communications 4: 1941.
  5. Pyropia yezoensis [link]
    Nakamura Y., Sasaki N., Kobayashi M., Ojima N., Yasuike M., Shigenobu Y., et al. (2013) The first symbiont-free genome sequence of marine red alga, Susabi-nori (Pyropia yezoensis). PLoS One 8: e57122.
Glaucophyceae
  1. Cyanophora paradoxa [link]
    Price D. C., Chan C. X., Yoon H. S., Yang E. C., Qiu H., Weber A. P., et al. (2012) Cyanophora paradoxa genome elucidates origin of photosynthesis in algae and plants. Science 335: 843–7.
Oomycetes
  1. Phytophthora capsici [link]
    Lamour K. H., Mudge J., Gobena D., Hurtado-Gonzales O. P., Schmutz J., Kuo A., et al. (2012) Genome Sequencing and Mapping Reveal Loss of Heterozygosity as a Mechanism for Rapid Adaptation in the Vegetable Pathogen Phytophthora capsici. Molecular Plant-Microbe Interactions 25: 1350–1360.
  2. Phytophthora infestans [link]
    Haas B. J., Kamoun S., Zody M. C., Jiang R. H., Handsaker R. E., Cano L. M., et al. (2009) Genome sequence and analysis of the Irish potato famine pathogen Phytophthora infestans. Nature 461: 393–8.
  3. Phytophthora ramorum [link]
    Tyler B. M., Tripathy S., Zhang X., Dehal P., Jiang R. H., Aerts A., et al. (2006) Phytophthora genome sequences uncover evolutionary origins and mechanisms of pathogenesis. Science 313: 1261–6.
  4. Phytophthora sojae [link]
    Tyler B. M., Tripathy S., Zhang X., Dehal P., Jiang R. H., Aerts A., et al. (2006) Phytophthora genome sequences uncover evolutionary origins and mechanisms of pathogenesis. Science 313: 1261–6.
Diatoms
  1. Fragilariopsis cylindrus CCMP1102 [link]
    http://genome.jgi.doe.gov/Fracy1/Fracy1.info.html
  2. Phaeodactylum tricornutum [link]
    Bowler C., Allen A. E., Badger J. H., Grimwood J., Jabbari K., Kuo A., et al. (2008) The Phaeodactylum genome reveals the evolutionary history of diatom genomes. Nature 456: 239–44.
  3. Thalassiosira pseudonana [link]
    Armbrust E. V., Berges J. A., Bowler C., Green B. R., Martinez D., Putnam N. H., et al. (2004) The genome of the diatom Thalassiosira pseudonana: ecology, evolution, and metabolism. Science 306: 79–86.
Other microalgae
  1. Aureococcus anophagefferens [link]
    Gobler C. J., Berry D. L., Dyhrman S. T., Wilhelm S. W., Salamov A., Lobanov A. V., et al. (2011) Niche of harmful alga Aureococcus anophagefferens revealed through ecogenomics. Proceedings of the National Academy of Sciences of the United States of America 108: 4352–4357.
  2. Bigelowiella natans [link]
    Curtis B. A., Tanifuji G., Burki F., Gruber A., Irimia M., Maruyama S., et al. (2012) Algal genomes reveal evolutionary mosaicism and the fate of nucleomorphs. Nature 492: 59–65.
  3. Ectocarpus siliculosus [link]
    Cock J. M., Sterck L., Rouze P., Scornet D., Allen A. E., Amoutzias G., et al. (2010) The Ectocarpus genome and the independent evolution of multicellularity in brown algae. Nature 465: 617–21.
  4. Emiliania huxleyi [link]
    Read B. A., Kegel J., Klute M. J., Kuo A., Lefebvre S. C., Maumus F., et al. (2013) Pan genome of the phytoplankton Emiliania underpins its global distribution. Nature 499: 209–13.
  5. Guillardia theta [link]
    Curtis B. A., Tanifuji G., Burki F., Gruber A., Irimia M., Maruyama S., et al. (2012) Algal genomes reveal evolutionary mosaicism and the fate of nucleomorphs. Nature 492: 59–65.
  6. Symbiodinium minutum [link]
    Shoguchi E., Shinzato C., Kawashima T., Gyoja F., Mungpakdee S., Koyanagi R., et al. (2013) Draft assembly of the Symbiodinium minutum nuclear genome reveals dinoflagellate gene structure. Curr Biol 23: 1399–408.
Land plants
  1. Arabidopsis thaliana [link]
    Swarbreck D., Wilks C., Lamesch P., Berardini T. Z., Garcia-Hernandez M., Foerster H., et al. (2008) The Arabidopsis Information Resource (TAIR): gene structure and function annotation. Nucleic Acids Res 36: D1009–14.
  2. Physcomitrella patens [link]
    Rensing S. A., Lang D., Zimmer A. D., Terry A., Salamov A., Shapiro H., et al. (2008) The Physcomitrella genome reveals evolutionary insights into the conquest of land by plants. Science 319: 64–69.
  3. Selaginella moellendorffii [link]
    Banks J. A., Nishiyama T., Hasebe M., Bowman J. L., Gribskov M., dePamphilis C., et al. (2011) The Selaginella genome identifies genetic changes associated with the evolution of vascular plants. Science 332: 960–3.

Photos

  1. Ectocarpus siliculosus sur Ulva [link]
    Ectocarpus siliculosus, hag a zo ur spesad bezhin gell, o kreskiƱ war Ulva, bezhin glas. 03 a viz Gouere 2003, Perharidi, Rosko (Bro Leon, Breizh).
    Date : 4 July 2003
    Author : Akirapeters
  2. Chondrus crispus [link]
    Irish moss.
    Date : 24 October 2007
    Author : Kontos
  3. Microalgae - Nannochloropsis sp. [link]
    This pennate diatom is the 'lab rat' of diatoms, and its genome sequence is currently being determined.
    Date : 12 October 2004
    Author : Commonwealth Scientific and Industrial Research Organisation
  4. Light Micrograph of Phaeodactylum tricornutum [link]
    Bradbury J: Nature's Nanotechnologists: Unveiling the Secrets of Diatoms. PLoS Biol 2/10/2004: e306.
    Date : 7 December 2009
    Author : Image courtesy of Alessandra de Martino and Chris Bowler, Stazione Zoologica and Ecole Normale SupƩrieure.

Our Plant Databases

Metabolomics -GC/MS, LC/MS, CE/MS ...
  1. PRIMe [Link]
    A Web site that assembles tools for metabolomics and transcriptomics
    Akiyama K. et al. In Silico Biology 8 339–345 (2008) [PubMed][Link]
  2. PRIMeLink [Link]
    Innovative content for plant metabolomics and integration of gene expression and metabolite accumulation
    Sakurai S. et al. Plant Cell Physiology 54(2) e5(1–8) (2013) [PubMed][Link]
  3. ReSpect [Link]
    A collection of literature and in-house MSn spectra data for research on plant metabolomics
    Sawada Y. et al. Phytochemistry 82 38–45 (2012) [PubMed][Link]
Phenotype, Genomics -phenome, cDNA, mutant ...
  1. RARGE II [Link]
    An integrated phenotype database of Arabidopsis mutant traits using a controlled vocabulary
    Akiyama K. et al. Plant Cell Physiology 55(1) e4(1–10) (2014) [PubMed] [Link]
  2. RARGE [Link]
    A large-scale database of RIKEN Arabidopsis resources ranging from transcriptome to phenome
    Akiyama K. et al. Nucleic Acids Research 33:D647–650 (2005) [PubMed] [Link]
  3. The Chloroplast Function Database II [Link]
    A comprehensive collection of homozygous mutants and their phenotypic/genotypic traits for nuclear-encoded chloroplast proteins
    Myouga F. et al. Plant Cell Physiology 54(2) e2(1–10) (2013) [PubMed] [Link]
  4. Cassava Online Archive [Link]
    Information resource of cassava for genetic improvement
    Sakurai T. et al. PLOS One 11 8(9) e74056 (2013) [PubMed] [Link]
  5. UniVio [Link]
    A multiple omics database with hormonome and transcriptome data from rice
    Kudo T. et al. Plant Cell Physiology 54(2) e9(1–12) (2013) [PubMed] [Link]
  6. rsoy [Link]
    Sequencing and analysis of approximately 40,000 soybean cDNA clones from a full-length-enriched cDNA library
    Umezawa T. et al. DNA Research 15 333–346 (2008) [PubMed] [Link]

Ohter Links

  1. DLP-SVM [Link]
    DLP-SVM is a prediction tool of protein domain linkers.
    Ebina T. et al. Biopolymers 92(1) 1–8 (2009) [PubMed] [Link]
  2. H-DROP [Link]
    H-DROP is a SVM based helical domain linker predictor for proteomics search.
    Ebina T. et al. J. Comput. Aided. Mol. Des. 28(8) 831–839 (2014) [PubMed] [Link]
  3. IS-Dom[Link]
    IS-Dom is a database of independent structural domains automatically delineated from protein structures.
    Ebina T. et al. J. Comput. Aided. Mol. Des. 27(5) 419–426 (2013) [PubMed] [Link]
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