RLPredictiOme, a Machine Learning-Derived Method for High-Throughput Prediction of Plant Receptor-like Proteins, Reveals Novel Classes of Transmembrane Receptors
Abstract
:1. Introduction
2. Results
2.1. Revisiting the Ectodomain of the RLK Superfamily in Plants
2.2. Feature Analysis
2.3. ML Model Capacity of Distinguishing RLPs from NRLPs
2.4. ML Model Abilities to Distinguish RLPs from RLKs
2.5. The Ability of ML Models to Classify RLP Subfamilies
2.6. Validation of RLPredictiOme
2.7. High Throughput Prediction of RLPs in the Arabidopsis Genome Using RLPredictiOme
2.8. GDPDL Family Downstream Analysis
2.9. Identification of GDPDLs- and SNC4-Interacting Proteins from Arabidopsis
2.10. The Expression Profile of the GDPDLs in Response to Pathogens and Different Organs
3. Discussion
4. Materials and Methods
4.1. Reclassification of the Plant RLK Ectodomains for Composing Datasets
4.2. Dataset Composition
4.3. Feature Extraction
4.4. Dealing with Imbalanced Datasets
4.5. Machine Learning Algorithms
4.6. Performance Assessment of the Models
4.7. Bayesian Inference in Ensemble Methods
4.8. Classifier Evaluation Strategy
4.9. RLP Subfamilies Downstream Analysis
4.10. Protein-Protein Interaction (PPI) Network Analysis
4.11. Plant Growth, Treatment with flg22, and Viral infection with TRV and CabLCV
4.12. RNA Extraction, Synthesis of cDNA, and qRT-PCR Analysis
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
ACC | accuracy | ML | machine learning |
BAK1 | BRI1-ASSOCIATED RECEPTOR KINASE1 | MLPL | major latex protein-like |
BRI1 | BRASSINOSTEROID INSENSITIVE 1 | MS | Murashige and Skoog |
CabLCV | cabbage leaf curl virus | NEP1 | NECROSIS- AND ETHYLENE-INDUCING PEPTIDE 1 |
CAP | adenylate-cyclase-associated | NLPs | NEP1-LIKE PROTEINS |
CERK1 | CHITIN ELICITOR RECEPTOR KINASE 1 | NRLPs | non-RLPs |
CLV1 | CLAVATA1 | nsLTP | non-specific lipid transfer proteins |
CPAASC2 | chemical properties of amino acid side chains 2 | PAMPs | pathogen-associated molecular patterns |
DAMPs | damage-associated molecular patterns | PEPR1 | PEP1 RECEPTOR 1 |
ECD | extracellular domain | PEPR2 | PEP1 RECEPTOR 2 |
EPF1 | EPIDERMAL PATTERNING FACTOR 1 | PPI | protein-protein interaction |
EPF2 | EPIDERMAL PATTERNING FACTOR 2 | PRRs | pattern recognition receptors |
ER | endoplasmic reticulum | PSK | PHYTOSULFOKINE |
ERL1 | ERECTA-LIKE 1 | PSKR1 | PHYTOSULFOKINE RECEPTOR 1 |
ETI | effector-triggered immunity | PPI | protein-protein interactions |
FDR | false discovery rate | PTI | PAMP-triggered immunity |
GDPDL | glycerophosphoryl diester phosphodiesterase family | RLCK | receptor-like cytoplasmic kinases |
HMM | hidden Markov model | RLP | receptor-like protein |
LRR | leucine-rich repeat | SOBIR1 | SUPPRESSOR OF BIR1-1 |
LRR-RLK | leucine-rich repeat kinase receptor-like kinase | SP | signal peptide |
LYM1 | LYSIN-MOTIF 1 | TMM | RLP TOO MANY MOUTHS |
LYM3 | LYSIN-MOTIF 3 | TN | true negatives |
LysM | lysin-motifs | TP | true positives |
MCC | Mathew’s correlation coefficient | TRV | tobacco rattle virus |
References
- Tang, D.; Wang, G.; Zhou, J.M. Receptor kinases in plant-pathogen interactions: More than pattern recognition. Plant Cell 2017, 29, 618–637. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- He, Y.; Zhou, J.; Shan, L.; Meng, X. Plant cell surface receptor-mediated signaling–a common theme amid diversity. J. Cell Sci. 2018, 131, jcs209353. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Shiu, S.H.; Karlowski, W.M.; Pan, R.; Tzeng, Y.H.; Mayer, K.F.; Li, W.H. Comparative analysis of the receptor-like kinase family in Arabidopsis and rice. Plant Cell 2004, 16, 1220–1234. [Google Scholar] [CrossRef] [Green Version]
- Ma, X.; Xu, G.; He, P.; Shan, L. SERKing coreceptors for receptors. Trends Plant Sci. 2016, 21, 1017–1033. [Google Scholar] [CrossRef]
- Botos, I.; Segal, D.M.; Davies, D.R. The structural biology of Toll-like receptors. Structure 2011, 19, 447–459. [Google Scholar] [CrossRef] [Green Version]
- Shiu, S.H.; Bleecker, A.B. Plant receptor-like kinase gene family: Diversity, function, and signaling. Sci. STKE 2001, 2001, re22. [Google Scholar] [CrossRef]
- Shiu, S.H.; Bleecker, A.B. Expansion of the receptor-like kinase/Pelle gene family and receptor-like proteins in Arabidopsis. Plant Physiol. 2003, 132, 530–543. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Gao, L.L.; Xue, H.W. Global analysis of expression profiles of rice receptor-like kinase genes. Mol. Plant 2012, 5, 143–153. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Sakamoto, T.; Deguchi, M.; Brustolini, O.J.; Santos, A.A.; Silva, F.F.; Fontes, E.P. The tomato RLK superfamily: Phylogeny and functional predictions about the role of the LRRII-RLK subfamily in antiviral defense. BMC Plant Biol. 2012, 12, 229. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zhou, F.; Guo, Y.; Qiu, L.J. Genome-wide identification and evolutionary analysis of leucine-rich repeat receptor-like protein kinase genes in soybean. BMC Plant Biol. 2016, 16, 58. [Google Scholar] [CrossRef]
- Li, J.; Chory, J. A putative leucine-rich repeat receptor kinase involved in brassinosteroid signal transduction. Cell 1997, 90, 929–938. [Google Scholar] [CrossRef] [Green Version]
- Lee, J.S.; Kuroha, T.; Hnilova, M.; Khatayevich, D.; Kanaoka, M.M.; McAbee, J.M.; Sarikaya, M.; Tamerler, C.; Torii, K.U. Direct interaction of ligand–receptor pairs specifying stomatal patterning. Genes Dev. 2012, 26, 126–136. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Jia, G.; Liu, X.; Owen, H.A.; Zhao, D. Signaling of cell fate determination by the TPD1 small protein and EMS1 receptor kinase. Proc. Natl. Acad. Sci. USA 2008, 105, 2220–2225. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Cho, S.K.; Larue, C.T.; Chevalier, D.; Wang, H.; Jinn, T.L.; Zhang, S.; Walker, J.C. Regulation of floral organ abscission in Arabidopsis thaliana. Proc. Natl. Acad. Sci. USA 2008, 105, 15629–15634. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kumpf, R.P.; Shi, C.L.; Larrieu, A.; Stø, I.M.; Butenko, M.A.; Péret, B.; Riiser, E.S.; Bennett, M.J.; Aalen, R.B. Floral organ abscission peptide IDA and its HAE/HSL2 receptors control cell separation during lateral root emergence. Proc. Natl. Acad. Sci. USA 2013, 110, 5235–5240. [Google Scholar] [CrossRef] [Green Version]
- Chen, D.; Guo, H.; Chen, S.; Yue, Q.; Wang, P.; Chen, X. Receptor-like kinase HAESA-like 1 positively regulates seed longevity in Arabidopsis. Planta 2022, 256, 21. [Google Scholar] [CrossRef] [PubMed]
- Ogawa, M.; Shinohara, H.; Sakagami, Y.; Matsubayashi, Y. Arabidopsis CLV3 peptide directly binds CLV1 ectodomain. Science 2008, 319, 294. [Google Scholar] [CrossRef] [PubMed]
- Ou, Y.; Kui, H.; Li, J. Receptor-like kinases in root development: Current progress and future directions. Mol. Plant 2021, 14, 166–185. [Google Scholar] [CrossRef]
- Hirakawa, Y.; Shinohara, H.; Kondo, Y.; Inoue, A.; Nakanomyo, I.; Ogawa, M.; Sawa, S.; Ohashi-Ito, K.; Matsubayashi, Y.; Fukuda, H. Non-cell-autonomous control of vascular stem cell fate by a CLE peptide/receptor system. Proc. Natl. Acad. Sci. USA 2008, 105, 15208–15213. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wang, J.; Li, H.; Han, Z.; Zhang, H.; Wang, T.; Lin, G.; Chang, J.; Yang, W.; Chai, J. Allosteric receptor activation by the plant peptide hormone phytosulfokine. Nature 2015, 525, 265–268. [Google Scholar] [CrossRef]
- Haruta, M.; Sabat, G.; Stecker, K.; Minkoff, B.B.; Sussman, M.R. A peptide hormone and its receptor protein kinase regulate plant cell expansion. Science 2014, 343, 408–411. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zhong, S.; Li, L.; Wang, Z.; Ge, Z.; Li, Q.; Bleckmann, A.; Wang, J.; Song, Z.; Shi, Y.; Liu, T.; et al. RALF peptide signaling controls the polytubey block in Arabidopsis. Science 2022, 375, 290–296. [Google Scholar] [CrossRef] [PubMed]
- Macho, A.P.; Zipfel, C. Plant PRRs and the activation of innate immune signaling. Mol. Cell 2014, 54, 263–272. [Google Scholar] [CrossRef] [Green Version]
- Gómez-Gómez, L.; Boller, T. FLS2: An LRR receptor–like kinase involved in the perception of the bacterial elicitor flagellin in Arabidopsis. Mol. Cell 2000, 5, 1003–1011. [Google Scholar] [CrossRef]
- Zipfel, C.; Kunze, G.; Chinchilla, D.; Caniard, A.; Jones, J.D.; Boller, T.; Felix, G. Perception of the bacterial PAMP EF-Tu by the receptor EFR restricts Agrobacterium-mediated transformation. Cell 2006, 125, 749–760. [Google Scholar] [CrossRef]
- Yamaguchi, Y.; Pearce, G.; Ryan, C.A. The cell surface leucine-rich repeat receptor for At Pep1, an endogenous peptide elicitor in Arabidopsis, is functional in transgenic tobacco cells. Proc. Natl. Acad. Sci. USA 2006, 103, 10104–10109. [Google Scholar] [CrossRef] [Green Version]
- Yamaguchi, Y.; Huffaker, A.; Bryan, A.C.; Tax, F.E.; Ryan, C.A. PEPR2 is a second receptor for the Pep1 and Pep2 peptides andcontributes to defense responses in Arabidopsis. Plant Cell 2010, 22, 508–522. [Google Scholar] [CrossRef] [Green Version]
- Miya, A.; Albert, P.; Shinya, T.; Desaki, Y.; Ichimura, K.; Shirasu, K.; Narusaka, Y.; Kawakami, N.; Kaku, H.; Shibuya, N. CERK1, a LysM receptor kinase, is essential for chitin elicitor signaling in Arabidopsis. Proc. Natl. Acad. Sci. USA 2007, 104, 19613–19618. [Google Scholar] [CrossRef] [Green Version]
- Wan, J.; Zhang, X.C.; Neece, D.; Ramonell, K.M.; Clough, S.; Kim, S.y.; Stacey, M.G.; Stacey, G. A LysM receptor-like kinase plays a critical role in chitin signaling and fungal resistance in Arabidopsis. Plant Cell 2008, 20, 471–481. [Google Scholar] [CrossRef] [Green Version]
- Wan, J.; Tanaka, K.; Zhang, X.C.; Son, G.H.; Brechenmacher, L.; Nguyen, T.H.N.; Stacey, G. LYK4, a lysin motif receptor-like kinase, is important for chitin signaling and plant innate immunity in Arabidopsis. Plant Physiol. 2012, 160, 396–406. [Google Scholar] [CrossRef]
- Petutschnig, E.K.; Jones, A.M.; Serazetdinova, L.; Lipka, U.; Lipka, V. The lysin motif receptor- like kinase (LysM-RLK) CERK1 is a major chitin-binding protein in Arabidopsis thaliana and subject to chitin-induced phosphorylation. Plant Biotechnol. J. 2010, 285, 28902–28911. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Cao, Y.; Liang, Y.; Tanaka, K.; Nguyen, C.T.; Jedrzejczak, R.P.; Joachimiak, A.; Stacey, G. The kinase LYK5 is a major chitin receptor in Arabidopsis and forms a chitin-induced complex with related kinase CERK1. eLife 2014, 3, e03766. [Google Scholar] [CrossRef] [PubMed]
- Ranf, S.; Gisch, N.; Schäffer, M.; Illig, T.; Westphal, L.; Knirel, Y.A.; Sánchez-Carballo, P.M.; Zähringer, U.; Hückelhoven, R.; Lee, J.; et al. A lectin S-domain receptor kinase mediates lipopolysaccharide sensing in Arabidopsis thaliana. Nat. Immun. 2015, 16, 426–433. [Google Scholar] [CrossRef]
- Yu, H.; Ruan, H.; Xia, X.; Chicowski, A.S.; Whitham, S.A.; Li, Z.; Wang, G.; Liu, W. Maize FERONIA-like receptor genes are involved in the response of multiple disease resistance in maize. Mol. Plant Pathol. 2022, 23, 1331–1345. [Google Scholar] [CrossRef] [PubMed]
- Ortiz-Morea, F.A.; Liu, J.; Shan, L.; He, P. Malectin-like receptor kinases as protector deities in plant immunity. Nat. Plants 2022, 8, 27–37. [Google Scholar] [CrossRef] [PubMed]
- Chen, X.; Ding, Y.; Yang, Y.; Song, C.; Wang, B.; Yang, S.; Guo, Y.; Gong, Z. Protein kinases in plant responses to drought, salt, andcold stress. J. Integr. Plant Biol. 2021, 63, 53–78. [Google Scholar] [CrossRef] [PubMed]
- Invernizzi, M.; Hanemian, M.; Keller, J.; Libourel, C.; Roby, D. PERKing up our understanding of the proline-rich extensin-like receptor kinases, a forgotten plant receptor kinase family. New Phytol. 2022, 235, 875–884. [Google Scholar] [CrossRef]
- Xie, Y.; Sun, P.; Li, Z.; Zhang, F.; You, C.; Zhang, Z. FERONIA receptor kinase integrates with hormone signaling to regulate plant growth, development, and responses to environmental stimuli. Int. J. Mol. Sci. 2022, 23, 3730. [Google Scholar] [CrossRef]
- Xie, Y.H.; Zhang, F.J.; Sun, P.; Li, Z.Y.; Zheng, P.F.; Gu, K.D.; Hao, Y.J.; Zhang, Z.; You, C.X. Apple receptor-like kinase FERONIA regulates salt tolerance and ABA sensitivity in Malus domestica. J. Plant Physiol. 2022, 270, 153616. [Google Scholar] [CrossRef]
- Yang, L.; Gao, C.; Jiang, L. Leucine-rich repeat receptor-like protein kinase AtORPK1 promotes oxidative stress resistance in and AtORPK1-AtKAPP mediated module in Arabidopsis. Plant Sci. J. 2022, 315, 111147. [Google Scholar] [CrossRef]
- Zhou, H.; Xiao, F.; Zheng, Y.; Liu, G.; Zhuang, Y.; Wang, Z.; Zhang, Y.; He, J.; Fu, C.; Lin, H. PAMP-INDUCED SECRETED PEPTIDE 3 modulates salt tolerance through RECEPTOR-LIKE KINASE 7 in plants. Plant Cell 2022, 34, 927–944. [Google Scholar] [CrossRef] [PubMed]
- Liu, Z.; Hou, S.; Rodrigues, O.; Wang, P.; Luo, D.; Munemasa, S.; Lei, J.; Liu, J.; Ortiz-Morea, F.A.; Wang, X.; et al. Phytocytokine signalling reopens stomata in plant immunity and water loss. Nature 2022, 605, 332–339. [Google Scholar] [CrossRef] [PubMed]
- Lin, G.; Zhang, L.; Han, Z.; Yang, X.; Liu, W.; Li, E.; Chang, J.; Qi, Y.; Shpak, E.D.; Chai, J. A receptor-like protein acts as a specificity switch for the regulation of stomatal development. Genes Dev. 2017, 31, 927–938. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Jeong, S.; Trotochaud, A.E.; Clark, S.E. The Arabidopsis CLAVATA2 gene encodes a receptor-like protein required for the stability of the CLAVATA1 receptor-like kinase. Plant Cell 1999, 11, 1925–1933. [Google Scholar] [CrossRef] [Green Version]
- Willmann, R.; Lajunen, H.M.; Erbs, G.; Newman, M.A.; Kolb, D.; Tsuda, K.; Katagiri, F.; Fliegmann, J.; Bono, J.J.; Cullimore, J.V.; et al. Arabidopsis lysin-motif proteins LYM1 LYM3 CERK1 mediate bacterial peptidoglycan sensing and immunity to bacterial infection. Proc. Natl. Acad. Sci. USA 2011, 108, 19824–19829. [Google Scholar] [CrossRef] [Green Version]
- Albert, I.; Böhm, H.; Albert, M.; Feiler, C.E.; Imkampe, J.; Wallmeroth, N.; Brancato, C.; Raaymakers, T.M.; Oome, S.; Zhang, H.; et al. An RLP23–SOBIR1–BAK1 complex mediates NLP-triggered immunity. Nat. Plants 2015, 1, 15140. [Google Scholar] [CrossRef]
- Jones, D.A.; Thomas, C.M.; Hammond-Kosack, K.E.; Balint-Kurti, P.J.; Jones, J.D. Isolation of the tomato Cf-9 gene for resistance to Cladosporium fulvum by transposon tagging. Science 1994, 266, 789–793. [Google Scholar] [CrossRef]
- Thomas, C.M.; Jones, D.A.; Parniske, M.; Harrison, K.; Balint-Kurti, P.J.; Hatzixanthis, K.; Jones, J. Characterization of the tomato Cf-4 gene for resistance to Cladosporium fulvum identifies sequences that determine recognitional specificity in Cf-4 and Cf-9. Plant Cell 1997, 9, 2209–2224. [Google Scholar] [CrossRef] [Green Version]
- Postma, J.; Liebrand, T.W.; Bi, G.; Evrard, A.; Bye, R.R.; Mbengue, M.; Kuhn, H.; Joosten, M.H.; Robatzek, S. Avr4 promotes Cf-4 receptor-like protein association with the BAK1/SERK3 receptor-like kinase to initiate receptor endocytosis and plant immunity. New Phytol. 2016, 210, 627–642. [Google Scholar] [CrossRef] [Green Version]
- Nielsen, H. Predicting secretory proteins with SignalP. In Protein Function Prediction; Springer: New York, NY, USA, 2017; pp. 59–73. [Google Scholar] [CrossRef]
- Wang, Y.; Xu, Y.; Sun, Y.; Wang, H.; Qi, J.; Wan, B.; Ye, W.; Lin, Y.; Shao, Y.; Dong, S.; et al. Leucine-rich repeat receptor-like gene screen reveals that Nicotiana RXEG1 regulates glycoside hydrolase 12 MAMP detection. Nat. Commun. 2018, 9, 594. [Google Scholar] [CrossRef] [Green Version]
- Yu, H.; Xie, W.; Li, J.; Zhou, F.; Zhang, Q. A whole-genome SNP array (RICE 6 K) for genomic breeding in rice. Plant Biotechnol. J. 2014, 12, 28–37. [Google Scholar] [CrossRef]
- Jamieson, P.A.; Shan, L.; He, P. Plant cell surface molecular cypher: Receptor-like proteins and 957 their roles in immunity and development. Plant Sci. J. 2018, 274, 242–251. [Google Scholar] [CrossRef] [PubMed]
- Silva, J.C.F.; Teixeira, R.M.; Silva, F.F.; Brommonschenkel, S.H.; Fontes, E.P. Machine learning approaches and their current application in Plant Mol Biol: A systematic review. Plant Sci. J. 2019, 284, 37–47. [Google Scholar] [CrossRef] [PubMed]
- Gastaldo, P.; Pinna, L.; Seminara, L.; Valle, M.; Zunino, R. A tensor-based approach to touch modality classification by using machine learning. Rob. Auton. Syst. 2015, 63, 268–278. [Google Scholar] [CrossRef]
- Kang, J.; Schwartz, R.; Flickinger, J.; Beriwal, S. Machine learning approaches for predicting radiation therapy outcomes: A clinician’s perspective. Int. J. Radiat. Oncol. Biol. Phys. 2015, 93, 1127–1135. [Google Scholar] [CrossRef] [PubMed]
- Zhang, B.; He, X.; Ouyang, F.; Gu, D.; Dong, Y.; Zhang, L.; Mo, X.; Huang, W.; Tian, J.; Zhang, S. Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma. Cancer Lett. 2017, 403, 21–27. [Google Scholar] [CrossRef]
- Silva, J.C.F.; Carvalho, T.F.; Fontes, E.P.; Cerqueira, F.R. Fangorn Forest (F2): A machine learning approach to classify genes and genera in the family Geminiviridae. BMC Bioinform. 2017, 18, 431. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Pineda, M.; Pérez-Bueno, M.L.; Barón, M. Detection of bacterial infection in melon plants by classification methods based on imaging data. Front. Plant Sci. 2018, 9, 164. [Google Scholar] [CrossRef] [Green Version]
- Moghimi, A.; Yang, C.; Miller, M.E.; Kianian, S.F.; Marchetto, P.M. A novel approach to assess salt stress tolerance in wheat using hyperspectral imaging. Front. Plant Sci. 2018, 9, 1182. [Google Scholar] [CrossRef] [PubMed]
- Gutiérrez, S.; Fernández-Novales, J.; Diago, M.P.; Tardaguila, J. On-the-go hyperspectral imaging under field conditions and machine learning for the classification of grapevine varieties. Front. Plant Sci. 2018, 9, 1102. [Google Scholar] [CrossRef]
- Ma, C.; Xin, M.; Feldmann, K.A.; Wang, X. Machine learning–based differential network analysis: A study of stress-responsive transcriptomes in Arabidopsis. Plant Cell 2014, 26, 520–537. [Google Scholar] [CrossRef] [Green Version]
- Carvalho, T.F.M.; Silva, J.C.F.; Calil, I.P.; Fontes, E.P.B.; Cerqueira, F.R. Rama: A machine learning approach for ribosomal protein prediction in plants. Sci. Rep. 2017, 7, 16273. [Google Scholar] [CrossRef] [Green Version]
- Fritz-Laylin, L.K.; Krishnamurthy, N.; Tör, M.; Sjölander, K.V.; Jones, J.D. Phylogenomic analysis of the receptor-like proteins of rice and Arabidopsis. Plant Physiol. 2005, 138, 611–623. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Buendia, L.; Girardin, A.; Wang, T.; Cottret, L.; Lefebvre, B. LysM receptor-like kinase and LysM receptor-like protein families: An update on phylogeny and functional characterization. Front. Plant Sci. 2018, 9, 1531. [Google Scholar] [CrossRef] [Green Version]
- Jonak, C.; Hirt, H. Glycogen synthase kinase 3/SHAGGY-like kinases in plants: An emerging family with novel functions. Trends Plant Sci. 2002, 7, 457–461. [Google Scholar] [CrossRef]
- Nie, J.; Zhou, W.; Liu, J.; Tan, N.; Zhou, J.M.; Huang, L. A receptor-like protein from Nicotiana benthamiana mediates VmE02 PAMP-triggered immunity. New Phytol. 2021, 229, 2260–2272. [Google Scholar] [CrossRef]
- Petersen, N.H.; Joensen, J.; McKinney, L.V.; Brodersen, P.; Petersen, M.; Hofius, D.; Mundy, J. Identification of proteins interacting with Arabidopsis ACD11. J. Plant Physiol. 2009, 166, 661–666. [Google Scholar] [CrossRef]
- Li, Q.; Ai, G.; Shen, D.; Zou, F.; Wang, J.; Bai, T.; Chen, Y.; Li, S.; Zhang, M.; Jing, M.; et al. A Phytophthora capsici effector targets ACD11 binding partners that regulate ROS-mediated defense response in Arabidopsis. Mol. Plant 2019, 12, 565–581. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ascencio-Ibánez, J.T.; Sozzani, R.; Lee, T.J.; Chu, T.M.; Wolfinger, R.D.; Cella, R.; Hanley-Bowdoin, L. Global analysis of Arabidopsis gene expression uncovers a complex array of changes impacting pathogen response and cell cycle during geminivirus infection. Plant Physiol. 2008, 148, 436–454. [Google Scholar] [CrossRef]
- Liu, J.; Chen, N.; Grant, J.N.; Cheng, Z.M.; Stewart Jr, C.N.; Hewezi, T. Soybean kinome: Functional classification and gene expression patterns. J. Exp. Bot. 2015, 66, 1919–1934. [Google Scholar] [CrossRef] [PubMed]
- Yan, J.; Su, P.; Wei, Z.; Nevo, E.; Kong, L. Genome-wide identification, classification, evolutionary analysis and gene expression patterns of the protein kinase gene family in wheat and Aegilops tauschii. Plant Mol. Biol. 2017, 95, 227–242. [Google Scholar] [CrossRef]
- Zuo, C.; Liu, H.; Lv, Q.; Chen, Z.; Tian, Y.; Mao, J.; Chu, M.; Ma, Z.; An, Z.; Chen, B. Genome-wide analysis of the apple (Malus domestica) cysteine-rich receptor-like kinase (CRK) family: Annotation, genomic organization, and expression profiles in response to fungal infection. Plant Mol. Biol. Rep. 2020, 38, 14–24. [Google Scholar] [CrossRef]
- Yan, J.; Li, G.; Guo, X.; Li, Y.; Cao, X. Genome-wide classification, evolutionary analysis and gene expression patterns of the kinome in Gossypium. PLoS ONE 2018, 13, e0197392. [Google Scholar] [CrossRef] [Green Version]
- Dezhsetan, S. Genome scanning for identification and mapping of receptor-like kinase (RLK) gene superfamily in Solanum tuberosum. Physiol. Mol. Biol. Plants 2017, 23, 755–765. [Google Scholar] [CrossRef]
- Pal, T.; Jaiswal, V.; Chauhan, R.S. DRPPP: A machine learning based tool for prediction of disease resistance proteins in plants. Comput. Biol. Med. 2016, 78, 42–48. [Google Scholar] [CrossRef] [PubMed]
- Ni, Y.; Aghamirzaie, D.; Elmarakeby, H.; Collakova, E.; Li, S.; Grene, R.; Heath, L.S. A machine learning approach to predict gene regulatory networks in seed development in Arabidopsis. Front. Plant Sci. 2016, 7, 1936. [Google Scholar] [CrossRef] [Green Version]
- Kushwaha, S.K.; Chauhan, P.; Hedlund, K.; Ahrén, D. NBSPred: A support vector machine-based high-throughput pipeline for plant resistance protein NBSLRR prediction. Bioinformatics 2016, 32, 1223–1225. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Dietterich, T.G. Ensemble methods in machine learning. In Proceedings of the International Workshop on Multiple Classifier Systems, Cagliari, Italy, 21–23 June 2000; pp. 1–15. [Google Scholar]
- Wang, X.; Li, Q.; Cheng, C.; Zhang, K.; Lou, Q.; Li, J.; Chen, J. Genome-wide analysis of a putative lipid transfer protein LTP_2 gene family reveals CsLTP_2 genes involved in response of cucumber against root-knot nematode (Meloidogyne incognita). Genome 2020, 63, 225–238. [Google Scholar] [CrossRef] [PubMed]
- Torres-Schumann, S.; Godoy, J.A.; Pintor-Toro, J.A. A probable lipid transfer protein gene is induced by NaCl in stems of tomato plants. Plant Mol. Biol. 1992, 18, 749–757. [Google Scholar] [CrossRef] [PubMed]
- Kapoor, R.; Kumar, G.; Arya, P.; Jaswal, R.; Jain, P.; Singh, K.; Sharma, T.R. Genome-wide analysis and expression profiling of rice hybrid proline-rich proteins in response to biotic and abiotic stresses, and hormone treatment. Plants 2019, 8, 343. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Bi, D.; Cheng, Y.T.; Li, X.; Zhang, Y. Activation of plant immune responses by a gain-of-function mutation in an atypical receptor-like kinase. Plant Physiol. 2010, 153, 1771–1779. [Google Scholar] [CrossRef] [Green Version]
- Zhang, Z.; Liu, Y.; Ding, P.; Li, Y.; Kong, Q.; Zhang, Y. Splicing of receptor-like kinase-encoding SNC4 and CERK1 is regulated by two conserved splicing factors that are required for plant immunity. Mol. Plant 2014, 7, 1766–1775. [Google Scholar] [CrossRef] [Green Version]
- Duruflé, H.; Hervé, V.; Ranocha, P.; Balliau, T.; Zivy, M.; Chourré, J.; San Clemente, H.; Burlat, V.; Albenne, C.; Déjean, S.; et al. Cellwall modifications of two Arabidopsis thaliana ecotypes, Col, and Sha, in response to sub-optimal growth conditions: An integrative study. PlantSci.J. 2017, 263, 183–193. [Google Scholar]
- Hayashi, S.; Ishii, T.; Matsunaga, T.; Tominaga, R.; Kuromori, T.; Wada, T.; Shinozaki, K.; Hirayama, T. The glycerophosphoryl diester phosphodiesterase-like proteins SHV3 and its homologs play important roles in cell wall organization. Plant Cell Physiol. 2008, 49, 1522–1535. [Google Scholar] [CrossRef] [Green Version]
- Salazar-Henao, J.E.; Lin, W.D.; Schmidt, W. Discriminative gene co-expression network analysis uncovers novel modules involved in the formation of phosphate deficiency-induced root hairs in Arabidopsis. Sci. Rep. 2016, 6, 26820. [Google Scholar] [CrossRef] [PubMed]
- Käll, L.; Krogh, A.; Sonnhammer, E.L. A combined transmembrane topology and signal peptide prediction method. J. Mol. Biol. 2004, 338, 1027–1036. [Google Scholar] [CrossRef]
- Sonnhammer, E.L.; Von Heijne, G.; Krogh, A. A hidden Markov model for predicting transmembrane helices in protein sequences. In Proceedings of the ISMB, Montréal, QC, Canada, 28 June–1 July 1998; Volume 6, pp. 175–182. [Google Scholar]
- Fu, L.; Niu, B.; Zhu, Z.; Wu, S.; Li, W. CD-HIT: Accelerated for clustering the next-generation sequencing data. Bioinformatics 2012, 28, 3150–3152. [Google Scholar] [CrossRef]
- Shiu, S.H.; Bleecker, A.B. Receptor-like kinases from Arabidopsis form a monophyletic gene family related to animal receptor kinases. Proc. Natl. Acad. Sci. USA 2001, 98, 10763–10768. [Google Scholar] [CrossRef] [Green Version]
- Saravanan, V.; Gautham, N. Harnessing computational biology for exact linear B-cell epitope prediction: A novel amino acid composition-based feature descriptor. OMICS 2015, 19, 648–658. [Google Scholar] [CrossRef] [PubMed]
- Bhasin, M.; Raghava, G.P. Classification of nuclear receptors based on amino acid composition and dipeptide composition. J. Biol. Chem. 2004, 279, 23262–23266. [Google Scholar] [CrossRef]
- Chawla, N.V.; Bowyer, K.W.; Hall, L.O.; Kegelmeyer, W.P. SMOTE: Synthetic minority over-sampling technique. J. Artif. Intell. Res. 2002, 16, 321–357. [Google Scholar] [CrossRef]
- Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; et al. Scikit-learn: Machine learning in Python. J. Mach. Learn Res. 2011, 12, 2825–2830. [Google Scholar]
- Freund, Y.; Schapire, R.E. A decision-theoretic generalization of online learning and an application to boosting. In Proceedings of the European Conference on Computational Learning Theory, Barcelona, Spain, 13–15 March 1995. [Google Scholar]
- Platt, J. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv. Large Margin Classif. 1999, 10, 61–74. [Google Scholar]
- Friedman, J.H. Stochastic gradient boosting. Comput. Stat. Data Anal. 2002, 38, 367–378. [Google Scholar] [CrossRef]
- Samworth, R.J. Optimal weighted nearest neighbour classifiers. Ann. Stat. 2012, 40, 2733–2763. [Google Scholar] [CrossRef]
- Hastie, T.; Tibshirani, R.; Friedman, J.H.; Friedman, J.H. The Elements of Statistical Learning: Data Mining, Inference, and Prediction; Springer: New York, NY, USA, 2009; Volume 2. [Google Scholar]
- Kim, K.S.; Choi, H.H.; Moon, C.S.; Mun, C.W. Comparison of k-nearest neighbor, quadratic discriminant and linear discriminant analysis in classification of electromyogram signals based on the wrist-motion directions. Curr. Appl. Phys. 2011, 11, 740–745. [Google Scholar] [CrossRef]
- Schmidt, M.; LeRoux, N.; Bach, F. Minimizing finite sums with the stochastic average gradient. Math. Program. 2017, 162, 83–112. [Google Scholar] [CrossRef] [Green Version]
- King, G.; Zeng, L. Logistic regression in rare events data. Polit. Anal. 2001, 9, 137–163. [Google Scholar] [CrossRef] [Green Version]
- Hinton, G.; Deng, L.; Yu, D.; Dahl, G.E.; Mohamed, A.-r.; Jaitly, N.; Senior, A.; Vanhoucke, V.; Nguyen, P.; Sainath, T.N.; et al. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Process Mag. 2012, 29, 82–97. [Google Scholar] [CrossRef]
- Haghighi, S.; Jasemi, M.; Hessabi, S.; Zolanvari, A. PyCM: Multiclass confusion matrix library in Python. J. Open Source Softw. 2018, 3, 729. [Google Scholar] [CrossRef] [Green Version]
- Feller, W. An Introduction to Probability Theory and Its Applications; John Wiley & Sons: Hoboken, NJ, USA, 2008; Volume 2. [Google Scholar]
- Gupta, A.K.; Nadarajah, S. Handbook of Beta Distribution and Its Applications; CRC Press: Boca Raton, FL, USA, 2004. [Google Scholar]
- Salvatier, J.; Wiecki, T.V.; Fonnesbeck, C. Probabilistic programming in Python using PyMC3. PeerJ Comput. Sci. 2016, 2, e55. [Google Scholar] [CrossRef] [Green Version]
- Geman, S.; Geman, D. Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. J. Appl. Stat. 1993, 20, 25–62. [Google Scholar] [CrossRef]
- Faulkner, C.; Petutschnig, E.; Benitez-Alfonso, Y.; Beck, M.; Robatzek, S.; Lipka, V.; Maule, A.J. LYM2-dependent chitin perception limits molecular flux via plasmodesmata. Proc. Natl. Acad. Sci. USA 2013, 110, 9166–9170. [Google Scholar] [CrossRef] [Green Version]
- Liu, B.; Li, J.F.; Ao, Y.; Qu, J.; Li, Z.; Su, J.; Zhang, Y.; Liu, J.; Feng, D.; Qi, K.; et al. Lysin motif–containing proteins LYP4 and LYP6 play dual roles in peptidoglycan and chitin perception in rice innate immunity. Plant Cell 2012, 24, 3406–3419. [Google Scholar] [CrossRef] [Green Version]
- Omasits, U.; Ahrens, C.H.; Müller, S.; Wollscheid, B. Protter: Interactive protein feature visualization and integration with experimental proteomic data. Bioinformatics 2014, 30, 884–886. [Google Scholar] [CrossRef] [Green Version]
- Price, M.N.; Dehal, P.S.; Arkin, A.P. FastTree 2–approximately maximum-likelihood trees for large alignments. PLoS ONE 2010, 5, e9490. [Google Scholar] [CrossRef]
- Hruz, T.; Laule, O.; Szabo, G.; Wessendorp, F.; Bleuler, S.; Oertle, L.; Widmayer, P.; Gruissem, W.; Zimmermann, P. Genevestigator v3: A reference expression database for the meta-analysis of transcriptomes. Adv. Bioinform. 2008, 2008, 420747. [Google Scholar] [CrossRef] [Green Version]
- Waese, J.; Fan, J.; Pasha, A.; Yu, H.; Fucile, G.; Shi, R.; Cumming, M.; Kelley, L.A.; Sternberg, M.J.; Krishnakumar, V.; et al. ePlant: Visualizing and exploring multiple levels of data for hypothesis generation in plant biology. Plant Cell 2017, 29, 1806–1821. [Google Scholar] [CrossRef] [Green Version]
- Li, B.; Ferreira, M.A.; Huang, M.; Camargos, L.F.; Yu, X.; Teixeira, R.M.; Carpinetti, P.A.; Mendes, G.C.; Gouveia-Mageste, B.C.; Liu, C.; et al. The receptor-like kinase NIK1 targets FLS2/BAK1 immune complex and inversely modulates antiviral and antibacterial immunity. Nat. Commun. 2019, 10, 4996. [Google Scholar] [CrossRef] [Green Version]
- Fontes, E.P.; Santos, A.A.; Luz, D.F.; Waclawovsky, A.J.; Chory, J. The geminivirus nuclear shuttle protein is a virulence factor that suppresses transmembrane receptor kinase activity. Genes Dev. 2004, 18, 2545–2556. [Google Scholar] [CrossRef] [Green Version]
- Santos, A.A.; Carvalho, C.M.; Florentino, L.H.; Ramos, H.J.; Fontes, E.P. Conserved threonine residues within the A-loop of the receptor NIK differentially regulate the kinase function required for antiviral signaling. PLoS ONE 2009, 4, e5781. [Google Scholar] [CrossRef] [Green Version]
- Zorzatto, C.; Machado, J.P.B.; Lopes, K.V.; Nascimento, K.J.; Pereira, W.A.; Brustolini, O.J.; Reis, P.A.; Calil, I.P.; Deguchi, M.; Sachetto-Martins, G.; et al. NIK1-mediated translation suppression functions as a plant antiviral immunity mechanism. Nature 2015, 520, 679–682. [Google Scholar] [CrossRef]
Description | Total | Description | Total | Description | Total |
---|---|---|---|---|---|
LRR-RLK | 14,087 | CHASE-RLK | 8 | CUB-RLK | 2 |
Unknown-RLK | 10,020 | Cysteine-rich-secretory-RLK | 7 | DUF1084-RLK | 2 |
S-domain-RLK | 3859 | GDPDL-RLK | 7 | DUF726-RLK | 2 |
Malectin-RLK | 3299 | Universal-stress-RLK | 6 | Endomembrane-RLK | 2 |
Salt-stress-response/antifungal-RLK | 2345 | ACT-RLK | 5 | GAF-domain | 2 |
L-Lectin-RLK | 2213 | Probable-lipid-transfer-RLK | 5 | GTPase-RLK | 2 |
WAK-RLK | 1844 | Ankyrin-Kinase | 4 | Glycosyl hydrolases-RLK | 2 |
B-lectin-RLK | 549 | Chromo-RLK | 4 | Glycosyltransferase-RLK | 2 |
LysM-RLK | 381 | PAN-like-Kinase | 4 | HAD-RLK | 2 |
WAK-EGF-RLK | 285 | PB1-RLK | 4 | HAD-hyrolase-like-RLK | 2 |
EGF-like-RLK | 212 | Sel1-RLK | 4 | MSP-RLK | 2 |
WAK-EFG-RLK | 177 | Alpha/beta-hydrolase-RLK | 3 | NB-ARC-RLK | 2 |
RCC1-RLK | 148 | Cytochrome P450-RLK | 3 | PQQ-enzyme-RLK | 2 |
B-Lectin-RLK | 145 | Helix-loop-helix-DNA-binding-RLK | 3 | Peptidase-RLK | 2 |
PAN-RLK | 131 | Histidine-phosphatase-RLK | 3 | PfkB-RLK | 2 |
C-Lectin-RLK | 90 | Major-Facilitator-RLK | 3 | Wnt-and-FGF-inhibitory-regulator-RLK | 2 |
Glycosyl-hydrolases-RLK | 90 | MatE-RLK | 3 | Adenylate-cyclase-associated-(CAP)-N-terminal-RLK | 1 |
Thaumatin-RLK | 86 | PPR | 3 | Alcohol-dehydrogenase-GroES-like-RLK | 1 |
NAF-RLK | 79 | PPR-RLK | 3 | Aldose-1-epimerase-RLK | 1 |
Ethylene-responsive-RLK | 74 | Phospholipase-RLK | 3 | Ankyrin-RLK | 1 |
EF-hand-RLK | 50 | Proline-rich-RLK | 3 | Castor-and-Pollux-RLK | 1 |
Cache-RLK | 32 | Sugar-(and other)-transporter-RLK | 3 | Cyclic nucleotide-binding-RLK | 1 |
Chitinase-RLK | 15 | Transmembrane-RLK | 3 | Cyclic-nucleotide-binding-RLK | 1 |
PAS-RLK | 12 | Alpha-amylase-catalytic-RLK | 2 | Cytochrome-P450-RLK | 1 |
Plastocyanin-like-RLK | 12 | Barwin-RLK | 2 | DEAD/DEAH-box-helicase-RLK | 1 |
Ring-finger-RLK | 9 | C2-RLK | 2 | DUF1221-RLK | 1 |
Adenovirus E3-RLK | 8 |
No | Label | Count |
---|---|---|
1 | L-Lectin-RLK | 980 |
2 | LRR-RLK | 5404 |
3 | S-domain-RLK | 1626 |
4 | Malectin-RLK | 1313 |
5 | Salt-stress-response/antifungal-RLK | 1004 |
6 | WAK-RLK | 1362 |
7 | B-Lectin-RLK | 362 |
8 | Unknown-RLK | 3285 |
10 | PAN-RLK | 41 |
11 | Ethylene-responsive-RLK | 29 |
12 | Thaumatin-RLK | 52 |
13 | RCC1-RLK | 65 |
14 | Glycosyl-hydrolases-RLK | 40 |
15 | C-Lectin-RLK | 21 |
16 | Other-RLKs | 192 |
Data Set | Algorithm | ACC | F1 | FDR | MCC | Precision | Sensitivity | Specificity |
---|---|---|---|---|---|---|---|---|
AAComposition_1 | Logistic RegressionCV | 0.9173 | 0.9211 | 0.0878 | 0.8343 | 0.9303 | 0.9303 | 0.9032 |
AAComposition_2 | Logistic RegressionCV | 0.9205 | 0.9241 | 0.0839 | 0.8407 | 0.9322 | 0.9322 | 0.9078 |
AAComposition_3 | Logistic RegressionCV | 0.9209 | 0.9245 | 0.0831 | 0.8416 | 0.9321 | 0.9321 | 0.9088 |
AAComposition_N_C terminal_1 | MLP Classifier | 0.9457 | 0.9478 | 0.0534 | 0.8912 | 0.9490 | 0.9490 | 0.9421 |
AAComposition_N_C terminal_2 | MLP Classifier | 0.9468 | 0.9487 | 0.0513 | 0.8934 | 0.9487 | 0.9487 | 0.9446 |
AAComposition_N_C terminal_3 | MLP Classifier | 0.9482 | 0.9499 | 0.0457 | 0.8964 | 0.9456 | 0.9456 | 0.9511 |
CPAASC_1 | Linear Discriminant Analysis | 0.9020 | 0.9102 | 0.1315 | 0.8074 | 0.9561 | 0.9561 | 0.8436 |
CPAASC_2 | Linear Discriminant Analysis | 0.9042 | 0.9120 | 0.1282 | 0.8116 | 0.9562 | 0.9562 | 0.8481 |
CPAASC_3 | Linear Discriminant Analysis | 0.9040 | 0.9119 | 0.1288 | 0.8113 | 0.9566 | 0.9566 | 0.8473 |
CPAASC_N_C terminal_1 | Linear Discriminant Analysis | 0.9104 | 0.9172 | 0.1183 | 0.8232 | 0.9558 | 0.9558 | 0.8614 |
CPAASC_N_C terminal_2 | Linear Discriminant Analysis | 0.9132 | 0.9196 | 0.1148 | 0.8284 | 0.9569 | 0.9569 | 0.8660 |
CPAASC_N_C terminal_3 | Linear Discriminant Analysis | 0.9140 | 0.9204 | 0.1137 | 0.8301 | 0.9572 | 0.9572 | 0.8674 |
Dipeptide_1 | MLP Classifier | 0.9439 | 0.9457 | 0.0497 | 0.8878 | 0.9412 | 0.9412 | 0.9468 |
Dipeptide_2 | MLP Classifier | 0.9481 | 0.9500 | 0.0501 | 0.8960 | 0.9500 | 0.9500 | 0.9459 |
Dipeptide_3 | MLP Classifier | 0.9447 | 0.9466 | 0.0497 | 0.8894 | 0.9428 | 0.9428 | 0.9468 |
Tripeptide_1 | Logistic RegressionCV | 0.9535 | 0.9551 | 0.0410 | 0.9069 | 0.9511 | 0.9511 | 0.9561 |
Tripeptide_2 | Logistic RegressionCV | 0.9550 | 0.9565 | 0.0389 | 0.9100 | 0.9519 | 0.9519 | 0.9584 |
Tripeptide_3 | Logistic RegressionCV | 0.9534 | 0.9549 | 0.0404 | 0.9067 | 0.9502 | 0.9502 | 0.9568 |
Mean | 0.9303 | 0.9342 | 0.0784 | 0.8615 | 0.9480 | 0.9480 | 0.9112 |
Data Set | Algorithm | ACC | F1 | FDR | MCC | Precision | Sensitivity | Specificity |
---|---|---|---|---|---|---|---|---|
AAComposition_N_C terminal | Quadratic Discriminant Analysis | 0.9775 | 0.9773 | 0.0337 | 0.9552 | 0.9884 | 0.9884 | 0.9670 |
Tripeptide | Gradient Boosting Classifier | 0.9762 | 0.9760 | 0.0367 | 0.9527 | 0.9890 | 0.9890 | 0.9639 |
CPAASC_N_C_terminal | Linear Discriminant Analysis | 0.9707 | 0.9706 | 0.0479 | 0.9421 | 0.9899 | 0.9899 | 0.9523 |
CPAASC | Linear Discriminant Analysis | 0.9647 | 0.9647 | 0.0572 | 0.9304 | 0.9877 | 0.9877 | 0.9426 |
Dipeptide | MLP Classifier | 0.9627 | 0.9617 | 0.0344 | 0.9254 | 0.9579 | 0.9579 | 0.9673 |
AAComposition | Quadratic Discriminant Analysis | 0.9571 | 0.9571 | 0.0627 | 0.9151 | 0.9777 | 0.9777 | 0.9374 |
Mean | 0.9681 | 0.9679 | 0.0454 | 0.9368 | 0.9818 | 0.9818 | 0.9551 |
Data Set | Algorithm | ACC | F1 | MCC | Precision | Sensitivity |
---|---|---|---|---|---|---|
AAComposition_10 | Linear Discriminant Analysis | 0.984 | 0.872 | 0.864 | 0.872 | 0.872 |
AAComposition_1 | Calibrated ClassifierCV | 0.984 | 0.869 | 0.861 | 0.869 | 0.869 |
AAComposition_2 | Calibrated ClassifierCV | 0.984 | 0.874 | 0.866 | 0.874 | 0.874 |
AAComposition_3 | Linear Discriminant Analysis | 0.984 | 0.873 | 0.864 | 0.873 | 0.873 |
AAComposition_4 | Linear Discriminant Analysis | 0.984 | 0.870 | 0.862 | 0.870 | 0.870 |
AAComposition_5 | Linear Discriminant Analysis | 0.983 | 0.867 | 0.858 | 0.867 | 0.867 |
AAComposition_6 | Linear Discriminant Analysis | 0.984 | 0.871 | 0.863 | 0.871 | 0.871 |
AAComposition_7 | Calibrated ClassifierCV | 0.984 | 0.869 | 0.861 | 0.869 | 0.869 |
AAComposition_8 | Calibrated ClassifierCV | 0.985 | 0.876 | 0.868 | 0.876 | 0.876 |
AAComposition_9 | Linear Discriminant Analysis | 0.984 | 0.875 | 0.867 | 0.875 | 0.875 |
Mean | 0.984 | 0.872 | 0.863 | 0.872 | 0.872 | |
AAComposition_N_C_terminal_10 | Calibrated ClassifierCV | 0.989 | 0.911 | 0.905 | 0.911 | 0.911 |
AAComposition_N_C_terminal_1 | Calibrated ClassifierCV | 0.988 | 0.904 | 0.897 | 0.904 | 0.904 |
AAComposition_N_C_terminal_2 | Calibrated ClassifierCV | 0.989 | 0.908 | 0.902 | 0.908 | 0.908 |
AAComposition_N_C_terminal_3 | Calibrated ClassifierCV | 0.988 | 0.902 | 0.896 | 0.902 | 0.902 |
AAComposition_N_C_terminal_4 | KNeighbors Classifier | 0.989 | 0.911 | 0.905 | 0.911 | 0.911 |
AAComposition_N_C_terminal_5 | KNeighbors Classifier | 0.989 | 0.909 | 0.903 | 0.909 | 0.909 |
AAComposition_N_C_terminal_6 | KNeighbors Classifier | 0.988 | 0.903 | 0.896 | 0.903 | 0.903 |
AAComposition_N_C_terminal_7 | KNeighbors Classifier | 0.988 | 0.900 | 0.894 | 0.900 | 0.900 |
AAComposition_N_C_terminal_8 | Calibrated ClassifierCV | 0.988 | 0.903 | 0.897 | 0.903 | 0.903 |
AAComposition_N_C_terminal_9 | Calibrated ClassifierCV | 0.988 | 0.907 | 0.900 | 0.907 | 0.907 |
Mean | 0.988 | 0.906 | 0.899 | 0.906 | 0.906 | |
CPAASC_10 | Linear Discriminant Analysis | 0.972 | 0.778 | 0.764 | 0.778 | 0.778 |
CPAASC_1 | AdaBoost Classifier | 0.971 | 0.772 | 0.757 | 0.772 | 0.772 |
CPAASC_2 | AdaBoost Classifier | 0.972 | 0.776 | 0.761 | 0.776 | 0.776 |
CPAASC_3 | AdaBoost Classifier | 0.972 | 0.773 | 0.759 | 0.773 | 0.773 |
CPAASC_4 | Linear Discriminant Analysis | 0.971 | 0.770 | 0.755 | 0.770 | 0.770 |
CPAASC_5 | Linear Discriminant Analysis | 0.972 | 0.773 | 0.759 | 0.773 | 0.773 |
CPAASC_6 | Linear Discriminant Analysis | 0.971 | 0.771 | 0.756 | 0.771 | 0.771 |
CPAASC_7 | AdaBoos tClassifier | 0.972 | 0.773 | 0.758 | 0.773 | 0.773 |
CPAASC_8 | Linear Discriminant Analysis | 0.972 | 0.778 | 0.763 | 0.778 | 0.778 |
CPAASC_9 | AdaBoost Classifier | 0.972 | 0.774 | 0.759 | 0.774 | 0.774 |
Mean | 0.972 | 0.774 | 0.759 | 0.774 | 0.774 | |
CPAASC_N_C_terminal_10 | AdaBoost Classifier | 0.975 | 0.800 | 0.787 | 0.800 | 0.800 |
CPAASC_N_C_terminal_1 | Linear Discriminant Analysis | 0.976 | 0.810 | 0.797 | 0.810 | 0.810 |
CPAASC_N_C_terminal_2 | AdaBoost Classifier | 0.975 | 0.803 | 0.790 | 0.803 | 0.803 |
CPAASC_N_C_terminal_3 | Linear Discriminant Analysis | 0.976 | 0.804 | 0.792 | 0.804 | 0.804 |
CPAASC_N_C_terminal_4 | Linear Discriminant Analysis | 0.976 | 0.805 | 0.793 | 0.805 | 0.805 |
CPAASC_N_C_terminal_5 | AdaBoost Classifier | 0.975 | 0.802 | 0.789 | 0.802 | 0.802 |
CPAASC_N_C_terminal_6 | Linear Discriminant Analysis | 0.976 | 0.808 | 0.795 | 0.808 | 0.808 |
CPAASC_N_C_terminal_7 | Linear Discriminant Analysis | 0.976 | 0.808 | 0.795 | 0.808 | 0.808 |
CPAASC_N_C_terminal_8 | AdaBoost Classifier | 0.975 | 0.802 | 0.789 | 0.802 | 0.802 |
CPAASC_N_C_terminal_9 | Linear Discriminant Analysis | 0.976 | 0.805 | 0.792 | 0.805 | 0.805 |
Mean | 0.976 | 0.805 | 0.792 | 0.805 | 0.805 | |
Dipeptide_10 | KNeighbors Classifier | 0.992 | 0.935 | 0.931 | 0.935 | 0.935 |
Dipeptide_1 | KNeighbors Classifier | 0.992 | 0.937 | 0.933 | 0.937 | 0.937 |
Dipeptide_2 | KNeighbors Classifier | 0.992 | 0.935 | 0.931 | 0.935 | 0.935 |
Dipeptide_3 | KNeighbors Classifier | 0.992 | 0.934 | 0.930 | 0.934 | 0.934 |
Dipeptide_4 | KNeighbors Classifier | 0.991 | 0.932 | 0.927 | 0.932 | 0.932 |
Dipeptide_5 | KNeighbors Classifier | 0.992 | 0.934 | 0.930 | 0.934 | 0.934 |
Dipeptide_6 | KNeighbors Classifier | 0.991 | 0.931 | 0.926 | 0.931 | 0.931 |
Dipeptide_7 | KNeighbors Classifier | 0.992 | 0.933 | 0.929 | 0.933 | 0.933 |
Dipeptide_8 | KNeighbors Classifier | 0.991 | 0.925 | 0.920 | 0.925 | 0.925 |
Dipeptide_9 | KNeighbors Classifier | 0.991 | 0.929 | 0.925 | 0.929 | 0.929 |
Mean | 0.992 | 0.932 | 0.928 | 0.932 | 0.932 | |
Tripeptide_1 | KNeighbors Classifier | 0.995 | 0.957 | 0.954 | 0.957 | 0.957 |
Tripeptide_2 | KNeighbors Classifier | 0.994 | 0.955 | 0.952 | 0.955 | 0.955 |
Tripeptide_3 | KNeighbors Classifier | 0.994 | 0.956 | 0.953 | 0.956 | 0.956 |
Tripeptide_4 | KNeighbors Classifier | 0.995 | 0.958 | 0.955 | 0.958 | 0.958 |
Tripeptide_5 | KNeighbors Classifier | 0.995 | 0.958 | 0.955 | 0.958 | 0.958 |
Tripeptide_6 | KNeighbors Classifier | 0.994 | 0.954 | 0.951 | 0.954 | 0.954 |
Tripeptide_7 | KNeighbors Classifier | 0.994 | 0.955 | 0.952 | 0.955 | 0.955 |
Tripeptide_8 | KNeighbors Classifier | 0.994 | 0.951 | 0.948 | 0.951 | 0.951 |
Tripeptide_9 | KNeighbors Classifier | 0.995 | 0.958 | 0.955 | 0.958 | 0.958 |
Tripeptide_10 | KNeighbors Classifier | 0.995 | 0.959 | 0.957 | 0.959 | 0.959 |
Mean | 0.994 | 0.956 | 0.953 | 0.956 | 0.956 |
Accession | SP | TM | RLP-NRLP | RLP-NRLP Probability | RLP-RLK | RLP-RLK Probability | RLP-Subfamily | RLP-Subfamily Probability | Classification | Decision Probability |
---|---|---|---|---|---|---|---|---|---|---|
NP_001234733.2 | Y | Y | RLP | 0.9961 | RLP | 0.5751 | LRR-RLP | 0.7666 | (LRR-RLP) | 0.9894 |
sQ9LNV9.2_RLP1 | Y | Y | RLP | 0.9961 | RLP | 0.7161 | LRR-RLP | 0.7671 | (LRR-RLP) | 0.9891 |
sp—Q93ZH0.1—LYM1 | Y | Y | RLP | 0.8941 | RLP | 0.9915 | LysM-RLP | 0.467 | (LysM-RLP) | 0.989 |
CAC40826.1_HcrVf2 | Y | Y | RLP | 0.9961 | RLP | 0.9895 | LRR-RLP | 0.8333 | (LRR-RLP) | 0.9888 |
AAA65235.1_Cf-9 | Y | Y | RLP | 0.9965 | RLP | 0.9906 | LRR-RLP | 0.8331 | (LRR-RLP) | 0.9887 |
AAC78594.1_Hcr2-2A | Y | Y | RLP | 0.9965 | RLP | 0.8569 | LRR-RLP | 0.849 | (LRR-RLP) | 0.9885 |
Q9SSD1.1 | Y | Y | RLP | 0.9966 | RLP | 0.991 | LRR-RLP | 0.4667 | (LRR-RLP) | 0.9885 |
AAC15779.1_Cf-2.1 | Y | Y | RLP | 0.9965 | RLP | 0.855 | LRR-RLP | 0.85 | (LRR-RLP) | 0.9882 |
sp—Q7FZR1.1—RLP52 | Y | Y | RLP | 0.9966 | RLP | 0.9903 | LRR-RLP | 0.8336 | (LRR-RLP) | 0.9882 |
QED40966.1 | Y | Y | RLP | 0.9962 | RLP | 0.7168 | LRR-RLP | 0.8506 | (LRR-RLP) | 0.9881 |
CAC40827.1_HcrVf3 | Y | Y | RLP | 0.9964 | RLP | 0.9909 | LRR-RLP | 0.8501 | (LRR-RLP) | 0.988 |
sp—Q9LJS0.1—RLP42 | Y | Y | RLP | 0.9966 | RLP | 0.9911 | LRR-RLP | 0.8502 | (LRR-RLP) | 0.988 |
AAC78593.1_Hcr2-0B | Y | Y | RLP | 0.9962 | RLP | 0.991 | LRR-RLP | 0.8495 | (LRR-RLP) | 0.9879 |
Q9FK66.1_RLP55 | Y | Y | RLP | 0.9958 | RLP | 0.9915 | LRR-RLP | 0.6669 | (LRR-RLP) | 0.9879 |
sQ9SN38.1_RLP5 | Y | Y | RLP | 0.9963 | RLP | 0.9912 | LRR-RLP | 0.8497 | (LRR-RLP) | 0.9879 |
AAC78596.1_Hcr2-5D | Y | Y | RLP | 0.9959 | RLP | 0.9909 | LRR-RLP | 0.85 | (LRR-RLP) | 0.9878 |
BAE95828.1 (LysM) | Y | Y | RLP | 0.9964 | RLP | 0.99 | Undefined | 0.4169 | (Undefined) | 0.9878 |
Q9LJS2.1 | Y | Y | RLP | 0.9964 | RLP | 0.9906 | LRR-RLP | 0.8505 | (LRR-RLP) | 0.9878 |
AJG42080.1_RLM2 | Y | Y | RLP | 0.9963 | RLP | 0.9908 | LRR-RLP | 0.8493 | (LRR-RLP) | 0.9877 |
CAA05269.1_Hcr9-4E | Y | Y | RLP | 0.9962 | RLP | 0.9893 | LRR-RLP | 0.8332 | (LRR-RLP) | 0.9877 |
AJG42091.1_LEPR3 | Y | Y | RLP | 0.9967 | RLP | 0.9911 | LRR-RLP | 0.8508 | (LRR-RLP) | 0.9875 |
Q9M2Y3.1_RLP44 | Y | Y | RLP | 0.9962 | RLP | 0.9902 | LRR-RLP | 0.7503 | (LRR-RLP) | 0.9875 |
CAC40825.1_HcrVf1 | Y | Y | RLP | 0.9965 | RLP | 0.9921 | LRR-RLP | 0.8166 | (LRR-RLP) | 0.9874 |
NP_001234474.2 | Y | Y | RLP | 0.9963 | RLP | 0.991 | LRR-RLP | 0.8332 | (LRR-RLP) | 0.9874 |
Solyc08g016270.1.1 | Y | Y | RLP | 0.9961 | RLP | 0.72 | LRR-RLP | 0.6335 | (LRR-RLP) | 0.9874 |
AAC78595.1_Hcr2-5B | Y | Y | RLP | 0.9963 | RLP | 0.8517 | LRR-RLP | 0.85 | (LRR-RLP) | 0.9873 |
O80809.1_CLV2 | Y | Y | RLP | 0.9964 | RLP | 0.991 | LRR-RLP | 0.8496 | (LRR-RLP) | 0.9873 |
sp—O23006.1—LYM2 | Y | Y | RLP | 0.9962 | RLP | 0.9908 | Undefined | 0.5005 | (Undefined) | 0.9873 |
sp—O48849.1—RLP23 | Y | Y | RLP | 0.9959 | RLP | 0.9906 | LRR-RLP | 0.7833 | (LRR-RLP) | 0.9873 |
AAC78592.1_Hcr2-0A | Y | Y | RLP | 0.9966 | RLP | 0.8518 | LRR-RLP | 0.8513 | (LRR-RLP) | 0.9872 |
sp—Q6NPN4.1—LYM3 | Y | Y | RLP | 0.9452 | RLP | 0.99 | LysM-RLP | 0.4501 | (LysM-RLP) | 0.9872 |
AAC78591.1 | Y | Y | RLP | 0.9966 | RLP | 0.9899 | LRR-RLP | 0.8507 | (LRR-RLP) | 0.9871 |
AJV90937.1 | Y | Y | RLP | 0.9968 | RLP | 0.8507 | LRR-RLP | 0.8332 | (LRR-RLP) | 0.9871 |
AUT14025.1 | Y | Y | RLP | 0.9962 | RLP | 0.8537 | LRR-RLP | 0.7329 | (LRR-RLP) | 0.987 |
AAC15780.1_Cf-2.2 | Y | Y | RLP | 0.9961 | RLP | 0.8555 | LRR-RLP | 0.8491 | (LRR-RLP) | 0.9863 |
AGI92782.1_RLP1.813 | Y | Y | RLP | 0.9963 | RLP | 0.9906 | LRR-RLP | 0.4005 | (LRR-RLP) | 0.9862 |
NP_187187.1 | Y | Y | RLP | 0.9964 | RLP | 0.9913 | LRR-RLP | 0.6497 | (LRR-RLP) | 0.986 |
AKR80573.1_I-7 | Y | Y | RLP | 0.9963 | RLP | 0.8605 | LRR-RLP | 0.65 | (LRR-RLP) | 0.9855 |
NP_001362850.1_EIX2 | Y | Y | RLP | 0.9961 | RLP | 0.8581 | LRR-RLP | 0.6005 | (LRR-RLP) | 0.985 |
sp—Q9SHI4.1—RLP3 | N | Y | RLP | 0.9965 | RLP | 0.9904 | LRR-RLP | 0.8328 | (LRR-RLP) | 0.8015 |
NP_001355132.1 | N | Y | RLP | 0.9965 | RLP | 0.9903 | LRR-RLP | 0.5163 | (LRR-RLP) | 0.8012 |
Q940E8.1_FEA2 | Y | N | RLP | 0.9487 | RLP | 0.8554 | LRR-RLP | 0.849 | NRLP | 0.2048 |
sp—Q67UE8.1—LYP4 | Y | N | RLP | 0.7894 | RLP | 0.8564 | Undefined | 0.0 | NRLP | 0.2017 |
AFB75328.1 | Y | N | RLP | 0.9472 | RLP | 0.857 | LRR-RLP | 0.5667 | NRLP | 0.2012 |
AKP45167.1 | Y | N | RLP | 0.9462 | RLP | 0.8543 | Undefined | 0.4495 | NRLP | 0.201 |
sp—Q69T51.1—LYP6 | Y | N | RLP | 0.8422 | RLP | 0.8544 | Undefined | 0.0 | NRLP | 0.2007 |
LOC_Os04g56430.1 | Y | N | RLP | 0.9471 | RLP | 0.8518 | Salt-stress-response/antifungal-RLP | 0.4334 | NRLP | 0.1986 |
Accession | SP | TM | RLP-NRLP | RLP-NRLP Probability | RLP-RLK | RLP-RLK Probability | RLP-Subfamily | RLP-Subfamily Probability | Classification | Decision Probability |
---|---|---|---|---|---|---|---|---|---|---|
AT1G65380.1 | Y | Y | RLP | 0.9962 | RLP | 0.9907 | LRR-RLP | 0.8505 | (LRR-RLP) | 0.9902 |
AT1G17240.1 | Y | Y | RLP | 0.9962 | RLP | 0.9913 | LRR-RLP | 0.8497 | (LRR-RLP) | 0.9886 |
AT4G13880.1 | Y | Y | RLP | 0.9963 | RLP | 0.9899 | LRR-RLP | 0.8001 | (LRR-RLP) | 0.9884 |
AT5G27060.1 | Y | Y | RLP | 0.9962 | RLP | 0.991 | LRR-RLP | 0.6669 | (LRR-RLP) | 0.9884 |
AT3G23110.1 | Y | Y | RLP | 0.9964 | RLP | 0.9912 | LRR-RLP | 0.6502 | (LRR-RLP) | 0.9883 |
AT1G80080.1 | Y | Y | RLP | 0.9961 | RLP | 0.9911 | LRR-RLP | 0.5506 | (LRR-RLP) | 0.9883 |
AT2G32680.1 | Y | Y | RLP | 0.9967 | RLP | 0.9918 | LRR-RLP | 0.7838 | (LRR-RLP) | 0.9882 |
AT1G74180.1 | Y | Y | RLP | 0.9959 | RLP | 0.858 | LRR-RLP | 0.8163 | (LRR-RLP) | 0.988 |
AT3G05370.1 | Y | Y | RLP | 0.9962 | RLP | 0.8556 | LRR-RLP | 0.6337 | (LRR-RLP) | 0.988 |
AT3G11080.1 | Y | Y | RLP | 0.9962 | RLP | 0.991 | LRR-RLP | 0.8496 | (LRR-RLP) | 0.988 |
AT3G28890.1 | Y | Y | RLP | 0.9966 | RLP | 0.8561 | LRR-RLP | 0.6336 | (LRR-RLP) | 0.988 |
AT2G25440.1 | Y | Y | RLP | 0.9962 | RLP | 0.9902 | LRR-RLP | 0.4832 | (LRR-RLP) | 0.9878 |
AT5G45770.1 | Y | Y | RLP | 0.9965 | RLP | 0.99 | LRR-RLP | 0.683 | (LRR-RLP) | 0.9878 |
AT2G42800.1 | Y | Y | RLP | 0.9963 | RLP | 0.9908 | LRR-RLP | 0.6665 | (LRR-RLP) | 0.9876 |
AT3G05360.1 | Y | Y | RLP | 0.9967 | RLP | 0.9913 | LRR-RLP | 0.6668 | (LRR-RLP) | 0.9876 |
AT5G65830.1 | Y | Y | RLP | 0.9966 | RLP | 0.8566 | LRR-RLP | 0.667 | (LRR-RLP) | 0.9876 |
AT1G28340.1 | Y | Y | RLP | 0.8425 | RLP | 0.9905 | Malectin-RLP | 0.4502 | (Malectin-RLP) | 0.9875 |
AT1G74190.1 | Y | Y | RLP | 0.9959 | RLP | 0.8564 | LRR-RLP | 0.8499 | (LRR-RLP) | 0.9871 |
AT2G15080.1 | Y | Y | RLP | 0.9965 | RLP | 0.9904 | LRR-RLP | 0.8502 | (LRR-RLP) | 0.987 |
AT3G05650.1 | Y | Y | RLP | 0.9964 | RLP | 0.9906 | LRR-RLP | 0.6664 | (LRR-RLP) | 0.9868 |
AT1G45616.1 | Y | Y | RLP | 0.9961 | RLP | 0.9913 | LRR-RLP | 0.7665 | (LRR-RLP) | 0.9868 |
AT3G05660.1 | Y | Y | RLP | 0.9966 | RLP | 0.8557 | LRR-RLP | 0.85 | (LRR-RLP) | 0.9866 |
AT1G58190.1 | Y | Y | RLP | 0.9962 | RLP | 0.8521 | LRR-RLP | 0.6663 | (LRR-RLP) | 0.9866 |
AT3G49750.1 | Y | Y | RLP | 0.9963 | RLP | 0.9909 | LRR-RLP | 0.7502 | (LRR-RLP) | 0.9865 |
AT4G13920.1 | Y | Y | RLP | 0.9967 | RLP | 0.9911 | LRR-RLP | 0.8498 | (LRR-RLP) | 0.9865 |
AT5G25910.1 | Y | Y | RLP | 0.9964 | RLP | 0.9899 | LRR-RLP | 0.8501 | (LRR-RLP) | 0.9864 |
AT2G33060.1 | Y | Y | RLP | 0.9966 | RLP | 0.9914 | LRR-RLP | 0.8332 | (LRR-RLP) | 0.9863 |
AT4G04220.1 | Y | Y | RLP | 0.9962 | RLP | 0.9911 | LRR-RLP | 0.8506 | (LRR-RLP) | 0.9863 |
AT2G33050.1 | Y | Y | RLP | 0.9964 | RLP | 0.9915 | LRR-RLP | 0.7498 | (LRR-RLP) | 0.986 |
AT1G71400.1 | Y | Y | RLP | 0.996 | RLP | 0.8563 | LRR-RLP | 0.6831 | (LRR-RLP) | 0.9851 |
AT4G18760.1 | Y | Y | RLP | 0.9967 | RLP | 0.9903 | LRR-RLP | 0.8495 | (LRR-RLP) | 0.9885 |
AT1G71390.1 | N | Y | RLP | 0.9966 | RLP | 0.99 | LRR-RLP | 0.6667 | (LRR-RLP) | 0.8021 |
AT2G25470.1 | N | Y | RLP | 0.9964 | RLP | 0.8556 | LRR-RLP | 0.8502 | (LRR-RLP) | 0.8014 |
AT1G47890.1 | N | Y | RLP | 0.9967 | RLP | 0.9908 | LRR-RLP | 0.8501 | (LRR-RLP) | 0.8001 |
AT4G13810.1 | N | Y | RLP | 0.9964 | RLP | 0.9907 | LRR-RLP | 0.833 | (LRR-RLP) | 0.7997 |
AT3G23010.1 | N | Y | RLP | 0.9965 | RLP | 0.9908 | LRR-RLP | 0.667 | (LRR-RLP) | 0.7995 |
AT1G74170.1 | N | Y | RLP | 0.9964 | RLP | 0.8561 | LRR-RLP | 0.7164 | (LRR-RLP) | 0.7994 |
AT3G24982.1 | N | Y | RLP | 0.9963 | RLP | 0.989 | LRR-RLP | 0.8512 | (LRR-RLP) | 0.7993 |
AT1G17250.1 | N | Y | RLP | 0.9965 | RLP | 0.9911 | LRR-RLP | 0.8496 | (LRR-RLP) | 0.799 |
AT3G23120.1 | N | Y | RLP | 0.997 | RLP | 0.9905 | LRR-RLP | 0.6835 | (LRR-RLP) | 0.7976 |
AT3G53240.1 | N | Y | RLP | 0.9961 | RLP | 0.9905 | LRR-RLP | 0.783 | (LRR-RLP) | 0.7973 |
AT1G07390.1 | N | Y | RLP | 0.9957 | RLP | 0.7119 | LRR-RLP | 0.7826 | (LRR-RLP) | 0.7969 |
AT3G11010.1 | N | Y | RLP | 0.9961 | RLP | 0.9902 | LRR-RLP | 0.6665 | (LRR-RLP) | 0.7958 |
AT1G34290.1 | Y | Y | RLP | 0.9964 | RLP | 0.9898 | Undefined | 0.2166 | (Undefined) | 0.7949 |
AT5G49290.1 | N | Y | RLP | 0.9966 | RLP | 0.9901 | LRR-RLP | 0.6833 | (LRR-RLP) | 0.7941 |
AT2G32660 | N | |||||||||
AT2G33020 | N | |||||||||
AT2G33030 | N | |||||||||
AT2G33080 | N | |||||||||
AT3G24900 | N | |||||||||
AT3G25010 | N | |||||||||
AT4G13900 | N | |||||||||
AT5G40170 | N | |||||||||
AT3G25020 | N |
Accession | SP | TM | RLP-NRLP | RLP-NRLP Probability | RLP-RLK | RLP-RLK Probability | RLP-Subfamily | RLP-Subfamily Probability | Classification | Decision Probability |
---|---|---|---|---|---|---|---|---|---|---|
Alien_71_464 | Y | Y | NRLP | 0.0532 | RLP | 0.7145 | Other-RLP | 0.4166 | NRLP | 0.4033 |
Alien_78_801 | Y | Y | NRLP | 0.0532 | RLP | 0.857 | WAK-RLP | 0.3169 | NRLP | 0.4014 |
Alien_88_471 | N | Y | NRLP | 0.369 | RLP | 0.855 | Unknown | 0.2837 | NRLP | 0.2068 |
Alien_90_956 | N | Y | NRLP | 0.0527 | RLK-like | 0.5721 | Other-RLP | 0.3499 | NRLP | 0.2064 |
Alien_94_666 | N | Y | NRLP | 0.0535 | RLP | 0.8558 | S-domain-RLP | 0.3164 | NRLP | 0.2045 |
Alien_11_789 | N | Y | NRLP | 0.0524 | RLK-like | 0.4288 | Other-RLP | 0.4331 | NRLP | 0.2034 |
Alien_34_248 | N | Y | NRLP | 0.2093 | RLP | 0.8571 | Other-RLP | 0.4004 | NRLP | 0.2022 |
Alien_70_660 | N | Y | NRLP | 0.3677 | RLP | 0.8564 | Unknown | 0.2491 | NRLP | 0.2002 |
Alien_59_959 | N | Y | NRLP | 0.052 | RLK-like | 0.576 | S-domain-RLP | 0.417 | NRLP | 0.1994 |
Alien_20_195 | Y | N | NRLP | 0.3704 | RLP | 0.8544 | Unknown | 0.2671 | NRLP | 0.1987 |
Alien_23_503 | N | Y | NRLP | 0.3698 | RLP | 0.8596 | Unknown | 0.3 | NRLP | 0.1987 |
Alien_69_854 | N | Y | NRLP | 0.0542 | RLP | 0.7198 | Other-RLP | 0.4327 | NRLP | 0.1985 |
Alien_2_750 | N | Y | NRLP | 0.0526 | RLK-like | 0.5768 | Other-RLP | 0.3331 | NRLP | 0.1956 |
Alien_66_528 | N | N | NRLP | 0.0001 | RLP | 0.8549 | S-domain-RLP | 0.3829 | NRLP | 0.0195 |
Alien_1_268 | N | N | NRLP | 0.0002 | RLP | 0.8536 | Other-RLP | 0.3831 | NRLP | 0.0093 |
Alien_51_917 | N | N | NRLP | 0.0002 | RLK-like | 0.573 | Unknown | 0.283 | NRLP | 0.0044 |
Alien_79_429 | N | N | NRLP | 0.3166 | RLP | 0.8588 | Other-RLP | 0.3001 | NRLP | 0.0041 |
Alien_61_779 | N | N | NRLP | 0.0002 | RLP | 0.7131 | S-domain-RLP | 0.3834 | NRLP | 0.0036 |
Alien_67_112 | N | N | NRLP | 0.1591 | RLP | 0.7131 | Other-RLP | 0.3342 | NRLP | 0.0035 |
Alien_42_363 | N | N | NRLP | 0.316 | RLP | 0.8576 | S-domain-RLP | 0.3336 | NRLP | 0.003 |
Alien_4_417 | N | N | NRLP | 0.0002 | RLK-like | 0.5712 | WAK-RLP | 0.4337 | NRLP | 0.0029 |
Alien_24_102 | N | N | NRLP | 0.4222 | RLP | 0.861 | WAK-RLP | 0.3498 | NRLP | 0.0027 |
Alien_9_882 | N | N | NRLP | 0.0002 | RLP | 0.7132 | S-domain-RLP | 0.3664 | NRLP | 0.0019 |
Alien_7_199 | N | N | NRLP | 0.3166 | RLP | 0.8564 | WAK-RLP | 0.3504 | NRLP | 0.0018 |
Alien_29_460 | N | N | NRLP | 0.2089 | RLP | 0.8554 | Unknown | 0.284 | NRLP | 0.0017 |
Alien_50_474 | N | N | NRLP | 0.0009 | RLP | 0.8548 | Unknown | 0.2495 | NRLP | 0.0017 |
Alien_72_442 | N | N | NRLP | 0.0002 | RLP | 0.8498 | Unknown | 0.2333 | NRLP | 0.0017 |
Alien_97_120 | N | N | NRLP | 0.3685 | RLP | 0.8566 | Unknown | 0.2999 | NRLP | 0.0017 |
Alien_38_893 | N | N | NRLP | 0.0003 | RLK-like | 0.5771 | S-domain-RLP | 0.4499 | NRLP | 0.0016 |
Alien_73_528 | N | N | NRLP | 0.0002 | RLP | 0.857 | S-domain-RLP | 0.3665 | NRLP | 0.0016 |
Alien_83_641 | N | N | NRLP | 0.0003 | RLP | 0.7085 | Other-RLP | 0.3502 | NRLP | 0.0016 |
Alien_44_248 | N | N | NRLP | 0.0003 | RLP | 0.7133 | S-domain-RLP | 0.3833 | NRLP | 0.0015 |
Alien_62_945 | N | N | NRLP | 0.0002 | RLK-like | 0.5733 | S-domain-RLP | 0.4834 | NRLP | 0.0015 |
Alien_16_855 | N | N | NRLP | 0.0002 | RLK-like | 0.4308 | Unknown | 0.2658 | NRLP | 0.0014 |
Alien_40_703 | N | N | NRLP | 0.0002 | RLP | 0.711 | S-domain-RLP | 0.3499 | NRLP | 0.0014 |
Alien_45_534 | N | N | NRLP | 0.0002 | RLP | 0.8553 | WAK-RLP | 0.3165 | NRLP | 0.0014 |
Alien_74_665 | N | N | NRLP | 0.0001 | RLP | 0.8547 | Unknown | 0.2503 | NRLP | 0.0014 |
Alien_18_925 | N | N | NRLP | 0.0001 | RLK-like | 0.5679 | Other-RLP | 0.4166 | NRLP | 0.0013 |
Alien_33_955 | N | N | NRLP | 0.0003 | RLK-like | 0.4348 | Unknown | 0.2332 | NRLP | 0.0013 |
Alien_39_171 | N | N | NRLP | 0.1577 | RLP | 0.8516 | Unknown | 0.2665 | NRLP | 0.0012 |
Alien_49_350 | N | N | NRLP | 0.0002 | RLP | 0.8573 | S-domain-RLP | 0.4842 | NRLP | 0.0012 |
Alien_63_622 | N | N | NRLP | 0.0002 | RLP | 0.8555 | Unknown | 0.2664 | NRLP | 0.0012 |
Alien_89_627 | N | N | NRLP | 0.0002 | RLP | 0.8567 | Other-RLP | 0.3835 | NRLP | 0.0012 |
Alien_91_929 | N | N | NRLP | 0.0003 | RLK-like | 0.573 | Other-RLP | 0.4331 | NRLP | 0.0012 |
Alien_14_450 | N | N | NRLP | 0.3148 | RLP | 0.7157 | WAK-RLP | 0.333 | NRLP | 0.0011 |
Alien_15_536 | N | N | NRLP | 0.0007 | RLP | 0.8566 | Unknown | 0.2668 | NRLP | 0.0011 |
Alien_22_586 | N | N | NRLP | 0.001 | RLP | 0.8562 | S-domain-RLP | 0.3993 | NRLP | 0.0011 |
Alien_3_226 | N | N | NRLP | 0.0003 | RLK-like | 0.431 | Unknown | 0.2991 | NRLP | 0.0011 |
Alien_57_326 | N | N | NRLP | 0.3151 | RLP | 0.8605 | Unknown | 0.2502 | NRLP | 0.0011 |
Alien_13_137 | N | N | NRLP | 0.2113 | RLK-like | 0.5764 | Unknown | 0.1667 | NRLP | 0.001 |
Alien_35_659 | N | N | NRLP | 0.0002 | RLK-like | 0.5687 | Other-RLP | 0.3829 | NRLP | 0.001 |
Alien_37_440 | N | N | NRLP | 0.0003 | RLK-like | 0.5743 | Unknown | 0.2666 | NRLP | 0.001 |
Alien_48_571 | N | N | NRLP | 0.0002 | RLP | 0.8586 | Unknown | 0.2999 | NRLP | 0.001 |
Alien_54_839 | N | N | NRLP | 0.0004 | RLP | 0.7158 | Unknown | 0.2674 | NRLP | 0.001 |
Alien_12_553 | N | N | NRLP | 0.3185 | RLP | 0.858 | Unknown | 0.2335 | NRLP | 0.0009 |
Alien_17_304 | N | N | NRLP | 0.3169 | RLP | 0.8541 | Unknown | 0.2828 | NRLP | 0.0009 |
Alien_25_176 | N | N | NRLP | 0.0003 | RLP | 0.8568 | Unknown | 0.2667 | NRLP | 0.0009 |
Alien_30_623 | N | N | NRLP | 0.0002 | RLP | 0.8547 | Other-RLP | 0.3833 | NRLP | 0.0009 |
Alien_32_240 | N | N | NRLP | 0.1576 | RLP | 0.8531 | Unknown | 0.2499 | NRLP | 0.0009 |
Alien_53_589 | N | N | NRLP | 0.0006 | RLP | 0.7103 | Unknown | 0.3 | NRLP | 0.0009 |
Alien_58_715 | N | N | NRLP | 0.0001 | RLK-like | 0.5748 | S-domain-RLP | 0.3842 | NRLP | 0.0009 |
Alien_82_456 | N | N | NRLP | 0.0001 | RLP | 0.855 | S-domain-RLP | 0.3165 | NRLP | 0.0009 |
Alien_85_415 | N | N | NRLP | 0.0004 | RLP | 0.715 | Unknown | 0.2167 | NRLP | 0.0009 |
Alien_8_947 | N | N | NRLP | 0.0001 | RLK-like | 0.5689 | Unknown | 0.25 | NRLP | 0.0009 |
Alien_10_555 | N | N | NRLP | 0.0002 | RLP | 0.8536 | Unknown | 0.2996 | NRLP | 0.0008 |
Alien_19_229 | N | N | NRLP | 0.0003 | RLP | 0.8599 | PAN-RLP | 0.3336 | NRLP | 0.0008 |
Alien_27_824 | N | N | NRLP | 0.0002 | RLP | 0.7111 | Unknown | 0.3337 | NRLP | 0.0008 |
Alien_41_731 | N | N | NRLP | 0.0004 | RLP | 0.7117 | Unknown | 0.2666 | NRLP | 0.0008 |
Alien_43_686 | N | N | NRLP | 0.0001 | RLP | 0.7129 | S-domain-RLP | 0.3662 | NRLP | 0.0008 |
Alien_47_420 | N | N | NRLP | 0.0004 | RLP | 0.8546 | Other-RLP | 0.4172 | NRLP | 0.0008 |
Alien_52_779 | N | N | NRLP | 0.0003 | RLK-like | 0.4383 | Unknown | 0.2999 | NRLP | 0.0008 |
Alien_55_478 | N | N | NRLP | 0.0002 | RLP | 0.7179 | Other-RLP | 0.3997 | NRLP | 0.0008 |
Alien_60_817 | N | N | NRLP | 0.0002 | RLP | 0.7135 | Unknown | 0.2999 | NRLP | 0.0008 |
Alien_64_626 | N | N | NRLP | 0.0002 | RLP | 0.7138 | Other-RLP | 0.4 | NRLP | 0.0008 |
Alien_75_673 | N | N | NRLP | 0.0002 | RLP | 0.8548 | Unknown | 0.2832 | NRLP | 0.0008 |
Alien_81_442 | N | N | NRLP | 0.0003 | RLK-like | 0.5736 | S-domain-RLP | 0.4833 | NRLP | 0.0008 |
Alien_87_495 | N | N | NRLP | 0.0005 | RLP | 0.8555 | S-domain-RLP | 0.3838 | NRLP | 0.0008 |
Alien_93_110 | N | N | NRLP | 0.3149 | RLP | 0.8597 | WAK-RLP | 0.467 | NRLP | 0.0008 |
Alien_99_622 | N | N | NRLP | 0.0002 | RLP | 0.8568 | Unknown | 0.25 | NRLP | 0.0008 |
Alien_21_499 | N | N | NRLP | 0.0002 | RLP | 0.86 | S-domain-RLP | 0.3498 | NRLP | 0.0007 |
Alien_31_429 | N | N | NRLP | 0.0002 | RLP | 0.7128 | Unknown | 0.2996 | NRLP | 0.0007 |
Alien_46_860 | N | N | NRLP | 0.0002 | RLK-like | 0.571 | Unknown | 0.2995 | NRLP | 0.0007 |
Alien_56_859 | N | N | NRLP | 0.0005 | RLK-like | 0.5724 | S-domain-RLP | 0.3328 | NRLP | 0.0007 |
Alien_5_855 | N | N | NRLP | 0.0003 | RLK-like | 0.572 | Unknown | 0.2997 | NRLP | 0.0007 |
Alien_65_609 | N | N | NRLP | 0.0002 | RLK-like | 0.4257 | Unknown | 0.2667 | NRLP | 0.0007 |
Alien_6_529 | N | N | NRLP | 0.0001 | RLP | 0.8565 | Unknown | 0.2504 | NRLP | 0.0007 |
Alien_86_232 | N | N | NRLP | 0.1581 | RLP | 0.8535 | Other-RLP | 0.3495 | NRLP | 0.0007 |
Alien_92_960 | N | N | NRLP | 0.0005 | RLK-like | 0.5741 | Other-RLP | 0.3168 | NRLP | 0.0007 |
Alien_95_597 | N | N | NRLP | 0.157 | RLP | 0.8588 | Unknown | 0.2833 | NRLP | 0.0007 |
Alien_96_597 | N | N | NRLP | 0.3704 | RLP | 0.8544 | WAK-RLP | 0.3999 | NRLP | 0.0007 |
Alien_0_119 | N | N | NRLP | 0.0528 | RLP | 0.7163 | PAN-RLP | 0.4339 | NRLP | 0.0006 |
Alien_26_112 | N | N | NRLP | 0.5285 | RLP | 0.8585 | Unknown | 0.2664 | NRLP | 0.0006 |
Alien_76_327 | N | N | NRLP | 0.0003 | RLP | 0.7066 | Other-RLP | 0.4002 | NRLP | 0.0006 |
Alien_77_685 | N | N | NRLP | 0.0002 | RLK-like | 0.569 | Unknown | 0.2494 | NRLP | 0.0006 |
Alien_98_323 | N | N | NRLP | 0.1046 | RLP | 0.7172 | Other-RLP | 0.5328 | NRLP | 0.0006 |
Alien_28_468 | N | N | NRLP | 0.0001 | RLP | 0.8563 | Unknown | 0.2831 | NRLP | 0.0005 |
Alien_36_821 | N | N | NRLP | 0.0001 | RLP | 0.717 | Unknown | 0.2337 | NRLP | 0.0005 |
Alien_68_626 | N | N | NRLP | 0.0002 | RLP | 0.8541 | Unknown | 0.2835 | NRLP | 0.0005 |
Alien_80_637 | N | N | NRLP | 0.0002 | RLK-like | 0.5715 | S-domain-RLP | 0.4333 | NRLP | 0.0005 |
Alien_84_494 | N | N | NRLP | 0.1614 | RLP | 0.8574 | S-domain-RLP | 0.3501 | NRLP | 0.0005 |
Class (Subfamily) | RLP | Correctly Classified * | Unknown Function ** | Incorrectly Subfamily Classified *** | Mistakenly Classified **** | RLKs in Arabidopsis |
---|---|---|---|---|---|---|
LRR-RLP | 49 | 46 | 3 | 0 | 2 | 235 |
L-Lectin-RLP | 5 | 0 | 5 | 5 | 45 | |
Salt stress response/antifungal-RLP | 9 | 3 | 1 | 5 | 0 | 44 |
WAK-RLP | 6 | 5 | 1 | 4 | 42 | |
S-domain-RLP | 1 | 1 | 1 | 37 | ||
Unknown-RLP (Extensin, PERK, RKF3, URKI) | 43 | 43 | 11 | 28 | ||
Malectin-RLP | 6 | 2 | 3 | 1 | 5 | 15 |
RCC1-RLP | 4 | 4 | 8 | |||
LysM-RLP | 4 | 2 | 2 | 3 | ||
B-lectin-RLP | 1 | 1 | 2 | |||
C-Lectin-RLP | 0 | 2 | ||||
Ethylene-responsive-RLP | 3 | 3 | 3 | 2 | ||
PAS-RLP | 0 | 2 | ||||
Thaumatin-RLP | 6 | 6 | 2 | |||
PPR-RLP | 0 | 1 | ||||
Glycosyl-hydrolases-RLP | 3 | 3 | 0 | |||
PAN-RLP | 1 | 1 | 1 | 0 | ||
Other-RLP | 35 | 11 | 24 | 13 | 0 | |
Undefined | 78 | |||||
Total | 176 | 122 | 47 | 7 | 45 | 468 |
Sequence | Ka | Ks | Ka/Ks | Selection | Date (Mya) | p-Value |
---|---|---|---|---|---|---|
GDPDL5-GDPDL3 | 0.382 | 1.578 | 0.242 | Purifying | 129.316 | 7.98 × 10−49 |
GDPD (ectodomain)- GDPDL4 | 0.214 | 1.466 | 0.146 | Purifying | 120.193 | 2.22 × 10-45 |
GDPDL4-GDPD-RLK | 0.214 | 1.288 | 0.166 | Purifying | 105.602 | 9.31 × 10−45 |
GDPDL1-GDPDL4 | 0.180 | 0.940 | 0.192 | Purifying | 77.037 | 1.60 × 10−51 |
GDPDL3-GDPDL4 | 0.164 | 0.852 | 0.192 | Purifying | 69.822 | 1.12 × 10−46 |
GDPDL4-GDPDL6 | 0.646 | 0.802 | 0.805 | Purifying | 65.744 | 0.146094 |
GDPD-RLK-GDPDL6 | 0.695 | 0.638 | 1.090 | Positive | 52.286 | 0.109708 |
GDPD (ectodomain)- GDPDL3 | 0.170 | 0.397 | 0.428 | Purifying | 32.525 | 4.56 × 10−13 |
GDPDL3-GDPD-RLK | 0.167 | 0.394 | 0.423 | Purifying | 32.333 | 3.06 × 10−13 |
GDPD-RLK-GDPDL3 | 0.167 | 0.394 | 0.423 | Purifying | 32.333 | 3.06 × 10−13 |
GDPDL1-GDPDL3 | 0.141 | 0.390 | 0.363 | Purifying | 31.961 | 1.05 × 10−17 |
GDPDL1-GDPD-RLK | 0.120 | 0.327 | 0.368 | Purifying | 26.786 | 5.38 × 10−16 |
GDPD-RLK-GDPDL1 | 0.120 | 0.327 | 0.368 | Purifying | 26.786 | 5.38 × 10−16 |
GDPDL1-GDPD (ectodomain) | 0.125 | 0.326 | 0.384 | Purifying | 26.730 | 5.08 × 10−15 |
Name | Betweenness Centrality | Closeness Centrality | Degree | Eccentricity | Description |
---|---|---|---|---|---|
SNC4 | 0.19234075 | 0.37614679 | 12 | 3 | glycerophosphoryl diester phosphodiesterase family protein, putative, expressed |
RLP51 | 0.0 | 0.27516779 | 2 | 4 | leucine rich repeat family protein, putative, expressed |
SNC1 | 3.0111 × 10−4 | 0.27702703 | 4 | 4 | rp3 protein, putative, expressed |
SUA | 1.0037 × 10−4 | 0.27702703 | 4 | 4 | RNA recognition motif family protein, expressed |
DRT111 | 1.0037 × 10−4 | 0.27702703 | 4 | 4 | G-patch domain containing protein, expressed |
AT2G20050 | 0.0 | 0.27424749 | 1 | 4 | AGC_PKA/PKG_like.1-ACG kinases include homologs to PKA, PKG and PKC, expressed |
AT1G59780 | 0.0 | 0.27424749 | 1 | 4 | NBS-LRR disease resistance protein, putative, expressed |
AT3G55350 | 0.0 | 0.27609428 | 3 | 4 | trp repressor/replication initiator, putative, expressed |
BPA1 | 0.30818366 | 0.51898734 | 6 | 2 | RNA recognition motif containing protein, putative, expressed |
AT4G1772 | 0.30818366 | 0.51898734 | 6 | 2 | RNA recognition motif, putative, expressed |
AT1G22920 | 0.0 | 0.27424749 | 2 | 4 | COP9 signalosome complex subunit 5b, putative, expressed |
GDPDL5 | 0.17835276 | 0.37104072 | 10 | 3 | glycerophosphoryl diester phosphodiesterase family protein, putative, expressed |
MLP328 | 0.0 | 0.27702703 | 7 | 4 | pathogenesis-related Bet v I family protein, putative, expressed |
AGL46 | 0.0 | 0.27702703 | 7 | 4 | OsMADS89-MADS-box family gene with M-gamma type-box, expressed |
AT2G47115 | 0.04302 | 0.2779661 | 8 | 4 | expressed protein |
AT1G29660 | 0.04302 | 0.2779661 | 8 | 4 | GDSL-like lipase/acylhydrolase, putative, expressed |
AT5G51950 | 0.04302 | 0.2779661 | 8 | 4 | HOTHEAD precursor, putative, expressed |
AT1G20680 | 0.04302 | 0.2779661 | 8 | 4 | Ser/Thr-rich protein T10 in DGCR region, putative, expressed |
AT2G17710 | 0.04302 | 0.2779661 | 8 | 4 | expressed protein |
AT5G42530 | 0.04302 | 0.2779661 | 8 | 4 | |
BPA1 | 0.30818366 | 0.51898734 | 6 | 2 | RNA recognition motif containing protein, putative, expressed |
AT4G17720 | 0.30818366 | 0.51898734 | 6 | 2 | RNA recognition motif, putative, expressed |
GDPDL3 | 0.1693342 | 0.37104072 | 10 | 3 | glycerophosphoryl diester phosphodiesterase family protein, putative, expressed |
SHV2 | 0.0 | 0.27516779 | 5 | 4 | COBRA-like protein 7 precursor, putative, expressed |
MRH1 | 0.0 | 0.27516779 | 5 | 4 | MRH1, putative, expressed |
BST1 | 0.0 | 0.27516779 | 5 | 4 | endonuclease/exonuclease/phosphatase family domain containing protein, expressed |
MRH6 | 0.0 | 0.27516779 | 5 | 4 | universal stress protein domain containing protein, putative, expressed |
MRH2 | 0.0 | 0.27516779 | 5 | 4 | kinesin motor domain containing protein, expressed |
ATCOAE | 0.0 | 0.27152318 | 1 | 4 | dephospho-CoA kinase, putative, expressed |
AT3G23750 | 0.0 | 0.27152318 | 1 | 4 | receptor protein kinase TMK1 precursor, putative, expressed |
BPA1 | 0.30818366 | 0.51898734 | 6 | 2 | RNA recognition motif containing protein, putative, expressed |
AT4G17720 | 0.30818366 | 0.51898734 | 6 | 2 | RNA recognition motif, putative, expressed |
GDPDL1 | 0.12794717 | 0.37442922 | 10 | 3 | glycerophosphoryl diester phosphodiesterase family protein, putative, expressed |
AT1G49750 | 0.0 | 0.27333333 | 1 | 4 | uncharacterized protein At4g06744 precursor, putative, expressed |
AT3G45710 | 0.0 | 0.27333333 | 1 | 4 | peptide transporter PTR2, putative, expressed |
PLDGAMMA1 | 0.00779455 | 0.29181495 | 3 | 4 | phospholipase D, putative, expressed |
MAP18 | 0.0 | 0.27333333 | 1 | 4 | Unknown function |
CDS1 | 0.0 | 0.28275862 | 2 | 4 | phosphatidate cytidylyltransferase, putative, expressed |
BPA1 | 0.30818366 | 0.51898734 | 6 | 2 | RNA recognition motif containing protein, putative, expressed |
AT4G17720 | 0.30818366 | 0.51898734 | 6 | 2 | RNA recognition motif, putative, expressed |
GDPDL4 | 0.21573054 | 0.38497653 | 14 | 3 | glycerophosphoryl diester phosphodiesterase family protein, putative, expressed |
AT5G38480 | 0.0 | 0.27891156 | 1 | 4 | 14-3-3 protein, putative, expressed |
FLA7 | 0.00445805 | 0.29390681 | 6 | 4 | fasciclin domain containing protein, expressed |
SKU5 | 0.0 | 0.2877193 | 4 | 4 | monocopper oxidase, putative, expressed |
FLA8 | 0.0 | 0.2877193 | 4 | 4 | fasciclin-like arabinogalactan protein, putative, expressed |
ZW9 | 0.00445805 | 0.29390681 | 6 | 4 | ubiquitin carboxyl-terminal hydrolase, putative, expressed |
AT1G32860 | 0.00853443 | 0.29496403 | 2 | 4 | glycosyl hydrolases family 17, putative, expressed |
AT3G56370 | 0.0 | 0.27891156 | 1 | 4 | receptor-like protein kinase precursor, putative, expressed |
AT4G09000 | 0.0 | 0.27891156 | 1 | 4 | 14-3-3 protein, putative, expressed |
BG_PPAP | 0.0 | 0.27891156 | 1 | 4 | glycosyl hydrolases family 17, putative, expressed |
AT1G01080 | 0.06480132 | 0.39047619 | 3 | 4 | RNA recognition motif containing protein, putative, expressed |
AT5G65430 | 0.0 | 0.27891156 | 1 | 4 | 14-3-3 protein, putative, expressed |
BPA1 | 0.30818366 | 0.51898734 | 6 | 2 | RNA recognition motif containing protein, putative, expressed |
AT4G17720 | 0.30818366 | 0.51898734 | 6 | 2 | RNA recognition motif, putative, expressed |
GDPDL6 | 0.67455299 | 0.4969697 | 38 | 3 | glycerophosphoryl diester phosphodiesterase family protein, putative, expressed |
AT4G11860 | 0.0 | 0.27891156 | 1 | 4 | ubiquitin interaction motif family protein, expressed |
AT3G23410 | 0.0 | 0.33333333 | 1 | 4 | alcohol oxidase, putative, expressed |
AT4G23400 | 0.0 | 0.33333333 | 1 | 4 | aquaporin protein, putative, expressed |
AT4G30850 | 0.0 | 0.33333333 | 1 | 4 | haemolysin-III, putative, expressed |
AT1G57870 | 0.0 | 0.33333333 | 1 | 4 | CGMC_GSK.5-CGMC includes CDA, MAPK, GSK3, and CLKC kinases, expressed |
AT1G31812 | 0.0 | 0.33333333 | 1 | 4 | acyl CoA binding protein, putative, expressed |
AT1G14360 | 0.0 | 0.33333333 | 1 | 4 | solute carrier family 35 member B1, putative, expressed |
AT5G06320 | 0.0 | 0.33333333 | 1 | 4 | harpin-induced protein 1 domain containing protein, expressed |
AT1G07550 | 0.0 | 0.33333333 | 1 | 4 | senescence-induced receptor-like serine/threonine-protein kinase precursor, putative, expressed |
AT5G07340 | 0.0 | 0.33333333 | 1 | 4 | calreticulin precursor protein, putative, expressed |
AT2G41705 | 0.0 | 0.33333333 | 1 | 4 | crcB-like protein, expressed |
AT3G12180 | 0.0 | 0.33333333 | 1 | 4 | cornichon protein, putative, expressed |
AT5G11890 | 0.0 | 0.33333333 | 1 | 4 | harpin-induced protein 1 domain containing protein, expressed |
AT1G14020 | 0.0 | 0.33333333 | 1 | 4 | auxin-independent growth promoter protein, putative, expressed |
AT1G34640 | 0.0 | 0.33333333 | 1 | 4 | expressed protein |
AT3G66654 | 0.0 | 0.33333333 | 1 | 4 | peptidyl-prolyl cis-trans isomerase, putative, expressed |
AT2G22425 | 0.0 | 0.33333333 | 1 | 4 | signal peptidase complex subunit 1, putative, expressed |
AT2G27290 | 0.0 | 0.33333333 | 1 | 4 | protein of unknown function DUF1279 domain containing protein, expressed |
AT5G49540 | 0.0 | 0.33333333 | 1 | 4 | transmembrane protein 93, putative, expressed |
AT1G13770 | 0.0 | 0.33333333 | 1 | 4 | DUF647 domain containing protein, putative, expressed |
AT1G29060 | 0.0 | 0.33333333 | 1 | 4 | expressed protein |
AT4G14455 | 0.0 | 0.33333333 | 1 | 4 | SNARE domain containing protein, putative, expressed |
AT4G25360 | 0.0 | 0.33333333 | 1 | 4 | leaf senescence related protein, putative, expressed |
AT4G12250 | 0.0 | 0.33333333 | 1 | 4 | UDP-glucuronate 4-epimerase, putative, expressed |
AT5G35460 | 0.0 | 0.33333333 | 1 | 4 | integral membrane protein, putative, expressed |
AT1G16170 | 0.0 | 0.33333333 | 1 | 4 | expressed protein |
AT5G03345 | 0.0 | 0.33333333 | 1 | 4 | expressed protein |
AT1G47640 | 0.0 | 0.33333333 | 1 | 4 | SSA2-2S albumin seed storage family protein precursor, putative, expressed |
AT5G52420 | 0.0 | 0.33333333 | 1 | 4 | expressed protein |
BPA1 | 0.30818366 | 0.51898734 | 6 | 2 | RNA recognition motif containing protein, putative, expressed |
AT4G17720 | 0.30818366 | 0.51898734 | 6 | 2 | RNA recognition motif, putative, expressed |
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Silva, J.C.F.; Ferreira, M.A.; Carvalho, T.F.M.; Silva, F.F.; de A. Silveira, S.; Brommonschenkel, S.H.; Fontes, E.P.B. RLPredictiOme, a Machine Learning-Derived Method for High-Throughput Prediction of Plant Receptor-like Proteins, Reveals Novel Classes of Transmembrane Receptors. Int. J. Mol. Sci. 2022, 23, 12176. https://doi.org/10.3390/ijms232012176
Silva JCF, Ferreira MA, Carvalho TFM, Silva FF, de A. Silveira S, Brommonschenkel SH, Fontes EPB. RLPredictiOme, a Machine Learning-Derived Method for High-Throughput Prediction of Plant Receptor-like Proteins, Reveals Novel Classes of Transmembrane Receptors. International Journal of Molecular Sciences. 2022; 23(20):12176. https://doi.org/10.3390/ijms232012176
Chicago/Turabian StyleSilva, Jose Cleydson F., Marco Aurélio Ferreira, Thales F. M. Carvalho, Fabyano F. Silva, Sabrina de A. Silveira, Sergio H. Brommonschenkel, and Elizabeth P. B. Fontes. 2022. "RLPredictiOme, a Machine Learning-Derived Method for High-Throughput Prediction of Plant Receptor-like Proteins, Reveals Novel Classes of Transmembrane Receptors" International Journal of Molecular Sciences 23, no. 20: 12176. https://doi.org/10.3390/ijms232012176