A General Overview of Sweet Sorghum Genomics

Rosas-Quijano, Raymundo; Ontiveros-Cisneros, Abraham; Montes-García, Noé; Díaz-Franco, Arturo; Vázquez-Ovando, Alfredo; Gálvez-López, Didiana

doi:10.5772/intechopen.98539

Cited by 2 publications

(1 citation statement)

References 69 publications

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“…On the other hand, genetic duplication in sorghum is relatively low compared to other grass-member crops like maize and rice. Oryza sativa genome is bigger than sorghum's genome and went through more gene expansion events than sorghum [60]. Although the number of lectin genes homologs in rice are triple the gene size of sorghum, the majority of rice lectin genes located in nonsegmental regions were expanded through tandem duplication events [14], whereas around 85.8% of sorghum's lectin genes were expanded by various segmental events compared to the 16.6% tandemly duplicated lectin genes, mainly by dispersion or retrogene copies created by transposition event.…”

Section: Discussionmentioning

confidence: 99%

Genome-wide screening of lectin putative genes from Sorghum bicolor L., distribution in QTLs and a probable implications of lectins in abiotic stress tolerance

2022

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Background Sorghum bicolor is one of the most important crops worldwide with the potential to provide resilience when other economic staples might fail against the continuous environmental changes. Many physiological, developmental and tolerance traits in plants are either controlled or influenced by lectins; carbohydrate binding proteins. Hence, we aimed at providing a comprehensive in silico account on sorghum’s lectins and study their possible implication on various desired agronomical traits. Results We have searched sorghum’s genome from grain and sweet types for lectins putative genes that encode proteins with domains capable of differentially binding carbohydrate moieties and trigger various physiological responses. Of the 12 known plant lectin families, 8 were identified regarding their domain architectures, evolutionary relationships, physiochemical characteristics, and gene expansion mechanisms, and they were thoroughly addressed. Variations between grain and sweet sorghum lectin homologs in term of the presence/absence of certain other joint domains like dirigent and nucleotide-binding adaptor shared by APAF-1, R-proteins, and CED-4 (NB-ARC) indicate a possible neofunctionalization. Lectin sequences were found to be preferentially overrepresented in certain quantitative trait loci (QTLs) related to various traits under several subcategories such as cold, drought, salinity, panicle/grain composition, and leaf morphology. The co-localization and distribution of lectins among multiple QTLs provide insights into the pleiotropic effects that could be played by one lectin gene in numerous traits. Conclusion Our study offers a first-time inclusive details on sorghum lectins and their possible role in conferring tolerance against abiotic stresses and other economically important traits that can be informative for future functional analysis and breeding studies.

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Section: Discussionmentioning

confidence: 99%

Genome-wide screening of lectin putative genes from Sorghum bicolor L., distribution in QTLs and a probable implications of lectins in abiotic stress tolerance

2022

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SVM-Root: Identification of Root-Associated Proteins in Plants by Employing the Support Vector Machine with Sequence-Derived Features

Meher

Hati

Sahu

et al. 2024

CBIO

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Background: Root is a desirable trait for modern plant breeding programs, as the roots play a pivotal role in the growth and development of plants. Therefore, the identification of the genes governing the root traits is an essential research component. With regard to the identification of root-associated genes/proteins, the existing wet-lab experiments are resource intensive and the gene expression studies are species-specific. Thus, we proposed a supervised learning-based computational method for the identification of root-associated proteins. Method: The problem was formulated as a binary classification, where the root-associated proteins and non-root-associated proteins constituted the two classes. Four different machine learning algorithms such as support vector machine (SVM), extreme gradient boosting, random forest, and adaptive boosting were employed for the classification of proteins of the two classes. Sequence-derived features such as AAC, DPC, CTD, PAAC, and ACF were used as input for the learning algorithms. Results: The SVM achieved higher accuracy with the 250 selected features of AAC+DPC+CTD than that of other possible combinations of feature sets and learning algorithms. Specifically, the SVM with the selected features achieved overall accuracies of 0.74, 0.73, and 0.73 evaluated with single 5-fold cross-validation (5F-CV), repeated 5F-CV, and independent test set, respectively. Conclusions: A web-enabled prediction tool SVM-Root (https://iasri-sg.icar.gov.in/svmroot/) has been developed for the computational prediction of the root-associated proteins. Being the first of its kind, the proposed model is believed to supplement the existing experimental methods and high throughput GWAS and transcriptome studies.

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A General Overview of Sweet Sorghum Genomics

Cited by 2 publications

References 69 publications

Genome-wide screening of lectin putative genes from Sorghum bicolor L., distribution in QTLs and a probable implications of lectins in abiotic stress tolerance

Genome-wide screening of lectin putative genes from Sorghum bicolor L., distribution in QTLs and a probable implications of lectins in abiotic stress tolerance

SVM-Root: Identification of Root-Associated Proteins in Plants by Employing the Support Vector Machine with Sequence-Derived Features

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