SSR-based genetic analysis of sweet corn inbred lines using artificial neural networks

Ferreira, Fernando Rafael Alves; Scapim, Carlos Alberto; Maldonado, Carlos; Mora, Freddy

doi:10.1590/1984-70332018v18n3n45

Cited by 15 publications

(15 citation statements)

References 25 publications

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“…These results agree with those indicated by Ferreira et al (2018) and Peña-Malavera et al (2014), who report that the UPGMA method may create highly unbalanced clusters, which produces an increase in the error rates of the UPGMA compared with both SOM and PCoA procedures. The clustering analysis using competitive learning-based neural networks (via SOM) is proposed as an alternative method to analyze populatio n structure and has a good adaptation to multi-allelic data (Peña-Malavera et al, 2014;Ferreira et al, 2018), is computationally faster than MCMC methods (Nikolic, Park, Sancristobal, Lek, & Chevalet, 2009) and does not consider the assumption of Hardy-Weinberg equilibrium in the population under study (Ferreira et al, 2018). Moreover, the artificial neural networks has the advantage of being non-parametric, does not require detailed information about the physical processes to be modeled and is tolerant of data loss (Azevedo et al, 2015).…”

Section: Resultssupporting

confidence: 92%

“…The clustering results from the neural network procedure agreed with those of the PCoA. This aspect was highlighted by Ferreira, Scapim, Maldonado, and Mora (2018) in an SSR -based genetic analysis. The UPGMA dendrogram showed that the IAC-766 variety was grouped individually and, on the other hand, that the Kober 5BB was grouped with Traviú and IAC-572.…”

Section: Resultssupporting

confidence: 65%

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High genetic differentiation of grapevine rootstock varieties determined by molecular markers and artificial neural networks

et al. 2019

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The genetic differentiation of grapevine rootstock varieties was inferred by the Artificial Neural Network approach based on the Self-Organizing Map algorithm. A combination of RAPD and SSR molecular markers, yielding polymorphic informative loci, was used to determine the genetic characterization among the rootstock varieties 420-A, Schwarzmann, IAC-766 Campinas, Traviú, Kober 5BB, and IAC-572 Jales. A neural network algorithm, based on allelic frequency, showed that the individual grapevine rootstocks (n = 64) were grouped into three genetically differentiated clusters. Cluster 1 included only the Kober 5BB rootstock, Cluster 2 included rootstocks of the varieties Traviú and IAC-572, and Cluster 3 included 420-A, Schwarzmann and IAC-766 plants. Evidence from the current study indicates that, despite the morphological similarities of the 420-A and Kober 5BB varieties, which share the same genetic origin, two new varieties were generated that are genetically divergent and show differences in performance.

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Section: Resultssupporting

confidence: 92%

Section: Resultssupporting

confidence: 65%

High genetic differentiation of grapevine rootstock varieties determined by molecular markers and artificial neural networks

et al. 2019

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“…Hence, the development of an effective marker system to assess genetic diversity in avocado collections facilitates the maintenance of germplasm and cultivar improvement. Transcriptome sequencing and de novo assembly has proven to be an important tool for gene discovery in many organisms and an effective method for molecular marker development [23][24][25]35]. De novo assembly of avocado cv.…”

Section: Discussionmentioning

confidence: 99%

“…Of the many DNA markers that have been developed, SSRs, which consist of repeated nucleotide motifs of between one and six bases, are widely preferred in plant genetics and breeding because they are widely distributed and abundant in plant genomes, and they are genetically codominant, highly reproducible, multi-allelic, and well suited for high-throughput genotyping [22][23][24][25]. Transcriptome sequencing, which is based on next-generation sequencing technologies, is a high-throughput technique that facilitates the acquisition of a large number of unigene sequences for expressed sequence tag (EST)-derived marker development [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Transcriptome Sequencing of Different Avocado Ecotypes: de novo Transcriptome Assembly, Annotation, Identification and Validation of EST-SSR Markers

Tan

et al. 2019

Forests

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Avocado (Persea americana Mill.) could be considered as an important tropical and subtropical woody oil crop with high economic and nutritional value. Despite the importance of this species, genomic information is currently unavailable for avocado and closely related congeners. In this study, we generated more than 216 million clean reads from different avocado ecotypes using Illumina HiSeq high-throughput sequencing technology. The high-quality reads were assembled into 154,310 unigenes with an average length of 922 bp. A total of 55,558 simple sequence repeat (SSR) loci detected among the 43,270 SSR-containing unigene sequences were used to develop 74,580 expressed sequence tag (EST)-SSR markers. From these markers, a subset of 100 EST-SSR markers was randomly chosen to identify polymorphic EST-SSR markers in 28 avocado accessions. Sixteen EST-SSR markers with moderate to high polymorphism levels were detected, with polymorphism information contents ranging from 0.33 to 0.84 and averaging 0.63. These 16 polymorphic EST-SSRs could clearly and effectively distinguish the 28 avocado accessions. In summary, our study is the first presentation of transcriptome data of different avocado ecotypes and comprehensive study on the development and analysis of a set of EST-SSR markers in avocado. The application of next-generation sequencing techniques for SSR development is a potentially powerful tool for genetic studies.

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“…Of the many available DNA markers, simple sequence repeats (SSRs) are commonly used for investigating plant genetics and breeding because they are widely distributed and abundant in plant genomes. They are also genetically codominant, highly reproducible, multi-allelic, and perfectly suitable for high-throughput genotyping [21][22][23][24][25]. Expressed sequence tag (EST)-derived markers in the genomic coding regions have an advantage over genomic DNA-derived markers, and can be efficiently amplified to reveal conserved sequences among related species [26].…”

Section: Introductionmentioning

confidence: 99%

Single-Molecule Long-Read Sequencing of Avocado Generates Microsatellite Markers for Analyzing the Genetic Diversity in Avocado Germplasm

Zang

Tan

et al. 2019

Agronomy

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Avocado (Persea americana Mill.) is an important fruit crop commercially grown in tropical and subtropical regions. Despite the importance of avocado, there is relatively little available genomic information regarding this fruit species. In this study, we functionally annotated the full-length avocado transcriptome sequence based on single-molecule real-time sequencing technology, and predicted the coding sequences (CDSs), transcription factors (TFs), and long non-coding RNA (lncRNA) sequences. Moreover, 76,777 simple sequence repeat (SSR) loci detected among the 42,096 SSR-containing transcript sequences were used to develop 149,733 expressed sequence tag (EST)-SSR markers. A subset of 100 EST-SSR markers was randomly chosen for an analysis that detected 15 polymorphicEST-SSR markers, with an average polymorphism information content of 0.45. These 15markers were able to clearly and effectively characterize46 avocado accessions based on geographical origin. In summary, our study is the first to generate a full-length transcriptome sequence and develop and analyze a set of EST-SSR markers in avocado. The application of third-generation sequencing techniques for developing SSR markers is a potentially powerful tool for genetic studies.

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SSR-based genetic analysis of sweet corn inbred lines using artificial neural networks

Cited by 15 publications

References 25 publications

High genetic differentiation of grapevine rootstock varieties determined by molecular markers and artificial neural networks

High genetic differentiation of grapevine rootstock varieties determined by molecular markers and artificial neural networks

Transcriptome Sequencing of Different Avocado Ecotypes: de novo Transcriptome Assembly, Annotation, Identification and Validation of EST-SSR Markers

Single-Molecule Long-Read Sequencing of Avocado Generates Microsatellite Markers for Analyzing the Genetic Diversity in Avocado Germplasm

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