Sequence Motif Identification and Protein Family Classification Using Probabilistic Trees

Leonardi, Florencia; Galves, Antonio

doi:10.1007/11532323_20

Cited by 6 publications

(5 citation statements)

References 6 publications

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“…The statistical significance of motif prediction was correlated with biological significance, indicating a valuable reason for the motifs analysis as also reported earlier by Zhang et al, (2009). Motifs information can be used for developing resistance genes and makers, to perform clustering (Broin et al, 2015), gene expression analysis study (Jensen et al, 2005, Huber andBulyk, 2006) and discovery of homology relations (Stewart, 2016), family classification (Blekas et al, 2005;Eser et al, 2013), discovery of sub-families in large protein families (Leonardi and Galves, 2005) and new signalling pathways (Ma et al, 2013).…”

Section: Cis-regulatory Elementssupporting

confidence: 63%

Molecular Characterization and Evolutionary Analysis of Potential Fusarium Resistant Genes for Crop Improvement

Yadav¹,

Kalidindi²,

Jayaswal³

et al. 2018

Int.J.Curr.Microbiol.App.Sci

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Section: Cis-regulatory Elementssupporting

confidence: 63%

Molecular Characterization and Evolutionary Analysis of Potential Fusarium Resistant Genes for Crop Improvement

Yadav¹,

Kalidindi²,

Jayaswal³

et al. 2018

Int.J.Curr.Microbiol.App.Sci

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“…Conserved motifs can provide evidence for further classification into different subgroups as it is possible that proteins within a subgroup, which share identical motifs, are likely to exhibit similar functions. Motifs information can be used to develop resistance genes and markers, perform clustering (Broin et al, 2015 ), assess the gene expression (Jensen et al, 2005 ; Huber and Bulyk, 2006 ), discover the homology relations, classify the families (Blekas et al, 2005 ; Jensen et al, 2005 ), discover the sub-families in large protein families (Leonardi and Galves, 2005 ) and identify new signaling pathways (Ma et al, 2013 ).…”

Section: Resultsmentioning

confidence: 99%

Structural, Functional, and Evolutionary Characterization of Major Drought Transcription Factors Families in Maize

et al. 2018

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Drought is one of the major threats to the maize yield especially in subtropical production systems. Understanding the genes and regulatory mechanisms of drought tolerance is important to sustain the yield. Transcription factors (TFs) play a major role in gene regulation under drought stress. In the present study, a set of 15 major TF families comprising 1,436 genes was structurally and functionally characterized. The functional annotation indicated that the genes were involved in ABA signaling, ROS scavenging, photosynthesis, stomatal regulation, and sucrose metabolism. Duplication was identified as the primary force in divergence and expansion of TF families. Phylogenetic relationship was developed for individual TF and combined TF families. Phylogenetic analysis clustered the genes into specific and mixed groups. Gene structure analysis revealed that more number of genes were intron-rich as compared to intron-less. Drought-responsive cis-regulatory elements such as ABREA, ABREB, DRE1, and DRECRTCOREAT have been identified. Expression and interaction analyses identified leaf-specific bZIP TF, GRMZM2G140355, as a potential contributor toward drought tolerance in maize. Protein-protein interaction network of 269 drought-responsive genes belonging to different TFs has been provided. The information generated on structural and functional characteristics, expression, and interaction of the drought-related TF families will be useful to decipher the drought tolerance mechanisms and to breed drought-tolerant genotypes in maize.

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“…Recently, it was proposed an algorithm to estimate the set of sparse contexts and the transition probabilities given by 2.2 (Leonardi and Galves;2005;Leonardi;2006). This algorithm represents internally the set of sparse contexts as a tree, as described above.…”

Section: The Elements In P Jmentioning

confidence: 99%

“…In this work we propose to use the framework of Sparse Probabilistic Suffix Trees (SPST) to analyze the similarity between sequences and to infer the evolution of protein families. SPST was first introduced in Leonardi and Galves (2005) as a generalization of the PST algorithm, proposed in Ron et al (1996). SPST has shown to be useful in protein modeling and classification, performing better than the PST algorithm (Leonardi;2006).…”

Section: Introductionmentioning

confidence: 99%

Detecting phylogenetic relations out from sparse context trees

Leonardi,

Matioli,

Armelin

et al. 2008

Preprint

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The goal of this paper is to study the similarity between sequences using a distance between the context trees associated to the sequences. These trees are defined in the framework of Sparse Probabilistic Suffix Trees (SPST), and can be estimated using the SPST algorithm. We implement the Phyl-SPST package to compute the distance between the sparse context trees estimated with the SPST algorithm. The distance takes into account the structure of the trees, and indirectly the transition probabilities. We apply this approach to reconstruct a phylogenetic tree of protein sequences in the globin family of vertebrates. We compare this tree with the one obtained using the well-known PAM distance.

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Sequence Motif Identification and Protein Family Classification Using Probabilistic Trees

Cited by 6 publications

References 6 publications

Molecular Characterization and Evolutionary Analysis of Potential Fusarium Resistant Genes for Crop Improvement

Molecular Characterization and Evolutionary Analysis of Potential Fusarium Resistant Genes for Crop Improvement

Structural, Functional, and Evolutionary Characterization of Major Drought Transcription Factors Families in Maize

Detecting phylogenetic relations out from sparse context trees

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