An Alignment-Free Approach for Eukaryotic ITS2 Annotation and Phylogenetic Inference

Agüero-Chapin, Guillermin; Sánchez-Rodríguez, Aminael; Hidalgo-Yanes, Pedro I; Pérez-Castillo, Yunierkis; Ruiz, Reinaldo Molina; Marchal, Kathleen; Vasconcelos, Vı́tor; Antunes, Agostinho

doi:10.1371/journal.pone.0026638

Cited by 10 publications

(19 citation statements)

References 56 publications

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“…This section summarizes the main results derived from the application of TI2BioP to the functional classification of protein bacteriocins [5], RNase III [69], ITS2 [21] and NRPS Adomains [66]. All these classes show high sequence divergence among their members, which represent a handicap for the good performance of alignment algorithms.…”

Section: Resultsmentioning

confidence: 99%

“…The bactericide action of this domain was only confirmed by experimental evidences; no alignment algorithm could anticipate such activity [5]. In addition, new ITS2 and RNase III members were registered using alignmentfree models based on the same biopolymer representation [21,69]. The predictions of these two members were verified through enzymatic assay for the new RNase III member and by evaluating both queries against profiles HMM ( Table 1).…”

Section: Resultsmentioning

confidence: 99%

“…nucleotide/amino acid distribution into a 2D space. Such graphical features can be quantified by the TIs to easily compare a great number of sequences/ maps [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

“…These TIs represent alignment-free predictors to detect functional signatures in members of gene and protein classes. Its practical importance for bioinformatics consisted of dealing with gene/protein classes sharing low sequence similarity and in estimating alignment-free distances for inferring phylogenetic relationships [21].…”

Section: Introductionmentioning

confidence: 99%

“…All alignment-based methods, the dynamic programming algorithms implemented by Needleman-Wunsch [45] and Smith-Waterman [46], the heuristic algorithm for basic local alignment search tool (BLAST) [47] and the probabilistic hidden Markov models (HMM) [48,49] have a friendly interface to search structural and functional sequence classifications, but they may fail in detecting gene/protein members that share low similarity to others of the family [21,50,51]. There are several evidences showing a low reliability for the biological functional prediction when protein families have pairwise sequence similarities below 50% [50,52,53].…”

Section: Introductionmentioning

confidence: 99%

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TI2BioP — Topological Indices to BioPolymers. A Graphical– Numerical Approach for Bioinformatics

Agüero-Chapin¹,

Ruiz²,

Pérez-Machado³

et al. 2016

Recent Advances in Biopolymers

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We developed a new graphical-numerical method called TI2BioP (Topological Indices to BioPolymers) to estimate topological indices (TIs) from two-dimensional (2D) graphical approaches for the natural biopolymers DNA, RNA and proteins The methodology mainly turns long biopolymeric sequences into 2D artificial graphs such as Cartesian and four-color maps but also reads other 2D graphs from the thermodynamic folding of DNA/RNA strings inferred from other programs. The topology of such 2D graphs is either encoded by node or adjacency matrixes for the calculation of the spectral moments as TIs. These numerical indices were used to build up alignment-free models to the functional classification of biosequences and to calculate alignment-free distances for phylogenetic purposes. The performance of the method was evaluated in highly diverse gene/protein classes, which represents a challenge for current bioinformatics algorithms. TI2BioP generally outperformed classical bioinformatics algorithms in the functional classification of Bacteriocins, ribonucleases III (RNases III), genomic internal transcribed spacer II (ITS2) and adenylation domains (A-domains) of nonribosomal peptide synthetases (NRPS) allowing the detection of new members in these target gene/protein classes. TI2BioP classification performance was contrasted and supported by predictions with sensitive alignment-based algorithms and experimental outcomes, respectively. The new ITS2 sequence isolated from Petrakia sp. was used in our graphical-numerical approach to estimate alignment-free distances for phylogenetic inferences. Despite TI2BioP having been developed for application in bioinformatics, it can be extended to predict interesting features of other biopolymers than DNA and protein sequences. TI2BioP version 2.0 is freely available from http://ti2biop.sourceforge.net/.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…nucleotide/amino acid distribution into a 2D space. Such graphical features can be quantified by the TIs to easily compare a great number of sequences/ maps [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

TI2BioP — Topological Indices to BioPolymers. A Graphical– Numerical Approach for Bioinformatics

Agüero-Chapin¹,

Ruiz²,

Pérez-Machado³

et al. 2016

Recent Advances in Biopolymers

Self Cite

View full text Add to dashboard Cite

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Alignment-Free Methods for the Detection and Specificity Prediction of Adenylation Domains

Agüero-Chapin

Pérez-Machado

Sánchez-Rodríguez

et al. 2016

Methods in Molecular Biology

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Identifying adenylation domains (A-domains) and their substrate specificity can aid the detection of nonribosomal peptide synthetases (NRPS) at genome/proteome level and allow inferring the structure of oligopeptides with relevant biological activities. However, that is challenging task due to the high sequence diversity of A-domains (~10-40 % of amino acid identity) and their selectivity for 50 different natural/unnatural amino acids. Altogether these characteristics make their detection and the prediction of their substrate specificity a real challenge when using traditional sequence alignment methods, e.g., BLAST searches. In this chapter we describe two workflows based on alignment-free methods intended for the identification and substrate specificity prediction of A-domains. To identify A-domains we introduce a graphical-numerical method, implemented in TI2BioP version 2.0 (topological indices to biopolymers), which in a first step uses protein four-color maps to represent A-domains. In a second step, simple topological indices (TIs), called spectral moments, are derived from the graphical representations of known A-domains (positive dataset) and of unrelated but well-characterized sequences (negative set). Spectral moments are then used as input predictors for statistical classification techniques to build alignment-free models. Finally, the resulting alignment-free models can be used to explore entire proteomes for unannotated A-domains. In addition, this graphical-numerical methodology works as a sequence-search method that can be ensemble with homology-based tools to deeply explore the A-domain signature and cope with the diversity of this class (Aguero-Chapin et al., PLoS One 8(7):e65926, 2013). The second workflow for the prediction of A-domain's substrate specificity is based on alignment-free models constructed by transductive support vector machines (TSVMs) that incorporate information of uncharacterized A-domains. The construction of the models was implemented in the NRPSpredictor and in a first step uses the physicochemical fingerprint of the 34 residues lining the active site of the phenylalanine-adenylation domain of gramicidin synthetase A [PDB ID 1 amu] to derive a feature vector. Homologous positions were extracted for A-domains with known and unknown substrate specificities and turned into feature vectors. At the same time, A-domains with known specificities towards similar substrates were clustered by physicochemical properties of amino acids (AA). In a second step, support vector machines (SVMs) were optimized from feature vectors of characterized A-domains in each of the resulting clusters. Later, SVMs were used in the variant of TSVMs that integrate a fraction of uncharacterized A-domains during training to predict unknown specificities. Finally, uncharacterized A-domains were scored by each of the constructed alignment-free models (TSVM) representing each substrate specificity resulting from the clustering. The model producing the largest score for the uncharacterized A-domain assigns t...

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Non-standard bioinformatics characterization of SARS-CoV-2

Bielińska-Wąż

Wąż

2021

Computers in Biology and Medicine

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A non-standard bioinformatics method, 4D-Dynamic Representation of DNA/RNA Sequences , aiming at an analysis of the information available in nucleotide databases, has been formulated. The sequences are represented by sets of “material points” in a 4D space - 4D-dynamic graphs. The graphs representing the sequences are treated as “rigid bodies” and characterized by values analogous to the ones used in the classical dynamics. As the graphical representations of the sequences, the projections of the graphs into 2D and 3D spaces are used. The method has been applied to an analysis of the complete genome sequences of the 2019 novel coronavirus. As a result, 2D and 3D classification maps are obtained. The coordinate axes in the maps correspond to the values derived from the exact formulas characterizing the graphs: the coordinates of the centers of mass and the 4D moments of inertia. The points in the maps represent sequences and their coordinates are used as the classifiers. The main result of this work has been derived from the 3D classification maps. The distribution of clusters of points which emerged in these maps, supports the hypothesis that SARS-CoV-2 may have originated in bat and in pangolin. Pilot calculations for Zika virus sequence data prove that the proposed approach is also applicable to a description of time evolution of genome sequences of viruses.

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An Alignment-Free Approach for Eukaryotic ITS2 Annotation and Phylogenetic Inference

Cited by 10 publications

References 56 publications

TI2BioP — Topological Indices to BioPolymers. A Graphical– Numerical Approach for Bioinformatics

TI2BioP — Topological Indices to BioPolymers. A Graphical– Numerical Approach for Bioinformatics

Alignment-Free Methods for the Detection and Specificity Prediction of Adenylation Domains

Non-standard bioinformatics characterization of SARS-CoV-2

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