A dynamic programming algorithm for identification of triplex-forming sequences

Lexa, Matej; Martínek, Tomáš; Burgetová, Ivana; Kopeček, Daniel

doi:10.1093/bioinformatics/btr439

Cited by 28 publications

(26 citation statements)

References 41 publications

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“…In both types of analysis, there is a strong enrichment of PTS sequences in low complexity sequences and simple repeats (Figure 2(c)). Such findings are compatible with the limited number of nucleotide triplets found in stable H-DNA (Soyfer and Potaman, 1995) (Lexa et al, 2011) and the general requirement for polypurine and polypyrimidine tracts in triplexes. We also found SVA and to a limited extent also SINE and scRNA elements to have above average PTS association ( Figure 2(c)).…”

Section: Distribution Of Potential Triplex Sequences In the Human Genomesupporting

confidence: 84%

Uneven Distribution of Potential Triplex Sequences in the Human Genome - In Silico Study using the R/Bioconductor Package Triplex

Lexa

Martínek

2014

Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms

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Abstract:Eukaryotic genomes are rich in sequences capable of forming non-B DNA structures. These structures are expected to play important roles in natural regulatory processes at levels above those of individual genes, such as whole genome dynamics or chromatin organization, as well as in processes leading to the loss of these functions, such as cancer development. Recently, a number of authors have mapped the occurrence of potential quadruplex sequences in the human genome and found them to be associated with promoters. In this paper, we set out to map the distribution and characteristics of potential triplex-forming sequences (PTS) in the human genome sequence. Using the R/Bioconductor package triplex, we found these sequences to be excluded from exons, while present mostly in a small number of repetitive sequence classes, especially short sequence tandem repeats (microsatellites), Alu and combined elements, such as SVA. We also introduce a novel way of classifying potential triplex sequences, using a lexicographically minimal rotation of the most frequent k-mer to assign class membership automatically. Members of such classes typically have different propensities to form parallel and antiparallel intramolecular triplexes (H-DNA). We observed an interesting pattern, where the predicted third strands of antiparallel H-DNA were much less likely to contain a deletion than their duplex structural counterpart than were their parallel versions.

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Section: Distribution Of Potential Triplex Sequences In the Human Genomesupporting

confidence: 84%

Uneven Distribution of Potential Triplex Sequences in the Human Genome - In Silico Study using the R/Bioconductor Package Triplex

Lexa

Martínek

2014

Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms

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“…Efficacy of the concept of residual twist in predicting mechanistic effects of base triplet nonisostericity in DNA triplexes has been demonstrated Yathindra 2005, 2006) and has been applied in developing an algorithm to identify triplex-forming sequences (Lexa et al 2011). It is envisaged that the knowledge of residual twist and radial difference can be effectively utilized for a comprehensive understanding of the structural influence of base-pair nonisostericity in nucleic acids duplexes/ triplexes.…”

Section: Discussionmentioning

confidence: 99%

An innate twist between Crick’s wobble and Watson-Crick base pairs

Prakash

Goldsmith

Yathindra

2013

RNA

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Non-Watson-Crick pairs like the G·U wobble are frequent in RNA duplexes. Their geometric dissimilarity (nonisostericity) with the Watson-Crick base pairs and among themselves imparts structural variations decisive for biological functions. Through a novel circular representation of base pairs, a simple and general metric scheme for quantification of base-pair nonisostericity, in terms of residual twist and radial difference that can also envisage its mechanistic effect, is proposed. The scheme is exemplified by G·U and U·G wobble pairs, and their predicable local effects on helical twist angle are validated by MD simulations. New insights into a possible rationale for contextual occurrence of G·U and other non-WC pairs, as well as the influence of a G·U pair on other non-Watson-Crick pair neighborhood and RNA-protein interactions are obtained from analysis of crystal structure data. A few instances of RNA-protein interactions along the major groove are documented in addition to the well-recognized interaction of the G·U pair along the minor groove. The nonisostericity-mediated influence of wobble pairs for facilitating helical packing through long-range interactions in ribosomal RNAs is also reviewed.

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“…Our analysis used the F b¼2 measure, which gives a higher preference to "recall" over "precision" (Lexa et al, 2011). This approach was based on the consideration that "precision" was difficult to measure because the positive samples could include an unknown fraction of stress-related genes that might not respond in shoots and roots within 24 h, and the control samples could also include an unknown fraction of genes that respond to stress with different degrees of intensity.…”

Section: The Candidate Stress-related Genes Predicted By Mldnamentioning

confidence: 99%

Machine Learning–Based Differential Network Analysis: A Study of Stress-Responsive Transcriptomes in Arabidopsis

et al. 2014

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ORCID IDs: 0000-0001-9612-7898 (C.M.); 0000-0002-3306-5594 (M.X.); 0000-0002-6406-5597 (X.W.)Machine learning (ML) is an intelligent data mining technique that builds a prediction model based on the learning of prior knowledge to recognize patterns in large-scale data sets. We present an ML-based methodology for transcriptome analysis via comparison of gene coexpression networks, implemented as an R package called machine learning-based differential network analysis (mlDNA) and apply this method to reanalyze a set of abiotic stress expression data in Arabidopsis thaliana. The mlDNA first used a ML-based filtering process to remove nonexpressed, constitutively expressed, or non-stressresponsive "noninformative" genes prior to network construction, through learning the patterns of 32 expression characteristics of known stress-related genes. The retained "informative" genes were subsequently analyzed by ML-based network comparison to predict candidate stress-related genes showing expression and network differences between control and stress networks, based on 33 network topological characteristics. Comparative evaluation of the network-centric and gene-centric analytic methods showed that mlDNA substantially outperformed traditional statistical testing-based differential expression analysis at identifying stress-related genes, with markedly improved prediction accuracy. To experimentally validate the mlDNA predictions, we selected 89 candidates out of the 1784 predicted salt stress-related genes with available SALK T-DNA mutagenesis lines for phenotypic screening and identified two previously unreported genes, mutants of which showed salt-sensitive phenotypes.

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A dynamic programming algorithm for identification of triplex-forming sequences

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 28 publications

References 41 publications

Uneven Distribution of Potential Triplex Sequences in the Human Genome - In Silico Study using the R/Bioconductor Package Triplex

Uneven Distribution of Potential Triplex Sequences in the Human Genome - In Silico Study using the R/Bioconductor Package Triplex

An innate twist between Crick’s wobble and Watson-Crick base pairs

Machine Learning–Based Differential Network Analysis: A Study of Stress-Responsive Transcriptomes in Arabidopsis

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