Fuzzy splicing in precursor-mRNA sequences: prediction of aberrant splice-junctions in viral DNA context

Abstract

RNA splicing normally generates stable splice-junction sequences in viruses that are important in the context of virus mimicry. Potential variability in envelop proteins may occur with point-mutations inducing cryptic splice-junctions, which would remain unrecognized by T-memory cells of higher organisms in vaccine trials. Such aberrant splicejunctions result from evolution-specific non-conservation of actual splice-junction sites due to mutations; as such, locations of splice-junctions in a test DNA sequence could only be imprecisely specified. Such impreciseness of splice-junction locations (or cryptic sites) in a sequence is evaluated in this study via "noisy" attributes (with associated stochastics) to the mutated subspace; and, relevant fuzzy considerations are invoked with membership attributes expressed in terms of a spatial signal-to-noise ratio (SSNR). That is, SSNR adopted as a membership function expresses the belongingness of a site-region to exon/intron subspaces. An illustrative example with actual (Dengue 1 viral) DNA data is furnished demonstrating the pursuit developed in predicting aberrant splice-junctions at cryptic sites in the test sequence.

Bioinformatics-Inspired Algorithms for 2D-Image Analysis-Application to Synthetic and Medical Images Part I

“…They enable a sequence comparison and offer optimal, (if not sub-optimal), scores on the similarity/dissimilarity between the compared pair of linear sequences (Jones & Pevzer, 2009) Both NW and SW pursuits imply a matrixbased approach with backtracking used to reconstruct the gapped alignments between the sequences being compared; hence, the aligned pair so obtained is assessed for the similarity score as explained earlier. Relevant dynamic programming structures of NW/SW computations are well known in bioinformatics and described widely across an archive of literature (e.g., Arredondo et al, 2005;Needleman & Wunsch, 1970;Neelakanta et al, 2011;Riedel et al, 2008;Smith & Waterman, 1981). In order to understand the basics of NW/SW algorithms, however, relevant pseudocodes are presented in Appendix A and Appendix B with illustrative examples.…”

“…Notwithstanding the prevalence of such methods, the study addressed in this paper is conceived and motivated by the consideration of finding locally-significant micro-, meso-or macroscopic artifacts in a test image vis-à-vis in a template using a novel binary correlator approach with the underlying correlation exercised via the so-called Needleman-Wunsch (NW) and Smith-Waterman (SW) algorithms. These algorithms are traditionally adopted in bioinformatics (Arredondo et al, 2005;Needleman & Wunsch, 1970;Neelakanta et al, 2011;Riedel et al, 2008;Smith & Waterman, 1981) to compare a pair of 1D-genomic sequences and deduce the "similarity" features between them. The underlying concept thereof is extended here to compare 2D-images and decide their contrasting and comparable details in quantifiable terms using the NW/SW algorithms.…”

Bioinformatics-Inspired Algorithms for 2D-Image Analysis­­—Application to Medical Images Part II

“…The set of synthetic (It is suggested that, in addition to the original descriptions in (Needleman & Wunsch, 1970) and (Smith, & Waterman, 1981). More relevant details on basic and applied considerations of 1D-NW/SW algorithms can be found across an archival of literature (e.g., Attwood & Parry-Smith, 1999;Riedel et al, 2008;Arredondo et al, 2005;Neelakanta et al, 2011).…”

Parameters Optimization in Entropic Segmentation Processes for the Analysis of Genetic Sequences

“…This method also uses nucleotides and stop codons statistics to analyze the DNA sequence. In [24], authors developed a method for prediction of aberrant splicejunctions in viral DNA context. In this work, the sequence of the Dengue type 1 is analyzed in order to demonstrate the pursuit developed in predicting aberrant splice-junctions at cryptic sites in the test sequence.…”