Novel methodologies for spectral classification of exon and intron sequences

Abstract

Digital processing of a nucleotide sequence requires it to be mapped to a numerical sequence in which the choice of nucleotide to numeric mapping affects how well its biological properties can be preserved and reflected from nucleotide domain to numerical domain. Digital spectral analysis of nucleotide sequences unfolds a period-3 power spectral value which is more prominent in an exon sequence as compared to that of an intron sequence. The success of a period-3 based exon and intron classification depends on the choice of a threshold value. The main purposes of this article are to introduce novel codes for 1-sequence numerical representations for spectral analysis and compare them to existing codes to determine appropriate representation, and to introduce novel thresholding methods for more accurate period-3 based exon and intron classification of an unknown sequence. The main findings of this study are summarized as follows: Among sixteen 1-sequence numerical representations, the K-Quaternary Code I offers an attractive performance. A windowed 1-sequence numerical representation (with window length of 9, 15, and 24 bases) offers a possible speed gain over non-windowed 4-sequence Voss representation which increases as sequence length increases. A winner threshold value (chosen from the best among two defined threshold values and one other threshold value) offers a top precision for classifying an unknown sequence of specified fixed lengths. An interpolated winner threshold value applicable to an unknown and arbitrary length sequence can be estimated from the winner threshold values of fixed length sequences with a comparable performance. In general, precision increases as sequence length increases. The study contributes an effective spectral analysis of nucleotide sequences to better reveal embedded properties, and has potential applications in improved genome annotation.

A Novel Circular Mapping Technique for Spectral Classification of Exons and Introns in Human DNA Sequences

“…Here the circular mapping is adopted for exons and introns classifications. The exons classification (EXCLASS), introns classification (INCLASS) and the precision value are evaluated by the following three equations [22] ; EXCLASS = Where, NCEC, NCIC and TEIN are the number of correct exons and the number of correct introns classifications and the total number of exons and introns respectively. Equations (13) - (19) are applied to training and testing sequences downloaded from USCS Assembly database: Feb. 2009 (GRCh37/hg19) (Clade: Mammal, Genome: Human, Assembly: Feb.2009 (GRCh37/hg19), Group: Genes and Gene rediction Tracks, Track: UCSC Genes, Table: knownGene) [23] - [26] .…”

Time-frequency analysis of resting state and evoked EEG data recorded at higher magnetic fields up to 9.4T

“…Eq. (3)) as introduced in (Abbasi et al, 2011;Kwan et al, 2012). Using this approach, we were able to successfully separate artefactual components from components of interest.…”

“…The component selection, however, remains difficult as it involves finding an appropriate threshold criterion for MI analysis. In this study, we introduce a novel approach by combining ICA and MI with the threshold criteria concept introduced by Abbasi and colleagues (Abbasi et al, 2011;Kwan et al, 2012). Consequently, it is possible to find a subset of components that jointly have maximal dependency on the ECG signal.…”

Rejecting deep brain stimulation artefacts from MEG data using ICA and mutual information

“…One can then define an optimum threshold MI thresh based on Gaussian variates to identify ICs attributed to brain activity (Abbasi et al, 2011;Kwan et al, 2012):…”