Ensemble learning based assessment of the role of transcription factors in gene expression.

Cancer cells are formed when the associated, active genes fail to function the way they are meant to function. Multiple genes collectively control cell growth by activating a proper set of genes. Regulation of gene expression is controlled through the combined effort of multiple regulatory elements. Transcription of each gene is affected differently according to the combinatorial patterns of regulatory elements bound in the nearby regions. Identifying and analysing such patterns will give a better insight into the cell function. The main focus of this study is on developing a computational model to predict the functional role of transcriptional factors residing between divergent gene pairs. Acute Myeloid Leukaemia (AML) gene expression data from GEO and the two TFs EP300 and CTCF binding data calibrated in k562 cell line from ENCODE consortium are taken as a case study.

Exon Mapping in Long Noncoding RNAs Using Digital Filters.

Long noncoding RNAs (lncRNAs) which were initially dismissed as "transcriptional noise" have become a vital area of study after their roles in biological regulation were discovered. Long noncoding RNAs have been implicated in various developmental processes and diseases. Here, we perform exon mapping of human lncRNA sequences (taken from National Center for Biotechnology Information GenBank) using digital filters. Antinotch digital filters are used to map out the exons of the lncRNA sequences analyzed. The period 3 property which is an established indicator for locating exons in genes is used here. Discrete wavelet transform filter bank is used to fine-tune the exon plots by selectively removing the spectral noise. The exon locations conform to the ranges specified in GenBank. In addition to exon prediction, G-C concentrations of lncRNA sequences are found, and the sequences are searched for START and STOP codons as these are indicators of coding potential.

Novel Approach to Analyzing MFE of Noncoding RNA Sequences.

Genomic studies have become noncoding RNA (ncRNA) centric after the study of different genomes provided enormous information on ncRNA over the past decades. The function of ncRNA is decided by its secondary structure, and across organisms, the secondary structure is more conserved than the sequence itself. In this study, the optimal secondary structure or the minimum free energy (MFE) structure of ncRNA was found based on the thermodynamic nearest neighbor model. MFE of over 2600 ncRNA sequences was analyzed in view of its signal properties. Mathematical models linking MFE to the signal properties were found for each of the four classes of ncRNA analyzed. MFE values computed with the proposed models were in concordance with those obtained with the standard web servers. A total of 95% of the sequences analyzed had deviation of MFE values within ±15% relative to those obtained from standard web servers.

Discrete wavelet transform de-noising in eukaryotic gene splicing.

BACKGROUND: This paper compares the most common digital signal processing methods of exon prediction in eukaryotes, and also proposes a technique for noise suppression in exon prediction. The specimen used here which has relevance in medical research, has been taken from the public genomic database - GenBank. METHODS: Here exon prediction has been done using the digital signal processing methods viz. binary method, EIIP (electron-ion interaction psuedopotential) method and filter methods. Under filter method two filter designs, and two approaches using these two designs have been tried. The discrete wavelet transform has been used for de-noising of the exon plots. RESULTS: Results of exon prediction based on the methods mentioned above, which give values closest to the ones found in the NCBI database are given here. The exon plot de-noised using discrete wavelet transform is also given. CONCLUSION: Alterations to the proven methods as done by the authors, improves performance of exon prediction algorithms. Also it has been proven that the discrete wavelet transform is an effective tool for de-noising which can be used with exon prediction algorithms.