Riboswitches, the small structured RNA elements, were discovered about a decade ago. It has been the subject of intense interest to identify riboswitches, understand their mechanisms of action and use them in genetic engineering. The accumulation of genome and transcriptome sequence data and comparative genomics provide unprecedented opportunities to identify riboswitches in the genome. In the present study, we have evaluated the following six machine learning algorithms for their efficiency to classify riboswitches: J48, BayesNet, Naïve Bayes, Multilayer Perceptron, sequential minimal optimization, hidden Markov model (HMM). For determining effective classifier, the algorithms were compared on the statistical measures of specificity, sensitivity, accuracy, F-measure and receiver operating characteristic (ROC) plot analysis. The classifier Multilayer Perceptron achieved the best performance, with the highest specificity, sensitivity, F-score and accuracy, and with the largest area under the ROC curve, whereas HMM was the poorest performer. At present, the available tools for the prediction and classification of riboswitches are based on covariance model, support vector machine and HMM. The present study determines Multilayer Perceptron as a better classifier for the genome-wide riboswitch searches.

Stresses have been known to cause various responses like cellular physiology, gene regulation, and genome remodeling in the organism to cope and survive. Here, we assessed the impact of stress conditions on the chromatin-interactome network of Arabidopsis thaliana. We identified thousands of chromatin interactions in native as well as in salicylic acid treatment and high temperature conditions in a genome-wide fashion. Our analysis revealed the definite pattern of chromatin interactions and stress conditions could modulate the dynamics of chromatin interactions. We found the heterochromatic region of the genome actively involved in the chromatin interactions. We further observed that the establishment or loss of interactions in response to stress does not result in the global change in the expression profile of interacting genes; however, interacting regions (genes) containing motifs for known TFs showed either lower expression or no difference than non-interacting genes. The present study also revealed that interactions preferred among the same epigenetic state (ES) suggest interactions clustered the same ES together in the 3D space of the nucleus. Our analysis showed that stress conditions affect the dynamics of chromatin interactions among the chromatin loci and these interaction networks govern the folding principle of chromatin by bringing together similar epigenetic marks.

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Histone variants replace canonical histones in nucleosomes, sometimes changing nucleosome function. Histone variant evolution is poorly characterized, and, as we show here, reconstruction of histone protein evolution can be challenging given large differences in rates across gene lineages and across sites. The positions of introns that interrupt genes can provide complementary phylogenetic information. We combined sequence and intron data to reconstruct the evolution of three histone H2A variants in Caenorhabditis elegans to reveal disparate histories. For the variant HIS-35 (which differs from H2A by only a single glycine-to-alanine C-terminal change), we find no evidence for the hypothesis of distinct protein function: the HIS-35 alanine is ancestral and common across canonical Caenorhabditis H2A sequences, with one species encoding identical HIS-35 and canonical H2A proteins. We propose instead that HIS-35 allows for H2A expression outside of the S-phase. Genes encoding such backup functions could be functionally important yet readily replaceable; consistent with this notion, both HTAS-1 and HIS-35 exhibit phylogenetic patterns that combine long-term evolutionary persistence and recurrent loss. Finally, the H2A.Z homolog, HTZ-1, exhibits recurrent intron loss and gain, suggesting that it is intron presence, rather than a specific intron sequence or position, that may be important in histone variant expression.