Miniature inverted repeat transposable elements (MITEs) are interesting transposable elements (TEs) because of their high copy numbers and mysterious identifies. Despite their low DNA content percentage in a genome, their numerous copies can disturb genomic stability and cause important genetic variations. Historically, MITE families were often discovered individually, a practice cannot keep pace with the large scale genome sequencing. Automated genome wide discovery of MITE families and their characterization are desirable. We developed a whole pipeline from MITE discovery to detailed analyses of MITEs at genomic scales. Using the pipeline, we performed in depth analyses of the MITE families in multiple recently release crop genomes. These analyses revealed the diversity of MITE families and their evolution in the host genome. We have also predicted the transposases that may be responsible for the mobilization of some MITE families. These MITE families can potentially be used as genetic markers for the improvement of these crop species.
Developing high sensitivity/specificity detection systems for proteomic studies using Adhirons coupled with fluorescence for studying protein
Big Data is another term used to recognize the datasets because of their huge size and unpredictability. We cannot oversee them with our present procedures or information mining programming apparatuses. Huge Data mining is the capacity of extricating helpful data from these extensive datasets or surges of information, that because of its volume, variability, and speed, it was unrealistic before to do it. The Big Data test is getting to be a standout among the most energizing open doors for the following years. This paper proposes Dynamic Energy Management using Big Data Analytics where, our application model of brilliant power lattice empowers a two-path stream of force and information in the middle of suppliers and buyers so as to encourage the force stream advancement as far as financial productivity, unwavering quality and supportability. An attempt is made to present a wide outline of the point, its present status, debate, and figure to what’s to come. We present the same by considering four articles as reference composed by compelling researchers in this field, covering the most fascinating and best in class themes on Big Data mining.
This research concerns cognitive modeling and emotion recognition based on collections of the real time human brain waves (EEG). In this talk, we present the current status of the research field as it is known to us and discuss the two-phase modeling approach under consideration in our study. The raw temporal EEG data are collected from the humans performing a set of given tasks. The data is analyzed using standard signal processing techniques plus some more specific methods, such as the correlational, wavelet and fractals analysis. The EEG electrodes are located in the areas of the scalp specified by American Electroencephalographic Society Standard (in the experimental part of our study, we use Emotiv wireless headset with 14 electrodes located according to the Standard). The collected EEG data corresponding to different cognitive states and emotional responses of the human subjects are used to feed the supervised learning models. The research relies on the Deep Learning models with different levels of depth. The task is to identify the most effective parameters of the learning procedure that would allow to reliably recognizing such emotions, considered to be basic, as anger, disgust, fear, happiness, sadness, and surprise as well as their derivatives: amusement, contempt, contentment, embarrassment, excitement, guilt, pride in achievement, relief, satisfaction, sensory pleasure, and shame. The EEG potentials reflect, indirectly, the spatially distributed brain neuronal signals induced by human cognitive and emotional activities. We suggest that the information about such localized (voxel based) data will be a better input for the Deep Learning model than the surface cranial information since it should provide the larger dimensionality of the supervised learning. Thus, the two-phase model is introduced: The first phase concerns the solution of the large scale inverse problem requiring the Big Data representation while at the second phase the Deep Learning model is used with the output of the first phase as its input. Assuming a model with a given number of the cranial measurements and a number of voxels obtained by subdividing, uniformly, the brain volume, the task is to find the density of the localized currents as a solution of the inverse problem. The method called LORETA is considered to be useful for solving the inverse problem. We work towards some specific applications of human emotion recognition such as Brain Computer Interface (BCI), and evaluation of educational materials and presentations. The methodology and the results obtained so far will be presented.
Genome biology aims at using the complete genome sequences to reconstruct all metabolic and signaling pathways that could operate in the target organisms and identify the likely regulatory hubs and potential drug targets. Such analysis requires comprehensive functional annotation of all proteins encoded in each sequenced genome. Standard sequence analysis typically fails to provide (confident) functional assignment for at least a third of the genes even in the relatively small prokaryotic genomes. As a result, comparative genomics has to deal with the constantly growing numbers of proteins whose functions remain unknown. This talk will discuss using comparative genomics to improve our understanding of microbial metabolic and signaling pathways, including some recent examples of identification of missing enzymes and prediction of alternative enzyme variants. It will show that the number of truly enigmatic conserved hypothetical proteins is relatively small, particularly in the reduced genomes of pathogenic bacteria, which suggests that most of their cellular functions are already accounted for. In contrast, the number of uncharacterized genes in free-living organisms remains quite large and their functions remain obscure. Our current hypothesis is that many of these genes have “house-cleaning” function, which is almost as important as house-keeping, particularly for aerobic bacteria and for eukaryotic cells. We shall also briefly discuss how comparative genomics could be used for identification of priority targets for future research and the challenges in characterization of their functions
