Genome-wide Prediction Of Gene Cooperation Regulation In Arabidopsis Thaliana

As we know, if a transcription factor can bind on a specific transcriptional regulatory region of DNA, the specific transcription factor binding sites are needed. Since the amount of data is little and the research methods are simple, for transcription factor binding sites of exploration have been very difficult. With a variety of transcription factors and promoter-related databases to improve, the researchers will study the beginning of the focus shifted to the research of the details of promoter and transcription factors. Such as identification of target genes of specific transcription factors, prediction of cis-regulatory elements, the co-occurrence of transcription factors and so on. At the same time, with the rapid development of bioinformatics, life science research methods are also changing fastly. A large number of high-throughput methods allow the emergence of bioinformatics methods based on large-scale studies to identify transcription factor-specific promoter as a possible event. With the start time after the advent of genome, a variety of reliable experimental data to speed up the study of bioinformatics related to technology in the promoter-specific research to identify the speed and further improve the accuracy of the prediction.In addition, all DNA-related activity in the lives of the assistance of specific proteins occurred. The key to the molecular level of these lives are affected by activity and protein-DNA interaction. One of the most important protein-DNA interactions is DNA transcription regulatory region with a combination of transcription factor. Since the molecular level of life activity directly activated the gene transcription regulation. So, for the transcription factor and DNA transcription regulatory region of the study of the combination model is very significant.Not only that, but the protein interaction is becoming the focus of life science research topics. Protein-protein interaction study has been set up means including a series of traditional experimental methods such as: yeast two-hybrid system, mass spectrometry and protein Chip-chips. In recent years, with the development of computer science, machine learning methods of scientific research in biology has become a powerful tool. Protein-protein interaction data and information integration protein notes protein-protein interaction databases are currently the main features. Protein-protein interaction from scientific literature mining automation and information are protein-protein interaction databases to promote the development of one of the main. The interaction between transcription factors is extra special. They may not combine directly, even may not have a higher tendency to co-expression. But they exert an imperceptible influence of synergy in the control of one or more genes. The most crucial fact is that the research of the interaction between transcription factors will be fundamental for us to speed up the cognitive speed of the mode of transcriptional regulation.After the introduction of the transcription factor interactions with DNA and transcription factor interactions, naturally, we will think of that if the co-occurrence of transcription factors is exist, or whether there is value in research. Facts have proved that the co-occurrence of transcription factors regulation do exist, and in some species has been proven. From the experimental results or statistical perspective, the coordination of transcription factors is the most available assume to explain facts. The number of Genes in various species is much greater than transcription factors', and furthermore it is unimaginable to prove that a single transcription factor can regulate a gene. First of all, TFBS of transcription factors in vivo biology are not noticeably different, even transcription factors of different genes almost share the same TFBS! Therefore, regulation of a specific gene is only by a particular transcription factor to locate the TFBS is obviously impossible. This can not help but make people ask: What in the end so that these transcription factors have the meaning of existence? In the end, what kind of role does transcription factors play in transcriptional regulation when there is subtle difference between what?In order to answer this series of questions, life scientists designed a program and a set of experiments to try to solve this puzzle. Among them, the comparison is forming a program at the corresponding transcription factor known case of TFBS algorithm derived from a pair of a common transcription factor gene regulation. And the results of their prediction score in combination with the experimental results obtained by the synergistic regulation of transcription factors to verify. However, the research material in this experiment (Arabidopsis thaliana), there are two difficulties:(1)The vast TFBS of transcription factors are unknown (transcription factor in 2290, only six have a specific TFBS);(2) There is no fact of interaction between transcription factors in Arabidopsis thaliana for reference;In order to solve these two difficulties, first I search the homologous transcription factors in JARSPAR for each DATF proteins (using BLAST, protein sequence alignment, the local version). And these TFBS as a homologous protein of Arabidopsis transcription factors of these TFBS. Once again, use algorithms to identify the transcription factor synergy points and the selection of infants. Finally, I evaluated the results of the experiment and the model of the prediction.My study proposes a new calculation of the prediction of transcription factor target gene. With the transcription factor-related accumulation of information, this method can be applied to transcription factors of other species. In this article, I consider the 6 transcription factors from JARSPAR database as the core data, and predicted their target genes. The result of this prediction is very useful for analysis of further research of transcription factor-related molecular network.