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Identified Cis-Regulatory Element in the Genome Through the Use HOMER (Research Paper Sample)


Identification of the Cis element involved in regulation allows us to confirm whether it is the same factor that is present in the promoter involved in THE response of the heat shock factor gene to drought stress and to heat stress.
In Vivo Footprinting


There exists different conditions under which HSFs are expressed. Identification of the Cis element involved in regulation allows us to confirm whether it is the same factor that is present in the promoter involved in response of the heat shock factor gene to drought stress and to heat stress.
In Vivo Footprinting
The method can be used via analysis of interaction of the proteins present in DNA and those in apx1 by making use of ligation – mediated PCR-DMS (Harbison et al., 2004). This is done through the use of 2 diverse modifications that make up the model. Visualization, association and blotting process ensures DNA hybridization as well as the extension of the P-labeled primer. In an experiment containing protoplasts obtained from DMS-treated Arabidopsis leaf and a control experiment containing naked DNAs that are DMS-treated, the G factor at different levels such as; G-273, G-272, G-271 and G-269 is protected from modification by DMS on the noncoding strand indicating hypersensitivity to DMS (Dai, et al., 2000). Sequences that are rich in G/C are present in a number of vegetal agents with homology to beans that can be educed with the synthase of ethylene chalcone as well as the fiber agents of avaocado. The Footprinting method indicates that at G-55 there is a very strong hypersensitivity of DMS in HSE (near TATA box) structure settling that HSE plays a critical role in apx1 promoter.
Searching for the Identified Cis-Regulatory Element in the Genome Through the Use Of HOMER
There exists an algorithm designed for novel motive discovery in HOMER that was aimed at fostering the process of regulatory element analysis in genomic uses where DNA is considered instead of the proteins (Du et, al, 2010). The algorithm is distinct in that it is made up of double sequence sets with the motive of identifying regulatory elements precisely enriched in a single set in relation to the other. In its functionality, the zero or one alternative per sequence (ZOOP) coupled with the calculations of hypergeometric enrichment have an aim of determining the motive enrichment. However, there are a number of methods that can be used when performing a motive analysis with HOMER. Nonetheless, there exists only two tools in HOMER that are used in managing all the phases employed in ascertaining motives in genomic and promoter regions. These tools include the and The existing scripts are essential in determining analyzing the genomic sites or a list of genes for motive of enrichment. Nonetheless, similar primary steps are employed in in discovering regulatory elements. Processing involves the extraction of the sequence through the use of the tools, background selection, GC, normalization, autonormalization, parsing input sequence into an oligo table, oligo autonormalization(which is optional), global search phase, matrix optimization, mask and repeat, screening for enrichment of known motifs(through load motif library which involves the search for known motives in the existing data and screening each motif) as well as motif analysis output ( involves motif files and De novo motif output) (Dai, et al, 2000).
Extracting Transcript Abundance Information Using Public Data
A number of tools that perform RNA sequence analysis are available online for public use. One of the famous tools that is available is the cufflinks that contains a lot of information and commands that can be used in execution of numerous features of RNA-Sequence analysis (Zhu, et al., 2006). Cufflinks is known to be famous for its ability to act as a point of reference for de novo transcriptome assembling. This means that it has the capability to make use of the provided transcription info to examine the configuration of novel transcripts to the genome. The functionality of this tool is distinct compared to other assemblers like Trinity that are known to work openly from the structures of RNA whose structure and existence does not depend on the genome as a necessity. All this data present in Cufflinks is available as public data for anyone who is interested in obtaining transcript abundance information for the Cis regulatory element (Trapnell et al, 2012)
Transcription Factor Activity
There exists a number of methods that are known to predict the changes in activities within the transcription factor. These methods are known to measure expression info from a specified regulatory network. However the expression measurements are faced with numerous challenges due to the existence of irrelevant regulations and conditions (Zhu, et al, 2006). More challenge is originates from the fact that most of genes regulatory networks contain incomplete information that is hard to rely on. Nonetheless, the nature and combination ratio of the active TFs that can trigger an alteration on the marked gene has remained to be mystery. A method that can be absolutely relied on to do the required evaluations on the changes in target gene activities is missing.
However, there exists an evaluation strategy that provides an indication for the number of target genes the viewer mien vicissitudes can be clarified by specific active TFs set. The meticulous grouping of active TFS that can act as a gene activator has not yet been known. This has presented itself as a problem that must be dealt with. To overcome it, we assume the explanation of a gene can only be made if there exists a certain combination where the active TFs predicted creates a possibility of explaining the variations being perceived in a gene (Zhu, et al, 2006). The inconsistency score (i-score) in such a case is introduced to determine the quantity of the genes that could not be explained by the changes of the activities of TFs. The Act-SATA and Act-A are methods that can be presented to yield ideal sets of TF action vicissitudes. The yielded ideal TF sets can then be used in the investigation of the complex interaction of the countenance and network data.
A weighted max-SATA problem can be moulded as an optimization of the i-score. This problem can then be solved by using the Max-SAT solver. A SAT formulae that contains every clause’s weight makes it easy for the solvers to determine the minimal i-score. There exists three variables for every TFi. One of them () indicates that TF is less active, the second one () shows that TF is more active and the last one () indicates that TF is neutral. There is additional of a single clause in the formulae for every target gene (Zhu, et al, 2006).
Due to the time length presumed to be taken by SAT solvers, a more flexible formulae of informed search algorithm base on A* can be employed. Here, there is extension of the incomplete solutions that yields complete elucidations that can be termed as relevant. The formulae can be employed where there is a need to find the best elucidation with active TFs domain extending to the point N as the optimum. Partial solutions are made up of active TFs that are less than N. partial elucidations where no ctive TFs exists acts as the initial point for the search. The extension junctures of the TFs that are not active are then set to a less (A−) or more active (A+) state (Zhu, et al, 2006).
Identifying the Additional Target of Heat Shock Factor
HSF is known to be the primary regulator of the transcriptional responses of the heat shock. The target genes for the HSF1 can be identified by conductive a comparative transcriptomic analysis (Pheasant & Mattick, 2007). This is done HSF that has a deficiency of oocytes and wild types. The process indicates a network of meiotic genes that play the role of synaptonemal complex structures as well as cohesion. This network is also involved in spindle assembly checkpoint and recombination of DNA. They were all seen to regulated by HSF1 in both adults and the female embryotic phase (Li et al, 2009).
How to Expand the Gene Regulatory Network
A developmental gene regulatory network is studied in the lab by the use of sea urchins since they are perfect specimens to perform on experiment on analyzing the gene network and the development of an embryo (Li et al, 2009). Two genes are identified with (Kirrell) acting as an encoder for the cell proteins that act as a mediator for the cell-cell fusion. The other gene (tgfbrtll) acts as a medium for encoding the receptors on the cell surface responsible for transducing signals originating from the family of TGFβ ligands (Pheasant & Mattick, 2007). The protein is later on involved in the development and signal mediation within the embryonic tissues involved in bone formation.
Rather than relying so much on the traditional in vivo footprinting techniques, one can employ the modern techniques which can allow the identification of the elements of cis that are involved in the regulation of the condition-dependent gene. Such methods can be, the use of the data obtained from optimized ligation, HCI DNA cleavage and DMS methylation for analysis by ivFAST. The method allows automation and fast processing of the data. It also allows quantitative and high-throughput approaches since it facilitates the comparison of several results from in...
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