There exists different conditionsunder which HSFs are expressed.
Identification of the Cis element involved inregulation allows us to confirm whether it is the same factor that is presentin the promoter involved in response of the heat shock factor gene to droughtstress and to heat stress. InVivo Footprinting The method can be used via analysisof interaction of the proteins present in DNA and those in apx1 by making useof ligation – mediated PCR-DMS (Harbison et al., 2004). This is done throughthe use of 2 diverse modifications that make up the model. Visualization, association and blottingprocess ensures DNA hybridization as well as the extension of the P-labeledprimer. In an experiment containing protoplasts obtained from DMS-treatedArabidopsis leaf and a control experiment containing naked DNAs that areDMS-treated, the G factor at different levels such as; G-273, G-272, G-271 andG-269 is protected from modification by DMS on the noncoding strand indicatinghypersensitivity to DMS (Dai, et al.
, 2000). Sequences that are rich in G/C arepresent in a number of vegetal agents with homology to beans that can be educedwith 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 stronghypersensitivity of DMS in HSE (near TATA box) structure settling that HSEplays a critical role in apx1 promoter.Searchingfor the Identified Cis-Regulatory Element in the Genome Through the Use OfHOMER There exists an algorithm designedfor novel motive discovery in HOMER that was aimed at fostering the process ofregulatory element analysis in genomic uses where DNA is considered instead ofthe proteins (Du et, al, 2010).
The algorithm is distinct in that it is made upof double sequence sets with the motive of identifying regulatory elementsprecisely enriched in a single set in relation to the other. In itsfunctionality, the zero or one alternative per sequence (ZOOP) coupled with thecalculations of hypergeometric enrichment have an aim of determining the motiveenrichment. However, there are a number of methods that can be used whenperforming a motive analysis with HOMER.
Nonetheless, there exists only twotools in HOMER that are used in managing all the phases employed inascertaining motives in genomic and promoter regions. These tools include thefindMotiveGenome.pl and findMotive.pl. The existing scripts are essential indetermining analyzing the genomic sites or a list of genes for motive of enrichment.
Nonetheless, similar primary steps are employed in in discovering regulatoryelements. Processing involves the extraction of the sequence through the use ofthe tools, background selection, GC, normalization, autonormalization, parsinginput sequence into an oligo table, oligo autonormalization(which is optional),global search phase, matrix optimization, mask and repeat, screening forenrichment of known motifs(through load motif library which involves the searchfor known motives in the existing data and screening each motif) as well asmotif analysis output ( involves motif files and De novo motif output) (Dai, etal, 2000).ExtractingTranscript Abundance Information Using Public Data A number of tools that perform RNAsequence analysis are available online for public use. One of the famous toolsthat is available is the cufflinks that contains a lot of information andcommands that can be used in execution of numerous features of RNA-Sequenceanalysis (Zhu, et al.
, 2006). Cufflinks is known to be famous for its abilityto act as a point of reference for de novo transcriptome assembling. This meansthat it has the capability to make use of the provided transcription info toexamine the configuration of novel transcripts to the genome.
The functionalityof this tool is distinct compared to other assemblers like Trinity that areknown to work openly from the structures of RNA whose structure and existencedoes not depend on the genome as a necessity. All this data present inCufflinks is available as public data for anyone who is interested in obtainingtranscript abundance information for the Cis regulatory element (Trapnell etal, 2012)TranscriptionFactor Activity There exists a number of methodsthat are known to predict the changes in activities within the transcriptionfactor. These methods are known to measure expression info from a specifiedregulatory network.
However the expression measurements are faced with numerouschallenges due to the existence of irrelevant regulations and conditions (Zhu,et al, 2006). More challenge is originates from the fact that most of genesregulatory networks contain incomplete information that is hard to rely on.Nonetheless, the nature and combination ratio of the active TFs that cantrigger an alteration on the marked gene has remained to be mystery. A methodthat can be absolutely relied on to do the required evaluations on the changesin target gene activities is missing. However, there exists an evaluationstrategy that provides an indication for the number of target genes the viewermien vicissitudes can be clarified by specific active TFs set. The meticulousgrouping 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 certaincombination where the active TFs predicted creates a possibility of explainingthe variations being perceived in a gene (Zhu, et al, 2006).
The inconsistencyscore (i-score) in such a case is introduced to determine the quantity of thegenes that could not be explained by the changes of the activities of TFs. TheAct-SATA and Act-A are methods that can be presented to yield ideal sets of TFaction vicissitudes. The yielded ideal TF sets can then be used in theinvestigation of the complex interaction of the countenance and network data. A weighted max-SATA problem can bemoulded as an optimization of the i-score. This problem can then be solved byusing the Max-SAT solver.
A SAT formulae that contains every clause’s weightmakes it easy for the solvers to determine the minimal i-score. There existsthree variables for every TFi. Oneof them () indicates that TF is less active,the second one () shows that TF is more active andthe 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 tobe taken by SAT solvers, a more flexible formulae of informed search algorithmbase on A* can be employed.
Here, there is extension of the incompletesolutions that yields complete elucidations that can be termed as relevant. Theformulae can be employed where there is a need to find the best elucidationwith active TFs domain extending to the point N as the optimum. Partialsolutions are made up of active TFs that are less than N. partial elucidationswhere no ctive TFs exists acts as theinitial point for the search. The extension junctures of the TFs that are notactive are then set to a less (A?) or more active (A+)state (Zhu, et al, 2006).
Identifyingthe Additional Target of Heat Shock Factor HSF is known to be the primaryregulator of the transcriptional responses of the heat shock. The target genesfor the HSF1 can be identified by conductive a comparative transcriptomicanalysis (Pheasant& Mattick, 2007). This is done HSF that has a deficiencyof oocytes and wild types. The process indicates a network of meiotic genesthat play the role of synaptonemal complex structures as well as cohesion. Thisnetwork is also involved in spindle assembly checkpoint and recombination ofDNA. They were all seen to regulated by HSF1 in both adults and the femaleembryotic phase (Li et al, 2009).
Howto Expand the Gene Regulatory Network Adevelopmental gene regulatory network is studied in the lab by the use of seaurchins since they are perfect specimens to perform on experiment on analyzing the gene network and the development ofan embryo (Li et al, 2009). Two genes are identified with (Kirrell) acting asan encoder for the cell proteins that act as a mediator for the cell-cellfusion. The other gene (tgfbrtll) acts asa medium for encoding the receptors on the cell surface responsible fortransducing signals originating from the family of TGF? ligands (Pheasant & Mattick, 2007).The protein is later on involved in the development and signal mediation withinthe embryonic tissues involved in bone formation. Recommendation Rather than relying so much on thetraditional in vivo footprinting techniques, one can employ the moderntechniques which can allow the identification of the elements of cis that areinvolved in the regulation of the condition-dependent gene.
Such methods canbe, the use of the data obtained from optimized ligation, HCI DNA cleavage andDMS methylation for analysis by ivFAST. The method allows automation and fastprocessing of the data. It also allows quantitative and high-throughputapproaches since it facilitates the comparison of several results from in vivofootprinting obtained from diverse conditions. The HOMER software is an amazingsoftware to use since it is an automated process that aims at finding theenriched motifs present in the sequence peaks of ChlP-Seq. The users are onlyin need of files that contain genomic coordinates that are then fed into thesoftware that takes care of the rest of the processes that are required.However, it is recommended that the users of the software should configure theappropriate genome with the software to achieve the desired results. Also, theusers of the software should ensure that they have write permissions if theywant to access the genomic directory to avoid being locked out since thesoftware derives the data for use from the directory it creates when it is run. When extracting Transcript AbundanceInformation from public data using cufflinks, it is recommended for the user tomake use of single BAM file for every experiment that the user wants toanalyze.
The user must also make sure files produced by any read alignmentprogram should first be converted into BAM file form to ensure that they workcorrectly with the cufflink. Whenexamining the transcription factor activity, i-score formulae can be used. Theoptimization counterpart of the SAT is the max-SAT which is used to thesatisfaction of the maximum number of clauses in Boolean formulae. However, inthis case, the DPLL branch bound algorithm can be used. This is because thealgorithm is known to be one the most exact and competitive algorithms forsolving the max-SAT problems hence yielding the desired results.
When identifying the additionaltarget of heat factor, the tests can be applied in growth experiments. To easethe work, the combination of drug effects can be determined by calculating theCI (combination effect) values by the use of calcusyn software rather than theuse of traditional manual calculation mode which was tedious and susceptible toerrors. Additive effects on the software are represented by a CI value of 1.
Avalue that is less than 1 designates synergistic drug interfaces. In an expansion of gene regulatorynetwork, GRACE (Gene Regulatory network interface Accuracy Enhancement)algorithm can be used which generates predictions of the high-confidencenetwork through the use of Markov Random Fields in semi-supervised techniquesfor eukaryotes. This helps to facilitate candidate selection for experiments aswell as well as in the improvement of the accuracy of the interface of generegulatory network.