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.
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.
for the Identified Cis-Regulatory Element in the Genome Through the Use Of
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
findMotiveGenome.pl and findMotive.pl. 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
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
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).
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
& 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).
to Expand the Gene Regulatory Network
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 TGF? 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 vivo
footprinting obtained from diverse conditions.
The HOMER software is an amazing
software to use since it is an automated process that aims at finding the
enriched motifs present in the sequence peaks of ChlP-Seq. The users are only
in need of files that contain genomic coordinates that are then fed into the
software that takes care of the rest of the processes that are required.
However, it is recommended that the users of the software should configure the
appropriate genome with the software to achieve the desired results. Also, the
users of the software should ensure that they have write permissions if they
want to access the genomic directory to avoid being locked out since the
software derives the data for use from the directory it creates when it is run.
When extracting Transcript Abundance
Information from public data using cufflinks, it is recommended for the user to
make use of single BAM file for every experiment that the user wants to
analyze. The user must also make sure files produced by any read alignment
program should first be converted into BAM file form to ensure that they work
correctly with the cufflink.
examining the transcription factor activity, i-score formulae can be used. The
optimization counterpart of the SAT is the max-SAT which is used to the
satisfaction of the maximum number of clauses in Boolean formulae. However, in
this case, the DPLL branch bound algorithm can be used. This is because the
algorithm is known to be one the most exact and competitive algorithms for
solving the max-SAT problems hence yielding the desired results.
When identifying the additional
target of heat factor, the tests can be applied in growth experiments. To ease
the work, the combination of drug effects can be determined by calculating the
CI (combination effect) values by the use of calcusyn software rather than the
use of traditional manual calculation mode which was tedious and susceptible to
errors. Additive effects on the software are represented by a CI value of 1. A
value that is less than 1 designates synergistic drug interfaces.
In an expansion of gene regulatory
network, GRACE (Gene Regulatory network interface Accuracy Enhancement)
algorithm can be used which generates predictions of the high-confidence
network through the use of Markov Random Fields in semi-supervised techniques
for eukaryotes. This helps to facilitate candidate selection for experiments as
well as well as in the improvement of the accuracy of the interface of gene