This document describes using a Bayesian network approach to build a gene regulatory network in response to cold stress in plants. Microarray data from Arabidopsis plants exposed to cold temperatures over time was analyzed using local pooled error tests to identify differentially expressed genes. The GeneNet algorithm was then applied to the differentially expressed genes to infer partial directed networks and identify master regulatory genes that may play important roles in cold stress response signaling pathways. The network provides a framework for further experimental validation of key genes.
Network pharmacology: From BioAssay Response Data to NetworkBin Chen
Network pharmacology, comparing protein pharmacology networks built from Ligand based approach (Similarity Ensemble Approach) with those built from BioAssay response data.
Austin Neurology & Neurosciences is an open access, peer reviewed, scholarly journal dedicated to publish articles covering all areas of Neurology & Neurological Sciences.
The journal aims to promote research communications and provide a forum for doctors, researchers, physicians and healthcare professionals to find most recent advances in all areas of Neurology & Neurological Sciences. Austin Neurology & Neurosciences accepts original research articles, reviews, mini reviews, case reports and rapid communication covering all aspects of neurology & neurosciences.
Austin Neurology & Neurosciences strongly supports the scientific up gradation and fortification in related scientific research community by enhancing access to peer reviewed scientific literary works. Austin Publishing Group also brings universally peer reviewed journals under one roof thereby promoting knowledge sharing, mutual promotion of multidisciplinary science.
Network pharmacology: From BioAssay Response Data to NetworkBin Chen
Network pharmacology, comparing protein pharmacology networks built from Ligand based approach (Similarity Ensemble Approach) with those built from BioAssay response data.
Austin Neurology & Neurosciences is an open access, peer reviewed, scholarly journal dedicated to publish articles covering all areas of Neurology & Neurological Sciences.
The journal aims to promote research communications and provide a forum for doctors, researchers, physicians and healthcare professionals to find most recent advances in all areas of Neurology & Neurological Sciences. Austin Neurology & Neurosciences accepts original research articles, reviews, mini reviews, case reports and rapid communication covering all aspects of neurology & neurosciences.
Austin Neurology & Neurosciences strongly supports the scientific up gradation and fortification in related scientific research community by enhancing access to peer reviewed scientific literary works. Austin Publishing Group also brings universally peer reviewed journals under one roof thereby promoting knowledge sharing, mutual promotion of multidisciplinary science.
Genome-wide association studies (GWAS) have been providing valuable insight to the genetics of common and complex diseases for many years. In this webcast we will walk through one possible workflow for completing GWAS in Golden Helix SNP & Variation Suite (SVS) with special attention paid to adjusting analysis for population stratification.
A simple immune system model of effector cells and regulator cells is coupled to a compartment model tuned to neuroblastoma xenograft data. Radiation is modeled as a raised death rate on a fixed day that reduces tumor size by transferring a fraction of cells into a compartment of “doomed” cells whose presence can be detected by effector cells, producing an immune response. The model predicts that the
time required to regrow the tumor to its initial size is positively correlated with the ratio of effector to regulator cells naturally present in the patient. Individual immune cell distribution is likely to affect the patient’s response to radiation therapy.
When infected by virus, plants put up a defense!
The defense mechanism of Cassava when infected with CMV using a computational technique based on NGS high throughput sequencing data.
In this work, we aim to study the defense mechanism of Cassava
when infected with CMV using a computational technique based on
NGS high throughput sequencing data
1. Identify the small RNAs that are produced upon CMV infection
2. Predict the gene targets of these small RNAs
3. Compare the expression profiles of the susceptible clones with
respect to the resistant clones to CMD
This is a presentation that was given at the Berlin Erlounge Erlang meetup. It is about the distributed cluster scheduler we use at Game Analytics. It was open sourced at the end of the presentation. You can find out more at GitHub, https://github.com/GameAnalytics/gascheduler.
Esto es un trabajo realizado para mi clase de Topics in Linguistics de la carrera de Lingüística Aplicada. Se trata de un portafolio donde yo y mi grupo respondemos preguntas sacadas de un libro sobre la enseñanza de lenguas y la tecnología.
Saxo Capital Market's new infographic unveils the scale of the fintech ‘boom’, ignited by a level of global investment which has more than tripled since 2008. The most significant growth has occurred throughout northern Europe – in the UK, Germany and the Nordics – enabling new industry players greater access to new opportunities in the fintech space.
Shree Sony Elevators Company is based in Pune and one of the fastest growing elevators Company in India, established in 2002; since the last 12 years the company precisely offers you service such as erection, inspection, maintenance, repairs and modernization of your lift.
Genome-wide association studies (GWAS) have been providing valuable insight to the genetics of common and complex diseases for many years. In this webcast we will walk through one possible workflow for completing GWAS in Golden Helix SNP & Variation Suite (SVS) with special attention paid to adjusting analysis for population stratification.
A simple immune system model of effector cells and regulator cells is coupled to a compartment model tuned to neuroblastoma xenograft data. Radiation is modeled as a raised death rate on a fixed day that reduces tumor size by transferring a fraction of cells into a compartment of “doomed” cells whose presence can be detected by effector cells, producing an immune response. The model predicts that the
time required to regrow the tumor to its initial size is positively correlated with the ratio of effector to regulator cells naturally present in the patient. Individual immune cell distribution is likely to affect the patient’s response to radiation therapy.
When infected by virus, plants put up a defense!
The defense mechanism of Cassava when infected with CMV using a computational technique based on NGS high throughput sequencing data.
In this work, we aim to study the defense mechanism of Cassava
when infected with CMV using a computational technique based on
NGS high throughput sequencing data
1. Identify the small RNAs that are produced upon CMV infection
2. Predict the gene targets of these small RNAs
3. Compare the expression profiles of the susceptible clones with
respect to the resistant clones to CMD
This is a presentation that was given at the Berlin Erlounge Erlang meetup. It is about the distributed cluster scheduler we use at Game Analytics. It was open sourced at the end of the presentation. You can find out more at GitHub, https://github.com/GameAnalytics/gascheduler.
Esto es un trabajo realizado para mi clase de Topics in Linguistics de la carrera de Lingüística Aplicada. Se trata de un portafolio donde yo y mi grupo respondemos preguntas sacadas de un libro sobre la enseñanza de lenguas y la tecnología.
Saxo Capital Market's new infographic unveils the scale of the fintech ‘boom’, ignited by a level of global investment which has more than tripled since 2008. The most significant growth has occurred throughout northern Europe – in the UK, Germany and the Nordics – enabling new industry players greater access to new opportunities in the fintech space.
Shree Sony Elevators Company is based in Pune and one of the fastest growing elevators Company in India, established in 2002; since the last 12 years the company precisely offers you service such as erection, inspection, maintenance, repairs and modernization of your lift.
RT-PCR and DNA microarray measurement of mRNA cell proliferationIJAEMSJORNAL
For mRNA quantification, RT-PCR and DNA microarrays have been compared in few studies
(RT-PCR). Healing callus of adult and juvenile rats after femur injury was found to be rich in mRNA at
various stages of the healing process. We used both methods to examine ten samples and a total of 26 genes.
Internal DNA probes tagged with 32P were employed in reverse transcription-polymerase chain reaction
(RT-PCR) to identify genes (RT-PCR). Ten Affymetrix® Rat U34A cRNA microarrays were hybridized with
biotin-labeled cRNA generated from mRNA. There was a wide range of correlation coefficients (r) between
RT-PCR and microarray data for each gene. Meaning became genetically unique because of this diversity.
Relatively lowly expressed genes had the highest r values. The distance between PCR primers and
microarray probes was found to be higher than previously assumed, leading to a drop in agreement between
microarray calls and PCR outcomes. Microarray research showed that RT-PCR expression levels for two
genes had a "floor effect." As a result, PCR primers and microarray probes that overlap in mRNA expression
levels can provide good agreement between these two techniques.
Talk and poster presented at the American Society for Biochemistry and Molecular Biology/Experimental Biology Conference on April 4, 2016. Abstract: A gene regulatory network (GRN) consists of genes, transcription factors, and the regulatory connections between them that govern the level of expression of mRNA and proteins from those genes. Our group has developed a MATLAB software package, called GRNmap, that uses ordinary differential equations to model the dynamics of medium-scale GRNs. The program uses a penalized least squares approach (Dahlquist et al. 2015, DOI: 10.1007/s11538-015-0092-6) to estimate production rates, expression thresholds, and regulatory weights for each transcription factor in the network based on gene expression data, and then performs a forward simulation of the dynamics of the network. GRNmap has options for using a sigmoidal or Michaelis-Menten production function. Parameters for a series of related networks, ranging in size from 15 to 35 genes, were optimized against DNA microarray data measuring the transcriptional response to cold shock in wild type and five strains individually deleted for the transcription factors, Cin5, Gln3, Hap4, Hmo1, Zap1, of budding yeast, Saccharomyces cerevisiae BY4741. Model predictions fit the experimental data well, within the 95% confidence interval. Open source code and a compiled executable that can run without a MATLAB license are available from http://kdahlquist.github.io/GRNmap/. GRNsight is an open source web application for visualizing such models of gene regulatory networks. GRNsight accepts GRNmap- or user-generated spreadsheets containing an adjacency matrix representation of the GRN and automatically lays out the graph of the GRN model. The application colors the edges and adjusts their thicknesses based on the sign (activation or repression) and the strength (magnitude) of the regulatory relationship, respectively. Users can then modify the graph to define the best visual layout for the network. The GRNsight open source code and application are available from http://dondi.github.io/GRNsight/index.html.
A clonal based algorithm for the reconstruction of genetic network using s sy...eSAT Journals
Abstract Motivation: Gene regulatory network is the network based approach to represent the interactions between genes. DNA microarray is the most widely used technology for extracting the relationships between thousands of genes simultaneously. Gene microarray experiment provides the gene expression data for a particular condition and varying time periods. The expression of a particular gene depends upon the biological conditions and other genes. In this paper, we propose a new method for the analysis of microarray data. The proposed method makes use of S-system, which is a well-accepted model for the gene regulatory network reconstruction. Since the problem has multiple solutions, we have to identify an optimized solution. Evolutionary algorithms have been used to solve such problems. Though there are a number of attempts already been carried out by various researchers, the solutions are still not that satisfactory with respect to the time taken and the degree of accuracy achieved. Therefore, there is a need of huge amount further work in this topic for achieving solutions with improved performances. Results: In this work, we have proposed Clonal selection algorithm for identifying optimal gene regulatory network. The approach is tested on the real life data: SOS Ecoli DNA repairing gene expression data. It is observed that the proposed algorithm converges much faster and provides better results than the existing algorithms. Index Terms: Microarray analysis, Evolutionary Algorithm, Artificial Immune System, S-system, Gene Regulatory Network, SOS Ecoli DNA repairing, Clonal Selection Algorithm.
Analysis of gene expression microarray data of patients with Spinal Muscular ...Anton Yuryev
By examining experimental gene expression data researchers can identify potential upstream regulatory factors that may control key biological processes. In this paper we examine the effectiveness of two similar approaches to this type of identification using a publicly available data set from research done on Spinal Muscular Atrophy.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Genome-wide transcription profiling is a powerful technique in studying disease susceptible footprints. Moreover, when applied to disease tissue it may reveal quantitative and qualitative alterations in gene expression that give information on the context or underlying basis for the disease and may provide a new diagnostic approach. However, the data obtained from high-density microarrays is highly complex and poses considerable challenges in data mining. Past researches prove that neuro diseases damage the brain network interaction, protein- protein interaction and gene-gene interaction. A number of neurological research paper also analyze the relationship among damaged part. Analysis of gene-gene interaction network drawn by using state-of-the-art gene database of Alzheimer’s patient can conclude a lot of information. In this paper we used gene dataset affected with Alzheimer’s disease and normal patient’s dataset from NCBI databank. After proper processing the .CEL affymetrix data using RMA, we use the processed data to find gene interaction outputs. Then we filter the output files using probe set filtering attributes p-value and fold count and draw a gene-gene interaction network. Then we analyze the interaction network using GeneMania software.
ABSTRACT
Genome-wide transcription profiling is a powerful technique in studying disease susceptible footprints. Moreover, when applied to disease tissue it may reveal quantitative and qualitative alterations in gene expression that give information on the context or underlying basis for the disease and may provide a new diagnostic approach. However, the data obtained from high-density microarrays is highly complex and poses considerable challenges in data mining. Past researches prove that neuro diseases damage the brain network interaction, protein- protein interaction and gene-gene interaction. A number of neurological research paper also analyze the relationship among damaged part. Analysis of gene-gene interaction network drawn by using state-of-the-art gene database of Alzheimer’s patient can conclude a lot of information. In this paper we used gene dataset affected with Alzheimer’s disease and normal patient’s dataset from NCBI databank. After proper processing the .CEL affymetrix data using RMA, we use the processed data to find gene interaction outputs. Then we filter the output files using probe set filtering attributes p-value and fold count and draw a gene-gene interaction network. Then we analyze the interaction network using GeneMania software.
Widespread and precise reprogramming of yeast protein–genome interactions in ...Cornell University
Gene expression is controlled by a variety of proteins that interact with the genome. Their precise organization and mech- anism of action at every promoter remains to be worked out. To better understand the physical interplay among genome- interacting proteins, we examined the temporal binding of a functionally diverse subset of these proteins: nucleosomes (H3), H2AZ (Htz1), SWR (Swr1), RSC (Rsc1, Rsc3, Rsc58, Rsc6, Rsc9, Sth1), SAGA (Spt3, Spt7, Ubp8, Sgf11), Hsf1, TFIID (Spt15/TBP and Taf1), TFIIB (Sua7), TFIIH (Ssl2), FACT (Spt16), Pol II (Rpb3), and Pol II carboxyl-terminal domain (CTD) phosphory- lation at serines 2, 5, and 7. They were examined under normal and acute heat shock conditions, using the ultrahigh reso- lution genome-wide ChIP-exo assay in Saccharomyces cerevisiae. Our findings reveal a precise positional organization of proteins bound at most genes, some of which rapidly reorganize within minutes of heat shock. This includes more precise positional transitions of Pol II CTD phosphorylation along the 5′ ends of genes than previously seen. Reorganization upon heat shock includes colocalization of SAGA with promoter-bound Hsf1, a change in RSC subunit enrichment from gene bodies to promoters, and Pol II accumulation within promoter/+1 nucleosome regions. Most of these events are widespread and not necessarily coupled to changes in gene expression. Together, these findings reveal protein–genome interactions that are robustly reprogrammed in precise and uniform ways far beyond what is elicited by changes in gene expression.
2. 1
A Bayesian Network Approach to Building Gene
Regulatory Networks
Jeremy Burrell, Chao-Jen Wong, Joan Chen
Abstract
Continuous abiotic stresses (such as extreme temperatures, high winds and edaphic
conditions) can have adverse effects on plant life. This can become a major constraint in
crop production. In order to alleviate these problems, it is important to understand the
cold stress mechanisms at the molecular and cellular levels, regarding the signaling
pathways from cold perception to activation of gene expression. We took a system
biology approach, aiming to build the gene regulatory network using the cold stress
microarray data that have been obtained, to gain a better understanding of plant responses
to cold stress at the molecular level. The results generated from this approach will be
utilized to generate hypothesis, which can be tested by other experimental approaches.
To discover the gene regulatory network in response to cold stress on plants, we have
employed a new proposed reverse engineering method implemented in the "GeneNet" R
package (Opgen-Rhein and Strimmer 2007). This method is a heuristic algorithm
applying an extended graphical Gaussian model and dynamical correlation shrinkage
estimators to the inference of partial directed causal networks from high-dimensional
time series expression data.
1 Introduction
Background:
Plants respond to various environmental stresses such as cold stress by activating the
expression of a large number of genes through a series of signal transduction cascades. These
signal transduction cascades, often times interact with each other, compose the signaling
networks, which eventually govern the transcriptional regulation of expression of genes which
are involved in protecting plants from cold damage. Thus understanding the molecular
mechanisms of the signal transduction network will greatly help in plant’s stress tolerance, which
in turn, will have a big economic influence on agricultural business.
Previous research has suggested that plant cold responsive pathways involve a large
number of genes which interact with each other. Some of the smaller signaling networks have
already provided valuable information about how genes interact with each other. To gain a more
complete hierarchical view of the networks in plant cold stress response, we employed a large
dataset which contains the microarray data from more than 20 ATH1 chips. This data was
obtained from plants which were cold treated for various periods of time, thus we aim to identify
genes which might play central regulatory roles in cold response.
3. 2
Methods and Materials:
The GeneChip®
microarray data was downloaded from publicly available database
(http://www.arabidopsis.org/info/expression/ATGenExpress.jsp). Plant total RNA were extracted
from leaves of 18-day old plants treated with cold (40
C) for the indicated time periods, according
to the standard protocol. cRNA labeling, hybridization and scanning was described in Hannah et
al (2006). The Arabidopsis ATH1 whole genome microarray from Affymetrix was used for this
set of cold stress experiments.
All the data analysis was performed in R. The raw data file (.cel) files from chip
hybridization were read directly into R. Robust Multi-Chip Analysis (RMA) algorithm was used
for chip background correction, normalization and to obtain gene expression estimates.
2 Local Pooled Error (LPE) Test
Using the Local-Pooled Error (LPE) test we estimate the significance of each genes
differential expression. LPE test is based on pooling errors within genes and between replicate
arrays for genes in which expression values are similar (Jain et. al. 2003).
LPE is most useful when dealing with a low number of replicates (i.e. 2-3). This is one reason
for using the LPE test rather than the traditional 2-sample t-statistics. Also, within-gene estimates
of variance do not provide a reliable hypothesis-testing framework.
We applied the LPE test to genes at each time point (30 min, 1 hr, 3 hr, 6 hr, 12 hr, 24 hr)
in relation to the initial time point (0 minutes) and computed the corresponding p-values for each
gene. Genes with a significant p-value of p ≤ 0.01 were chosen to input into the GeneNet
algorithm.
3 GeneNet
GeneNet implements a statistical learning algorithm proceeding in two steps: (i)
transform correlation network into a partial correlation network, which is an undirected graph
displaying the linear associations, and (ii) convert the undirected graph into a partially directed
graph by estimating pairwise log-ratios of standardized partial variances.
Consider a linear regression with Y as response and the set of vectors {X1,...,Xk,...,XK }
as covariates. The regression coefficient estimator of Y is defined by
βk
γ
= ˜ργk
A
SPVγ
SPVk
B
σγ
2
σk
2
C
,
where ˜ργk is the partial correlation between Y and Xk , and SPVγ = ˜σγ
2
σγ
2
is called the
standardized partial variance.
A: Establish edges between nodes. An edge is drawn by the multiple testing A ≠ 0.
B: Determine the directionality of edges of a causal network. If logB ≠ 0 evaluated by the
multiple testing, edges are directed in a fashion such that the direction of an arrow points from
the node with larger standardized partial variance to the node with smaller standardized partial
variance.
4. 3
4 Conclusion
Using the union of up-regulated genes (figure 1), the union of down-regulated genes as
well as the union of both up and down-regulated genes together, we have built a gene regulatory
network in plant cold stress response, based on the time point microarray data. We have
identified a number of master regulatory genes which might play important roles in transducing
cold stress signals to other genes. The network built in this work sets up a framework for bench
scientists to design experiments to further test the importance of those genes, which might be
important in cold stress response.
Figure 1: Up Regulated Network
5. 4
References
[1] Efron, B. (2004), “Large-Scale Simultaneous Hypothesis Testing: The Choice of a Null
Hypothesis”, J Amer Statist Assoc, 99:96-104.
[2] Hannah MA, Wiese D, Freund S, Fiehn O, Heyer AG, Hincha DK. “Natural Genetic
Variation of Freezing Tolerance in Arabidopsis”, Plant Physiol. 2006 Sep;142(1):98-112.
Epub 2006 Jul 14.
[3] Jain, N. et. al. (2003), “Local-Pooled-Error Test for Identifying Differentially Expressed
Genes With a Small Number of Replicated Microarrys”, Bioinformatics, 19, 15:1945-1951.
[4] Opgen-Rhein, R. and Strimmer, K. (2007), “From Correlation to Causation Networks: A
Simple Approximate Learning Algorithm and its Application to High-Dimensional Plant
Gene Expression Data”, BMC Systems Biology, 1:37.