Yeast Cell-Cycle Regulation Network inference Wang Lin.

Slides:

Advertisements

Similar presentations

STRING Prediction of protein networks through integration of diverse large-scale data sets Lars Juhl Jensen EMBL Heidelberg.

Advertisements

Gene Set Enrichment Analysis Genome 559: Introduction to Statistical and Computational Genomics Elhanan Borenstein.

Outlines Background & motivation Algorithms overview

Periodic clusters. Non periodic clusters That was only the beginning…

Gene Set Enrichment Analysis Genome 559: Introduction to Statistical and Computational Genomics Elhanan Borenstein.

Combined analysis of ChIP- chip data and sequence data Harbison et al. CS 466 Saurabh Sinha.

Global Mapping of the Yeast Genetic Interaction Network Tong et. al, Science, Feb 2004 Presented by Bowen Cui.

. Inferring Subnetworks from Perturbed Expression Profiles D. Pe’er A. Regev G. Elidan N. Friedman.

Orm family proteins mediate sphingolipid homeostasis Presentation by: Gillian Dekkers and Soledad Ordoñez Supervisor: Joost Holthuis David K. Breslow et.

Gene regulation in cancer 11/14/07. Overview The hallmark of cancer is uncontrolled cell proliferation. Oncogenes code for proteins that help to regulate.

Open Day 2006 From Expression, Through Annotation, to Function Ohad Manor & Tali Goren.

Work Process Using Enrich Load biological data Check enrichment of crossed data sets Extract statistically significant results Multiple hypothesis correction.

Genome-wide prediction and characterization of interactions between transcription factors in S. cerevisiae Speaker: Chunhui Cai.

Basis State Prediction of Cell-Cycle Transcription Factors in Saccharomyces cerevisiae Dr. Matteo Pellegrini Dr. Shawn Cokus Sherri Rose UCLA Molecular,

Gene expression analysis summary Where are we now?

Regulatory Network (Part II) 11/05/07. Methods Linear –PCA (Raychaudhuri et al. 2000) –NIR (Gardner et al. 2003) Nonlinear –Bayesian network (Friedman.

Yeast Dataset Analysis Hongli Li Final Project Computer Science Department UMASS Lowell.

Reconstructing Transcription Network in S.cerevisiae WANG Chao Oct. 4, 2004.

Microarrays and Cancer Segal et al. CS 466 Saurabh Sinha.

Modularity in Biological networks.  Hypothesis: Biological function are carried by discrete functional modules.  Hartwell, L.-H., Hopfield, J. J., Leibler,

Functional annotation and network reconstruction through cross-platform integration of microarray data X. J. Zhou et al

Indiana University Bloomington, IN Junguk Hur Computational Omics Lab School of Informatics Differential location analysis A novel approach to detecting.

1 Is Gene Position Adaptively Favored?. 2 Why do we care? Genomic clusters of genes Yeast 98% of genes in metabolic pathways cluster (Lee & Sonnhammer)

Finding Transcription Modules from large gene-expression data sets Ned Wingreen – Molecular Biology Morten Kloster, Chao Tang – NEC Laboratories America.

Microarray analysis Algorithms in Computational Biology Spring 2006 Written by Itai Sharon.

Classical tree view of cell cycle data (Spellman, et al MolBiolCell 9, 3273)

Inferring the nature of the gene network connectivity Dynamic modeling of gene expression data Neal S. Holter, Amos Maritan, Marek Cieplak, Nina V. Fedoroff,

1 Predicting Gene Expression from Sequence Michael A. Beer and Saeed Tavazoie Cell 117, (16 April 2004)

Regulatory element detection using correlation with expression (REDUCE) Literature search WANG Chao Sept 14, 2004.

Promoter Analysis using Bioinformatics, Putting the Predictions to the Test Amy Creekmore Ansci 490M November 19, 2002.

Introduction to molecular networks Sushmita Roy BMI/CS 576 Nov 6 th, 2014.

Modeling the Gene Expression of Saccharomyces cerevisiae Δcin5 Under Cold Shock Conditions Kevin McKay Laura Terada Department of Biology Loyola Marymount.

 Shai Shen-Orr  Nothing in Biology Makes Sense Except in the Light of Evolution  Theodosius Dobzhansky 1973.

Genetic network inference: from co-expression clustering to reverse engineering Patrik D’haeseleer,Shoudan Liang and Roland Somogyi.

DNA microarray technology allows an individual to rapidly and quantitatively measure the expression levels of thousands of genes in a biological sample.

Genetic Regulatory Network Inference Russell Schwartz Department of Biological Sciences Carnegie Mellon University.

Finish up array applications Move on to proteomics Protein microarrays.

Bioinformatics Brad Windle Ph# Web Site:

Using Bayesian Networks to Analyze Whole-Genome Expression Data Nir Friedman Iftach Nachman Dana Pe’er Institute of Computer Science, The Hebrew University.

Unraveling condition specific gene transcriptional regulatory networks in Saccharomyces cerevisiae Speaker: Chunhui Cai.

Analysis of the yeast transcriptional regulatory network.

CS5263 Bioinformatics Lecture 20 Practical issues in motif finding Final project.

Identification of cell cycle-related regulatory motifs using a kernel canonical correlation analysis Presented by Rhee, Je-Keun Graduate Program in Bioinformatics.

Bioinformatics MEDC601 Lecture by Brad Windle Ph# Office: Massey Cancer Center, Goodwin Labs Room 319 Web site for lecture:

Central dogma: the story of life RNA DNA Protein.

IMPROVED RECONSTRUCTION OF IN SILICO GENE REGULATORY NETWORKS BY INTEGRATING KNOCKOUT AND PERTURBATION DATA Yip, K. Y., Alexander, R. P., Yan, K. K., &

While gene expression data is widely available describing mRNA levels in different cancer cells lines, the molecular regulatory mechanisms responsible.

Sudhakar Jonnalagadda and Rajagopalan Srinivasan

Introduction to biological molecular networks

William S. Klug Michael R. Cummings Charlotte A. Spencer Concepts of Genetics Eighth Edition Chapter 21 Dissection of Gene Function: Mutational Analysis.

Evaluation of gene-expression clustering via mutual information distance measure Ido Priness, Oded Maimon and Irad Ben-Gal BMC Bioinformatics, 2007.

Nonlinear differential equation model for quantification of transcriptional regulation applied to microarray data of Saccharomyces cerevisiae Vu, T. T.,

Case Study: Characterizing Diseased States from Expression/Regulation Data Tuck et al., BMC Bioinformatics, 2006.

1 Genomics Advances in 1990 ’ s Gene –Expressed sequence tag (EST) –Sequence database Information –Public accessible –Browser-based, user-friendly bioinformatics.

Identifying submodules of cellular regulatory networks Guido Sanguinetti Joint work with N.D. Lawrence and M. Rattray.

Inferring Regulatory Networks from Gene Expression Data BMI/CS 776 Mark Craven April 2002.

Building and Analyzing Genome-Wide Gene Disruption Networks

1 Department of Engineering, 2 Department of Mathematics,

1 Department of Engineering, 2 Department of Mathematics,

Sensitivity of RNA‐seq.

1 Department of Engineering, 2 Department of Mathematics,

HIS-24 regulates expression of infection-inducible genes.

Quantitative Genetic Interactions Reveal Biological Modularity

Diego Calderon, Anand Bhaskar, David A

Integrative omic approaches for the study of host–pathogen interactions Integrative omic approaches for the study of host–pathogen interactions (A) Proteomic.

BRC Science Highlight Yeast evolved for enhanced xylose utilization reveal interactions between cell signaling and Fe-S cluster biogenesis Objective Obtain.

Volume 26, Issue 12, Pages e5 (March 2019)

Global analysis of the chemical–genetic interaction map.

Highly metastatic PDAC cells have a unique gene signature, which is not preserved in metastases but predicts poor patient outcome. Highly metastatic PDAC.

Presentation transcript:

Yeast Cell-Cycle Regulation Network inference Wang Lin

The topic we focus on--Genetic interactions.

Experience Methods EMAP(Epistatic Miniarray Profile) Double mutant Microarray Gene expression

EMAP Null hypothesis: where,, and represent the fitnesses (or growth rates) relative to wild-type organisms with mutation A, with mutation B, and with both mutations, respectively. + -

S score The definition of S score

Cluster Functional Organization of the S. cerevisiae Phosphorylation Network Dorothea Fiedler Cell 2009

Cluster Problem1: Cluster using the whole data set always did not show good results because of the noise and the complex network. Problem2: How to select a cluster method. – Single, Complete, SVM(MCL, Diffusion Kernel)

Problem1: Additional Information Cell-Cycle Related Genes could be separated to 4 periods - S, G1, M, G2

Microarray data Time cause microarray experiments

RPM Model From: “ A random-periods model for expression of cell-cycle genes ” Delong Liu et al. PNAS 2004 Model: A nonlinear regression model for quantitatively analyzing periodic gene expression

How to find cell cycle related genes? Use estimates from fitting the RPM to known cell-cycle genes to inform a correlation approach for selecting other cell-cycle-related genes.

Another way to use microarray data High positive S-score with microarray evidence Time-lagged Correlation Analysis The activation of a gene by a TF in a nonlinear (sigmodial) fashion where is relative expression at the time point for a given TF, is the mean of the TF expression profile over all time points and s is the standard deviation

Time-lagged Correlation Analysis The definition of time-lagged: – T : denote the estimated cell cycle period of a particular experiment. – : denote the estimated phase angle of gene g – :

Time-lagged Correlation Analysis The Correlation Analysis: Calculate the spearman rank correlation of and where

Combined EMAP and Microarray Microarray cluster + S-score cluster Significant S-score + Time-Lagged Correlation

Reference Sean R Collins, A strategy for extracting and analyzing large- scale quantitative epistatic interaction data, Genome Biology 2006 Dorothea Fiedler, Functional Organization of the S. cerevisiae Phosphorylation Network, Cell 2009 Delong Liu, A random-periods model for expression of cell- cycle genes, PNAS 2004 Pierre R. Bushel, Dissecting the fission yeast regulatory network reveals phase-specific control, Systems Biology 2009