A Tale of Three Inferences

Slides:

Advertisements

Similar presentations

BioInformatics (3).

Advertisements

Promoter and Module Analysis Statistics for Systems Biology.

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

20,000 GENES IN HUMAN GENOME; WHAT WOULD HAPPEN IF ALL THESE GENES WERE EXPRESSED IN EVERY CELL IN YOUR BODY? WHAT WOULD HAPPEN IF THEY WERE EXPRESSED.

McPromoter – an ancient tool to predict transcription start sites

Single Category Classification Stage One Additive Weighted Prototype Model.

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

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

Integrated analysis of regulatory and metabolic networks reveals novel regulatory mechanisms in Saccharomyces cerevisiae Speaker: Zhu YANG 6 th step, 2006.

A Data Mining Method to Predict Transcriptional Regulatory Sites Based On Differentially Expressed Genes in Human Genome HSIEN-DA HUANG, HUEI-LINA and.

Introduction to Bioinformatics - Tutorial no. 5 MEME – Discovering motifs in sequences MAST – Searching for motifs in databanks TRANSFAC – The Transcription.

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.

ENCODE enhancers 12/13/2013 Yao Fu Gerstein lab. ‘Supervised’ enhancer prediction Yip et al., Genome Biology (2012) Get enhancer list away to genes DNase.

Goals: Discuss 3 examples of transcriptional regulation -Lac operon -Coordinated gene regulation -Regulation of transcription without regulation of polymerase.

Detecting binding sites for transcription factors by correlating sequence data with expression. Erik Aurell Adam Ameur Jakub Orzechowski Westholm in collaboration.

Microarrays to Functional Genomics: Generation of Transcriptional Networks from Microarray experiments Joshua Stender December 3, 2002 Department of Biochemistry.

Module 9.  Digital logic circuits can be categorized based on the nature of their inputs either: Combinational logic circuit It consists of logic gates.

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

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

Gene Regulation, Part 1 Lecture 15 Fall Metabolic Control in Bacteria Regulate enzymes already present –Feedback Inhibition –Fast response Control.

Data Mining the Yeast Genome Expression and Sequence Data Alvis Brazma European Bioinformatics Institute.

Modelling Gene Regulatory Networks using the Stochastic Master Equation Hilary Booth, Conrad Burden, Raymond Chan, Markus Hegland & Lucia Santoso BioInfoSummer2004.

Motif Search and RNA Structure Prediction Lesson 9.

Circuits & Boolean Expressions. A ABC BC ABC C B A Example # 1: Boolean Expression: Develop a Boolean expression from a circuit.

Determine the sequence of genes along a chromosome based on the following recombination frequencies A-C 20% A-D 10% B-C 15% B-D 5%

Introduction to Bioinformatics - Tutorial no. 5 MEME – Discovering motifs in sequences MAST – Searching for motifs in databanks TRANSFAC – the Transcription.

Starter What do you know about DNA and gene expression?

DE MORGAN’S THEOREM. De Morgan’s Theorem De Morgan’s Theorem.

Enhancers and 3D genomics Noam Bar RESEARCH METHODS IN COMPUTATIONAL BIOLOGY.

Digital Logic Design. Truth Table  Logic Circuit 1. Start with truth table 2. When your output is a 1, figure out the combination of inputs, ANDs, and.

Integrative Genomics I BME 230. Probabilistic Networks Incorporate uncertainty explicitly Capture sparseness of wiring Incorporate multiple kinds of data.

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

Yiming Kang, Hien-haw Liow, Ezekiel Maier, & Michael Brent

Warm-Up (1/4) Explain how a cell expresses a gene.

Reverse-engineering transcription control networks timothy s

Monica Britton, Ph.D. Sr. Bioinformatics Analyst June 2016 Workshop

Detection of genome regulation sequences

Control of Gene Expression

Boolean Algebra.

Circuits & Boolean Expressions

Learning Sequence Motif Models Using Expectation Maximization (EM)

Inferring Models of cis-Regulatory Modules using Information Theory

Mechanisms of lncRNA function.

Effect of polymorphisms on transcriptional regulation in mice

CSE 370 – Winter Combinational Logic - 1

Dennis Shasha, Courant Institute, New York University With

Noise in cellular circuitry

DNA structure and gene expression

Patterns of control of gene expression

Control of Gene Expression in Eukaryotic cells

Boolean Algebra.

A Phase Separation Model for Transcriptional Control

Interactive Note-taking

Network Inference Chris Holmes Oxford Centre for Gene Function, &,

Mechanisms and Consequences of Alternative Polyadenylation

Interactive Note-taking

Presented by, Jeremy Logue.

Correlation of mouse gene expression with bacterial gene expression.

Ann Hochschild, Simon L Dove Cell

Figure 9. Categories of pha-siRNA-yielding genes

Nora Pierstorff Dept. of Genetics University of Cologne

Predicting Gene Expression from Sequence

GENE REGULATION Virtually every cell in your body contains a complete set of genes But they are not all turned on in every tissue Each cell in your body.

The Human Genome Source Code

Presented by, Jeremy Logue.

Overlap between changes in de novo protein synthesis after p53- or miR-34a-induction. Overlap between changes in de novo protein synthesis after p53- or.

Schematic representation of a transcriptomic evaluation approach.

Circuits & Boolean Expressions

Inferred promoter–metabolite regulation network (Table EV7)

Presentation transcript:

A Tale of Three Inferences Models of transcription and their consequences Philip Benfey, Ken Birnbaum, Dennis Shasha

What is the Logic of Transcription? Known: transcription factors bind to small subsequences of DNA, perhaps in a statistical mechanical (hence concentration dependent) way. Controversial: interaction among different transcription factor-binding events.

Models of Interaction Additive (Boolean OR): For promoter P on gene G, if T1 binds to c1 and T2 binds to c2 in an inductive way, then the expression of G will remain the same if the promoter were to have twice the number of c1 and c2 goes to 0. Boolean AND: Under same conditions, there will be no expression

Classical Approach Detailed nature of interaction is unknown. Find genes whose expressions correlate well with one another. Infer that common motifs among the promoters of those genes must be the binding cis-elements.

Note to ken Ken, please include a power point slide in which you show how this correlation method is meant to work.

Bussemaker, Siggia, et. al Model: expression of a promoter is: S coef * number, where coef[j] is the strength of the jth transcription factor-cis-element binding and number[j] is the number of times the cis-element appears. Additive, Boolean disjunctive model. No explicit model of transcription factors.

Bussemaker Discovery Consider one experiment consisting of many gene-promoter pairs some of which are expressed. Best cis-element (largest coefficient) is one that appears in promoters of many expressed genes and few unexpressed genes (inversely weighted by number of appearances per promoter)

Bussemaker Example Promoter 1: x, x, x, y. Expression: 1 Promoter 2: x, y. Expression 1. Promoter 3: x, y, y, y. Expression 3. Conclude: best hypothesis is that each y contributes to expression with a coefficient of 1. No good model for x.

NYU (Birn,Benf,Sha) Model Transcription factor/cis-elements form a boolean (AND/OR/NOT) model with amplifiers (e.g. TFA & TFB ==> TFC) Finding these circuits is a multi-stage affair: 1) find cis-element/transcription factor pairs 2) infer boolean circuit by seeing combinations that work.

NYU Find TF/Cis-element pairs Given the knowledge of genes that encode transcription factors and given a sequence of experiments (e.g. different times during sporulation), find cis-elements that correlate best with transcription factors over time.

NYU Model Details Cis-element c expression at time t = sum of expression of genes containing c in their promoter sequence. Transcription X expression at time t = extent of RNA production at time t. X binds to c potentially if two time sequences correlate well.

Note to Ken Ken, please put in our experiment with promoters A, B, C such that first promoter has AB, second has AC, and third has BC. First time has TA, TB; second time has TA, TC; and third time has TB, TC. Gene correlation doesn’t work. Bussemaker only partly works.