'Gene 1' presentation slideshows

Elements of Bioinformatics (14F001) TP2: Gene prediction 22 October 2012 CORRECTIONS

960 views • 86 slides

Microarray Data Normalization and Analysis

Microarray Data Normalization and Analysis. John Quackenbush 15 February 2003 MGED/AAAS Denver. The Experimental Design. The Experimental Design dictates a good deal of what you can do with the data Good normalization and processing reflects the experimental design

872 views • 64 slides

Microarray Data Analysis - II

Microarray Data Analysis - II. FIOCRUZ Bioinformatics Workshop 6 June, 2001. Challenges in Microarray Data Analysis. Spot Identification and Quantitation. Normalization of data from each experiment. Identification of Differentially Expressed Genes

698 views • 49 slides

Cis-regulation

Cis-regulation. Trans-regulation. 5. Objective: pathway reconstruction. Identify candidate causal genes within the eQTL confidence interval around a marker by (partial) gene expression correlation analysis. Target gene. Genome with potential candidate genes. Target gene. Marker.

660 views • 48 slides

A Quantitative Overview to Gene Expression Profiling in Animal Genetics

552 views • 41 slides

Molecular biology databases

Molecular biology databases. Based on Chapter 2 of Post-genome Informatics by Minoru Kanehisa, Oxford University Press, 2000 2.1 History 2.2 Information Technology

550 views • 39 slides

Rich Probabilistic Models for Gene Expression

Rich Probabilistic Models for Gene Expression. Eran Segal (Stanford) Ben Taskar (Stanford) Audrey Gasch (Berkeley) Nir Friedman (Hebrew University) Daphne Koller (Stanford). Our Goals. Find patterns in gene expression data. j. i. A ij - mRNA level of gene i in experiment j.

500 views • 37 slides

From Sequence to Expression: A Probabilistic Framework

From Sequence to Expression: A Probabilistic Framework. Joint work with:. Eran Segal (Stanford). Nir Friedman (Hebrew U.) Daphne Koller (Stanford). Yoseph Barash (Hebrew U.) Itamar Simon (Whitehead Inst.). G1. S. M. G2. Understanding Cellular Processes.

411 views • 33 slides

Review of statistical modeling and probability theory

Review of statistical modeling and probability theory. Alan Moses ML4bio. What is modeling?. Describe some observations in a simple, more compact way. X = (X 1 ,X 2 ). What is modeling?. Describe some observations in a simple, more compact way. G m. Model: a = -. r 2.

438 views • 33 slides

Introduction of Genome Research

Introduction of Genome Research. Bioinformatics Research Center Institute of Biomedical S ciences ACADEMIA SINICA. ???. www.sinica.edu.tw/~trees/bioinformatics E-mail: trees@gate.sinica.edu.tw. Introduction. Outline. Introduction. Some Research Topics. Related Links and Resources.

509 views • 31 slides

Welcome iGEM team 2007

Welcome iGEM team 2007. Research at the Interface. Biology. Engineering. Chemistry. Economics. Physics. Computer Science. Programming Cells. Goal: Can we Program cells in a manner analogous to how we now program computers?. #include <iostream> main() { int input;

407 views • 27 slides

A Quantitative Overview to Gene Expression Profiling in Animal Genetics

A Quantitative Overview to Gene Expression Profiling in Animal Genetics. MPSS M assively P arallel S ignature S equencing. Lynx Therapeutics, Inc, Hayward, CA. www.lynxgen.com. Armidale Animal Breeding Summer Course, UNE, Feb. 2006.

414 views • 25 slides

A Quantitative Overview to Gene Expression Profiling in Animal Genetics

A Quantitative Overview to Gene Expression Profiling in Animal Genetics. Co-Expression. Validation of alternative methods of data normalization in gene co-expression studies. Reverter et al. 2005 Bioinformatics 21:1112. Armidale Animal Breeding Summer Course, UNE, Feb. 2006.

388 views • 25 slides

EPISTASIS

EPISTASIS. Dr. S. Ramgopal Rao. Epistatic Gene Interactions. Gene interactions occur when two or more different genes influence the outcome of a single trait Most morphological traits (height, weight, color) are affected by multiple genes

899 views • 20 slides

Presentations

Presentations. 27.07.09 (1 presentation: Team 1) 28.07.09 (2 presentations: Team 3, Team 2) 29.07.09 (1 presentation: Team 4). Goals of Functional Genomics.

228 views • 19 slides

Epistasis

Epistasis. Definition. Epistasis is a form of gene interaction in which one pair of alleles (gene) masks the phenotypic expression of another. There are no new phenotypes produced by this type of gene interaction. Epistatic versus Hypostatic.

658 views • 16 slides

Detection of gene-gene interactions in genome-wide association studies

Detection of gene-gene interactions in genome-wide association studies. Manuel A R Ferreira. Center for Human Genetic Research. Massachusetts General Hospital. Harvard Medical School. What is epistasis or GxG?. Phenotype. 5. 3. Locus B. 1. 0. 1. 1. Locus A.

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229 views • 11 slides

In-text figure Page 238

In-text figure Page 238. thymine dimer. Figure 15.2 Page 240. DNA. histone core. nucleosome. Figure 15.3a Page 241. operator. regulatory gene. operator. gene 1. gene 2. gene 3. transcription, translation. promoter. lactose operon. repressor protein. Figure 15.3a Page 241.

252 views • 11 slides

Grow on 5-FOA

ARS. ARS. Gene 1 knockout. Gene 2 knockout. CEN. CEN. HIS3. HIS3. ARS. ARS. CEN. CEN. ARS. ARS. CEN. CEN. URA3. URA3. Gene 2. Gene 2. Gene 1. Gene 1. LEU2. LEU2. Mutant Gene 1. Mutant Gene 1. Gene 1 knockout. Mutant Gene 2. Mutant Gene 2. Gene 2 knockout.

146 views • 4 slides