Statistical framework for phylogenomic analysis of gene family expression profiles.

نویسنده

  • Xun Gu
چکیده

Microarray technology has produced massive expression data that are invaluable for investigating the genome-wide evolutionary pattern of gene expression. To this end, phylogenetic expression analysis is highly desirable. On the basis of the Brownian process, we developed a statistical framework (called the E(0) model), assuming the independent expression of evolution between lineages. Several evolutionary mechanisms are integrated to characterize the pattern of expression diversity after gene duplications, including gradual drift and dramatic shift (punctuated equilibrium). When the phylogeny of a gene family is given, we show that the likelihood function follows a multivariate normal distribution; the variance-covariance matrix is determined by the phylogenetic topology and evolutionary parameters. Maximum-likelihood methods for multiple microarray experiments are developed, and likelihood-ratio tests are designed for testing the evolutionary pattern of gene expression. To reconstruct the evolutionary trace of expression diversity after gene (or genome) duplications, we developed a Bayesian-based method and use the posterior mean as predictors. Potential applications in evolutionary genomics are discussed.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Microarray analysis of gene expression patterns in Arabidopsis seedlings under trehalose, sucrose and sorbitol treatment

Trehalose is the non-reducing alpha-alpha-1, 1-linked glucose disaccharide. The biosynthesisprecursor of trehalose, trehalose-6-phosphate (T6P), is essential for plant development, growth,carbon utilization and alters photosynthetic capacity but its mode of action is not understood. In thecurrent research, 6 days old seedlings of Arabidopsis thaliana (Columbia ecotype) were grown inliquid cultu...

متن کامل

Multivariate Feature Extraction for Prediction of Future Gene Expression Profile

Introduction: The features of a cell can be extracted from its gene expression profile. If the gene expression profiles of future descendant cells are predicted, the features of the future cells are also predicted. The objective of this study was to design an artificial neural network to predict gene expression profiles of descendant cells that will be generated by division/differentiation of h...

متن کامل

Multivariate Feature Extraction for Prediction of Future Gene Expression Profile

Introduction: The features of a cell can be extracted from its gene expression profile. If the gene expression profiles of future descendant cells are predicted, the features of the future cells are also predicted. The objective of this study was to design an artificial neural network to predict gene expression profiles of descendant cells that will be generated by division/differentiation of h...

متن کامل

Construction of a rice glycoside hydrolase phylogenomic database and identification of targets for biofuel research

Glycoside hydrolases (GH) catalyze the hydrolysis of glycosidic bonds in cell wall polymers and can have major effects on cell wall architecture. Taking advantage of the massive datasets available in public databases, we have constructed a rice phylogenomic database of GHs (http://ricephylogenomics.ucdavis.edu/cellwalls/gh/). This database integrates multiple data types including the structural...

متن کامل

The rice kinase database. A phylogenomic database for the rice kinome.

The rice (Oryza sativa) genome contains 1,429 protein kinases, the vast majority of which have unknown functions. We created a phylogenomic database (http://rkd.ucdavis.edu) to facilitate functional analysis of this large gene family. Sequence and genomic data, including gene expression data and protein-protein interaction maps, can be displayed for each selected kinase in the context of a phyl...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:
  • Genetics

دوره 167 1  شماره 

صفحات  -

تاریخ انتشار 2004