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Biomedical subjects

E E Schadt

Publications and source records attributed to E E Schadt.

6 recordsLinked to original sources
PubMed2003

A new paradigm for drug discovery: integrating clinical, genetic, genomic and molecular phenotype data to identify drug targets.

Application of statistical genetics approaches to variations in mRNA transcript abundances in segregating populations can be used to identify genes and pathways associated with common human diseases. The combination of this genetic information with gene expression and clinical trait data can also be used to identify subtypes of a disease and the genetic loci specific to each subtype. Here we highlight results from some of our recent work in this area and further explore the many possibilities that exist in employing a more comprehensive genetics and functional genomics approach to the functional annotation of genomes, and in applying such methods to the validation of targets for complex traits in the drug discovery process.

Animals↗
PubMed2001

Experimental annotation of the human genome using microarray technology.

The most important product of the sequencing of a genome is a complete, accurate catalogue of genes and their products, primarily messenger RNA transcripts and their cognate proteins. Such a catalogue cannot be constructed by computational annotation alone; it requires experimental validation on a genome scale. Using 'exon' and 'tiling' arrays fabricated by ink-jet oligonucleotide synthesis, we devised an experimental approach to validate and refine computational gene predictions and define full-length transcripts on the basis of co-regulated expression of their exons. These methods can provide more accurate gene numbers and allow the detection of mRNA splice variants and identification of the tissue- and disease-specific conditions under which genes are expressed. We apply our technique to chromosome 22q under 69 experimental condition pairs, and to the entire human genome under two experimental conditions. We discuss implications for more comprehensive, consistent and reliable genome annotation, more efficient, full-length complementary DNA cloning strategies and application to complex diseases.

Algorithms↗
PubMed2001

Feature extraction and normalization algorithms for high-density oligonucleotide gene expression array data.

Algorithms for performing feature extraction and normalization on high-density oligonucleotide gene expression arrays, have not been fully explored, and the impact these algorithms have on the downstream analysis is not well understood. Advances in such low-level analysis methods are essential to increase the sensitivity and specificity of detecting whether genes are present and/or differentially expressed. We have developed and implemented a number of algorithms for the analysis of expression array data in a software application, the DNA-Chip Analyzer (dChip). In this report, we describe the algorithms for feature extraction and normalization, and present validation data and comparison results with some of the algorithms currently in use.

Algorithms↗
PubMed2000

Analyzing high-density oligonucleotide gene expression array data.

We have developed methods and identified problems associated with the analysis of data generated by high-density, oligonuceotide gene expression arrays. Our methods are aimed at accounting for many of the sources of variation that make it difficult, at times, to realize consistent results. We present here descriptions of some of these methods and how they impact the analysis of oligonucleotide gene expression array data. We will discuss the process of recognizing the "spots" (or features) on the Affymetrix GeneChip(R) probe arrays, correcting for background and intensity gradients in the resulting images, scaling/normalizing an array to allow array-to-array comparisons, monitoring probe performance with respect to hybridization efficiency, and assessing whether a gene is present or differentially expressed. Examples from the analyses of gene expression validation data are presented to contrast the different methods applied to these types of data.

Base Sequence↗
PubMed1998

Computational advances in maximum likelihood methods for molecular phylogeny.

We have developed a generalization of Kimura's Markov chain model for base substitution at a single nucleotide site. This generalized model incorporates more flexible transition rates and consequently allows irreversible as well as reversible chains. Because the model embodies just the right amount of symmetry, it permits explicit calculation of finite-time transition probabilities and equilibrium distributions. The model also meshes well with maximum likelihood methods for phylogenetic analysis. Quick calculation of likelihoods and their derivatives can be carried out by adapting Baum's forward and backward algorithms from the theory of hidden Markov chains. Analysis of HIV sequence data illustrates the speed of the algorithms on trees with many contemporary taxa. Analysis of some of Lake's data on the origin of the eukaryotic nucleus contrasts the reversible and irreversible versions of the model.

Algorithms↗