Detecting and profiling tissue-selective genes

Tissue Distributions DB
http://genome.dkfz-heidelberg.de/menu/tissue_db/
TissueDistributionDBs, is a repository of tissue distribution profiles for identifying and ranking the genes in the spectrum of tissue specificity based on Expressed Sequence Tags (ESTs). This repository is currently available for several model organisms across animal and plant kingdoms and is fundamentally based on the UniGene database.

http://physiolgenomics.physiology.org/content/26/2/158.full

Received 19 December 2005; accepted in final form 4 May 2006.
Physiological Genomics 26:158-162 (2006)
1094-8341/06 $8.00 © 2006 American Physiological Society
Detecting and profiling tissue-selective genes
Shuang Liang , Yizheng Li , Xiaobing Be , Steve Howes and Wei Liu
Bioinformatics, Wyeth Research, Cambridge, Massachusetts

ABSTRACT

The widespread use of DNA microarray technologies has generated large amounts of data from various tissue and/or cell types. These data set the stage to answer the question of tissue specificity of human transcriptome in a comprehensive manner. Our focus is to uncover the tissue-gene relationship by identifying genes that are preferentially expressed in a small number of tissue types. The tissue selectivity would shed light on the potential physiological functions of these genes and provides an indispensable reference to compare against disease pathophysiology and to identify or validate tissue-specific drug targets. Here we describe a systematic computational and statistical approach to profile gene expression data to identify tissue-selective genes with the use of a more extensive data set and a well-established multiple-comparison procedure with error rate control. Expression data of 35,152 probe sets in 97 normal human tissue types were analyzed, and 3,919 genes were identified to be selective to one or a few tissue types. We presented results of these tissue-selective genes and compared them to those identified by other studies.
tissue selectivity; differential expression; transcription profiling; Tukey; honest significant difference

Alignment of gene expression profiles from test samples against a reference database: New method for context-specific interpretation of microarray data.
http://www.ncbi.nlm.nih.gov/pubmed/21453538
Abstract
Background
Gene expression microarray data have been organized and made available as public databases, but the utilization of such highly heterogeneous reference datasets in the interpretation of data from individual test samples is not as developed as e.g. in the field of nucleotide sequence comparisons. We have created a rapid and powerful approach for the alignment of microarray gene expression profiles (AGEP) from test samples with those contained in a large annotated public reference database and demonstrate here how this can facilitate interpretation of microarray data from individual samples.
Methods
AGEP is based on the calculation of kernel density distributions for the levels of expression of each gene in each reference tissue type and provides a quantitation of the similarity between the test sample and the reference tissue types as well as the identity of the typical and atypical genes in each comparison. As a reference database, we used 1654 samples from 44 normal tissues (extracted from the Genesapiens database).
Results
Using leave-one-out validation, AGEP correctly defined the tissue of origin for 1521 (93.6%) of all the 1654 samples in the original database. Independent validation of 195 external normal tissue samples resulted in 87% accuracy for the exact tissue type and 97% accuracy with related tissue types. AGEP analysis of 10 Duchenne muscular dystrophy (DMD) samples provided quantitative description of the key pathogenetic events, such as the extent of inflammation, in individual samples and pinpointed tissue-specific genes whose expression changed (SAMD4A) in DMD. AGEP analysis of microarray data from adipocytic differentiation of mesenchymal stem cells and from normal myeloid cell types and leukemias provided quantitative characterization of the transcriptomic changes during normal and abnormal cell differentiation.
Conclusions
The AGEP method is a widely applicable method for the rapid comprehensive interpretation of microarray data, as proven here by the definition of tissue- and disease-specific changes in gene expression as well as during cellular differentiation. The capability to quantitatively compare data from individual samples against a large-scale annotated reference database represents a widely applicable paradigm for the analysis of all types of high-throughput data. AGEP enables systematic and quantitative comparison of gene expression data from test samples against a comprehensive collection of different cell/tissue types previously studied by the entire research community.

A Comparative Study of Mouse Hepatic and Intestinal Gene Expression Profiles under PPARα Knockout by Gene Set Enrichment Analysis.
http://www.ncbi.nlm.nih.gov/pubmed/21811494

Gene expression profiling of PPARα has been used in several studies, but fewer
studies went further to identify the tissue-specific pathways or genes involved
in PPARα activation in genome-wide. Here, we employed and applied gene set
enrichment analysis to two microarray datasets both PPARα related respectively in
mouse liver and intestine. We suggested that the regulatory mechanism of PPARα
activation by WY14643 in mouse small intestine is more complicated than in liver
due to more involved pathways. Several pathways were cancer-related such as
pancreatic cancer and small cell lung cancer, which indicated that PPARα may have
an important role in prevention of cancer development. 12 PPARα dependent
pathways and 4 PPARα independent pathways were identified highly common in both
liver and intestine of mice. Most of them were metabolism related, such as fatty
acid metabolism, tryptophan metabolism, pyruvate metabolism with regard to PPARα
regulation but gluconeogenesis and propanoate metabolism independent of PPARα
regulation. Keratan sulfate biosynthesis, the pathway of regulation of actin
cytoskeleton, the pathways associated with prostate cancer and small cell lung
cancer were not identified as hepatic PPARα independent but as WY14643 dependent
ones in intestinal study. We also provided some novel hepatic tissue-specific
marker genes.