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Cancer Genome Characterization Initiative

Visit the database of genomic characterization data for multiple tumor types.

All About the GLS Tool


The GLS Tool is a gene expression tool for a single cDNA library or a group of libraries. First it finds all the expressed genes in the one or more libraries that meet the chosen criteria of organism and library characteristics. Then it "summarizes" or organizes these genes based on a selected field. Finally, it classifies them into unique and non-unique, and known and unknown.

What to Put In the Search Fields

Search Field Options
Organism Select "Homo sapiens" or "Mouse" from the drop down box.
Library Group There are three options:
  • Keep the default setting of "CGAP Libraries" to search only CGAP libraries.
  • Select MGC libraries (The Mammalian Gene Collection).
  • Select all EST libraries, which include both subsets of CGAP and MGC libraries.
Tissue Type Keep the default setting of "Any" to search all tissue types, or select one specific tissue.
Tissue Preparation Keep the default setting "Any" that includes all library preparation methods listed in Tissue Preparation Overview, or choose one specific method.
Tissue Histology Keep the default setting "Any" that includes normal, pre-cancerous, and cancer histology, or select a specific histology.
Library Protocol Keep the default setting "Any" that includes all library protocol methods. The protocols listed below the line are CGAP specific protocols described in cDNA Library Protocols Overview.
Library Name With all of the above settings at default, enter the exact name of a CGAP or MGC library, e.g., NCI_CGAP_Pr1 or NIH_MGC_50.
Summarize results bySelect one of the five criteria to summarize the results based on this criterion. See "Summarizing the Results" below.

Examples of GLS Queries

Because of the many combinations of library criteria and result formats, the investigator can analyze tissues and libraries from many vantage points. Below are a few examples:

  • Select "prostate" and "cancer" to generate a list of unique genes that may be potential markers for prostate cancer and would be useful on a microarray.
  • Select mouse and NCI_CGAP_Mam6 to find the unique genes in this one library.
  • Compare the gene expression in "normalized" libraries from colon cancer with "non-normalized" colon cancer - are there differences?

Summarizing the Results

The expressed genes found by GLS may be "summarized" using five different criteria: tissue, histology, tissue preparation, library protocol, and library name, each of which takes the same pool of genes and places each unique and non-unique gene in the appropriate, differently labeled "bin". In the example below, the tool has summarized the human CGAP prostate libraries each way as an example. Using UniGene Build 127, the results appear as follows, summarized by:

a) Tissue

Subset Libraries Sequences Unique Genes Non-Unique Genes
KnownUnknown KnownUnknown
All prostate tissue19692925101765865423
Prostate tissue1969292 5101765865423

b) Histology

Subset Libraries Sequences Unique Genes Non-Unique Genes
KnownUnknown KnownUnknown
All histologies19692925101765865423

c) Tissue preparation

Subset Libraries Sequences Unique Genes Non-Unique Genes
KnownUnknown KnownUnknown
All preparations19692925101765865423
Cell line22878117940178

d) Library protocol

Subset Libraries Sequences Unique Genes Non-Unique Genes
KnownUnknown KnownUnknown
All protocols19692925101765865423
Krizman protocol 11331261130434301912
Soares non-
Soares normalized18559111527981343
Soares subtracted124301251548603592
Stratagene non-normalized338931231268269

e) Library name

Subset Libraries Sequences Unique Genes Non-Unique Genes
KnownUnknown KnownUnknown
All names19692925101765865423

Analyzing the Results

  • A "Unique" gene is one found only in a group of libraries and a "Non-Unique" gene is seen in at least one other library group.
  • A "Known" gene has an identified name and function and an "Unknown" gene does not.
  • The results are always returned with the UniGene Build number. Since UniGene is rebuilt every two or three weeks, gene clusters may disappear or ESTs may be grouped in new clusters as more ESTs enter the database.
  • The first line of each results table is "all", which is not necessarily the sum of the numbers in each gene column.
  • In the above example, the results for the same 19 libraries are summarized in five different ways, changing the groupings of unique and non-unique genes. Thus, both library criteria and the way the results are organized are important factors in each analysis.

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