Correct identification of genes from serial analysis of gene expression tag sequences

SAGE (serial analysis of gene expression) is a remarkable technique for genome-wide analysis of gene expression. It is crucial to understand the extent to which SAGE can accurately indicate a gene or expressed sequence tag (EST) with a single tag. We analyzed the effect of the size of SAGE tag on gene identification. Our observation indicates that SAGE tags are in general not long enough to achieve the degree of uniqueness of identification originally envisaged. Our observations also indicate that the limitation of using SAGE tag to identify a gene can be overcome by converting SAGE tags into longer 3' EST sequences with the generation of longer cDNA fragments from SAGE tages for gene identification (GLGI) method.     

Serial analysis of gene expression: rapid RT-PCR analysis of unknown SAGE tags.

In a pilot study on SAGE on Reed-Sternberg cells we have sequenced 1055 tags representing 701 genes. Screening of the GenBank database resulted in the identification of a corresponding gene or EST for 490 of them. For 211 of the tags no homology could be detected. A major problem of the serial analysis of gene expression (SAGE) approach is how to further analyse the unknown tags. We have developed an RT-PCR-based method, rapid analysis of unknown SAGE tags (RAST-PCR), to analyse the expression of the corresponding genes. This approach can be used as a screening method to investigate whether or not the gene is differentially expressed between several cell types of interest.     

Visualizing metabolic activity on a genome-wide scale

MOTIVATION: To enhance the exploration of gene expression data in a metabolic context, one requires an application that allows the integration of this data and which represents this data in a (genome-wide) metabolic map. The layout of this metabolic map must be highly flexible to enable discoveries of biological phenomena. Moreover, it must allow the simultaneous representation of additional information about genes and enzymes. Since the layout and properties of existing maps did not fulfill our requirements, we developed a new way of representing gene expression data in metabolic charts. RESULTS: ViMAc generates user-specified (genome-wide) metabolic maps to explore gene expression data. To enhance the interpretation of these maps information such as sub-cellular localization is included. ViMAc can be used to analyse human or yeast expression data obtained with DNA microarrays or SAGE. We introduce our metabolic map method and demonstrate how it can be applied to explore DNA microarray data for yeast. Availability: ViMAc is freely available for academic institutions on request from the authors.     

Gene expression profiling of human diseases by serial analysis of gene expression

Until recently, the approach to understanding the molecular basis of complex syndromes such as cancer, coronary artery disease, and diabetes was to study the behavior of individual genes. However, it is generally recognized that expression of a number of genes is coordinated both spatially and temporally and that this coordination changes during the development and progression of diseases. Newly developed functional genomic approaches, such as serial analysis of gene expression (SAGE) and DNA microarrays have enabled researchers to determine the expression pattern of thousands of genes simultaneously. One attractive feature of SAGE compared to microarrays is its ability to quantify gene expression without prior sequence information or information about genes that are thought to be expressed. SAGE has been successfully applied to the gene expression profiling of a number of human diseases. In this review, we will first discuss SAGE technique and contrast it to microarray. We will then highlight new biological insights that have emerged from its application to the study of human diseases.     

Serial analysis of gene expression in chronic hepatitis C and hepatocellular carcinoma

Hepatitis C virus (HCV) causes chronic hepatitis C (CH-C) and is epidemiologically linked with the occurrence of hepatocellular carcinoma (HCC). To elucidate the comprehensive gene expression profiles of CH-C and HCC, serial analysis of gene expression (SAGE) libraries were made from CH-C and HCC tissues of a patient, and compared with a reported SAGE library of a normal liver (NL). Scatter plots of the distribution of tags from the HCC library exhibited the existence of many differentially expressed genes compared with those from the CH-C and NL libraries. Up-regulation of IFN-gamma inducible genes and oxidative stress-inducible genes were identified in both the CH-C and HCC libraries, and some unpublished new genes were specifically up- or down-regulated in the HCC library. This genome-wide scanning study discloses the molecular portraits of CH-C and HCC, and provides novel candidate genes that should help clarify the mechanism of hepatocarcinogenesis in the chronically HCV-infected liver.     

Serial analysis of gene expression (SAGE): unraveling the bioinformatics tools

Serial analysis of gene expression (SAGE) is a powerful technique that can be used for global analysis of gene expression. Its chief advantage over other methods is that it does not require prior knowledge of the genes of interest and provides qualitative and quantitative data of potentially every transcribed sequence in a particular cell or tissue type. This is a technique of expression profiling, which permits simultaneous, comparative and quantitative analysis of gene-specific, 9- to 13-basepair sequences. These short sequences, called SAGE tags, are linked together for efficient sequencing. The sequencing data are then analyzed to identify each gene expressed in the cell and the levels at which each gene is expressed. The main benefit of SAGE includes the digital output and the identification of novel genes. In this review, we present an outline of the method, various bioinformatics methods for data analysis and general applications of this important technology.     

Clustering formal concepts to discover biologically relevant knowledge from gene expression data

The production of high-throughput gene expression data has generated a crucial need for bioinformatics tools to generate biologically interesting hypotheses. Whereas many tools are available for extracting global patterns, less attention has been focused on local pattern discovery. We propose here an original way to discover knowledge from gene expression data by means of the so-called formal concepts which hold in derived Boolean gene expression datasets. We first encoded the over-expression properties of genes in human cells using human SAGE data. It has given rise to a Boolean matrix from which we extracted the complete collection of formal concepts, i.e., all the largest sets of over-expressed genes associated to a largest set of biological situations in which their over-expression is observed. Complete collections of such patterns tend to be huge. Since their interpretation is a time-consuming task, we propose a new method to rapidly visualize clusters of formal concepts. This designates a reasonable number of Quasi-Synexpression-Groups (QSGs) for further analysis. The interest of our approach is illustrated using human SAGE data and interpreting one of the extracted QSGs. The assessment of its biological relevancy leads to the formulation of both previously proposed and new biological hypotheses.     

Effects of global system for mobile communications 1800 MHz radiofrequency electromagnetic fields on gene and protein expression in MCF-7 cells

Despite many studies over a decade, it still remains ambiguous as to the real biological effects induced by radiofrequency electromagnetic fields (RF EMF) utilized in mobile telephony. Here we investigated global gene and protein responses to RF EMF simulating the Global System for Mobile Communications (GSM) 1800 MHz signal in human breast cancer cell line MCF-7 using genomic and proteomic approaches. GeneChip analysis identified a handful of consistent changed genes after exposure to RF EMF at specific absorption rates (SAR) of up to 3.5 W/kg for 24 h. However, these differentially transcribed genes could not be further confirmed by real-time RT-PCR assay. Meanwhile, systematic proteome analysis of the MCF-7 cells revealed that a few but different proteins were differentially expressed under continuous or intermittent RF EMF exposure at SAR of 3.5 W/kg for 24 h or less, implying that the observed effects might have occurred by chance. Overall, the present study does not provide convincing evidence that RF EMF exposure under current experimental conditions can produce distinct effects on gene and protein expression in the MCF-7 cells.     

Stage-dependent gene expression profiles during natural killer cell development

Natural killer (NK) cells develop from hematopoietic stem cells (HSCs) in the bone marrow. To understand the molecular regulation of NK cell development, serial analysis of gene expression (SAGE) was applied to HSCs, NK precursor (pNK) cells, and mature NK cells (mNK) cultured without or with OP9 stromal cells. From 170,464 total individual tags from four SAGE libraries, 35,385 unique genes were identified. A set of genes was expressed in a stage-specific manner: 15 genes in HSCs, 30 genes in pNK cells, and 27 genes in mNK cells. Among them, lipoprotein lipase induced NK cell maturation and cytotoxic activity. Identification of genome-wide profiles of gene expression in different stages of NK cell development affords us a fundamental basis for defining the molecular network during NK cell development.     

The pattern of gene expression in mouse Gr-1(+) myeloid progenitor cells

To understand the pattern of gene expression in mouse myeloid progenitor cells, we carried out a genome-wide analysis of gene expression in mouse bone marrow Gr-1(+) cells using SAGE and GLGI techniques. We identified 22,033 unique SAGE tags with quantitative information from 73,869 collected SAGE tags. Among these unique tags, 64% match known sequences, including many genes important for myeloid differentiation, and 36% have no matches to known sequences and are likely to represent novel genes. We compared the expression of mouse Gr-1(+) and human CD15(+) myeloid progenitor cells and showed that the pattern of gene expression of these two cell populations had some similarities. We also compared the expression of mouse Gr-1(+) myeloid progenitor cells with that of mouse brain tissue and found a highly tissue-specific manner of gene expression in these two samples. Our data provide a basis for studying altered gene expression in myeloid disorders using mouse models.     

Extract‐SAGE: An integrated platform for cross­analysis and GA­based selection of SAGE data

Serial analysis of gene expression (SAGE) is a powerful quantification technique for gene expression data. The huge amount of tag data in SAGE libraries of samples is difficult to analyze with current SAGE analysis tools. Data is often not provided in a biologically significant way for cross‐analysis and ‐comparison, thus limiting its application. Hence, an integrated software platform that can perform such a complex task is required. Here, we implement set theory for cross‐analyzing gene expression data among different SAGE libraries of tissue sources; up‐ or down‐regulated tissue‐specific tags can be identified computationally. Extract‐SAGE employs a genetic algorithm (GA) to reduce the number of genes among the SAGE libraries. Its representative tag mining will facilitate the discovery of the candidate genes with discriminating gene expression.     

Generation of high-quantity and quality tag/ditag cDNAs for SAGE analysis

The serial analysis of gene expression (SAGE) technique is an important tool for genome-wide gene expression analysis. However, the requirement of a large amount of mRNA for the analysis and the difficulties in generating high-quality tag and ditag fragments for the construction of a SAGE library often interfere with the successful performance of the SAGE technique. We developed two procedures to solve these issues: (i) introducing low-cycle PCR amplification of the 3' cDNA before the BsmFI digestion of the 3' cDNAs and (ii) gel purifying the BsmFI-released tag fragments before ditag formation. These modifications provide a large quantity of initial 3' cDNAs and high-quality tags and ditags for the construction of SAGE libraries.