MD-SeeGH: a platform for integrative analysis of multi-dimensional genomic data.

BACKGROUND: Recent advances in global genomic profiling methodologies have enabled multi-dimensional characterization of biological systems. Complete analysis of these genomic profiles require an in depth look at parallel profiles of segmental DNA copy number status, DNA methylation state, single nucleotide polymorphisms, as well as gene expression profiles. Due to the differences in data types it is difficult to conduct parallel analysis of multiple datasets from diverse platforms. RESULTS: To address this issue, we have developed an integrative genomic analysis platform MD-SeeGH, a software tool that allows users to rapidly and directly analyze genomic datasets spanning multiple genomic experiments. With MD-SeeGH, users have the flexibility to easily update datasets in accordance with new genomic builds, make a quality assessment of data using the filtering features, and identify genetic alterations within single or across multiple experiments. Multiple sample analysis in MD-SeeGH allows users to compare profiles from many experiments alongside tracks containing detailed localized gene information, microRNA, CpG islands, and copy number variations. CONCLUSION: MD-SeeGH is a new platform for the integrative analysis of diverse microarray data, facilitating multiple profile analyses and group comparisons.

Identification of novel lung genes in bronchial epithelium by serial analysis of gene expression.

A description of the transcriptome of human bronchial epithelium should provide a basis for studying lung diseases, including cancer. We have deduced global gene expression profiles of bronchial epithelium and lung parenchyma, based on a vast dataset of nearly two million sequence tags from 21 serial analysis of gene expression (SAGE) libraries from individuals with a history of smoking. Our analysis suggests that the transcriptome of the bronchial epithelium is distinct from that of lung parenchyma and other tissue types. Moreover, our analysis has identified novel bronchial-enriched genes such as MS4A8B, and has demonstrated the use of SAGE for the discovery of novel transcript variants. Significantly, gene expression associated with ciliogenesis is evident in bronchial epithelium, and includes the expression of transcripts specifying axonemal proteins DNAI2, SPAG6, ASP, and FOXJ1 transcription factor. Moreover, expression of potential regulators of ciliogenesis such as MDAC1, NYD-SP29, ARMC3, and ARMC4 were also identified. This study represents a comprehensive delineation of the bronchial and parenchyma transcriptomes, identifying more than 20,000 known and hypothetical genes expressed in the human lung, and constitutes one of the largest human SAGE studies reported to date.

Recent advances in array comparative genomic hybridization technologies and their applications in human genetics.

Array comparative genomic hybridization (array CGH) is a method used to detect segmental DNA copy number alterations. Recently, advances in this technology have enabled high-resolution examination for identifying genetic alterations and copy number variations on a genome-wide scale. This review describes the current genomic array platforms and CGH methodologies, highlights their applications for studying cancer genetics, constitutional disease and human variation, and discusses visualization and analytical software programs for computational interpretation of array CGH data.

Gain of a region on 7p22.3, containing MAD1L1, is the most frequent event in small-cell lung cancer cell lines.

Small-cell lung cancer (SCLC) is a highly aggressive lung neoplasm, which accounts for 20% of yearly lung cancer cases. The lack of knowledge of the progenitor cell type for SCLC precludes the definition of a normal gene expression profile and has hampered the identification of gene expression changes, while the low resolution of conventional genomic screens such as comparative genomic hybridization (CGH) and loss of heterozygosity analysis limit our ability to fine-map genetic alterations. The recent advent of whole genome tiling path array CGH enables profiling of segmental DNA copy number gains and losses at a resolution 100 times that of conventional methods. Here we report the analysis of 14 SCLC cell lines and six matched normal B-lymphocyte lines. We detected 7p22.3 copy number gain in 13 of the 14 SCLC lines and 0 of the 6 matched normal lines. In 4 of the 14 cell lines, this gain is present as a 350 kbp gene specific copy number gain centered at MAD1L1 (the human homologue of the yeast gene MAD1). Fluorescence in situ hybridization validated the array CGH finding. Intriguingly, MAD1L1 has been implicated to have tumor-suppressing functions. Our data suggest a more complex role for this gene, as MAD1L1 is the most frequent copy number gain in SCLC cell lines.

SeeGH--a software tool for visualization of whole genome array comparative genomic hybridization data.

BACKGROUND: Array comparative genomic hybridization (CGH) is a technique which detects copy number differences in DNA segments. Complete sequencing of the human genome and the development of an array representing a tiling set of tens of thousands of DNA segments spanning the entire human genome has made high resolution copy number analysis throughout the genome possible. Since array CGH provides signal ratio for each DNA segment, visualization would require the reassembly of individual data points into chromosome profiles. RESULTS: We have developed a visualization tool for displaying whole genome array CGH data in the context of chromosomal location. SeeGH is an application that translates spot signal ratio data from array CGH experiments to displays of high resolution chromosome profiles. Data is imported from a simple tab delimited text file obtained from standard microarray image analysis software. SeeGH processes the signal ratio data and graphically displays it in a conventional CGH karyotype diagram with the added features of magnification and DNA segment annotation. In this process, SeeGH imports the data into a database, calculates the average ratio and standard deviation for each replicate spot, and links them to chromosome regions for graphical display. Once the data is displayed, users have the option of hiding or flagging DNA segments based on user defined criteria, and retrieve annotation information such as clone name, NCBI sequence accession number, ratio, base pair position on the chromosome, and standard deviation. CONCLUSIONS: SeeGH represents a novel software tool used to view and analyze array CGH data. The software gives users the ability to view the data in an overall genomic view as well as magnify specific chromosomal regions facilitating the precise localization of genetic alterations. SeeGH is easily installed and runs on Microsoft Windows 2000 or later environments.

A tiling resolution DNA microarray with complete coverage of the human genome.

We constructed a tiling resolution array consisting of 32,433 overlapping BAC clones covering the entire human genome. This increases our ability to identify genetic alterations and their boundaries throughout the genome in a single comparative genomic hybridization (CGH) experiment. At this tiling resolution, we identified minute DNA alterations not previously reported. These alterations include microamplifications and deletions containing oncogenes, tumor-suppressor genes and new genes that may be associated with multiple tumor types. Our findings show the need to move beyond conventional marker-based genome comparison approaches, that rely on inference of continuity between interval markers. Our submegabase resolution tiling set for array CGH (SMRT array) allows comprehensive assessment of genomic integrity and thereby the identification of new genes associated with disease.