Biomedical subjects

Stéphane Liva

Publications and source records attributed to Stéphane Liva.

4 recordsLinked to original sources

PubMed2006

VAMP: visualization and analysis of array-CGH, transcriptome and other molecular profiles.

MOTIVATION: Microarray-based CGH (Comparative Genomic Hybridization), transcriptome arrays and other large-scale genomic technologies are now routinely used to generate a vast amount of genomic profiles. Exploratory analysis of this data is crucial in helping to understand the data and to help form biological hypotheses. This step requires visualization of the data in a meaningful way to visualize the results and to perform first level analyses. RESULTS: We have developed a graphical user interface for visualization and first level analysis of molecular profiles. It is currently in use at the Institut Curie for cancer research projects involving CGH arrays, transcriptome arrays, SNP (single nucleotide polymorphism) arrays, loss of heterozygosity results (LOH), and Chromatin ImmunoPrecipitation arrays (ChIP chips). The interface offers the possibility of studying these different types of information in a consistent way. Several views are proposed, such as the classical CGH karyotype view or genome-wide multi-tumor comparison. Many functionalities for analyzing CGH data are provided by the interface, including looking for recurrent regions of alterations, confrontation to transcriptome data or clinical information, and clustering. Our tool consists of PHP scripts and of an applet written in Java. It can be run on public datasets at http://bioinfo.curie.fr/vamp AVAILABILITY: The VAMP software (Visualization and Analysis of array-CGH,transcriptome and other Molecular Profiles) is available upon request. It can be tested on public datasets at http://bioinfo.curie.fr/vamp. The documentation is available at http://bioinfo.curie.fr/vamp/doc.

Algorithms↗

PubMed2006

CAPweb: a bioinformatics CGH array Analysis Platform.

Assessing variations in DNA copy number is crucial for understanding constitutional or somatic diseases, particularly cancers. The recently developed array-CGH (comparative genomic hybridization) technology allows this to be investigated at the genomic level. We report the availability of a web tool for analysing array-CGH data. CAPweb (CGH array Analysis Platform on the Web) is intended as a user-friendly tool enabling biologists to completely analyse CGH arrays from the raw data to the visualization and biological interpretation. The user typically performs the following bioinformatics steps of a CGH array project within CAPweb: the secure upload of the results of CGH array image analysis and of the array annotation (genomic position of the probes); first level analysis of each array, including automatic normalization of the data (for correcting experimental biases), breakpoint detection and status assignment (gain, loss or normal); validation or deletion of the analysis based on a summary report and quality criteria; visualization and biological analysis of the genomic profiles and results through a user-friendly interface. CAPweb is accessible at http://bioinfo.curie.fr/CAPweb.

Chromosome Breakage↗

PubMed2006

Spatial normalization of array-CGH data.

BACKGROUND: Array-based comparative genomic hybridization (array-CGH) is a recently developed technique for analyzing changes in DNA copy number. As in all microarray analyses, normalization is required to correct for experimental artifacts while preserving the true biological signal. We investigated various sources of systematic variation in array-CGH data and identified two distinct types of spatial effect of no biological relevance as the predominant experimental artifacts: continuous spatial gradients and local spatial bias. Local spatial bias affects a large proportion of arrays, and has not previously been considered in array-CGH experiments. RESULTS: We show that existing normalization techniques do not correct these spatial effects properly. We therefore developed an automatic method for the spatial normalization of array-CGH data. This method makes it possible to delineate and to eliminate and/or correct areas affected by spatial bias. It is based on the combination of a spatial segmentation algorithm called NEM (Neighborhood Expectation Maximization) and spatial trend estimation. We defined quality criteria for array-CGH data, demonstrating significant improvements in data quality with our method for three data sets coming from two different platforms (198, 175 and 26 BAC-arrays). CONCLUSION: We have designed an automatic algorithm for the spatial normalization of BAC CGH-array data, preventing the misinterpretation of experimental artifacts as biologically relevant outliers in the genomic profile. This algorithm is implemented in the R package MANOR (Micro-Array NORmalization), which is described at http://bioinfo.curie.fr/projects/manor and available from the Bioconductor site http://www.bioconductor.org. It can also be tested on the CAPweb bioinformatics platform at http://bioinfo.curie.fr/CAPweb.

Algorithms↗

PubMed2005

Preferential occurrence of chromosome breakpoints within early replicating regions in neuroblastoma.

Neuroblastoma (NB) is a frequent paediatric extra cranial solid tumor characterized by the occurrence of unbalanced chromosome translocations, frequently, but not exclusively, involving chromosomes 1 and 17. We have used a 1 Mb resolution BAC array to further refine the mapping of breakpoints in NB cell lines. Replication timing profiles were evaluated in 7 NB cell lines, using DNAs from G1 and S phases flow sorted nuclei hybridised on the same array. Strikingly, these replication timing profiles were highly similar between the different NB cell lines. Furthermore, a significant level of similarity was also observed between NB cell lines and lymphoblastoid cells. A segmentation analysis using the Adaptative Weights Smoothing procedure was performed to determine regions of coordinate replication. More than 50% of the breakpoints mapped to early replicating regions, which account for 23.7% of the total genome. The breakpoints frequency per 10(8) bases was therefore 10.84 for early replicating regions, whereas it was only 2.94 for late replicating regions, these difference being highly significant (p < 10(-4)). This strong association was also observed when chromosomes 1 and 17, the two most frequent translocation partners in NB were excluded from the statistical analysis. These results unambiguously establish a link between unbalanced translocations, whose most likely mechanism of occurrence relies on break-induced replication, and early replication of the genome.

Cell Line, Tumor↗