Splicy: a web-based tool for the prediction of possible alternative splicing events from Affymetrix probeset data

Philipp Bucher
2007
Article

Résumé

BACKGROUND: The Affymetrix technology is nowadays a well-established method for the analysis of gene expression profiles in cancer research studies. However, changes in gene expression levels are not the only way to link genes and disease. The existence of gene isoforms specifically linked with cancer or apoptosis is increasingly found in literature. Hence it is of great interest to associate the results of a gene expression study with updated evidences on the transcript structure and its possible variants. RESULTS: We present here a web-based software tool, Splicy, whose primary task is to retrieve data on the mapping of Affymetrix probes to single exons of gene transcripts and displaying graphically this information projected on the gene physical structure.Starting from a list of Affymetrix probesets the program produces a series of graphical displays, each relative to a transcript associated with the gene targeted by a given probe. The information on the transcript-by-transcript and exon-by-exon mapping of probe pairs can be retrieved both graphically and in the form of tab-separated files. The mapping of single probes to NCBI RefSeq or EMBL cDNAs is handled by the ISREC mapping tables used in the CleanEx Expression Reference Database Project. We currently maintain these mappings for most popular human and mouse Affymetrix chips, and Splicy can be queried for matches with human and mouse NCBI RefSeq or EMBL cDNAs. CONCLUSION: Splicy generates probeset annotations and images describing the relation between the single probes and intron/exon structure of the target transcript in all its known variants. We think that Splicy will be useful for giving to the researcher a clearer picture of the possible transcript variants linked with a given gene and an additional view on the interpretation of microarray experiment data. Splicy is publicly available and has been realized in the framework of a bioinformatics grant from the Italian Cancer Research Association.

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https://infoscience.epfl.ch/record/114915?ln=fr

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Philipp Bucher
2007
Article

Résumé

Official source

https://infoscience.epfl.ch/record/114915?ln=fr

À propos de ce résultat

Concepts associés (19)

Concepts associés

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Publications associées (42)

Publications associées

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Gene expression profiles have been widely used in molecular classification, diagnosis and prediction, particularly in the area of oncology where accurate and early diagnosis is needed for appropriate treatment. Avoiding under-/over-treatment when it is not necessary can extend a patient's survival and prevent disease recurrence. These high-throughput assay technologies have generated terabytes of data exploited extensively to provide insights on cancer biology and the underlying mechanism of disease progression. The ultimate goal is to identify possibly tailored treatment and therapy for personalized medicine. Analysis of microarray data is constrained by the following characteristics: (i) noisy due to missing or erroneous values; (ii) high dimensional due to a large number of genes versus a few number of samples in which their expression levels are measured; (iii) costly due to expensive microarray experiments. Abundant microarray gene expression data should be processed by appropriate computational and statistical learning methodologies such as machine learning techniques. These methods are robust to noisy data and have a great capacity to analyze high dimensional data. Their computational power is nevertheless limited to sample size based on which these methods are built. These algorithms have been widely applied to microarray gene expression data to identify a set of genes known as a gene signature whose expressions are highly correlated to a target value or outcome such as disease status, tumor subtype, a patient's survival time, risk of mortality or cancer relapse. Prediction of survival time and a patient's risk which is unknown at diagnosis presents a more challenging task for machine learning methods than tumor subtype or disease classification, which is already established by oncologists. The properties of microarray data cited above, the limitation of the number of samples in cancer patients and dependency of the machine learning methods' performance on sample size justify joint analysis of microarray data to increase the number of samples. We applied joint analysis methods to breast and lung cancer data sets to improve survival prediction and risk assessment. In overall, no significant improvement or deterioration of the performance accuracy was obtained with joint analysis. However, increasing sample size helped to identify robust or stable gene signatures predictive of survival time and risk assessment. Our achievements and learned-lessons from joint analysis of microarray gene expression data can be used as a guideline for future research studies in classification and prediction.

EPFL2010

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A model-based optimization framework for the inference of regulatory interactions using time-course DNA microarray expression data

Vassily Hatzimanikatis, Eleftherios Papoutsakis

Background: Proteins are the primary regulatory agents of transcription even though mRNA expression data alone, from systems like DNA microarrays, are widely used. In addition, the regulation process in genetic systems is inherently non-linear in nature, and most studies employ a time-course analysis of mRNA expression. These considerations should be taken into account in the development of methods for the inference of regulatory interactions in genetic networks. Results: We use an S-system based model for the transcription and translation process. We propose an optimizationbased regulatory network inference approach that uses time-varying data from DNA microarray analysis. Currently, this seems to be the only model-based method that can be used for the analysis of time-course "relative" expressions (expression ratios). We perform an analysis of the dynamic behavior of the system when the number of experimental samples available is varied, when there are different levels of noise in the data and when there are genes that are not considered by the experimenter. Our studies show that the principal factor affecting the ability of a method to infer interactions correctly is the similarity in the time profiles of some or all the genes. The less similar the profiles are to each other the easier it is to infer the interactions. We propose a heuristic method for resolving networks and show that it displays reasonable performance on a synthetic network. Finally, we validate our approach using real experimental data for a chosen subset of genes involved in the sporulation cascade of Bacillus anthracis. We show that the method captures most of the important known interactions between the chosen genes. Conclusion: The performance of any inference method for regulatory interactions between genes depends on the noise in the data, the existence of unknown genes affecting the network genes, and the similarity in the time profiles of some or all genes. Though subject to these issues, the inference method proposed in this paper would be useful because of its ability to infer important interactions, the fact that it can be used with time-course DNA microarray data and because it is based on a non-linear model of the process that explicitly accounts for the regulatory role of proteins.

2007

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Gene expression profiling of the stromal reaction to invasive human breast and prostate cancer

Anne Planche

Primary tumor growth induces host tissue responses that are believed to support and promote tumor progression. Identification of the molecular characteristics of the tumor microenvironment and a deeper knowledge of its crosstalk with tumor cells may therefore be crucial for improving our understanding of the processes implicated in cancer progression, for discovering potential therapeutic targets, and for finding a stromal signature that may predict clinical outcome. To selectively address stromal gene expression changes during cancer progression, oligo-based Affymetrix microarray technology was used to analyze laser-microdissected stromal cells derived from invasive human breast and prostate carcinoma in comparison to their normal counterparts. Statistical analysis of the gene expression profile of breast and prostate stroma revealed genes differentially expressed in tumor stroma compared to normal stroma together with genes common to the stromal reaction in both tumor types. Several up and downregulated genes were validated by quantitative real-time RT-PCR and one common upregulated gene, periostin (POSTN), was validated by immunohistochemistry. The tumor-specific stromal genes as well as common stromal genes were observed to cluster breast and prostate cancer patients into two groups with statistically different clinical outcome. Univariate Cox analysis identified the genes whose expression level was most strongly associated with patient survival. Taken together, these observations provide evidence of the implication of the tumor microenvironment in tumor progression and its survival-predictive value.

2008

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EPFL2010

A model-based optimization framework for the inference of regulatory interactions using time-course DNA microarray expression data

Vassily Hatzimanikatis, Eleftherios Papoutsakis

2007