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pmid:31114869

MEXPRESS update 2019.

Alexander Koch · Jana Jeschke · Wim Van Criekinge · Manon van Engeland · Tim De Meyer

Nucleic acids research · 2019 doi:10.1093/nar/gkz445

Paper

complete

Experiment

partial

Data

partial

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complete

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MEXPRESS update 2019.
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Alexander Koch
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Jana Jeschke
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Wim Van Criekinge
[3]
Manon van Engeland
[4]
Tim De Meyer
journal
Nucleic acids research
year
2019
abstract
The recent growth in the number of publicly available cancer omics databases has been accompanied by the development of various tools that allow researchers to visually explore these data. In 2015, we built MEXPRESS, an online tool for the integration and visualization of gene expression, DNA methylation and clinical data from The Cancer Genome Atlas (TCGA), a large collection of publicly available multi-omics cancer data. MEXPRESS addresses the need for an easy-to-use, interactive application that allows researchers to identify dysregulated genes and their clinical relevance in cancer. Furthermore, while other tools typically do not support integrated visualization of expression and DNA methylation data in combination with the precise genomic location of the methylation, MEXPRESS is unique in how it depicts these diverse data types together. Motivated by the large number of users MEXPRESS has managed to attract over the past 3 years and the recent migration of all TCGA data to a new data portal, we developed a new version of MEXPRESS (https://mexpress.be). It contains the latest TCGA data, additional types of omics and clinical data and extra functionality, allowing users to explore mechanisms of gene dysregulation beyond expression and DNA methylation.
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The visual interpretation of the data (less GSTP1 expression in samples with more promoter methylation) is confirmed by the statistical analysis of the correlation between DNA methylation and gene expression (Pearson correlation coefficients ranging from −0.664 to −0.803 for promoter region probes).
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The visual interpretation of the data (less GSTP1 expression in samples with more promoter methylation) is confirmed by the statistical analysis of the correlation between DNA methylation and gene expression (Pearson correlation coefficients ranging from −0.664 to −0.803 for promoter region probes).
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Custom python scripts pull the following TCGA data from the Xena public data hubs and upload these data to the MEXPRESS database: gene expression (RNA-seq – HTSeq – FPKM-UQ), DNA methylation (Infinium HumanMethylation450 and HumanMethylation27), gene-level copy number (GISTIC2 (10) thresholded), miRNA expression (miRNA-seq RPM), somatic mutations (MuTect2 variant aggregation and masking), phenotype and survival.
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The visual interpretation of the data (less GSTP1 expression in samples with more promoter methylation) is confirmed by the statistical analysis of the correlation between DNA methylation and gene expression (Pearson correlation coefficients ranging from −0.664 to −0.803 for promoter region probes).
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Custom python scripts pull the following TCGA data from the Xena public data hubs and upload these data to the MEXPRESS database: gene expression (RNA-seq – HTSeq – FPKM-UQ), DNA methylation (Infinium HumanMethylation450 and HumanMethylation27), gene-level copy number (GISTIC2 (10) thresholded), miRNA expression (miRNA-seq RPM), somatic mutations (MuTect2 variant aggregation and masking), phenotype and survival.
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