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microarray analysis -凯发k8网页登录

gene expression and genetic variant analysis of microarray data

microarrays contain oligonucleotide or cdna probes to measure the expression levels of genes on a genomic scale. bioinformatics toolbox™ lets you preprocess expression data from microarrays using various normalization and filtering methods. use the normalized data to identify differentially expressed genes and perform enrichment analysis of expression results using gene ontology. you can also detect genetic variants such as copy number variations (cnvs) and single nucleotide polymorphism (snps) from comparative genomic hybridization (cgh) data. visualize gene and protein-protein interaction networks using graph theory algorithms.

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    • import data and annotations from affymetrix® genechip®, illumina®, agilent®, gene expression omnibus (geo), imagene®, spot, genepix® gpr, and gal; manage experimental data and sample metadata per miame standard

    • prepare raw microarray data for analysis using background adjustment, normalization, and expression filtering; extensive preprocessing of affymetrix and illumina data. retrieve probe annotations from library files

    • identify, visualize, and classify differentially expressed genes and expression profiles

    • find, analyze, and visualize genetic variants such as copy number variations (cnvs) and single nucleotide polymorphisms (snps)

    • real-time gene ontology (go) information; enrichment analysis of microarray expression results using gene ontology networks

    • apply basic graph theory algorithms to protein-protein interactions (ppi) and other gene networks; view network relationships using interactive maps, hierarchy plots, and pathways
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