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Bioinformatics techniques in microarray research: applied microarray data analysis using R and SAS software.

ABSTRACT: Exploration of the underlying biological mechanisms of disease is useful for many purposes such as the development of novel treatment modalities in addition to informing on-going risk factor research. DNA-microarray technology is a relatively recent and novel approach to conducting genome-wide gene expression studies to identify previously unknown biological pathways associated with disease. The copious data arising from microarray experiments is not conducive to traditional analytical approaches. Beyond the analytical challenges, there are equally important issues related to the interpretation and presentation of results. This chapter outlines appropriate techniques for analyzing microarray data in a fashion that also yields a list of top genes with differential expression in a given experiment. Derivatives of the top genes list can be used as a starting point for the presentation of study results. The list also serves as the basis for additional techniques related to enhanced interpretation and presentation of results. All analyses described in this chapter can be performed using relatively limited computational resources such as a lap top PC with at least 2.0 GB of memory and 2.0 GHz of processing speed.

SUBMITTER: Demmer RT

PROVIDER: S-EPMC6824719 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

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Bioinformatics techniques in microarray research: applied microarray data analysis using R and SAS software.

Demmer Ryan T RT Pavlidis Paul P Papapanou Panos N PN

Methods in molecular biology (Clifton, N.J.) 20100101

Exploration of the underlying biological mechanisms of disease is useful for many purposes such as the development of novel treatment modalities in addition to informing on-going risk factor research. DNA-microarray technology is a relatively recent and novel approach to conducting genome-wide gene expression studies to identify previously unknown biological pathways associated with disease. The copious data arising from microarray experiments is not conducive to traditional analytical approache ...[more]

PMID: 20717798

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Project description:Pancreatitis is a type of inflammation in the pancreas, which frequently occurs due to alcohol and gallstones. The present study aimed to identify pancreatitis‑associated microRNAs (miRNAs) by analyzing the microarray of GSE24279. GSE24279 was downloaded from the Gene Expression Omnibus, composed of a collective of 27 pancreatitis and 22 normal control samples. The differentially expressed miRNAs (DE‑miRNAs) in pancreatitis samples were screened using the Limma package in Bioconductor. Subsequently, target genes of the DE‑miRNAs were predicted using the miRecords and miRWalk databases. Their potential functions were analyzed by functional and pathway enrichment analysis using the Database for Annotation, Visualization and Integrated Discovery online tool. Finally, pancreatitis‑associated genes among the target genes identified were searched using the Comparative Toxicogenomics Database, and a regulatory network of pancreatitis‑associated genes and their target miRNAs were constructed using Cytoscape software. A total 14 upregulated and 39 downregulated miRNAs were identified in pancreatitis samples compared with control samples and 290 target genes of DE‑miRNAs were determined. Cyclin D1 (CCND1), v‑akt murine thymoma viral oncogene homolog 2 (AKT2), cyclin‑dependent kinase 6 (CDK6) and SMAD family member 2 (SMAD2) were involved in the pathway of pancreatic cancer. Among the target genes, 279 genes were pancreatitis‑associated genes, which in turn were targeted by 37 miRNAs in the regulatory network. Hsa‑miR‑15a, hsa‑miR‑16, hsa‑miR‑155, hsa‑miR‑375 and hsa‑miR‑429 in particular may be involved in pancreatitis by targeting genes in the regulatory network, including hsa‑miR‑15a→CCND1, hsa‑miR‑16→CCND1, hsa‑miR‑155→CCND1/SMAD2, hsa‑miR‑375→AKT2/CDK6 and hsa‑miR‑429→CCND1. The above miRNAs and their targets may contribute to the pathogenesis of pancreatitis; therefore, they may be potential therapeutic targets.

Dataset Information

Bioinformatics techniques in microarray research: applied microarray data analysis using R and SAS software.

Publications

Bioinformatics techniques in microarray research: applied microarray data analysis using R and SAS software.

Similar Datasets

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