Paper review: An overview on microarray technologies

Abstract

Bioinformatics is a branch in Statistics which is still unpopular among statistics students in Indonesia. Bioinformatics research used microarray technology, because data is available through to microarray experiment on tissue sample at hand. Microarray technology has been widely used to provide data for bioinformatics research, since it was first introduced in late 1990, particularly in life sciences and biotechnology research. The emergence and development of the Covid-19 disease further reinforces the need to understand bioinformatics and its technology. There are two of the most advance platforms in microarray technology, namely, are the Affymetrix GeneChip and Illumina BeadArray. This paper aims to give an overview about microarray technology on the two platforms and the advantage of using them on bioinformatics research.

The interplay of long noncoding RNAs and hepatitis B virus

“…62 Detailed reviews focused on microarray technologies and applications can be found elsewhere. [63][64][65] For differential gene expression analysis of deep transcriptome sequencing data, RNA sequencing files are obtained and run through bioinformatics pipelines that generally follow a workflow of file preparation, sequence read mapping to a reference (e.g., human) genome, and quantification and differential expression calculations between samples. Different software packages with varying algorithms and computational methods are available to employ in any step of these modular pipelines-for example, DESeq2 or Cuffdiff 2 are widely used tools for transcript assembly and differential analysis.…”

“…Deep transcriptome sequencing and targeted RT‐qPCR with standard curves can be utilized to provide quantitative data on absolute expression levels 62 . Detailed reviews focused on microarray technologies and applications can be found elsewhere 63–65 . For differential gene expression analysis of deep transcriptome sequencing data, RNA sequencing files are obtained and run through bioinformatics pipelines that generally follow a workflow of file preparation, sequence read mapping to a reference (e.g., human) genome, and quantification and differential expression calculations between samples.…”