5. The Present and Future of Microarrays

5.1. Effective use of Public Databases for Microarray Data
Microarray expression studies are producing massive quantities of gene expression and other functional genomic data, which will help provide key insights into gene function. It is widely acknowledged that there is a need for public repositories for microarray data [116] whose functions would include providing free access to supporting data for publications based on microarray experiments. Such repositories are under development by the National Center for Biotechnology Information (which has developed the Gene Expression Omnibus) [117], the DNA Database of Japan [118], and the European Bioinformatics Institute (which has developed ArrayExpress) [119]. The miRBase database is also a searchable database of published miRNA sequences and annotation [120].
In addition, the system can search for a specific gene and a specific disease and use properly for each purpose. It is necessary for other researchers to be careful to have access to the underlying data. Even the most carefully conducted studies should require intensive review and consideration of previously published data before embarking on new studies [121].
Although many microarray results have been derived from public databases, one problem was the lack of standards for presenting and exchanging such databases. To address these issues, the members of the Function Genomics Data Society created the MIAME (Minimum Information About a Microarray Experiment) standards for the description of microarray experiments [122]. Making microarray data public in a MIAME-compliant manner has become a precondition for publication for many journals [117]. Publishing original data and protocols facilitates independent evaluation of results and re-analysis, and maintains the spirit of open access [123].

5.2. The Relevance of Microarray Quality Control
DNA microarray technologies have had some problems regarding reproducibility and comparability between laboratories and across inter- and intra-platforms of gene expression measurements [124,125,126,127,128,129,130]. The MicroArray Quality Control (MAQC) project was initiated to address these concerns and showed intra-platform consistency across test sites as well as a high level of inter-platform concordance in terms of genes identified as differentially expressed by microarray methods. This study provides a resource that represents an important first step toward establishing a framework for the use of microarrays in clinical and regulatory settings [131,132]. International organizations such as External RNA Control Consortium [133], the Microarray Gene Expression Data Society [123], and the MAQC project are providing the microarray community with standardization of data reporting, common analysis tools, and useful controls that can help provide confidence in the consistency and reliability of these gene expression platforms [131].

5.3. Next-Generation Sequencing (NGS) Compared with Microarrays
Recently, the advent of NGS, or massively parallel sequencing, has precipitated the discovery of variants in the human genome [134], allowed whole-genome sequencing of microorganisms [135], and has led the way towards novel applications in the fields of human genetics [136], cancer [137,138], and infectious diseases [139,140]. NGS technologies have had a great impact on the field of expression research. Compared to microarray technology, the NGS method has several distinct advantages. The detection range of NGS is not limited to a set of predetermined probes as with the microarray technology, therefore NGS is capable of identifying new genes. And, the analysis of a microarray is limited to the gene level for most arrays, whereas NGS can detect expression at the gene, transcript, and coding DNA sequence levels. Finally, NGS can be used for traditional transcriptome profiling [141,142], identification of novel transcripts [143], identification of expressed SNPs [144,145], alternative splicing, and for the detection of gene fusion events [146,147,148,149]. However, in comparison with a microarray, NGS provides enormous gene information and thus requires significant costs for analysis [132,150,151]. Therefore, it will be necessary to use each characteristic effectively.