Identification of reliable biomarkers by integration of transcriptomics and bioinformatics

Reliable biomarkers are indicators with distinctive traits that can be used as potential indicators for detecting the normality or abnormality of biological, physiological, or pharmacological processes. Depending on their applicability, these biomarkers can be generally categorized into five types including antecedent, screening, diagnostic, staging, and prognostic biomarkers. Also, biomarkers can be in different forms including proteins, DNAs, RNAs, and other metabolic compounds. Thus, different -omics fields and approaches have emerged in order to detect and analyze these biomarkers, including proteomics, genomics, transcriptomics, and metabolomics, generating huge data on these biomarkers’ forms. Then, the bioinformatics field would play a basic role in the analysis of this huge data, identifying, determining, and confirming these biomarkers’ suitability and linking them with a specific condition. So, transcriptomics is an example of these fields that study transcriptomes. Thus, the workflow of transcriptomics and bioinformatics fields to identify reliable biomarkers has been discussed. In fact, multi-omics research approaches are seen to have the potential to expand future studies of reliable biomarkers. So, an increasing number of scientists are adopting methods of integrating bioinformatics with proteomics, genomics, transcriptomics, metabolomics, and other multi-omics joint studies. Therefore, the purpose of this paper is to investigate technologies of transcriptomics as an example of emerging fields that are used for the detection and analysis of reliable biomarkers as well as investigating the integration of the transcriptomics field with the bioinformatics field in order to identify these biomarkers.