Bioinformatics for immunologists

Abstract

Immunology was once a specialty prone to cause dismay or even scepticism among outsiders for its struggles to visualize poorly understood, complex interactions through descriptive models integrating cell types, their factors and functions. This was the age of 'too many soft ideas propped up by too little hard data'. Twenty-first century immunologists have the advantage of being able to marry this rich conceptual legacy to a contemporary toolkit offering such depth of hard data across different 'omics' platforms, that they are faced by the opposite dilemma: 'too much hard data to comprehend or synthesize into a meaningful narrative'. Approaches including next-generation sequencing of host and pathogen genomes and transcriptomes, metagenomics of the microbiota, creative strategies for receptor repertoire sequencing, and then for proteomics and metabolomics, encompass all that is needed to tell the entire story, if only we are creative enough, not only to evaluate the message from any given omics platform, but to derive the tools that enable us to integrate the answers from diverse omics platforms in a meaningful way. To achieve this goal, there is an urgent need to ensure we train the next generation of bioinformatically literate researchers.

Unveiling the role of miR-137-3p/miR-296-5p/SERPINA3 signaling in colorectal cancer progression: integrative analysis of gene expression profiles and in vitro studies

“…It aims to unravel the mysteries behind a vast amount of complex biological data. Currently, bioinformatics analyses, such as gene expression profiling, metabolic network analysis, and proteomics data analysis are being widely conducted globally [ 13 – 17 ]. These analyses have gradually become important emerging research areas within the field of bioinformatics.…”

“…These analyses have gradually become important emerging research areas within the field of bioinformatics. In recent years, numerous genes related to tumor progression have been identified by researchers through database analysis methods [ 13 – 17 ]. These genes could potentially serve as tumor markers in the future.…”