Table 1 Overview of omics technologies: summary of strengths, limitations and clinical utility for each technology

Genomics

SNP

• Unbiased approach when using GWAS

• Difficult to find functional and structural gene variants

• Theragnostic approach

• Cost-effective large-scale genetic screening

• Only regulatory or coding regions are included

• Risk stratification

• Well-established analysis tools

• Tissue-specific alterations

Epigenetics

• Unbiased approach when using epigenome-wide association studies

• Different composition of cell types during sepsis

• Epigenetic signatures for sepsis diagnosis and/or prognosis

• Can elucidate the interplay between genetic and environmental factors

• Frequency of epigenetic changes not known

• Prediction of therapeutic response

• Reverse causation

Transcriptomics

Expression profiling

• Can generate global view transcriptome alterations

• Tissue-specific expression of genes

• mRNA expression signatures for sepsis diagnosis and/or prognosis

• Provide good coverage of genome

• Fails to measure low-expression genes with good sensitivity

• Prediction of therapeutic response

• Can elucidate alterations in signal transduction pathways during sepsis

High-throughput gene sequencing (for example, RNA-seq)

• Comprehensive sequence information

• Tissue-specific expression of genes

• No clinical utility

• Unbiased approach

• Estimates abundance of genes in term of copies

miRNA

• Stable in blood

• Functions not completely understood

• Novel diagnostic and/or prognostic biomarkers in sepsis.

• Suggestive evidence that miRNAs play an important role in regulation of networks

• Necessary for correctly interpretation of gene expression

• The inclusion of miRNA when interpreting mRNA expression

Proteomics

• Provides global or unbiased alteration

• Needs large amount of preprocessing or fractions

• Novel diagnostic and/or prognostic biomarkers in sepsis

• Highly sensitive

• Current instruments unable to measure all proteins from complex biological fluids

• Prediction of therapeutic response

• No need for antibody-based technologies for measuring proteins

• Inefficient quantification of low expression proteins

Metabolomics

• Relatively few targets

• Difficulty in identifying small molecules

• Novel diagnostic and/or prognostic biomarkers in sepsis

• Good translation to existing laboratory technology

• Diverse physical and chemical properties and thus no single extraction tool

• Prediction of therapeutic response

• Disease progression