Table 1.

Summary and comparison of various diagnostic modalities.

Technique	Description	Platform	Data Analysis	Pros	Cons
Whole-genome sequencing (WGS)	whole genome is analyzed	Illumina PacBio Complete Ion Torrent BGI/MGI Oxford Nanopore	sequenced reads as data output with read alignments or quality scores variant identification annotation visualization statistical analysis	whole genomic sequence can be analyzed can identify non-coding mutations	costly and time-consuming for data interpretation high chance of incidental findings
Whole-exome sequencing (WES)	entire exome is analyzed			cost-effective and time-efficient than WGS deep coverage in exonic regions	high risk of incidental findings information only on coding regions
Targeted gene panel	captures key genes or regions of interest set by prior knowledge			significant reduction in time and cost compared to WGS/WES suitable as a diagnostic modality	requires prior knowledge of targeted regions not suitable for biomarker discovery
RNA-sequencing (RNAseq)	number of mRNA or total RNA molecules in the transcriptome is directly sequenced and quantified			can detect novel transcripts, fusions, single-nucleotide variants, indels, alternative splicing, allele-specific expression and newly transcribed regions good for biomarker discovery	need high-quality RNA (RNA integrity number > 8) only the expressed markers can be detected, thereby missing alterations in regulatory regions or non-expressed genes
Multiplex gene expression panel	a variation on RNA microarrays that uses hybridization probes	NanoString QuantiGene Plex	color-coded probes are converted into counts counts are normalized using housekeeping genes	RNA from FFPE material can be used can be done with less amount of RNA compared to RNAseq amplification free minimal background signal	not suitable for biomarker discovery limited flexibility
Epigenetic techniques	heritable phenotypical alterations that do not involve DNA sequence	Illumina Nimblegen Axon Roche	different epigenetic techniques are integrated based on these annotations, epigenome differences are recognized	epigenetic changes, such as DNA methylation or histone modification can be assessed	risk of variations depending on time of harvest and different organs/samples difficulty in choosing the techniques depending on the modification
Proteomic techniques	quantifies protein/peptide abundance, modification and interaction	mass spectrometry-based protein microarray-based	covalent changes are quantified by determining the equivalent change in protein mass in contrast to the unmodified peptide	gives a different level of understanding from D/RNA sequencing by high-throughput analyses of thousands of proteins in cells or body fluids	weak reproducibility and repeatability compared to other genomics techniques
Immunohistochemistry (IHC)	detection of molecules using antibodies, enzymatic/fluorescent dyes used to visualize by secondary antibody conjugates	automated staining devices from several suppliers dedicated multiplexing techniques e.g. Roche DISCOVERY / PerkinElmer Opal™	brightfield IHC requires microscopes while more advanced image analysis requires whole-slide-scanners fluorescent dyes require specialized imaging devices	allows spatial distributions of cell types / molecules of interest	limited to single / multiple molecules of interest on a given slide and therefore requires predefined antibody panels
In situ hybridization (ISH)	hybridization of RNA/DNA molecules using fluorescent (F-ISH) or chromogenic (C-ISH) dyes	RNAscope® Technology for RNA DNAscope™ for DNA	brightfield microscopes for CISH and fluorescent imagers for FISH application of object detection and sementic segmentations allows automation and quantitative analysis	quantitative measure of RNA/DNA molecules at cellular level can be applied on Formalin-Fixed Paraffin-Embedded (FFPE) tissues	limited to single / multiple molecules of interest
Single cell sequencing (sc-seq)	measures DNA, RNA, epigenetic marks and protein at a single-cell resolution	Illumina Ion Torrent BGI/MGI 10X Genomics	annotations of individual cells using cellular barcodes rest are analyzed in a similar manner to bulk sequencing	provides more precise classification of cell types and states than bulk sequencing	introduction of noise due to experimental procedures computational burden due to high dimensionality data difficult to integrate data from various types of single-cell approaches