A multi-omics approach involves the combining of different “omics”: genomics, epigenomics, transcriptomics, and proteomics. The simultaneous study of each “omic” can provide a more accurate, holistic, and representative understanding of the complex molecular mechanisms that underpin biology.
Fundamentally, genomics investigates the structure, function, mapping, evolution and editing of information coded in our (and other species) genomes. That includes single nucleotide variants (SNVs), indels, insertions, deletions, copy number variations (CNVs), duplications, inversions… the list goes on.
In the past decade, genomics has allowed us to predict, diagnose and treat diseases in a more unbiased and precise way than we ever could before. And in research, genomics has revealed the genes or mutations involved in thousands of different phenotypes, biological processes and diseases. This has allowed us to identify new biomarkers, new drug targets and so much more.
Choosing a sequencing method
Selecting the right sequencing method is vital in any genomics analysis – and this all depends on your biological question. Today’s sequencing methods can be split into short-read or long-read technologies.
Short-read uses next-generation sequencing (NGS), also known as second-generation massively parallel sequencing, and is dominated by Illumina. Long-read sequencing, sometimes called third-generation sequencing, uses technology developed by Oxford Nanopore Technologies and Pacific Biosciences.
Short-read has the advantage of speed, scalability, lower cost and higher accuracy. Long-read has the advantage of de novo genome assembly and full-length isoform sequencing. There is also the possibility of integrating both approaches together, to get the best of both worlds.
Epigenomics investigates modifications of DNA or DNA-associated proteins, such as DNA methylation, chromatin interactions and histone modifications. Epigenetic regulation of DNA can determine cell fate and function, and the epigenome can change based on the environment. What’s more, these DNA alterations can be passed on.
These changes can act as markers for cancer, metabolic syndromes, cardiovascular disease and more. They can be tissue-specific, cell-specific and even more specific than that – down to subcellular compartments – and changes can occur during both healthy and disease states.
- Methylation sequencing: Cytosine methylation affects gene expression and chromatin remodelling and can be used to investigate the methylation status of the genome with single-nucleotide resolution.
- ChIP-seq: Chromatin immunoprecipitation sequencing combines immunoprecipitation assays with sequencing to identify genome-wide DNA binding sites for transcription factors and other proteins.
- ATAC-seq: Assay for transposase-accessible chromatin with sequencing to determine chromatin accessibility across the genome. Helps uncover how chromatin packaging and other factors affect gene expression.
- HiC/3C/Capture-C: Analyses chromatin interactions. Hi-C extends 3C-Seq to map chromatin contacts genome-wide, and it has also been applied to studying in situ chromatin interactions. Capture-C to the 3C method with pull-down of the biotinylated fragments with magnetic beads
Transcriptomics involves investigating RNA transcripts that are produced by the genome and how these transcripts are altered in response to regulatory processes. It’s the bridge between genotype and phenotype – the link between the genes and the proteins. Sandwiched nicely in the middle, it can tell us a lot about our biology.
RNA-seq is the method of choice for analysing the transcriptomes of disease states, biological processes and more. RNA-seq has a broad dynamic range, has very sensitive and accurate measurements of fold changes in gene expression and can be applied across a wide range of species. However, there is a lack of standardisation between sequencing platforms and read depth, which can compromise the reproducibility of this analysis.
Whole transcriptome analysis captures both known and novel features, allows researchers to identify biomarkers across the broadest range of transcripts and enables a more comprehensive understanding of phenotypes of interest.
Often, the most useful insight cannot be obtained by only studying genes – much more can be found out by looking at proteins too. The proteome is highly dynamic, as proteins can be modified in response to internal and external cues and different proteins are constructed by the cell as circumstances change. This is why proteomics examinations can be described as a ‘snapshot’ of the protein environment at any given time.
Proteomics has evolved over the past decades. This is mostly due to the accumulation of protein and DNA databases, with algorithms for searching through all the information generated, and improvements in technologies, such as mass spectrometry. Today, proteomics is essential for early disease diagnosis and monitoring. It also plays a crucial role in identifying target molecules for drug discovery and is used to understand complex gene functions.
Mass spectrometry measures the mass-to-charge ratio of ions to identify and quantify molecules in simple and complex mixtures. Methods can generally be segregated into either bottom-up or top-down.
Bottom-up means that proteins are digested by proteolytic enzymes before analysis with mass spec. This approach has been around longer and is the most widely used approach. Top-down involves the characterisation of intact proteins. This allows for almost 100% sequence coverage and the characterisation of proteoforms. However, top-down is far more expensive and less efficient.
Report: Multi-Omics: The Full Picture
An overview of different multi-omics approaches
Multi-omics data integration and bioinformatics