Genomics is an area of molecular biology and genetics that focuses on the structure, function, evolution and mapping of genomes. This field encompasses a wide range of research fields including genome sequencing, functional genomics, comparative genomics, bioinformatics, epigenomics, and gene regulation analysis. It also includes studies related to disease pathogenesis and personal genomics. A few popular areas of genomics research are highlighted below.
Functional genomics encompasses the research field describing the function and interaction of both proteins and genes. This area of study is a genome-wide approach that builds on our understanding of DNA structure and sequence to focus on the dynamic aspects of interactions, including gene transcription, translation, and protein-protein interactions.
The importance of functional genomics has increased following the completion of the human genome project. Genomics is now being used to understand the function of the ~30,000 human genes, non-coding regions, and sequence variations, including single nucleotide polymorphisms (SNPs). Understanding functional genomics, that is, how the genome and proteome interact to generate an organism's phenotype, provides insights into complex biological processes including human disease.
Since functional genomics includes investigation at both the genome and proteome level, a wide variety of technologies are utilized. Techniques that allow the measurement of multiple genes or proteins simultaneously, including multiplex and high-throughput screening approaches, are commonly used.
Common experimental approaches for functional genomics and their enabling technologies include:
Epigenetics is defined as the study of inherited changes in phenotype or gene expression caused by mechanisms other than mutations in the underlying DNA sequence. Epigenetic markers include DNA methylation and modification of histone tails.
In eukaryotes, DNA can be modified by methylation of cytosine bases, while in prokaryotes DNA methylation occurs primarily on adenosine bases. Aberrant or increased methylation has been correlated with gene silencing and the development of several cancers.
Histones are subject to several different covalent modifications, including methylation, acetylation, phosphorylation, ubiquitylation, and sumoylation. Acetylation is the most commonly studied of these modifications and a strong correlation between histone acetylation and active transcription has been established. Conversely, many histone methylation events are correlated with transcriptional silencing. Different histone modifications likely function in differing ways. For example, acetylation at one position may have a different effect than acetylation at another position.
Multiple modifications can exist simultaneously and are likely working together to influence chromatin state and gene expression. The concept of multiple dynamic modifications regulating gene expression in a systematic and reproducible fashion is known as the histone code.
Common techniques used to study DNA methylation and histone modifications include:
Biological pathways include metabolic, regulatory, and signaling pathways that organize and coordinate the activities of a cell. Biological pathways often interact with one another to form biological networks.
Pathway analysis is used in systems biology to build a better picture of how the individual components of a biological system interact to create the larger functional system.
Techniques that enable screening for expression of multiple gene or gene products simultaneously are typically used to generate data for pathway analysis. The data are then analyzed using complex software algorithms or biostatistics to create a map of how these individual components interact with one another in an interaction pathway. As deregulation of a pathway can result in disease, such as cancer, pathway analysis often involves comparisons of expression patterns between normal and pathological samples.
Although an understanding of biological pathways can be built up slowly by adding information generated from individual experiments, typically large data sets, often combined from a variety of sources including the published literature, are mined using automated network generating software tools. While almost any biological analysis tool can be used to generate data for pathway analysis, techniques that enable the comparison of multiple gene or gene products are most useful, and include:
The National Institutes of Health (NIH) defines a biomarker as a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. The goal of biomarker research is to discover gene or protein markers and use them to improve diagnostic, prognostic, or therapeutic outcomes for patients, and to assist in the development of novel drug candidates.
Biomarkers can be discovered through differential expression analysis, which is the indentification of mRNA, microRNA, or protein expression levels as influenced by disease state, therapy, or other differences between sample cohorts. Once differentially expressed targets are identified and validated, their expression levels can be used to classify organisms, individuals, disease states, metabolic conditions, or phenotypic responses to environmental or chemical challenges.
Common experimental approaches for biomarker discovery include:
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