Genomics can mean different things but from a broad perspective it includes a deep and detailed study of DNA and RNA sequences encompassing most or all of the genome using bioinformatics and machine learning computational approaches. The method can generate vast amounts of sequence data from a tissue sample, annotating, aligning, sorting and filtering the data into refined subsets of interest. The genomics approach allows an investigator to find any given biomarker needle in the proverbial genetic haystack repeatedly and with high accuracy. The combination of next generation sequencing (NGS) and bioinformatics pipelines have allowed genomics to revolutionize the study of biological systems. In the near future NGS is also likely to transform routine molecular diagnostics. Key concepts of the methodology and applications of genomics will be presented along with current strengths and limitations.
Review of published scientific NGS papers and experience from the Wolfson Institute NGS team.
Genomics, underpinned first by microarrays and more recently by NGS is an ideal and comprehensive approach to biomarker discovery and validation. Given an adequately powered high-depth NGS experiment a comprehensive dataset can be produced which is an excellent resource for data mining and a reference for the future. Publicly shared NGS datasets can be mined repeatedly with more and more refined computational methods, each time potentially yielding new information. There are some limitations to genomics studies, mostly related to high costs and data overload, these are temporary barriers that will disappear with newer platforms and more competition.
NGS can produce vast amounts of detailed nucleic acid information on biological samples. Bioinformatics pipelines, although cumbersome today, are evolving into more user-friendly formats and will soon become relatively easy to use for non-experts, which will pave the way for genomics to enter into routine diagnostic use.