Computational Epigenetics to Bioinformatics
Computational epigenetics uses bioinformatic methods to complement experimental research in epigenetics. Due to the recent explosion of epigenome datasets, computational methods play an increasing role in all areas of epigenetic research.
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Epigenetic data processing and analysis
Various experimental techniques have been developed for genome-wide mapping of epigenetic information, the most widely used being ChIP-on-chip, ChIP-seq and bisulfite sequencing. All of these methods generate large amounts of data and require efficient ways of data processing and quality control by bioinformatic methods.
Epigenome prediction
A substantial amount of bioinformatic research has been devoted to the prediction of epigenetic information from characteristics of the genome sequence. Such predictions serve a dual purpose. First, accurate epigenome predictions can substitute for experimental data, to some degree, which is particularly relevant for newly discovered epigenetic mechanisms and for species other than human and mouse.
Second, prediction algorithms build statistical models of epigenetic information from training data and can therefore act as a first step toward quantitative modeling of an epigenetic mechanism. Successful computational prediction of DNA and lysine methylation and acetylation has been achieved by combinations of various features.
Segmentation, By Product
- Reagents
- Kits
- Enzymes
- Instruments & Consumables
- Bioinformatics Tools
By Technology
- Histone Modification
- DNA Methylation
Applications
- Oncology
- Cardiovascular Diseases
End Users
- Academic & Research Institutes
- Biotechnology & Pharmaceutical Companies
- Contract Research Organization
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Declining Prices of Sequencing
The declining costs associated with different strategies and methods for sequencing supports to influence the scale and scope of almost all genomic research projects. The costs associated with DNA sequencing performed at the sequencing centers, which is funded by the Institutes, has tracked by the National Human Genome Research Institute (NHGRI) for many years.
This information has served as a key standard for establishing the DNA sequencing capacity and considering improvements in DNA sequencing technologies of the NHGRI Genome Sequencing Program (GSP). In the recent years, next generation sequencing price have declined substantially. For instance, first whole human genome sequencing cost over US$3.7 billion in 2000 and took 13 years for the completion.
However, the costs for the same in recent years has reduce to US$1,000 and the process requires less number of days. In 2000, cost for sequencing was US$ 3.7 billion, which dropped down to US$ 10 million in 2006 and declined to US$ 5,000 in 2012. Major market players such as Illumina and Roche have introduced breakthrough technologies that have enabled in the cost and time reduction in the sequencing.
Owing to factors such as advances in the field of genomics, development in different methods and strategies for sequencing, there is a notable decline in the cost of sequencing, that upsurge the growth of the market.
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