Personal genomics, bioinformatics, and variomics

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Personal genomics, bioinformatics, and variomics 


Jong Bhak1, Ho Ghang1, Rohit Reja1, and Sangsoo Kim2*

1KOBIC (Korean Bioinformation Center), KRIBB, Daejeon 305-806, Korea. 2Dept. of Bioinformatics, Soongsil Univ., Seoul 156-743, Korea.


Abstract

There are at least five complete genome sequences available in 2008. It is known that there are over 15,000,000 genetic variants called SNPs in the dbSNP database. The cost of a full genome sequencing in 2009 will be claimed to be less than $5000 USD. The genomics era has arrived in 2008. This review introduces technologies, bioinformatics, genomics visions, and variomics projects. Variomics is the study of the total genetic variation in an individual and populations. Research on genetic variation is the most valuable among many genomics research branches. Genomics and variomics projects will change biology and the society so dramatically that biology will become an everyday technology as personal computers and the internet. 'BioRevolution' is the term that can adequately describe this change.


Introduction
Since the launch of the Human Genome Project (HGP) in 1990 by NIH of USA, researchers have been developing faster DNA sequencers
(Shendure, Mitra et al. 2004; Chan 2005; Metzker 2005; Gupta 2008; Mardis 2008). HGP was said to be led by James Watson who modeled DNA in Cambridge, UK in 1953. In 2003, the International Human Genome Sequencing Consortium held a press conference to announce the completion of the human genome (IHGSC 2004). In 2008, after 55 years, his complete genome sequence was publicized by using 454 DNA sequencers developed by a company (Wheeler, Srinivasan et al. 2008). In 2007, Craig Venter of former Celera founder published his own personal genome in PLoS Biology (Levy, Sutton et al. 2007). We are entering the personalized biology era with the advent of next generation sequencing technologies.

DNA sequencing
The first breakthrough in genome sequencing came from Watson's colleague in Cambridge, Fred Sanger. In 1977, Sanger and his team produced the first useful DNA sequencing method and publicized the first complete genome
(Sanger, Air et al. 1977). It was a tiny virus genome known as phi X 174. Soon after phi X 174, he published the first complete organelle genome which was a mitochondrion (Anderson, Bankier et al. 1981). By 1998, researchers in the US evaluated multiplex genome sequencing technologies and were aware that one person's whole genome could be sequenced in one day using contemporary technologies. George Church was the Ph.D. student of Walter Gilbert who received a Nobel Prize with Sanger for developing a sequencing method. Gilbert's method was not used much. However, his colleague Church kept developing sequencing methods. One of them is based on Polony idea (Porreca, Shendure et al. 2006). This technology is used by KNOME Inc. that is a full genome sequencing company. Along with KNOME, other companies such as Complete Genomics are now producing DNA sequences cheaply and in an unprecedented capacity. The speed of sequencing is advancing many folds per year, much faster than the cycle of semiconductor chips in computer industries. Also, genome sequencing technology is becoming an everyday technology to the level as computer CPUs are universally used. In five years of time, experts predict that everyone in developed nations will have his or her own genome information if he/she wanted it. Due to its far reaching consequences in medicine, health, biology, nanotechnology, and information technology, DNA sequencing will become the most important industrial technology ever developed in the next decades. 

Personal Genomics
In 2009, genome sequencing technologies will achieve one person's whole genome per day in terms of DNA fragments sequenced. Personal genomics is a new term that utilizes such fast sequencers. In 2008, the cost for one personal genome is less than $350,000 USD. If the cost goes down below $1,000 USD, the impact of personal genomics is predicted to be the largest ever in biology on common people's life. Reflecting this technological advancement to the society is the PGP (Personal Genome Project), a project to sequence as many people as possible with lowest possible costs
(Church 2005). At present, Google Inc. and Church group are working together to sequence 100,000 people's genetic regions of DNA. In Saudi Arabia, the government is planning to sequence 100 Arabic people. In Europe, there are various groups of people and nations who have been genotyping the populations. Especially, Iceland has been successful in that effort by utilizing their well-kept genealogical data encompassing 100,000s people. In Asia, Jeongsun Seo of Seoul National University has been working on East Asia Genome Project in the past years. His group collected thousands of samples from Mongolian tribes with a gigantic genealogical tree among them (Park et al. 2008; Sung et al. 2008). Seo is said to be sequencing at least 100 Korean genomes in collaboration with Church and Green Cross Inc. of Korea. The aim of Seo's genome project is to produce a resource for the East Asians as well as Koreans. He is presently sequencing at least two Korean people. In China, Beijing Genome Institute has been successful in terms of sequencing. Their first achievement came from a plant genome, rice. After rice, they launched a 100 Han Chinese genome sequencing project. In Nov. 2008, they published their first Chinese genome in a joural, Nature. In Dec. 2008, another Korean group, Lee Gilyeo Cancer and Diabetes Institute (LCDI) and Korean Bioinformation Center (KOBIC) made a Korean genome sequence public. The genome was sequenced by Solexa paired-end sequencer and comparative genomics analyses and SNP data were uploaded as a public resource for everyone. The time taken to analyze the 7.8x Korean genome took only one week using 150 computer CPUs to produce mapping DNA fragments to a reference genome, generate new SNP information, compare with other individual genomes, and map it with 1600 already known phehotype information from the public literature.

Genome revolution 
These public genome data alongside previously known Craig Venter's and James Watson's mark that full genome sequences are not in academic domain anymore. Anyone who has money and will can sequence human genomes. This 'genomic revolution' will eventually lead to the 'BioRevolution' in terms of making the most essential human information completely mapped and publically available. These are revolutionary because humans can now engineer themselves with a map or a blue print not directly relying on trial and error style conventional evolutionary methods. This indicates that evolution went into a conscious level of driving evolution. It is almost designing the evolution using computers. 

Genomes and personalized medicine
The consequences of 'BioRevolution' where genomic information is utilized by scientists to engineers all kinds of biological processes including evolution itself will bring us the personalized medicine. The essence of personalized medicine is that enzymes in our tissues such as cytochrome P450 have distinct differences among individuals and populations. Certain drugs produce different responses in individuals. 

Cytochrome p450 family example

The cytochrome P450 (CYP) family of liver enzymes are responsible for breaking down more than 30 different classes of drugs during phase I of drug metabolism. Structural and SNP variations of the genes that code for these enzymes can influence their ability to metabolize certain drugs. Based upon this, a population can be categorized into four major types of drug metabolizers:
  • Extensive metabolizers: The individuals that can be administered with normal drug dosage
  • Intermediate metabolizers : The individuals that metabolizes drug with a rate slower than the normal rate.
  • Poor metabolizers: The individuals with poor metabolizing rate. Drugs make accumulate and cause serious adverse effects.
  • Ultra metabolizers: Individuals with metabolizing rate faster than extensive metabolizers. They may experience no effect of drug activity.

Variomics

The most important scientific data out of personal genomes are the precise sequence differences among individuals. Such differences have many types. There are structural differences between chromosomes. There can be insertions and deletions of DNA segments. There are certain fragments that appear as repeats in genomes. Mapping all these structural genetic variations can be briefly termed as 'variomics'. A variome is the totality of genetic variation found in an individual, a population, and a species. Among all the variations we know, the most common one is single nucleotide polymorphisms (SNP). In Korea, mapping the variome has been pursued relatively early and there are several groups who are mapping the genetic variations. KOBIC has several very early stage, if not the earliest in the world, variome servers; http://variome.net and http://variomics.net. Along with SNP variation, the copy number variation (CNV) is also important. Some recent reports tell us that CNVs can be as variable as or even more variable than SNPs that are simple DNA base changes in populations. Yeun-Jun Chung of Catholic University of Korea has been mapping CNVs among Korean people (Kim et al. 2008).
In early 2005, the US FDA cleared the AmpliChip® CYP450 Test, which measures variations in two genes of the CYP450 enzyme system: CYP2D6 and CYP2C19. The Roche AmpliChip CYP450 Test is intended to identify a patient's CYP2D6 and CYP2C19 genotype from genomic DNA extracted from a whole blood sample. Information about CYP2D6 and CYP2C19 genotype may be used as an aid to clinicians in determining therapeutic strategy and treatment dose for therapeutics that are metabolized by the CYP2D6 or CYP2C19 gene product.


Human Variome Project (HVP)

As an international collaboration, headed by Richard Cotton, HVP was launched in 2006 (http://humanvariomeproject.org) (Ring, Kwok et al. 2006). HVP aims to make clinicians who have been working on rare diseases, to work together with molecular biologists and bioinformaticians. Their goal is to link medical information with genotype information. Succinctly this process is called genotype to phenotype mapping. As several full human genome sequences are already available, mapping phenotypes to the full genomes will be the major challenge of biology in the next 20 years. 

Asian Variome Project (AVP)
Alongside and with the associations of eIMBL, A-IMBN, and HVP, a variome project that tries to map Asian population variome was launched in 2008. This was a group effort of Korean researchers who have been interested in genome sequences, SNPs, and CNVs. They have formed a Korean Variome Consortium (KOVAC: http://variome.kr) and supported AVP as one of the first projects. eIMBL that is the virtual laboratory network of Asia linking key biology groups modeled after EMBL has acquired $80,000 USD in 2008 to support AVP. eIMBL aims to establish a virtual bioinformatics center in Asia Pacific region that links many bioinformation processing scientists in Asia.

Bioinformatics for personal genomes and variomes
Bioinformatics is the key in personal genome projects and variome projects. Bioinformatics is not a set of tools but it is a proper scientific discipline. It regards life as a gigantic information processing phenomenon and tries to map its components and to model the emerging networks of the components. Bioinformatics in 2008 is driving biology into an information science. Most biology researches are now with massive amount of data that cannot be processed by hand. Nearly all the biological research outcomes in the next five years will have some form of high throughput data such as genome sequences, microarray data, proteome analyses, SNPs, epigenome chips, and large scale phenotype mapping. Bioinformatics tools in genomics and variomics can be found from various internet resources. There are various bioinformatics hubs such as NCBI (National Center for Biotechnology Information), EBI (European Bioinformatics Institute), DDBJ (Databank of Japan), and KOBIC. Some others are: Bioinformatics Organization (http://Bioinformatics.Org), EMBnet (http://www.embnet.org/), and The International Society for Computational Biology (http://iscb.org). The following are major bioinformatics journals:


Sequencing DNA, Metagenomics, and Ecogenomics
Next generation sequencing methods are not only mapping genomes. They can be used to map the environment. It is called ecogenomics. Environment to humans can be various microbial, plant, and animal interactions around us. Especially, microbial interaction is critical to our health. Gut bacteria are natural environment to us. Metagenomics is a methodology that sequences the whole set of microbes in our food tract. Researchers are realizing that human genome is complemented by such environmental genomes. A new term, 'ecogenomics' is now used to describe these concepts. Metagenomics and ecogenomics are for mapping the variation of environmental genetic factors.


Mapping expression using DNA sequencing
DNA sequencing technology used to be for mapping genotypes. However, they are now used to map expression levels in cells. Cells produce various RNAs. mRNA is the most abundant and important. In the past, microarray and DNA chips were used for measuring expression levels. They are not accurate and it takes many bioinformatic adjustments before it becomes reliable expression data. New sequencing technologies can measure expression levels much more accurately. By sequencing the RNAs, we can now quantify the mRNA levels by precisely knowing the RNA sequences. Sequencing technologies will restructure the expression analyses in the future.

 

 

Conclusion

In 2009 and onwards, personal genome projects will produce unprecedented amount of biological data. New bioinformatics technologies will be required to handle them. New sequencing technologies will drive the next decades of biology and transform the medical practices in hospitals within the next decades. Fast sequencing unexpectedly brought us interesting applications such as metagenomics and ecogenomics. We have examined the current trends in genomics and variomics.

Acknowledgements

SK was supported by Soongsil University Reserach Fund.

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