Category Archives: Transhuman News

Feature Video now available The Genomic Landscape of Breast Cancer in Women of African Ancestry

On Tuesday, June 7, Olufunmilayo I. Olopade, M.D., F.A.C.P., presented The Genomic Landscape of Breast Cancer in Women of African Ancestry, the final lecture in the 2016 Genomics and Health Disparities Lecture Series. Dr. Olufunmilayo is director of the Center for Clinical Cancer Genetics at the University of Chicago School of Medicine. Read more | Watch the video

The NHGRI History of Genomics Program closed its six-part seminar series featuring Human Genome Project (HGP) participants who helped launch the HGP with the talk: The Genome is for Life, by David Bentley, D.Phil., on Thursday, May 26th. Dr. Bentley is vice president and chief scientist at Illumina Inc. His long-term research interest is the study of human sequence variation and its impact on health and disease. Read more about the series

In this issue of The Genomics Landscape, we feature the use of model organisms to explore the function of genes implicated in human disease. This month's issue also highlights a recently completed webinar series to help professionals in the health insurance industry understand genetic testing, new funding for training in genomic medicine research, and NHGRI's Genome Statute and Legislation Database. Read more

Cristina Kapusti, M.S., has been named chief of the Policy and Program Analysis Branch (PPAB) at the National Human Genome Research Institute (NHGRI). In her new role, she will oversee policy activities and evaluation as well as program reporting and assessment to support institute priorities. PPAB is a part of the Division of Policy, Communications and Education (DPCE), whose mission is to promote the understanding and application of genomic knowledge to advance human health and society. Read more

Last Updated: July 7, 2016

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About NHGRI - Genome.gov | National Human Genome Research ...

Genomics is a discipline in genetics that applies recombinant DNA, DNA sequencing methods, and bioinformatics to sequence, assemble, and analyze the function and structure of genomes (the complete set of DNA within a single cell of an organism).[1][2] Advances in genomics have triggered a revolution in discovery-based research to understand even the most complex biological systems such as the brain.[3] The field includes efforts to determine the entire DNA sequence of organisms and fine-scale genetic mapping. The field also includes studies of intragenomic phenomena such as heterosis, epistasis, pleiotropy and other interactions between loci and alleles within the genome.[4] In contrast, the investigation of the roles and functions of single genes is a primary focus of molecular biology or genetics and is a common topic of modern medical and biological research. Research of single genes does not fall into the definition of genomics unless the aim of this genetic, pathway, and functional information analysis is to elucidate its effect on, place in, and response to the entire genomes networks.[5][6]

From the Greek [7]gen, "gene" (gamma, epsilon, nu, epsilon) meaning "become, create, creation, birth", and subsequent variants: genealogy, genesis, genetics, genic, genomere, genotype, genus etc. While the word genome (from the German Genom, attributed to Hans Winkler) was in use in English as early as 1926,[8] the term genomics was coined by Tom Roderick, a geneticist at the Jackson Laboratory (Bar Harbor, Maine), over beer at a meeting held in Maryland on the mapping of the human genome in 1986.[9]

Following Rosalind Franklin's confirmation of the helical structure of DNA, James D. Watson and Francis Crick's publication of the structure of DNA in 1953 and Fred Sanger's publication of the Amino acid sequence of insulin in 1955, nucleic acid sequencing became a major target of early molecular biologists.[10] In 1964, Robert W. Holley and colleagues published the first nucleic acid sequence ever determined, the ribonucleotide sequence of alanine transfer RNA.[11][12] Extending this work, Marshall Nirenberg and Philip Leder revealed the triplet nature of the genetic code and were able to determine the sequences of 54 out of 64 codons in their experiments.[13] In 1972, Walter Fiers and his team at the Laboratory of Molecular Biology of the University of Ghent (Ghent, Belgium) were the first to determine the sequence of a gene: the gene for Bacteriophage MS2 coat protein.[14] Fiers' group expanded on their MS2 coat protein work, determining the complete nucleotide-sequence of bacteriophage MS2-RNA (whose genome encodes just four genes in 3569 base pairs [bp]) and Simian virus 40 in 1976 and 1978, respectively.[15][16]

In addition to his seminal work on the amino acid sequence of insulin, Frederick Sanger and his colleagues played a key role in the development of DNA sequencing techniques that enabled the establishment of comprehensive genome sequencing projects.[4] In 1975, he and Alan Coulson published a sequencing procedure using DNA polymerase with radiolabelled nucleotides that he called the Plus and Minus technique.[17][18] This involved two closely related methods that generated short oligonucleotides with defined 3' termini. These could be fractionated by electrophoresis on a polyacrylamide gel and visualised using autoradiography. The procedure could sequence up to 80 nucleotides in one go and was a big improvement, but was still very laborious. Nevertheless, in 1977 his group was able to sequence most of the 5,386 nucleotides of the single-stranded bacteriophage X174, completing the first fully sequenced DNA-based genome.[19] The refinement of the Plus and Minus method resulted in the chain-termination, or Sanger method (see below), which formed the basis of the techniques of DNA sequencing, genome mapping, data storage, and bioinformatic analysis most widely used in the following quarter-century of research.[20][21] In the same year Walter Gilbert and Allan Maxam of Harvard University independently developed the Maxam-Gilbert method (also known as the chemical method) of DNA sequencing, involving the preferential cleavage of DNA at known bases, a less efficient method.[22][23] For their groundbreaking work in the sequencing of nucleic acids, Gilbert and Sanger shared half the 1980 Nobel Prize in chemistry with Paul Berg (recombinant DNA).

The advent of these technologies resulted in a rapid intensification in the scope and speed of completion of genome sequencing projects. The first complete genome sequence of an eukaryotic organelle, the human mitochondrion (16,568 bp, about 16.6 kb [kilobase]), was reported in 1981,[24] and the first chloroplast genomes followed in 1986.[25][26] In 1992, the first eukaryotic chromosome, chromosome III of brewer's yeast Saccharomyces cerevisiae (315 kb) was sequenced.[27] The first free-living organism to be sequenced was that of Haemophilus influenzae (1.8 Mb [megabase]) in 1995.[28] The following year a consortium of researchers from laboratories across North America, Europe, and Japan announced the completion of the first complete genome sequence of a eukaryote, S. cerevisiae (12.1 Mb), and since then genomes have continued being sequenced at an exponentially growing pace.[29] As of October 2011[update], the complete sequences are available for: 2,719 viruses, 1,115 archaea and bacteria, and 36 eukaryotes, of which about half are fungi.[30][31]

Most of the microorganisms whose genomes have been completely sequenced are problematic pathogens, such as Haemophilus influenzae, which has resulted in a pronounced bias in their phylogenetic distribution compared to the breadth of microbial diversity.[32][33] Of the other sequenced species, most were chosen because they were well-studied model organisms or promised to become good models. Yeast (Saccharomyces cerevisiae) has long been an important model organism for the eukaryotic cell, while the fruit fly Drosophila melanogaster has been a very important tool (notably in early pre-molecular genetics). The worm Caenorhabditis elegans is an often used simple model for multicellular organisms. The zebrafish Brachydanio rerio is used for many developmental studies on the molecular level, and the flower Arabidopsis thaliana is a model organism for flowering plants. The Japanese pufferfish (Takifugu rubripes) and the spotted green pufferfish (Tetraodon nigroviridis) are interesting because of their small and compact genomes, which contain very little noncoding DNA compared to most species.[34][35] The mammals dog (Canis familiaris),[36] brown rat (Rattus norvegicus), mouse (Mus musculus), and chimpanzee (Pan troglodytes) are all important model animals in medical research.[23]

A rough draft of the human genome was completed by the Human Genome Project in early 2001, creating much fanfare.[37] This project, completed in 2003, sequenced the entire genome for one specific person, and by 2007 this sequence was declared "finished" (less than one error in 20,000 bases and all chromosomes assembled).[37] In the years since then, the genomes of many other individuals have been sequenced, partly under the auspices of the 1000 Genomes Project, which announced the sequencing of 1,092 genomes in October 2012.[38] Completion of this project was made possible by the development of dramatically more efficient sequencing technologies and required the commitment of significant bioinformatics resources from a large international collaboration.[39] The continued analysis of human genomic data has profound political and social repercussions for human societies.[40]

The English-language neologism omics informally refers to a field of study in biology ending in -omics, such as genomics, proteomics or metabolomics. The related suffix -ome is used to address the objects of study of such fields, such as the genome, proteome or metabolome respectively. The suffix -ome as used in molecular biology refers to a totality of some sort; similarly omics has come to refer generally to the study of large, comprehensive biological data sets. While the growth in the use of the term has led some scientists (Jonathan Eisen, among others[41]) to claim that it has been oversold,[42] it reflects the change in orientation towards the quantitative analysis of complete or near-complete assortment of all the constituents of a system.[43] In the study of symbioses, for example, researchers which were once limited to the study of a single gene product can now simultaneously compare the total complement of several types of biological molecules.[44][45]

After an organism has been selected, genome projects involve three components: the sequencing of DNA, the assembly of that sequence to create a representation of the original chromosome, and the annotation and analysis of that representation.[4]

Historically, sequencing was done in sequencing centers, centralized facilities (ranging from large independent institutions such as Joint Genome Institute which sequence dozens of terabases a year, to local molecular biology core facilities) which contain research laboratories with the costly instrumentation and technical support necessary. As sequencing technology continues to improve, however, a new generation of effective fast turnaround benchtop sequencers has come within reach of the average academic laboratory.[46][47] On the whole, genome sequencing approaches fall into two broad categories, shotgun and high-throughput (aka next-generation) sequencing.[4]

Shotgun sequencing (Sanger sequencing is used interchangeably) is a sequencing method designed for analysis of DNA sequences longer than 1000 base pairs, up to and including entire chromosomes.[48] It is named by analogy with the rapidly expanding, quasi-random firing pattern of a shotgun. Since the chain termination method of DNA sequencing can only be used for fairly short strands (100 to 1000 base pairs), longer DNA sequences must be broken into random small segments which are then sequenced to obtain reads. Multiple overlapping reads for the target DNA are obtained by performing several rounds of this fragmentation and sequencing. Computer programs then use the overlapping ends of different reads to assemble them into a continuous sequence.[48][49] Shotgun sequencing is a random sampling process, requiring over-sampling to ensure a given nucleotide is represented in the reconstructed sequence; the average number of reads by which a genome is over-sampled is referred to as coverage.[50]

For much of its history, the technology underlying shotgun sequencing was the classical chain-termination method, which is based on the selective incorporation of chain-terminating dideoxynucleotides by DNA polymerase during in vitro DNA replication.[19][51] Developed by Frederick Sanger and colleagues in 1977, it was the most widely used sequencing method for approximately 25 years. More recently, Sanger sequencing has been supplanted by "Next-Gen" sequencing methods, especially for large-scale, automated genome analyses. However, the Sanger method remains in wide use in 2013, primarily for smaller-scale projects and for obtaining especially long contiguous DNA sequence reads (>500 nucleotides).[52] Chain-termination methods require a single-stranded DNA template, a DNA primer, a DNA polymerase, normal deoxynucleosidetriphosphates (dNTPs), and modified nucleotides (dideoxyNTPs) that terminate DNA strand elongation. These chain-terminating nucleotides lack a 3'-OH group required for the formation of a phosphodiester bond between two nucleotides, causing DNA polymerase to cease extension of DNA when a ddNTP is incorporated. The ddNTPs may be radioactively or fluorescently labelled for detection in automated sequencing machines.[4] Typically, these automated DNA-sequencing instruments (DNA sequencers) can sequence up to 96 DNA samples in a single batch (run) in up to 48 runs a day.[53]

The high demand for low-cost sequencing has driven the development of high-throughput sequencing (or next-generation sequencing [NGS]) technologies that parallelize the sequencing process, producing thousands or millions of sequences at once.[54][55] High-throughput sequencing technologies are intended to lower the cost of DNA sequencing beyond what is possible with standard dye-terminator methods. In ultra-high-throughput sequencing as many as 500,000 sequencing-by-synthesis operations may be run in parallel.[56][57]

Solexa, now part of Illumina, developed a sequencing method based on reversible dye-terminators technology acquired from Manteia Predictive Medicine in 2004. This technology had been invented and developed in late 1996 at Glaxo-Welcome's Geneva Biomedical Research Institute (GBRI), by Dr. Pascal Mayer and Dr Laurent Farinelli.[58] In this method, DNA molecules and primers are first attached on a slide and amplified with polymerase so that local clonal colonies, initially coined "DNA colonies", are formed. To determine the sequence, four types of reversible terminator bases (RT-bases) are added and non-incorporated nucleotides are washed away. Unlike pyrosequencing, the DNA chains are extended one nucleotide at a time and image acquisition can be performed at a delayed moment, allowing for very large arrays of DNA colonies to be captured by sequential images taken from a single camera.

Decoupling the enzymatic reaction and the image capture allows for optimal throughput and theoretically unlimited sequencing capacity. With an optimal configuration, the ultimately reachable instrument throughput is thus dictated solely by the analogic-to-digital conversion rate of the camera, multiplied by the number of cameras and divided by the number of pixels per DNA colony required for visualizing them optimally (approximately 10 pixels/colony). In 2012, with cameras operating at more than 10MHz A/D conversion rates and available optics, fluidics and enzymatics, throughput can be multiples of 1 million nucleotides/second, corresponding roughly to 1 human genome equivalent at 1x coverage per hour per instrument, and 1 human genome re-sequenced (at approx. 30x) per day per instrument (equipped with a single camera). The camera takes images of the fluorescently labeled nucleotides, then the dye along with the terminal 3' blocker is chemically removed from the DNA, allowing the next cycle.[59]

Ion Torrent Systems Inc. developed a sequencing approach based on standard DNA replication chemistry. This technology measures the release of a hydrogen ion each time a base is incorporated. A microwell containing template DNA is flooded with a single nucleotide, if the nucleotide is complementary to the template strand it will be incorporated and a hydrogen ion will be released. This release triggers an ISFET ion sensor. If a homopolymer is present in the template sequence multiple nucleotides will be incorporated in a single flood cycle, and the detected electrical signal will be proportionally higher.[60]

Overlapping reads form contigs; contigs and gaps of known length form scaffolds.

Paired end reads of next generation sequencing data mapped to a reference genome.

Multiple, fragmented sequence reads must be assembled together on the basis of their overlapping areas.

Sequence assembly refers to aligning and merging fragments of a much longer DNA sequence in order to reconstruct the original sequence.[4] This is needed as current DNA sequencing technology cannot read whole genomes as a continuous sequence, but rather reads small pieces of between 20 and 1000 bases, depending on the technology used. Typically the short fragments, called reads, result from shotgun sequencing genomic DNA, or gene transcripts (ESTs).[4]

Assembly can be broadly categorized into two approaches: de novo assembly, for genomes which are not similar to any sequenced in the past, and comparative assembly, which uses the existing sequence of a closely related organism as a reference during assembly.[50] Relative to comparative assembly, de novo assembly is computationally difficult (NP-hard), making it less favorable for short-read NGS technologies.

Finished genomes are defined as having a single contiguous sequence with no ambiguities representing each replicon.[61]

The DNA sequence assembly alone is of little value without additional analysis.[4]Genome annotation is the process of attaching biological information to sequences, and consists of three main steps:[62]

Automatic annotation tools try to perform these steps in silico, as opposed to manual annotation (a.k.a. curation) which involves human expertise and potential experimental verification.[63] Ideally, these approaches co-exist and complement each other in the same annotation pipeline (also see below).

Traditionally, the basic level of annotation is using BLAST for finding similarities, and then annotating genomes based on homologues.[4] More recently, additional information is added to the annotation platform. The additional information allows manual annotators to deconvolute discrepancies between genes that are given the same annotation. Some databases use genome context information, similarity scores, experimental data, and integrations of other resources to provide genome annotations through their Subsystems approach. Other databases (e.g. Ensembl) rely on both curated data sources as well as a range of software tools in their automated genome annotation pipeline.[64]Structural annotation consists of the identification of genomic elements, primarily ORFs and their localisation, or gene structure. Functional annotation consists of attaching biological information to genomic elements.

The need for reproducibility and efficient management of the large amount of data associated with genome projects mean that computational pipelines have important applications in genomics.[65]

Functional genomics is a field of molecular biology that attempts to make use of the vast wealth of data produced by genomic projects (such as genome sequencing projects) to describe gene (and protein) functions and interactions. Functional genomics focuses on the dynamic aspects such as gene transcription, translation, and proteinprotein interactions, as opposed to the static aspects of the genomic information such as DNA sequence or structures. Functional genomics attempts to answer questions about the function of DNA at the levels of genes, RNA transcripts, and protein products. A key characteristic of functional genomics studies is their genome-wide approach to these questions, generally involving high-throughput methods rather than a more traditional gene-by-gene approach.

A major branch of genomics is still concerned with sequencing the genomes of various organisms, but the knowledge of full genomes has created the possibility for the field of functional genomics, mainly concerned with patterns of gene expression during various conditions. The most important tools here are microarrays and bioinformatics.

Structural genomics seeks to describe the 3-dimensional structure of every protein encoded by a given genome.[66][67] This genome-based approach allows for a high-throughput method of structure determination by a combination of experimental and modeling approaches. The principal difference between structural genomics and traditional structural prediction is that structural genomics attempts to determine the structure of every protein encoded by the genome, rather than focusing on one particular protein. With full-genome sequences available, structure prediction can be done more quickly through a combination of experimental and modeling approaches, especially because the availability of large numbers of sequenced genomes and previously solved protein structures allow scientists to model protein structure on the structures of previously solved homologs. Structural genomics involves taking a large number of approaches to structure determination, including experimental methods using genomic sequences or modeling-based approaches based on sequence or structural homology to a protein of known structure or based on chemical and physical principles for a protein with no homology to any known structure. As opposed to traditional structural biology, the determination of a protein structure through a structural genomics effort often (but not always) comes before anything is known regarding the protein function. This raises new challenges in structural bioinformatics, i.e. determining protein function from its 3D structure.[68]

Epigenomics is the study of the complete set of epigenetic modifications on the genetic material of a cell, known as the epigenome.[69] Epigenetic modifications are reversible modifications on a cells DNA or histones that affect gene expression without altering the DNA sequence (Russell 2010 p.475). Two of the most characterized epigenetic modifications are DNA methylation and histone modification. Epigenetic modifications play an important role in gene expression and regulation, and are involved in numerous cellular processes such as in differentiation/development and tumorigenesis.[69] The study of epigenetics on a global level has been made possible only recently through the adaptation of genomic high-throughput assays.[70]

Metagenomics is the study of metagenomes, genetic material recovered directly from environmental samples. The broad field may also be referred to as environmental genomics, ecogenomics or community genomics. While traditional microbiology and microbial genome sequencing rely upon cultivated clonal cultures, early environmental gene sequencing cloned specific genes (often the 16S rRNA gene) to produce a profile of diversity in a natural sample. Such work revealed that the vast majority of microbial biodiversity had been missed by cultivation-based methods.[71] Recent studies use "shotgun" Sanger sequencing or massively parallel pyrosequencing to get largely unbiased samples of all genes from all the members of the sampled communities.[72] Because of its power to reveal the previously hidden diversity of microscopic life, metagenomics offers a powerful lens for viewing the microbial world that has the potential to revolutionize understanding of the entire living world.[73][74]

Bacteriophages have played and continue to play a key role in bacterial genetics and molecular biology. Historically, they were used to define gene structure and gene regulation. Also the first genome to be sequenced was a bacteriophage. However, bacteriophage research did not lead the genomics revolution, which is clearly dominated by bacterial genomics. Only very recently has the study of bacteriophage genomes become prominent, thereby enabling researchers to understand the mechanisms underlying phage evolution. Bacteriophage genome sequences can be obtained through direct sequencing of isolated bacteriophages, but can also be derived as part of microbial genomes. Analysis of bacterial genomes has shown that a substantial amount of microbial DNA consists of prophage sequences and prophage-like elements.[75] A detailed database mining of these sequences offers insights into the role of prophages in shaping the bacterial genome.[76][77]

At present there are 24 cyanobacteria for which a total genome sequence is available. 15 of these cyanobacteria come from the marine environment. These are six Prochlorococcus strains, seven marine Synechococcus strains, Trichodesmium erythraeum IMS101 and Crocosphaera watsonii WH8501. Several studies have demonstrated how these sequences could be used very successfully to infer important ecological and physiological characteristics of marine cyanobacteria. However, there are many more genome projects currently in progress, amongst those there are further Prochlorococcus and marine Synechococcus isolates, Acaryochloris and Prochloron, the N2-fixing filamentous cyanobacteria Nodularia spumigena, Lyngbya aestuarii and Lyngbya majuscula, as well as bacteriophages infecting marine cyanobaceria. Thus, the growing body of genome information can also be tapped in a more general way to address global problems by applying a comparative approach. Some new and exciting examples of progress in this field are the identification of genes for regulatory RNAs, insights into the evolutionary origin of photosynthesis, or estimation of the contribution of horizontal gene transfer to the genomes that have been analyzed.[78]

Genomics has provided applications in many fields, including medicine, biotechnology, anthropology and other social sciences.[40]

Next-generation genomic technologies allow clinicians and biomedical researchers to drastically increase the amount of genomic data collected on large study populations.[79] When combined with new informatics approaches that integrate many kinds of data with genomic data in disease research, this allows researchers to better understand the genetic bases of drug response and disease.[80][81]

The growth of genomic knowledge has enabled increasingly sophisticated applications of synthetic biology.[82] In 2010 researchers at the J. Craig Venter Institute announced the creation of a partially synthetic species of bacterium, Mycoplasma laboratorium, derived from the genome of Mycoplasma genitalium.[83]

Conservationists can use the information gathered by genomic sequencing in order to better evaluate genetic factors key to species conservation, such as the genetic diversity of a population or whether an individual is heterozygous for a recessive inherited genetic disorder.[84] By using genomic data to evaluate the effects of evolutionary processes and to detect patterns in variation throughout a given population, conservationists can formulate plans to aid a given species without as many variables left unknown as those unaddressed by standard genetic approaches.[85]

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Genomics - Wikipedia, the free encyclopedia

The Cost of Sequencing a Human Genome

Advances in the field of genomics over the past quarter-century have led to substantial reductions in the cost of genome sequencing. The underlying costs associated with different methods and strategies for sequencing genomes are of great interest because they influence the scope and scale of almost all genomics research projects. As a result, significant scrutiny and attention have been given to genome-sequencing costs and how they are calculated since the beginning of the field of genomics in the late 1980s. For example, NHGRI has carefully tracked costs at its funded 'genome sequencing centers' for many years (see Figure 1). With the growing scale of human genetics studies and the increasing number of clinical applications for genome sequencing, even greater attention is being paid to understanding the underlying costs of generating a human genome sequence.

Accurately determining the cost for sequencing a given genome (e.g., a human genome) is not simple. There are many parameters to define and nuances to consider. In fact, it is difficult to cite precise genome-sequencing cost figures that mean the same thing to all people because, in reality, different researchers, research institutions, and companies typically track and account for such costs in different fashions.

A genome consists of all of the DNA contained in a cell's nucleus. DNA is composed of four chemical building blocks or "bases" (for simplicity, abbreviated G, A, T, and C), with the biological information encoded within DNA determined by the order of those bases. Diploid organisms, like humans and all other mammals, contain duplicate copies of almost all of their DNA (i.e., pairs of chromosomes; with one chromosome of each pair inherited from each parent). The size of an organism's genome is generally considered to be the total number of bases in one representative copy of its nuclear DNA. In the case of diploid organisms (like humans), that corresponds to the sum of the sizes of one copy of each chromosome pair.

Organisms generally differ in their genome sizes. For example, the genome of E. coli (a bacterium that lives in your gut) is ~5 million bases (also called megabases), that of a fruit fly is ~123 million bases, and that of a human is ~3,000 million bases (or ~3 billion bases). There are also some surprising extremes, such as with the loblolly pine tree - its genome is ~23 million bases in size, over seven times larger than ours. Obviously, the cost to sequence a genome depends on its size. The discussion below is focused on the human genome; keep in mind that a single 'representative' copy of the human genome is ~3 billion bases in size, whereas a given person's actual (diploid) genome is ~6 billion bases in size.

Genomes are large and, at least with today's methods, their bases cannot be 'read out' in order (i.e., sequenced) end-to-end in a single step. Rather, to sequence a genome, its DNA must first be broken down into smaller pieces, with each resulting piece then subjected to chemical reactions that allow the identify and order of its bases to be deduced. The established base order derived from each piece of DNA is often called a 'sequence read,' and the collection of the resulting set of sequence reads (often numbering in the billions) is then computationally assembled back together to deduce the sequence of the starting genome. Sequencing human genomes are nowadays aided by the availability of available 'reference' sequences of the human genome, which play an important role in the computational assembly process. Historically, the process of breaking down genomes, sequencing the individual pieces of DNA, and then reassembling the individual sequence reads to generate a sequence of the starting genome was called 'shotgun sequencing' (although this terminology is used less frequently today). When an entire genome is being sequenced, the process is called 'whole-genome sequencing.' See Figure 2 for a comparison of human genome sequencing methods during the time of the Human Genome Project and circa ~ 2016.

An alternative to whole-genome sequencing is the targeted sequencing of part of a genome. Most often, this involves just sequencing the protein-coding regions of a genome, which reside within DNA segments called 'exons' and reflect the currently 'best understood' part of most genomes. For example, all of the exons in the human genome (the human 'exome') correspond to ~1.5% of the total human genome. Methods are now readily available to experimentally 'capture' (or isolate) just the exons, which can then be sequenced to generate a 'whole-exome sequence' of a genome. Whole-exome sequencing does require extra laboratory manipulations, so a whole-exome sequence does not cost ~1.5% of a whole-genome sequence. But since much less DNA is sequenced, whole-exome sequencing is (at least currently) cheaper than whole-genome sequencing.

Another important driver of the costs associated with generating genome sequences relates to data quality. That quality is heavily dependent upon the average number of times each base in the genome is actually 'read' during the sequencing process. During the Human Genome Project (HGP), the typical levels of quality considered were: (1) 'draft sequence' (covering ~90% of the genome at ~99.9% accuracy); and (2) 'finished sequence' (covering >95% of the genome at ~99.99% accuracy). Producing truly high-quality 'finished' sequence by this definition is very expensive; of note, the process of 'sequence finishing' is very labor-intensive and is thus associated with high costs. In fact, most human genome sequences produced today are 'draft sequences' (sometimes above and sometimes below the accuracy defined above).

There are thus a number of factors to consider when calculating the costs associated with genome sequencing. There are multiple different types and quality levels of genome sequences, and there can be many steps and activities involved in the process itself. Understanding the true cost of a genome sequence therefore requires knowledge about what was and was not included in calculating that cost (e.g., sequence data generation, sequence finishing, upfront activities such as mapping, equipment amortization, overhead, utilities, salaries, data analyses, etc.). In reality, there are often differences in what gets included when estimating genome-sequencing costs in different situations.

Below is summary information about: (1) the estimated cost of sequencing the first human genome as part of the HGP; (2) the estimated cost of sequencing a human genome in 2006 (i.e., roughly a decade ago); and (3) the estimated cost of sequencing a human genome in 2016 (i.e., the present time).

The HGP generated a 'reference' sequence of the human genome - specifically, it sequenced one representative version of all parts of each human chromosome (totaling ~3 billion bases). In the end, the quality of the 'finished' sequence was very high, with an estimated error rate of <1 in 100,000 bases; note this is much higher than a typical human genome sequence produced today. The generated sequence did not come from one person's genome, and, being a 'reference' sequence of ~3 billion bases, really reflects half of what is generated when an individual person's ~6-billion-base genome is sequenced (see below).

The HGP involved first mapping and then sequencing the human genome. The former was required at the time because there was otherwise no 'framework' for organizing the actual sequencing or the resulting sequence data. The maps of the human genome served as 'scaffolds' on which to connect individual segments of assembled DNA sequence. These genome-mapping efforts were quite expensive, but were essential at the time for generating an accurate genome sequence. It is difficult to estimate the costs associated with the 'human genome mapping phase' of the HGP, but it was certainly in the many tens of millions of dollars (and probably hundreds of millions of dollars).

Once significant human genome sequencing began for the HGP, a 'draft' human genome sequence (as described above) was produced over a 15-month period (from April 1999 to June 2000). The estimated cost for generating that initial 'draft' human genome sequence is ~$300 million worldwide, of which NIH provided roughly 50-60%.

The HGP then proceeded to refine the 'draft' and produce a 'finished' human genome sequence (as described above), which was achieved by 2003. The estimated cost for advancing the 'draft' human genome sequence to the 'finished' sequence is ~$150 million worldwide. Of note, generating the final human genome sequence by the HGP also relied on the sequences of small targeted regions of the human genome that were generated before the HGP's main production-sequencing phase; it is impossible to estimate the costs associated with these various other genome-sequencing efforts, but they likely total in the tens of millions of dollars.

The above explanation illustrates the difficulty in coming up with a single, accurate number for the cost of generating that first human genome sequence as part of the HGP. Such a calculation requires a clear delineation about what does and does not get 'counted' in the estimate; further, most of the cost estimates for individual components can only be given as ranges. At the lower bound, it would seem that this cost figure is at least $500 million; at the upper bound, this cost figure could be as high as $1 billion. The truth is likely somewhere in between.

The above estimated cost for generating the first human genome sequence by the HGP should not be confused with the total cost of the HGP. The originally projected cost for the U.S.'s contribution to the HGP was $3 billion; in actuality, the Project ended up taking less time (~13 years rather than ~15 years) and requiring less funding - ~$2.7 billion. But the latter number represents the total U.S. funding for a wide range of scientific activities under the HGP's umbrella beyond human genome sequencing, including technology development, physical and genetic mapping, model organism genome mapping and sequencing, bioethics research, and program management. Further, this amount does not reflect the additional funds for an overlapping set of activities pursued by other countries that participated in the HGP.

As the HGP was nearing completion, genome-sequencing pipelines had stabilized to the point that NHGRI was able to collect fairly reliable cost information from the major sequencing centers funded by the Institute. Based on these data, NHGRI estimated that the hypothetical 2003 cost to generate a 'second' reference human genome sequence using the then-available approaches and technologies was in the neighborhood of $50 million.

Since the completion of the HGP and the generation of the first 'reference' human genome sequence, efforts have increasingly shifted to the generation of human genome sequences from individual people. Sequencing an individual's 'personal' genome actually involves establishing the identity and order of ~6 billion bases of DNA (rather than a ~3-billion-base 'reference' sequence; see above). Thus, the generation of a person's genome sequence is a notably different endeavor than what the HGP did.

Within a few years following the end of the HGP (e.g., in 2006), the landscape of genome sequencing was beginning to change. While revolutionary new DNA sequencing technologies, such as those in use today, were not quite implemented at that time, genomics groups continued to refine the basic methodologies used during the HGP and continued lowering the costs for genome sequencing. Considerable efforts were being made to the sequencing of nonhuman genomes (much more so than human genomes), but the cost-accounting data collected at that time can be used to estimate the approximate cost that would have been associated with human genome sequencing at that time.

Based on data collected by NHGRI from the Institute's funded genome-sequencing groups, the cost to generate a high-quality 'draft' human genome sequence had dropped to ~$14 million by 2006. Hypothetically, it would have likely cost upwards of $20-25 million to generate a 'finished' human genome sequence - expensive, but still considerably less so than for generating the first reference human genome sequence.

The decade following the HGP brought revolutionary advances in DNA sequencing technologies that are fundamentally changing the nature of genomics. So-called 'next-generation' DNA sequencing methods arrived on the scene, and their effects quickly became evident in terms of lowering genome-sequencing costs; note that these NHGRI-collected data are 'retroactive' in nature, and do not always accurately reflect the 'projected' costs for genome sequencing going forward).

In 2015, the most common routine for sequencing an individual's human genome involves generating a 'draft' sequence and comparing it to a reference human genome sequence, so as to catalog all sequence variants in that genome; such a routine does not involve any sequence finishing. In short, nearly all human genome sequencing in 2015 yields high-quality 'draft' (but unfinished) sequence. That sequencing is typically targeted to all exons (whole-exome sequencing) or aimed at the entire ~6-billion-base genome (whole-genome sequencing), as discussed above. The quality of the resulting 'draft' sequences is heavily dependent on the amount of average base redundancy provided by the generated data (with higher redundancy costing more).

Adding to the complex landscape of genome sequencing in 2015 has been the emergence of commercial enterprises offering genome-sequencing services at competitive pricing. Direct comparisons between commercial versus academic genome-sequencing operations can be particularly challenging because of the many nuances about what each includes in any cost estimates (with such details often not revealed by private companies). The cost data that NHGRI collects from its funded genome-sequencing groups includes information about a wide range of activities and components, such as: reagents, consumables, DNA-sequencing instruments, certain computer equipment, other equipment, laboratory pipeline development, laboratory information management systems, initial data processing, submission of data to public databases, project management, utilities, other indirect costs, labor, and administration. Note that such cost-accounting does not typically include activities such as quality assurance/quality control (QA/QC), alignment of generated sequence to a reference human genome, sequence assembly, genomic variant calling, or annotation. Almost certainly, companies vary in terms of which of the items in the above lists get included in any cost estimates, making direct cost comparisons with academic genome-sequencing groups difficult. It is thus important to consider these variables - along with the distinction between retrospective versus projected costs - when comparing genome-sequencing costs claimed by different groups. Anyone comparing costs for genome sequencing should also be aware of the distinction between 'price' and 'cost' - a given price may be either higher or lower than the actual cost.

Based on the data collected from NHGRI-funded genome-sequencing groups, the cost to generate a high-quality 'draft' whole human genome sequence in mid-2015 was just above $4,000; by late in 2015, that figure had fallen below $1,500. The cost to generate a whole-exome sequence was generally below $1,000. Commercial prices for whole-genome and whole-exome sequences have often (but not always) been slightly below these numbers.

Innovation in genome-sequencing technologies and strategies does not appear to be slowing. As a result, one can readily expect continued reductions in the cost for human genome sequencing. The key factors to consider when assessing the 'value' associated with an estimated cost for generating a human genome sequence - in particular, the amount of the genome (whole versus exome), quality, and associated data analysis (if any) - will likely remain largely the same. With new DNA-sequencing platforms anticipated in the coming years, the nature of the generated sequence data and the associated costs will likely continue to be dynamic. As such, continued attention will need to be paid to the way in which the costs associated with genome sequencing are calculated.

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Last Updated: June 6, 2016

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The Cost of Sequencing a Human Genome