Information

How can I find a complete human genome file

How can I find a complete human genome file


We are searching data for your request:

Forums and discussions:
Manuals and reference books:
Data from registers:
Wait the end of the search in all databases.
Upon completion, a link will appear to access the found materials.

I'm trying to figure out how I can download a file that represents the complete human DNA sequence. I don't care too much about the format - I'm able to write C++ code to parse it. FASTA seems like a simple format though. What I haven't figured out yet is where I can find a complete file - I have found what appear to be subsets of genes or other sequences or single chromosomes but aren't there 46 chromosomes to include or are some of those duplicates (i.e. 22 chromosomes + 2 sex chromosomes)?

On this page, I have found this list of files under "Human > Genome assembly: GRCh38" but it appears to be broken up by chromosome or something? If so, would I merge these? My goal is to display all the letters via projector onto a wall and I want to be able to point at it and tell someone, that's all the DNA for a human (not a subset). Also, to double check, it is a "genome assembly" I want right? By the way, I don't care about allele variants right now.

Please consider in your response that I am not familiar with much of the lingo, thank you.


The National Centre for Biotechnology Information has a link to a genomes FTP site - on that page, there is a file labelled … /genomes/H_sapiens (this a direct link to that directory).

There are numerous files therein. From the README file:

Sequence data include chromosomes, contigs, RNAs, and proteins generated through the NCBI Reference Sequence and NCBI Genome Annotation projects. Map data presented in the Map Viewer resource are also provided here.


Non-biologist here stepping in.

@swbarnes2 has a good point pinning the fact that (approx) 3Giga nucleotides to display "on a wall" (as you state) even with a good projector is gonna be a hard task. You'll need several projectors and a hell of a big wall. (say you take the smallest readable police setting you'll have each letter take a space of 4*6 pixels which for the whole will bring you to ~[227k x 342k] pixels so around 35k HD-projectors)

Which led me to think of why you would want to do such a thing. The most plausible of which is : it is for some sort of artistic/cultural intent. In such case, rather than showing letters (ATGC) I recommend to encode it in binary (00,01,10,11) and make this value code for a colored pixel.

That will leave you with a square matrix of around 57k pixels edge (which remains humongous) of shaded in 4 tones black to white dots.

If you want to go even farther, trichromia stands to the rescue, don't make pixels code for just one nucleotide each. Make them code for one "pseudo-codon" (triplet) each. First nucleotide defining the red shade, Second nucleotide defining the green shade, last nucleotide defining the blue shade. (plain and simple Additive color RGB stuff).

-EDIT- Knowing that the notion of codon is invalid and that any nucleotide (except the leading and trailing 2 of each chromosome) could be part of three distinct codon (depending on wether they are in an intron, exon or even alternatively spliced) we see that this grouping by 3 is not THAT right.

In such case why not take even more liberties ? Group your nucletides by 12 (3 groups of 4) giving you more depths in the color shades.

-END OF EDITED SECTION-

you'll get a much nicer and significantly smaller matrix of [30k x 30k] (which is still gonna take you a big wall and a few HD-projectors ~150 but at this point you can compress the output with several methods and get merged pixels, yet 150 is far less than 35000).

I know I don't bring actual solutions to the asked question (but I really think @Omen did it pretty well) but I sensed that there maybe here some insight worth handing (at the risk of making a fool of myself)


but aren't there 46 chromosomes to include or are some of those duplicates

First of all, while each person has 2 copies of each chromosome, those copies are 99% identical. So it would be a waste to repeat the whole thing twice.

Second, the technology is such that it's not easy to generate, say, the whole sequence of a chromosome that came from their mother. You either get sanger traces which show the two sequences superimposed on each other, or very short reads that are not mixed, but you can't tell which parent generated which fragment.

So in general, a reference genome will just have one consensus letter at every position, even though that's not biologically realistic. It doesn't much matter what the reference is, as long as everyone knows that it's just a reference.

My goal is to display all the letters via projector onto a wall and I want to be able to point at it and tell someone, that's all the DNA for a human (not a subset).

Can you really display 3 billion characters like that?


If I understand your question correctly, you want a single file, i.e. a single string, which represents the sequence of an entire human genome. However, there is no such thing. The human genome is stored in 46 different strings (chromosome), and these strings have no natural order.

The numbers used to refer to the genomes are based on their order when arranged by size.

All operations on the genome (such as copying it before mitosis) happen in parallel, with proteins operating on each chromosome individually.

If you want to represent an entire human genome "honestly", I would say your best bet is to put 46 separate strings on the projector, perhaps running parallel to one another like the code in the Matrix.

If you want to display one big long string, any sequence of concatenation is as (in)correct as any other, so just open the files in alphabetical order and concatenate them all.


If you want to merge all the sequences as a single sequence then download the sequence of all the chromosomes and then concatenate them. Simple command for that if you use linux:

grep -v ">" chromosome*.fa > entire_genome.txt

Now it makes sense to separate the genome chromosome-wise because there is no physical connection between one chromosome and the other. Moreover there are many orders by which you can concatenate the chromosomes together which will give you23!number of genome sequences.

Now you should note that all of this can give you serious errors if you are trying to do study genomic context of any gene. So better go chromosome wise.

If I have interpreted you wrong and what you meant is to have all the chromosome fasta sequences in a single file, yet not merge the sequences then it is a pretty straightforward command.

cat chromosome*.fa > genome.fa

Now, what you download is a reference sequence. You have to find variants etc for your data by controlling your alignment parameters.

And I really don't understand why you want to project it on the wall. There are easier and better ways of analyzing the genome.


Human Genome - Case Study Example

A gene is one DNA molecule segment corresponding to the coding for one complete protein. 23 different types of DNA molecules or chromosomes constitute the entire human genome. Put another way, the genome of a species is the total set of chromosomes that are constituted to make up that species, and the human genome is the set of chromosomes that together define the human species. Genomics, in turn, is the investigation into the human genome and the definition of genomes in general in terms of being able to totally describe the genomic makeup of species and how the genomic characteristics translate into species characteristics such as physiology and the vulnerability of particular members of the species to certain conditions and diseases (Center for Biomolecular Science and Engineering 2014 Little et al.

2003 Nature Education 2013). Genetics meanwhile, in general, refers to the scientific investigation into differences in genes that have been inherited from parents to offspring, and human genetics is this study directed towards the human species (National Center for Biotechnology Information 2007 Saha 1998 New York State 2011 The 1000 Genomes Project Consortium 2012 Jha 2012 Centers for Disease Control and Prevention 2013 Wadhwa 2014).Genetic variation is simply the variation in the genetic makeup among human beings.

Variations are said to be small in relation to the total genome for all humanity, with variations between any two random human beings just accounting for 01. percent of their total base pairs. Among populations too, genetic variation is very small and below that which would classify peoples of different races as subspecies, indicating that the global population is just a single continuous genetic pool that interbreeds through time. On the other hand, a small portion of genetic variations among human beings is significant, in that they either confer advantages to people versus their environments or else that they predispose some people to different kinds of diseases.

Genetic variation is advantageous for instance for people who because of a genetic variation allow them to withstand malaria plagues in an environment, and make some people better able to resist infection with the AIDS virus. Recent studies for instance also associate historical resistance to the plague-causing bacterium to a gene mutation that at present also seems to shield people with the genetic variation from the ravages of AIDS and its complications. Early medical and academic literature on this have pointed out that there are single-gene variations that are causally linked to the development of certain diseases in human beings, among them cystic fibrosis and sickle cell disorder, as well as Huntington's.

On the other hand, as research progresses, the genetic variation bases of a range of other chronic and intractable modern diseases, from psychological diseases such as bipolar disorder and schizophrenia to cancer, diabetes, and cardiovascular diseases, are being established. Meanwhile, as the research also moves forward, it is increasingly clear that a host of other diseases have not just one basis in genetic variation nor in just one set of environmental conditions, but there are various genetic variations teamed up with various environmental constraints that together can give rise to disease.


Your genome is 3 billion letters, driving 3 trillion cells, for 3 billion seconds. Why this computational analysis and not that? What did I just find? Who cares? Unidentified caller from Stockholm at 3 in the morning?

We will introduce you to various aspects of genomic data such as what it looks like, how to get it, and what are some of the most (and less) interesting things you could do with it.

Class includes:
Human genome parts list, COVID-19 genome parts list, Genome sequencing technologies, and a taste of the three main forces of life, neutral, negative & positive selection, via, respectively: Population genomics & paternity testing Medical AI (disease) genomics (where you could really help really sick kids from your keyboard) and Comparative (evolutionary) genomics (bats, cats, rats, gnats, SARS-CoV-2). And maybe a dash of cryptogenomics and genomic privacy.

Get a taste of Machine Learning, Natural Language Processing, Cryptography and even Genomics in the service of humanity.

Background in Biology, ML or NLP purely optional. See class Explore page for more details.

All course materials will be available via this website and Piazza, not Canvas.

CS106 or equivalent (aka, some programming experience in any language)
Example: read string from a file, count some patterns in it, print counts (refer to tutorials from previous offerings linked below).

This course is cross-listed as DBIO273A and BIOMEDIN273A. Write to Gill if you want to help get it cross-listed elsewhere.

Mondays and Wednesdays 11:30AM-12:50PM.

The course will be taught entirely online.
Link for Zoom
No attendance taken, but lectures will not be recorded.

As a Stanford student you also have free access to many biomedical journals. To access all biomedical resources Stanford pays for from off campus, you can install a browser extension and a shortcut that allows you to directly search and access Lane Library online resources using your SUNetID. Many of the terms we teach are also well defined in wikipedia.

All course communication will be handled via Piazza. You can enroll by clicking this link (our class page). Course announcements and other private course resources will be communicated via Piazza.

Auditors are welcome. Please sign up to Piazza as well. Send us an email if you want to be included in the class mailing list.

Gill Bejerano
Office: Via Zoom
Office hours: Email for appointment
Phone: (650) 723-7666
Email:

Bo Yoo
Office: N/A
Office hours: No OH during the exam
Email:

There will be four homework assignments (programming and conceptual questions) and one final take home exam. Each homework will be 15% of your final grade, and the final exam will be 40% of your final grade.

All codes must be executable on stanford student machines (i.e. cardinal, myth, or rice). Jupyter notebooks are allowed for Homework 4 and the final exam. Include how to run your code in your README, and all your codes must be able to run without user modification (e.g. if the code takes in a file as an input the path or the file name should not be hard coded but should be passed in through command line. All files must be named appropriately and your submitted zipped file must include your name. Be as detailed as possible to ensure that you get all the points.

If you are registered with the Office of Accessible Education (OAE), please send the accommodation letter via email to the class staff email () in the beginning of the quarter.

All homework assignments are individual assignments and you may not work in a group. You are allowed to discuss ideas and compare final numeric outputs (e.g. number of lines in a file), but no part of your final code can be shared with other students. In your submitted writeup (e.g., README), you must note the names of your collaborators. You may not share any part of your submissions with each other until grades are returned. We take honor code violations seriously. Violations will be reported to the Office of Community Standards.

We may make mistakes when we grade your homework. If you find one please send an email to to ask for a regrade. We will regrade your entire homework, and your grade may go up or down as a result. You cannot redo your homework after grades have been returned. We will not accept anymore submissions after grades have been sent out.

Take home exam must be done independently. You may not discuss it with anyone.


The Open Human Genome, twenty years on

On 26th June 2000, the “working draft” of the human genome sequence was announced to great fanfare. Its availability has gone on to revolutionise biomedical research . But this iconic event, twenty years ago today, is also a reference point for the value and power of openness and its evolution.

Biology’s first mega project

Back in 1953, it was discovered that DNA was the genetic material of life. Every cell of every organism contains a copy of its genome, a long sequence of DNA letters, containing a complete set of instructions for that organism. The first genome of a free-living organism – a bacteria – was only determined in 1995 and contained just over half a million letters. At the time sequencing machines determined 500 letter fragments, 100 at a time, with each run taking hours. Since the human genome contains about three billion letters, sequencing it was an altogether different proposition, going on to cost of the order of three billion dollars.

A collective international endeavour, and a fight for openness

It was sequenced through a huge collective effort by thousands of scientists across the world in many stages, over many years. The announcement on 26th June 2000 was only of a draft – but still sufficiently complete to be analysed as a whole. Academic articles describing it wouldn’t be published for another year, but the raw data was completely open, freely available to all.

It might not have been so, as some commercial forces, seeing the value of the genome, tried to shut down government funding in the US and privatise access . However openness won out, thanks largely to the independence and financial muscle of Wellcome (which paid for a third of the sequencing at the Wellcome Sanger Institute ) and the commitment of the US National Institutes of Health. Data for each fragment of DNA was released onto the internet just 24hrs after it had been sequenced, with the whole genome accessible through websites such as Ensembl .

Openness for data, openness for publications

Scientists publish. Other scientists try to build on their work. However, as science has become increasingly data rich, access to the data has become as important as publication. In biology, long before genomes, there were efforts by scientists, funders and publishers to link publication with data deposition in public databases hosted by organisations such as EBI and NCBI . However, publication can take years and if a funder has made a large grant for data generation, should the research community have to wait until then?

The Human Genome Sequence, with its 24-hour data release model was at the vanguard of “pre-publication” data release in biology. Initially the human genome was seen as a special case – scientists worried about raw unchecked data being released to all or that others might beat them to publication if such data release became general – but gradually the idea took hold. Dataset generators have found that transparency has generally been beneficial to them and that community review of raw data has allowed errors to be spotted and corrected earlier. Pre-publication data release is now well established where funders are paying for data generation that has value as a community resource, including most genome related projects. And once you have open access data, you can’t help thinking about open access publication too. The movement to change the academic publishing business model to open access dates back to the 1990s, but long before open access became mandated by funders and governments it became the norm for genome related papers.

Big data comes to biology, forcing it to grow up fast

Few expected the human genome to be sequenced so quickly. Even fewer expected the price to sequence one to have dropped to less than $1000 today, or to only take 24 hours on a single machine. “Next Generation” sequencing technology has led to million-fold reductions in price and similar gains in output per machine in less than 20 years. This is the most rapid improvement in any technology, far exceeding the improvements in computing in the same period. The genomes of tens of thousands of different organisms have been sequenced as a result. Furthermore, the change in output and price has made sequencing a workhorse technology throughout biological and biomedical research – every cell of an organism has an identical copy of its genome, but each cell (37 trillion in each human) is potentially doing something different, which can also be captured by sequencing. Public databases have therefore been filling up with sequence data, doubling in size as much as every six months, as scientists probe how organisms function. Sequence is not the only biological data type being collected on a large scale, but it has been the driver to making biology a big data science.

Genomics and medicine, openness and privacy

Every individual’s genome is slightly different and some of those difference may cause disease. Clinical geneticists have been testing Individual genes of patients to find for cause of rare diseases for more than twenty years, but sequencing the whole genome to simplify the hunt is now affordable and practical. Right now our understanding of the genome is only sufficient to inform clinical care for a small number of conditions, but it’s already enough for the UK NHS to roll out whole genome sequencing as part of the new Genome Medicine Service, after testing this in the 100,000 genomes project . It is the first national healthcare system in the world to do this.

How much could your healthcare be personalised and improved through analysis of your genome? Right now, an urgent focus is on whether genome differences affects the severity of COVID-19 infections . Ultimately, understanding how the human genome works and how DNA differences affect health will depend on research on the genomes of large numbers of individuals alongside their medical records. Unlike the original reference human genome, this is not open data but highly sensitive, private, personal data.

The challenge has become to build systems that can allow research but are trusted by individuals sufficiently for them to consent to their data being used. What was developed for the 100,000 genomes project, in consultation with participants, was a research environment that functions as a reading library – researchers can run complex analysis on de-identified data within a secure environment but cannot take individual data out. They are restricted to just the statistical summaries of their research results. This Trusted Research Environment model is now being looked at for other sources of sensitive health data.

The open data movement has come a long way in twenty years, showing the benefits to society of organisational transparency that results from data sharing and the opportunities that come from data reuse. The Reference Human Genome Sequence as a public good has been part of that journey. However, not all data can be open, even if the ability to analyse it has great value to society. If we want to benefit from the analysis of private data, we have to find a middle ground which preserves some of strengths of openness, such as sharing analytical tools and summary results, while adapting to constrained analysis environments designed to protect privacy sufficiently to satisfy the individuals whose data it is.

• Professor Tim Hubbard is a board member of the Open Knowledge Foundation and was one of the organisers of the sequencing of the human genome.


Conclusions

At present, predicted transcript arrays allow for the discovery of most protein-coding genes genome wide when many different conditions are considered. Until the discovery and characterization of these protein-coding genes is completed, this method will continue to be a cost-effective solution to drive such discovery. In contrast, genomic tiling represents a completely unbiased method for monitoring transcriptional activity in genomes, but due to cost will probably be limited to screening a smaller number of conditions. However, as novel transcription regions are identified from the tiling data, these regions can be represented on predicted transcript arrays that are hybridized over many more conditions, as described in Figure 1. As the microarray technologies have evolved, tiling the entire human genome is now possible, with such efforts presently being supported by the ENCODE (Encyclopedia of DNA Elements) project of the National Human Genome Research Institute (NHGRI) [41].

We believe the steps taken here are necessary for querying all potential transcription activity in the genome, for the purpose of identifying novel genes, more completely characterizing existing genes, and identifying a more comprehensive set of probes for these genes that can be used to monitor transcription abundances in more standard gene expression studies. Not all uses of microarrays demand an exhaustive representation of probes to all genes in the genome under study. However, experiments that seek to identify key drivers of pathways [42] or that seek to discriminate between alternative splice forms of genes within a given tissue [21] require a more comprehensive set of arrays to ensure success. These data provide an essential first step to generating a comprehensive set of arrays that are based on experimental support combined with computational annotation, instead of relying solely on the latter. These comprehensive arrays will be invaluable as we seek to better understand mechanisms of action for existing and novel drug targets and elucidate pathways underlying complex diseases. In addition, further study of the extensive noncoding RNA identified via the methods described here and elsewhere [10, 12, 15, 16] is likely to open new fields of biology as the functional roles for these entities are determined.


SCIENTIFIC TEACHING THEMES

Active Learning

Students engage in think-pair-share discussions at the beginning of the laboratory to assess their knowledge of scientific databases. After the laboratory session, the whole class discusses the results of their bioinformatics exploration.

Assessment

Pre-assessment: In small group discussion and sharing out to the class, students describe what they think they can discover about a particular SNP based on bioinformatics approaches.

Assignment: Students turn in a screen shot from the UCSC Genome Browser representing the SNP of interest, along with a short description of the genomic region including nearby genes, conservation of the region in other vertebrate models, and citations of three published genome-wide association studies.

Participate in Discussion: After turning in the assignment, students participated in a class-wide discussion of what they learned about on-line genomic information.

Inclusive Teaching

  • Discussion of the similarities among all human genomes acknowledges the enormous genetic conservation between all of us.
  • Examination of particular health-related SNPs also demonstrates that all of us are at risk for some diseases regardless of age, gender, race, etc.
  • Enabling students to choose a particular SNP is inherently inclusive, since each student can pursue an individual interest.
  • The diversity of choices across the class will provide a variety of examples that may be more or less common in various backgrounds.

FINDING MY RELIGION / Leader of the Human Genome Project argues in a new book that science and religion can coexist happily

Science and religion have long had an uneasy relationship, at best. But Dr. Francis S. Collins believes the two can coexist happily and that a scientist can worship God equally well in a cathedral or a laboratory.

Collins, a physician-geneticist, led the Human Genome Project, an international research initiative that mapped all 3.1 billion base pairs in human DNA. The monumental project took a crew of scientists deep inside the uncharted landscape of the human body. At the end, they had what amounts to a blueprint for building a human being and a unique reference to use in developing diagnoses, treatments and, ultimately, ways to prevent genetic diseases. Collins is now the director of the National Human Genome Research Institute.

Once a staunch atheist and now a devout Christian, Collins puts forth in his book "The Language of God: A Scientist Presents Evidence for Belief" (Free Press, July 2006) the idea that "belief in God can be an entirely rational choice, and the principles of faith are, in fact, complementary with the principles of science." I spoke with him by phone last week from his home in Rockville, Maryland.

I grew up in a home where faith was not an important part of my experience. And when I got to college and people began discussing late at night in the dorm whether God exists, there were lots of challenges to that idea, and I decided I had no need for that. I was already moving in the direction of becoming a scientist, and it seemed to me that anything that really mattered could be measured by the tools of science.

I went on to become a graduate student in physical chemistry, and as I got more into this reductionist mode of thinking that characterizes a lot of the physical and biological sciences, it was even more attractive to just dismiss the concept of anything outside of the natural world. So I became a committed materialist and an obnoxious atheist, and it sounded very convenient to be so, because that meant I didn't have to be responsible to anybody other than myself.

What changed your mind? Did you have a sudden epiphany, or did religion sort of quietly sneak up on you?

It was a sneaking process. As a medical student I had the responsibility of taking care of patients who had terrible diseases. I watched some of these people really leaning on their faiths as a rock in the storm, and it didn't seem like some kind of psychological crutch. It seemed very real, and I was puzzled by that.

At one point, one of my patients challenged me, asking me what I believed, and I realized, as I stammered out something about "I don't believe any of this," that it all sounded rather thin in the face of this person's clearly very strong, dedicated belief in God. That forced me to recognize that I had done something that a scientist is not supposed to do: I had drawn a conclusion without looking at the data. I had decided to be an atheist without really understanding what the arguments were for and against the existence of God.

So where did you go from there?

With the full intention of shoring up my atheism, I decided I'd better investigate this thing called faith so that I could shoot it down more effectively and not have another one of those awkward moments. I read about the major world religions, and I found it all very confusing. It didn't occur to me to read the original texts -- I was in a hurry. But I did ultimately go and knock on the door of a Methodist minister who lived down the street and asked him if he could make any recommendations for somebody who, like me, was looking for some arguments for or against faith.

He took a book off his shelf -- "Mere Christianity" by C.S. Lewis. Lewis had been an atheist [and] set out as I did to convince himself of the correctness of his position and accidentally converted himself. I took the book home, and in the first few pages realized that all of my arguments in favor of atheism were quickly reduced to rubble by the simple logic of this clear-thinking Oxford scholar. I realized, "I've got to start over again here. Everything that I had based my position upon is really flawed to the core."

I can understand how you might make the change from being an atheist to an agnostic, given your scientific worldview. But moving from an agnostic to a believer, now that seems like a tougher transition.

And I made it in stages, so for a while I abandoned atheism and landed in the agnostic bin, but I found that in a certain way a cop-out. It did not seem that that was necessarily a place where one could comfortably stay unless you could say, "I have now considered all of the evidence, and I've concluded that there is no reason to actually make a real decision." This business of saying "I don't know" can't just be an "I don't want to know." And the more I looked at the evidence, the more I concluded that I wasn't really in a position where that was a viable choice.

Why not? What kind of evidence?

One piece of evidence was the argument, which is right there in Lewis' first chapter on moral law, [about] the knowledge of right and wrong, which I find to this day a puzzling feature of humanity if all we are is products of evolution. Moral law, which seems to be universal to humankind, calls us, on a regular basis, to do things that are not consistent with the idea that our only purpose is to propagate our own DNA.

It calls us sometimes to do things that are truly sacrificial, to help out somebody else at our own expense. And all of the arguments that the social biologists have put forward about how this kind of sacrificial love, this kind of agape, as the Greeks would call it, can be explained on the basis of evolution -- I find rather hollow. It doesn't work in many instances where we are called to do something really quite destructive to the possibility of propagating our own DNA.

I found with Lewis a compelling argument that there is something within us, a signpost, that is pointing us towards the importance of recognizing good and evil, and that is drawing us towards being good and not evil. As Lewis says, if you were looking somewhere around you and within you for some evidence of a God -- not a deist God who wandered off after starting the universe, but a God who really cares about people -- where else would you find more powerful evidence than in this particular thing you find in your own heart? I continue to find that a pretty interesting argument.

You said in your book that your scientific explorations had a lot to do with convincing you that God exists. Can you cite some aspects of your research that particularly confirmed God's existence for you?

Everything I do as a scientist reinforces my sense of God's presence because every new discovery is, if you believe in his role as creator, a glimpse into his mind. And I find that very meaningful and satisfying to be able to have the experience of discovery by both the natural world unveiling itself and also getting a glimpse into what God's plan was.

Can you give me an example?

Well, sequencing the human genome. This was an incredibly breathtaking experience, to unveil over the course of just a few short years the complete instruction book for human biology, the 3 billion letters of the code. That's something which will only be done once in human history, which has incredible power to reveal information about exactly how human biology works and which for me, as a believer, is the culmination of God's creative plan to put creatures on this planet. To have that laid out in front of you for the first time is breathtaking to any scientist, but particularly if you see it as that significant language of God, [which] as the title of the book suggests, carries it to a whole other plane.

Can you tell me about BioLogos, your theory of theistic evolution? How does it differ from intelligent design?

Intelligent design argues that there are certain molecular machines, like the human eye with all its remarkable engineering, that are just too darned complicated for evolution to have been able to develop, and that there had to be supernatural intervention in order to produce those functions. So it makes a very specific claim that there are failures, or gaps, in Darwinian evolution that God had to fix along the way.

In that context, I have trouble with intelligent design, because as science is progressing rapidly, particularly with the study of the DNA sequences of many, many organisms, it becomes pretty clear that some of these gaps are in fact not machines that came suddenly out of nowhere, but were built up bit by bit, component by component, in a way that's entirely compatible with evolution over long periods of time.

I believe in a different model, which I call BioLogos. It's a model that I find entirely consistent with what I know scientifically and what I believe about God, which is the following:

If God decided to create the universe and his purpose was to populate it with creatures in his image, with whom he could have fellowship and to whom he would give the knowledge of right and wrong, an ability to make decisions on their own free will and an immortal soul, and if he chose to use evolution to accomplish that goal, who are we to say that's not how he would have done it? It's an incredibly elegant means of creation. And because God is outside of time and space -- at least, I think that would make sense, given that he's not part of the natural world -- he could, at the very moment of creation, at the instant of the Big Bang, have this entire plan completely designed right down to our having this conversation. And it would seem perhaps a bit random and long and drawn out to us, but not to him.

Why do you think God would do that? What is the purpose of it?

Well, now we are into a really difficult question, which is trying to understand God's motivations, and I don't think I am qualified to have a clue about that. But I think any religion that people believe in has within it the idea that humans are in search of God, and that God is interested in our being in search of him. So if you accept that idea, then the mechanism by which he could carry that out could be almost anything, but I think in this case it was evolution.


Big Data and Bioinformatics in SHGP

The SHGP, by the scale and nature of its data, is a typical big data project, where the four “V”s (volume, velocity, variety, and veracity) characterizing big data are present. When running at full capacity, the project will produce 10–15 TB of raw sequence data per day. Therefore, establishing a highperformance and scalable information technology (IT) infrastructure and the use of advanced bioinformatics methods are major components of the SHGP. “The structure of the participating centers and the distribution of the genomic data production and analysis form an interesting IT challenge that is probably the first of its kind worldwide,” said Dr. Mohamed Abouelhoda, head of the SHGP bioinformatics team.
Figure 3: The high-performance computer SANAM, one of the top supercomputers worldwide in the green data center in the KACST.
All the labs produce significant amounts of data that should be analyzed and moved to the central storage for large-scale data analysis, with results to be shared among researchers inside and outside the kingdom. While each satellite lab has some computing power to participate in the data analysis, the main computing power for storage and analysis resides in the KACST. The SHGP has also access to the energy-efficient, high-performance computer, SANAM, with a performance of 532 TFlops and high-speed interconnects data rate of 56 Gb/s (Figure 3). “SANAM is one of the top supercomputers worldwide,” said Dr. Abdulqadir Alaqeeli from the KACST SANAM team.
To cope with this distributed IT infrastructure, the SHGP bioinformatics team has developed methods to manage the data and the analysis among the different sites using different computational resources. The transfer of data is prioritized and scheduled to reduce the required bandwidth. The use of commercial cloud computing solutions is also part of the design, to automatically scale the in-house IT resources in response to abrupt computation loads. Collectively, the central and satellite computer resources as well as the automatic extension with commercial cloud solutions work together like a hybrid multicloud system.


Geneticists sequence the complete human X chromosome for the first time

For the first time, scientists have determined the complete sequence of a human chromosome, namely the X chromosome, from ‘telomere to telomere’. This is truly a complete sequencing of a human chromosome, with no gaps in the base pair read and at an unprecedented level of accuracy.

A step closer towards the complete blueprint of a human being

The Human Genome Project was a 13-year-long, publicly funded project initiated in 1990 with the objective of determining the DNA sequence of the entire human genome.

Although the project was met with initial skepticism by scientists and non-scientists alike, the overwhelming success of the Human Genome Project is readily apparent. Not only did it usher in a new era in medicine, but it also led to significant advances in DNA sequencing technology.

When the Human Genome Project was finished, its running costs tallied $2.7 billion of taxpayers’ money. Today, a human genome can be sequenced for less than $200 — that’s a 13.5-million-fold reduction in cost. And, it’s still going down.

However, despite its resounding success, the human genome sequencing is still incomplete, as still unknown regions of the genome could not be finished due to technical reasons.

These gaps in the genome have been gradually filled as technically improved after the Human Genome Project was officially over in 2003.

But, until last year, there were still 100 or so regions that were yet unknown. Now, some of these regions have been brought to light, helping to complete the sequencing of the human X chromosome.

The X chromosome is one of two sex-determining chromosomes passed down from parent to child. A zygote that receives two X chromosomes – one from each parent – will grow into a female, while an X and a Y chromosome result in a male.

According to Karen Miga, a research scientist at the UC Santa Cruz Genomics Institute, this was all possible thanks to new sequencing technologies that enable “ultra-long reads,” such as the nanopore sequencing technology.

In the initial stages of the Human Genome Project, scientists could read 500 bases at a time, or 500 letters per sequence. In the mid-2000s, the amount of DNA that could be read at a time was reduced (100-200 bases), but the accuracy of technology increased. Then around 2010, new technology came on the market that could read 1,000-10,000, and now more recently 100,000 or more bases at a time thanks to nanopore technology.

Nanopore tech involves funneling single molecules of DNA through a tiny hole. Changes in current flow determine the genetic sequencing.

“These repeat-rich sequences were once deemed intractable, but now we’ve made leaps and bounds in sequencing technology,” Miga said. “With nanopore sequencing, we get ultra-long reads of hundreds of thousands of base pairs that can span an entire repeat region, so that bypasses some of the challenges.”

The technique itself was very simple: simply collect as much of these bases that scientists could from a single cell line of interest.

“We chose a unique cell line that has two copies of every chromosome, just like any normal cell, but each of those copies is identical to one another. Rather than having to resolve the genome of two genomes, we only had a single version to worry about. Then you can grow these cell lines clonally, so you don’t have variation in them, and then sequence them on these instruments,” Dr. Adam Phillippy of the National Human Genome Research Institute said in a statement.

Scientists collected data over the course of six months, and then used algorithms to stitch the puzzle pieces back together again.

This is how they sequenced the centromere, a large repetitive bit of sequence that is centered in the middle of the X chromosome as its name might suggest, and a number of other genome arrays on the X chromosome.

This work opens up a range of new possibilities in research, including the prospect of identifying new associations between genetic sequence variation and disease, as well as new clues into human biology and evolution.

“We’re starting to find that some of these regions where there were gaps in the reference sequence are actually among the richest for variation in human populations, so we’ve been missing a lot of information that could be important to understanding human biology and disease,” Miga said in a statement.

The complete sequencing of the X chromosome signifies yet another massive victory for science. However, there are still 23 other chromosomes to go — all of them might be completely mapped out by the end of this year, the researchers said.


Instructions for generating the dictionary and index files

Creating the FASTA sequence dictionary file

We use the CreateSequenceDictionary tool to create a .dict file from a FASTA file. Note that we only specify the input reference the tool will name the output appropriately automatically.

This produces a SAM-style header file named ref.dict describing the contents of our FASTA file.

Here we are using a tiny reference file with a single contig, chromosome 20 from the human b37 reference genome, that we use for demo purposes. If we were running on the full human reference genome there would be many more contigs listed.

Creating the fasta index file

We use the faidx command in Samtools to prepare the FASTA index file. This file describes byte offsets in the FASTA file for each contig, allowing us to compute exactly where to find a particular reference base at specific genomic coordinates in the FASTA file.

This produces a text file named ref.fasta.fai with one record per line for each of the FASTA contigs. Each record is of the contig, size, location, basesPerLine and bytesPerLine. The index file produced above looks like this:

This shows that our FASTA file contains chromosome 20, which is 63025520 bases long, then the coordinates within the file which you do not need to care about.


Watch the video: Τα γονίδια και η κληρονομικότητα (November 2022).