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Surely an ancestor with as much variation in the species as described in the article does support creationism. This ancestor had a diverse gene pool which supports devolution as races became more distinct over time, ie, a shallowing of their gene pool rather than random mutation.

Religion aside, if all our evidence points to a single common ancestor is it not just as possible that he was created by an alien than an accident of the universe. How long before mankind can make life? Why couldn't an alien do the same before us? Just saying this because creationism doesn't mean religion and is worth scientific debate

No, the evidence supports the idea that either (a) two or more species used the same site "about the same time" if you allow for "about the same time" to cover 6x recorded history OR the individuals found represent only one species that was about as varied as modern man. There's no evidence of "devolution." Genes change by random mutation in every generation. Your genes are not 100% faithful copies of your parents' genes. http://www.sanger.ac.uk/about/press/2011/110612.html On average, people have 60 new mutations with each generation. Most of those random genetic changes are harmless, but some of them matter and devolution WOULD occur if it were not for natural selection screening out genes that result in lowered rates of reproduction.

Even minor harmful mutations are screened out over many generations. Likewise a mutation that gave a mere 1% advantage in reproduction 1.8 million years ago (the time at which these proto-people lived) would have been present in every living descendant within 50,000 years. We all have the best of what those hominids had 1.8 million years ago plus a whole lot of mutations that occurred since.

Regarding the likelihood that life was somehow introduced to our planet a billion years ago, the burden is on those who would like to advance such ideas. There isn't any reason for the rest of us to entertain such complications.

If it is, then http://pfam.sanger.ac.uk/ is the Little Red Book.

No they will not patent this. The work was done by the Wellcome Trust Sanger Institute that is funded (unsurprisingly) by the Wellcome Trust which is a big medical research charity. The Sanger Institute releases all of its research into the public domain.

They started off with a couple of common cancers, but the plan is to do many more:

http://www.sanger.ac.uk/about/press/2008/080429.html

'The ICGC will identify a list of approximately 50 cancer types and subtypes that are of clinical significance around the globe, aiming to study cancers of all major organs, including breast, ovary, prostate, lung and blood cancers...All the data generated will be made rapidly and freely available to the global research community. '

'It seems that they should do this with cancer cells from several different patients and compare them to find out which mutations actually trigger the cancer.'

Believe it or not, they have thought of this! An international consortium has been set up to use exactly this technology on a really large scale. See e.g.:

http://www.sanger.ac.uk/about/press/2008/080429.html

'Each ICGC member will conduct a comprehensive, high-resolution analysis of the full range of genomic changes in at least one specific type or subtype of cancer, with studies built around common standards of data collection and analysis. Each project will each require cancer specimens from 500 patients and have an estimated cost of US$20 million.'

Now you know how I feel when there's an article about API's, Ubuntu, or codecs.

Human cells have and express p16-INK4A normally - it's part of the CDKN2A gene locus. It is a cell cycle control gene whose main function is to put the brakes on replication. p16 is expressed in human cells and is often mutated or outright deleted in many human cancers of all cell types.
COSMIC (new window)

The difference described in naked mole rats is that their cells induce p16 expression after minimal contact with neighboring cells while human and rat cells need more prodding to turn on cell cycle control genes.

This is a cool finding, but does not have a direct application in human cancers anytime soon. It's very hard to turn on a gene that has been mutated or deleted in cancer cells. You have to do it in practically every cell, otherwise, they grow back. Even then it may be too late. Loss of contact inhibition may be necessary in early oncogenesis, but restoration of p16 expression in a cancer cell that already has multiple genetic mutations, may not do much at that point. So, it's an interesting finding and I hope it leads to a better understanding of cancer and cancer prevention. But honestly, we have cool findings like this once a week. It just requires the right spin to sell it to the media - like calling something a "cancer-proof" gene - and it finds its way here.

My understanding is that one gets a nice nested hierarchy if one looks at SNPs for the great apes and that we share the right spots. Similar remarks apply to CNVs. (This is simply recall from ev bio and genetics classes back in college with out any specific citations) I don't know as much about either of those as I do about ERVs. The main reason I know a bit about ERVs is Abbie Smith's eponymous blog http://scienceblogs.com/erv/. The Sanger Institute http://www.sanger.ac.uk/humgen/cnv/ has done a lot of work related to Copy Number Variation and so poking around their stuff might help but I think they've mainly focused just on variation between humans. So, um, yeah, I guess the short answer is I don't know.

So, to account for the mutations caused by their amplification procedure, they double checked the twelve candidate mutations they found against the donor's DNA from blood samples (not amplified by cell culture) and against the same regions in very close male relatives of the donors (if you are male and have a biological full brother, then your Y chromosomes should be almost completely identical). They scratched eight candidate mutations off as coming from the cell culture process, leaving four.

Not sure this is entirely correct. From the original posting article...

Having identified 23 candidate SNPs - or single letter changes in the DNA - they amplified the regions containing these candidates and checked the sequences using the standard Sanger method. A total of four naturally occurring mutations were confirmed. Knowing this number of mutations, the length of the area that they had searched and the number of generations separating the individuals, the team were able to calculate the rate of mutation.

It seems that instead of the procedure that you describe involving blood samples and whatnot, they instead took the two sequences and compared them to a reference Y chromosome. They identified 23 places where either of the two sequences were potentially different from the reference Y chromosome in established SNP (Single Nucleotide Polymorphisms) locations. The doubled checked and found that 4 of the places the chromosome differed were in established SNPs of the Y chromosome (currently I believe there are 91,437 established naturally occuring SNPs in the Y chromosome out of 59,373,566 base pairs). So I think they are concluding that they observed a SNP mutation in 4 of the 12 cases where the DNA differs. The rest of the differences they seem to have attributed to the process used to sequence the DNA. I haven't had a chance to read the actual paper, though, to see how they might have accounted for unknown SNPs.

"I think that they will find many of them have the same sets of mutations. The reason is that I believe that many of these mutations are from virus[es], not from random mutations."

That would be an interesting direction of investigation.

Quote from the press release: "Fortunately, most of these [mutations] are harmless and have no apparent effect on our health or appearance." They don't know that. That is ENTIRELY speculation.

You can view mutations for various cancers here : Cancer Genome Workbench or Catologue of Somatic Mutations in Cancer.

Not to mention that zebrafish have a completely mapped genome, which also makes it a much better candidate for a lot of studies.

Another name for these microdeletions is copy number variation, a normal form of variation in the human genome. There is also a fundamental concept in population genetics called genetic load which are recessive lethal alleles present in any population as a result of new mutations and limited selection against rare recessive alleles. Just be glad we are not all the same because then a single bad virus like the 1918 influenza could wipe us all out. Besides life would be so boring.

We are predominantly Perl programmers at least on the European side - http://www.ensembl.org/ is the European based Genome browser (probably a million lines of Perl)... plus most of the http://www.sanger.ac.uk/ Wellcome Trust Sanger Institure data manipulation and presentation is in Perl...

See also http://www.bioperl.org/

Also check out

http://www.sanger.ac.uk/software/

not suprising really with webpages like this
--------

Decoding the Genome Needs Superpower

The Wellcome Trust Sanger Institute is one of the largest genomics data centers in the world. In "The Hum and the Genome," the Scientist writes about the IT infrastructure needed to handle the avalanche of data that researchers have to analyze. With its 2,000 processors and its 300 terabytes of storage, the data center uses today about 0.75 megawatts (MW) of power at a cost of 140,000 per year (about $170K). But the data center will need more than a petabyte of storage within three years, and its yearly electricity bill will reach 500,000 (more than $600K) for about 1.4 MW, enough to power more than a thousand homes. Read more...

Below is a small diagram showing the current IT infrastructure of the Wellcome Trust Sanger Institute, used by the Human Genome Project (Credit: Wellcome Trust Sanger Institute).

The current IT infrastructure of the Wellcome Trust Sanger Institute

Here is a link to a larger version of this chart.

Now, let's look at this IT infrastructure in detail.

* Computers
o Today: The datacenter hosts about 2,000 Alpha processors, originally designed by Digital Equipment (DEC), before its acquisition by Compaq, and later by Hewlett-Packard (HP).
o Tomorrow: The Sanger Institute is looking at cheaper solutions, especially now that HP has officially stopped any development on the Alpha front.
* Storage
o Today: Three different computer rooms have a total capacity of about 300 terabytes.
o Tomorrow: The IT management forecasts about a petabyte within three years -- at least.
* Databases
o Today: There are about 40 different databases, and only two of them are in the 50 terabytes area.
o Tomorrow: One of the databases, the Trace sequence archive currently contains about 700 million entries, and it doubles every 10 months.
* Power bills
o Today: The current equipment needs about 0.75 megawatts for a cost of 140,000 per year (about $170K).
o Tomorrow: The new setup will need about 1.4 megawatts, which will raise the yearly bill to about 500,000 (about $615K today).

The supercomputer vendors can say all they want about diminishing costs. But they almost never talk about the power bills...

Sources: Stuart Blackman, The Scientist, Volume 19, Issue 11, Page 15, June 6, 2005; and various websites

> "it also raises the spectre of a single large company owning all these combinations."

You might be interested to read our data release policy http://www.sanger.ac.uk/Projects/release-policy.sh tml which describes how the finished data is made publicly available, to all, no charge.

(I work at the Sanger Centre.)

Dave

No, going from sequence to structure is a big problem; see e.g. the CASP competition. The fundamental difficulty is that protein folding involves many complex interactions between amino acid side chains and solvent molecules, getting you into a world of nightmarish quantum chemistry where energy landscapes are rugged and rules are made to be broken.

In general there are two ways to approach structure prediction. The most reliable is homology modeling where you basically find a similar protein sequence (i.e. a close evolutionary relative) whose structure is known. Current protein database searches generally rely on probabilistic models borrowed from natural language processing and speech recognition, primarily hidden Markov models. Essentially, these models address the evolutionary process (which describes how different proteins are related), rather than the folding process (which describes how individual proteins fold).

If there aren't any similar proteins with known structure, you're into the domain of novel fold prediction, the second (harder) way to predict structure. The current best novel fold prediction methods begin by breaking the protein sequence into lots of tiny fragments (think words), then doing homology modeling on these fragments... e.g. the ROSETTA program from David Baker's group.

Simulating the full folding kinetics, as folding@home does, is even harder, and involves wading knee-deep into all that nightmarish quantum chemistry (or approximating it). Here you are interested in not only the final folded structure of the protein, but also its intermediate structures (hence the applicability of this approach to study of misfolding diseases, such as those involving prions).

Thank you DeepStream for pointing out the difference between folding@home and this ROSETTA-related project... teach me to respond without rtfa...

The submitter wouldn't need to 'ogle' for links if they paid attention to the BBC Article (which I had to google for since the article mised out the link).

It clearly says "use web tools such as the Ensembl Genome Browser at the Wellcome Trust Sanger Institute in Cambridge, UK, to mine the data." and on the right, under the heading "Related internet links" there is a link to the Wellcome Trust Sanger Institute who run the Ensembl Genome browser, as well as 3 other relevant links.

What the BBC site does not do is put inline links to other non BBC sites, they are always on the right, with a little disclaimer that " The BBC is not responsible for the content of external internet sites".

Yep. The genome has been mapped out.

If you have any interest in reading all those agct's, or just think you have what it takes to create a superworm of your own, there's more information here
Maybe the coolest thing about worms as a science tool is the reletively few number of neurons they have. (~300, I think. Its been a while) This allows researchers to use a laser to kill single neuron, and watching the effects on the whole pathway.

Several persons interviewed in the first article discussed dissection, experimentation, and preservation of Venter's body, which would probably be more useful than his previous contributions.

nice that DNA was discovered while stareing at Xrays now software does a good job

info: sanger center Cambridge was one of the centers that they helped sequence human DNA

why ? Because of the ability to patent squences of DNA
(that drug companies get rich off) they had to do it before evil companies did like Celera Genomics who used a more inactuate method (shotgun) but evily patented it

welcome trust is a huge Charity that funds research in this area

ptenting DNA is silly these are naturally occuring things (squences) they where not created just discovered its all very silly

Cuba and alot of africa are starting not to recognise these patents as they would like to build the drugs that help AIDS and HIV

its sad that AIDS and HIV has to come along just to show the world that patents are stupid on DNA

anyway

here is lots of software related to DNA

regards

John Jones

Last thing I heard from developmental biology/biochemistry, they hadn't yet euclidated all of the sub-steps involving thousands of hormones/enzymes/genetic control mechanisms required to turn a tissue into an organ. Sure, we can take some stem cells, hit them with some chemicals and have them start to make kidney cells or neurons or endothelial cells. Convincing these kidney cells to form an organ, however, is a HUGE leap which requires stem cells becoming vascular tissue ( +3 types of cells) and protective sheathing ( +2 types of cells) and accessory nervous/vacular connections ( +2 types of cells). Has anyone made these types of cells? Not that I know about.

Good news is - this type of human-controlled development is possible in C. elegans, a worm. We have sequenced it's entire genome http://www.sanger.ac.uk/Projects/C_elegans/ and more importantly, we know where every single cell in the adult originated from - starting with a 4-cell zygote. PubMed Abstract Link

Maybe in 20 or more years we will have this knowledge for some "higher" animal - Maybe even a vertebrate! Then we can start to understand human organ development.

The WTCHG is actually a cool place that looks for genes for complex diseases, writes useful software, and are heavily invested in using Linux as a scientific computing platform.

They have an 86 cpu Mosix/Linux cluster, and two 8 CPU, 8GB machines running Linux.

1 Small molecule metabolism

• 1.A Degradation [18]
• • 1.A.1 Carbon compounds [66]
  • 1.A.2 Amino acids [23]
• 1.B Energy metabolism
• • 1.B.1 Glycolysis [12]
  • 1.B.10 Glyoxylate bypass [3]
  • 1.B.2 Pyruvate dehydrogenase [4]
  • 1.B.3 Tricarboxylic acid cycle [15]
  • 1.B.5 Pentose phosphate pathway [3]
  • 1.B.5.a Oxidative branch
  • 1.B.5.b Non-oxidative branch [4]
• 1.B.6 Entner-Doudoroff pathway [2]

1 Small molecule metabolism

• 1.A Degradation [18]
• • 1.A.1 Carbon compounds [66]
  • 1.A.2 Amino acids [23]
• 1.B Energy metabolism
• • 1.B.1 Glycolysis [12]
  • 1.B.10 Glyoxylate bypass [3]
  • 1.B.2 Pyruvate dehydrogenase [4]
  • 1.B.3 Tricarboxylic acid cycle [15]
  • 1.B.5 Pentose phosphate pathway [3]
  • 1.B.5.a Oxidative branch
  • 1.B.5.b Non-oxidative branch [4]
• 1.B.6 Entner-Doudoroff pathway [2]

1 Small molecule metabolism

• 1.A Degradation [18]
• • 1.A.1 Carbon compounds [66]
  • 1.A.2 Amino acids [23]
• 1.B Energy metabolism
• • 1.B.1 Glycolysis [12]
  • 1.B.10 Glyoxylate bypass [3]
  • 1.B.2 Pyruvate dehydrogenase [4]
  • 1.B.3 Tricarboxylic acid cycle [15]
  • 1.B.5 Pentose phosphate pathway [3]
  • 1.B.5.a Oxidative branch
  • 1.B.5.b Non-oxidative branch [4]
• 1.B.6 Entner-Doudoroff pathway [2]

1 Small molecule metabolism

• 1.A Degradation [18]
• • 1.A.1 Carbon compounds [66]
  • 1.A.2 Amino acids [23]
• 1.B Energy metabolism
• • 1.B.1 Glycolysis [12]
  • 1.B.10 Glyoxylate bypass [3]
  • 1.B.2 Pyruvate dehydrogenase [4]
  • 1.B.3 Tricarboxylic acid cycle [15]
  • 1.B.5 Pentose phosphate pathway [3]
  • 1.B.5.a Oxidative branch
  • 1.B.5.b Non-oxidative branch [4]
• 1.B.6 Entner-Doudoroff pathway [2]

1 Small molecule metabolism

• 1.A Degradation [18]
• • 1.A.1 Carbon compounds [66]
  • 1.A.2 Amino acids [23]
• 1.B Energy metabolism
• • 1.B.1 Glycolysis [12]
  • 1.B.10 Glyoxylate bypass [3]
  • 1.B.2 Pyruvate dehydrogenase [4]
  • 1.B.3 Tricarboxylic acid cycle [15]
  • 1.B.5 Pentose phosphate pathway [3]
  • 1.B.5.a Oxidative branch
  • 1.B.5.b Non-oxidative branch [4]
• 1.B.6 Entner-Doudoroff pathway [2]

1 Small molecule metabolism

• 1.A Degradation [18]
• • 1.A.1 Carbon compounds [66]
  • 1.A.2 Amino acids [23]
• 1.B Energy metabolism
• • 1.B.1 Glycolysis [12]
  • 1.B.10 Glyoxylate bypass [3]
  • 1.B.2 Pyruvate dehydrogenase [4]
  • 1.B.3 Tricarboxylic acid cycle [15]
  • 1.B.5 Pentose phosphate pathway [3]
  • 1.B.5.a Oxidative branch
  • 1.B.5.b Non-oxidative branch [4]
• 1.B.6 Entner-Doudoroff pathway [2]

1 Small molecule metabolism

• 1.A Degradation [18]
• • 1.A.1 Carbon compounds [66]
  • 1.A.2 Amino acids [23]
• 1.B Energy metabolism
• • 1.B.1 Glycolysis [12]
  • 1.B.10 Glyoxylate bypass [3]
  • 1.B.2 Pyruvate dehydrogenase [4]
  • 1.B.3 Tricarboxylic acid cycle [15]
  • 1.B.5 Pentose phosphate pathway [3]
  • 1.B.5.a Oxidative branch
  • 1.B.5.b Non-oxidative branch [4]
• 1.B.6 Entner-Doudoroff pathway [2]

1 Small molecule metabolism

• 1.A Degradation [18]
• • 1.A.1 Carbon compounds [66]
  • 1.A.2 Amino acids [23]
• 1.B Energy metabolism
• • 1.B.1 Glycolysis [12]
  • 1.B.10 Glyoxylate bypass [3]
  • 1.B.2 Pyruvate dehydrogenase [4]
  • 1.B.3 Tricarboxylic acid cycle [15]
  • 1.B.5 Pentose phosphate pathway [3]
  • 1.B.5.a Oxidative branch
  • 1.B.5.b Non-oxidative branch [4]
• 1.B.6 Entner-Doudoroff pathway [2]

1 Small molecule metabolism

• 1.A Degradation [18]
• • 1.A.1 Carbon compounds [66]
  • 1.A.2 Amino acids [23]
• 1.B Energy metabolism
• • 1.B.1 Glycolysis [12]
  • 1.B.10 Glyoxylate bypass [3]
  • 1.B.2 Pyruvate dehydrogenase [4]
  • 1.B.3 Tricarboxylic acid cycle [15]
  • 1.B.5 Pentose phosphate pathway [3]
  • 1.B.5.a Oxidative branch
  • 1.B.5.b Non-oxidative branch [4]
• 1.B.6 Entner-Doudoroff pathway [2]

1 Small molecule metabolism

• 1.A Degradation [18]
• • 1.A.1 Carbon compounds [66]
  • 1.A.2 Amino acids [23]
• 1.B Energy metabolism
• • 1.B.1 Glycolysis [12]
  • 1.B.10 Glyoxylate bypass [3]
  • 1.B.2 Pyruvate dehydrogenase [4]
  • 1.B.3 Tricarboxylic acid cycle [15]
  • 1.B.5 Pentose phosphate pathway [3]
  • 1.B.5.a Oxidative branch
  • 1.B.5.b Non-oxidative branch [4]
• 1.B.6 Entner-Doudoroff pathway [2]

1 Small molecule metabolism

• 1.A Degradation [18]
• • 1.A.1 Carbon compounds [66]
  • 1.A.2 Amino acids [23]
• 1.B Energy metabolism
• • 1.B.1 Glycolysis [12]
  • 1.B.10 Glyoxylate bypass [3]
  • 1.B.2 Pyruvate dehydrogenase [4]
  • 1.B.3 Tricarboxylic acid cycle [15]
  • 1.B.5 Pentose phosphate pathway [3]
  • 1.B.5.a Oxidative branch
  • 1.B.5.b Non-oxidative branch [4]
• 1.B.6 Entner-Doudoroff pathway [2]

After some searching, I finally found some of the Sanger Center's hardware specs (by clicking on the Compaq Nonstop link at the bottom of the main page).

Basically, they used a boat load of Alpha's (R.I.P.) -- EV5's, EV56's and EV6's --, 48 Pentium Linux machines, and some more from SGI and Sun.

Here's a link to the slide.

• Sanger Center project page with additional info for the data hungry.
• Press release on which the news articles are based.
• Nature Science Update's take on the news

• Sanger Center project page with additional info for the data hungry.
• Press release on which the news articles are based.
• Nature Science Update's take on the news

They measure things such as the number of fragments (fewer=better) and their lengths (longer=better) and estimated coverage of the genome.

There is also a less biased comparison over at the Nature website. I don't know if you can get to read it without a paid subscription though. Their findings are less controversial, saying that the statistics are similar for the two assemblies, but that the annotations (i.e. descriptions of what is actually there, comparison: A group photo with note on peoples names and their relationships) are better in the public version.

Lars
__

The latest full release of EMBL (63) weighed in at about 4.7 Gb compressed. This took me about 30 hours to download.

GPL'd tools are available. Checkout EMBOSS for a start, BioPerl, BioJava, bioPython, and BioXML, all linking in with a common biocorba interfaces, and many more besides.

I run my bioinformatics service with a minimum of commercial software (only one commercial package which I am soon replacing with EMBOSS, and several non-open packages. The majority are open to some degree.

Needless to say it is based on Unix systems (IRIX/Linux in my case).

..d

Instead of reporting this not-news, why not link to yesterday's press release from the HGP (kindly sent to me by a friend who works on the project). They are moving from a "draft" sequence towards the finished version, having completed 85% of it.

Ade_
/

It seems odd that ./ is focussing on the commercial aspects of the HGP again.

Especially on a day when the public consortium have made this press release announcing 85% genome completion, which is freely available to the public, and the ensembl project, an open source project, making genome data, annotation, and analysis tools freely available, has reached Milestone 2.

Your question is too general. You do not need to install anything if your needs are limited to aligment of few DNA/protein fragments once a week. The web resources will be good enough for that. For any lab doing sequencing I highly recommend Staden The gap4 program, which is a part of a package is a category leader. You may also take a look at Sanger Centre web site: Software. Almost all what you may ever need is there. ;-)

I was thinking more along the lines of EMBOSS as well. I have been hacking a few bits on that and trying to get things going. Generally people like it a lot despite the command line.

Bioperl is cool. I put together a database indexing and retrieval script for my DBs with a non standard header. Works a charm (after tidying up a few wrinkles in the way my hack worked.)

We are looking to get ENSEMBL up and running shortly. It looks exciting and we want to build on it for our own purposes too.

Academic genomics has certainly produced a lot of good open source tools. ENSEMBL, EMBOSS, Bioperl, Biojava, Sean's HMMER and so on. Maybe we should start a new site? adopt_a_genome_scientist.org for those that can write good code but don't know what to write to meet those that know what they want doing but can't write code.

..d

Even without access to full public data (like the raw chromatograms instead of assembled sequence) celera is certain to use the public data to know where their shotgunned fragments go.
I wonder how that will affect their patents, it's very sticky.
And yes, Celera is short time competitor and Celera's parent a long time benefactee of HGP
Anonymous Coward

I am a bioinformaticist who is involved with a number of projects (most peripherally). Our aim is to provide tools that work for those that need them, so we GPL or LGPL almost everything. One spinoff of the major package I am involved with is that instead of paying a huge per processor fee for a package that is closed and requires expensive hardware to run, I can now put out a better package that is modifyable and extensible on a larger number of machines, each of which is far cheaper. Happy? ask my users in six months.

Progress? Standing on the shoulders of giants is a good way to get to see further. Before we had the OS package (EMBOSS for the interested) anyone wanting to write a new package had to start from scratch. Now, you just copy the bits that do what you want from the existing source, write a couple of new functions and have a new application in a day or two. With good design it is on the web based interface before the coding is even finished!!.

I expect a scientist saying 'I used this program' when they publish a paper to make that code available to other scientists to try to repeat the results and audit the accuracy of the work. It is understandable and acceptable to licence things to protect the original authors investment, but that does not mean closed source.

Enough ranting. Support your local open source scientists. EMBOSS, BioPerl, BioJava and so on.. That way we ALL get better tools to do better work. And the kudos go to the ones that dream up the best tools and ways to approach things.

..d

The European Molecular Biology Open Software Suite should get an award of some kind here.

• Usable and used now by tens of thousands of scientists worldwide which is a great advance on last year
• Really nice design for the interface allowing it to be easily merged with any frontend you wish for easy incorporation into GUI apps, workflow schemes etc.
• Open source by design, and updated every night
• An extra man year of funding would make a massive difference and you'll also be contributing to basic scientific research.
• Developed by Genome researchers for scientists worldwide

Visit the EMBOSS home page for more details. ..d

This is an interface that describes all the inputs to the programs. It means that the one program can be linked to whichever frontend you wish be it web, javabean, corba or whatever. Just parse the program description file (ACD file) and you are in..

The Sanger Centre's website is at www.sanger.ac.uk.

Gab

Congratulations to all who participated in its sequencing. We look forward to the first draft of the human genome by spring 2000.