Daily Archives: January 3, 2013

Re-design the Human Genome. Thanks to the Pentagone and the Industral Complex.

By: william ross

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Re-design the Human Genome. Thanks to the Pentagone and the Industral Complex. - Video

"To Redesign the Human Genome"
Source: SuperDeltaBravo1 http://www.youtube.com Human Genome Project Resources: http://www.kumc.edu Thoughts on eugenics and eugenicists: rense.com

By: irisphant

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"To Redesign the Human Genome" - Video

12/15/12 - Blast Camp! - Video Commentary - Genome
Trying something new with video commentary and I may do more but let me know what you guys think! Be sure to like us on Facebook! : http://www.facebook.com/SpartanTeam04

By: IllestAirsoft

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12/15/12 - Blast Camp! - Video Commentary - Genome - Video

January 3, 2013

April Flowers for redOrbit.com Your Universe Online

Genome sequencing is much more common than in the past. In a large part, this is attributable to advances in biotechnologies and computer software, however, there is still some question about both the accuracy of different sequencing methods and the best ways to evaluate these efforts. Computer scientists, led by New York University, have now devised a new tool to better measure the validity of genome sequencing.

By tracking a small group of key statistical features in the basic structure of the assembled genome, the new method allows for the evaluation of a wide range of genome sequencing procedures. To put together the complete genome sequence much like a complex jigsaw puzzle such sequence-assembly algorithm lays out the individual short reads, which are strings of DNAs four nucleic acid bases sampled from the target genome.

The method, as described in the journal PLOS ONE, uses techniques from statistical inference and learning theory to select the most significant features, concluding that many features thought by human experts to be important are actually misleading.

The research team, consisting of scientists from New York Universitys Courant Institute of Mathematical Sciences, NYU School of Medicine, Swedens KTH Royal Institute of Technology, and Cold Spring Harbor Laboratory, says current evaluation methods of genome sequencing are typically imprecise, relying on what amounts to crowd sourcing. Scientists weigh in on the accuracy of a sequencing method, creating a consensus. Still other methods use apples-to-oranges comparisons to make assessments, limiting their value as an evaluation.

The research team expanded upon a previous system they had created with this new work. The earlier system, Feature Response Curve (FRCurve) offers a global picture of how genome-sequencing methods, or assemblers, are able to deal with different regions and structures in a large complex genome. FRCurve points out how an assembler might have traded off one kind of quality measure at the expense of another. It shows how aggressively a genome assembler might have tried, for example, to pull together a group of genes into a contiguous piece of the genome, while at the same time incorrectly rearranging their correct order and copy numbers.

The team admits FRCurve has a significant limitation, however. The system can only gauge the accuracy of certain kinds of assemblers at one time. This excludes comparisons among the range of sequencing methods being used currently. Where FRCurve failed is with many of the new methods that are becoming highly popular because they are specifically designed to work with the most established next-generation sequencing technologies. These methods are also able to perform some error correction and data compression. The problem, however, is that by doing so, they discard the original signature of key statistical features position and orientation of the reads used to generate the candidate sequence that FRCurve needs for evaluation.

The PLOS ONE article unveiled a new method, FRCbam, with the capability to evaluate a much wider class of assemblers by reverse engineering the latent structures obscured by error-correction and data compression. This operation is performed rapidly by using efficient and scalable mapping algorithms.

FRCbam validates its analysis by examining a large ensemble of assemblers working on a large ensemble of genomes, which are selected from crowd-sourced competitions like GAGE and Assemblathons, instead of assumption-ridden simulations or expensive auxiliary methods. Thus, FRCbam is able to characterize the statistics that are expected, and then validate any individual system with respect to it.

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New Method Allows For The Evaluation Of A Wide Range Of Genome Sequencing Procedures

Public release date: 3-Jan-2013 [ | E-mail | Share ]

Contact: Sarah McDonnell s_mcd@mit.edu 617-253-8923 Massachusetts Institute of Technology

CAMBRIDGE, MA -- Researchers at MIT, the Broad Institute and Rockefeller University have developed a new technique for precisely altering the genomes of living cells by adding or deleting genes. The researchers say the technology could offer an easy-to-use, less-expensive way to engineer organisms that produce biofuels; to design animal models to study human disease; and to develop new therapies, among other potential applications.

To create their new genome-editing technique, the researchers modified a set of bacterial proteins that normally defend against viral invaders. Using this system, scientists can alter several genome sites simultaneously and can achieve much greater control over where new genes are inserted, says Feng Zhang, an assistant professor of brain and cognitive sciences at MIT and leader of the research team.

"Anything that requires engineering of an organism to put in new genes or to modify what's in the genome will be able to benefit from this," says Zhang, who is a core member of the Broad Institute and MIT's McGovern Institute for Brain Research.

Zhang and his colleagues describe the new technique in the Jan. 3 online edition of Science. Lead authors of the paper are graduate students Le Cong and Ann Ran.

Early efforts

The first genetically altered mice were created in the 1970s by adding small pieces of DNA to mouse embryonic cells. This method is now widely used to create transgenic mice for the study of human disease, but, because it inserts DNA randomly in the genome, researchers can't target the newly delivered genes to replace existing ones.

In recent years, scientists have sought more precise ways to edit the genome. One such method, known as homologous recombination, involves delivering a piece of DNA that includes the gene of interest flanked by sequences that match the genome region where the gene is to be inserted. However, this technique's success rate is very low because the natural recombination process is rare in normal cells.

More recently, biologists discovered that they could improve the efficiency of this process by adding enzymes called nucleases, which can cut DNA. Zinc fingers are commonly used to deliver the nuclease to a specific location, but zinc finger arrays can't target every possible sequence of DNA, limiting their usefulness. Furthermore, assembling the proteins is a labor-intensive and expensive process.

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Editing the genome with high precision

Advances in bio-technologies and computer software have helped make genome sequencing much more common than in the past. But still in question are both the accuracy of different sequencing methods and the best ways to evaluate these efforts. Now, computer scientists have devised a tool to better measure the validity of genome sequencing.

The method, which is described in the journal PLOS One, allows for the evaluation of a wide range of genome sequencing procedures by tracking a small group of key statistical features in the basic structure of the assembled genome.Such sequence-assembly algorithm lays out the individual short reads (strings of DNAs four nucleic acid bases sampled from the target genome) to put together the complete genome sequencemuch like a complex jig-saw puzzle. The method uses techniques from statistical inference and learning theory to select the most significant features. Surprisingly, the method concludes that many features thought by human experts to be the most important were actually highly misleading.

The work was conducted by researchers at New York Universitys Courant Institute of Mathematical Sciences, NYU School of Medicine, Swedens KTH Royal Institute of Technology, and Cold Spring Harbor Laboratory.

Current evaluation methods of genome sequencing are typically imprecise. They rely on what amounts to crowd sourcing, with scientists weighing in on the accuracy of a sequencing method. Other evaluations use apples-to-oranges comparisons in making assessments, thus limiting their value.

In the PLOS One work, the researchers expanded upon an earlier system they created, Feature Response Curve (FRCurve), which offers a global picture of how genome-sequencing methods, or assemblers, are able to deal with different regions and different structures in a large complex genome. Specifically, it points out how an assembler might have traded off one kind of quality measure at the expense of another kind. For instance, it shows how aggressively a genome assembler might have tried to pull together a group of genes into a contiguous piece of the genome, while incorrectly rearranging their correct order and copy numbers.

However, FRCurve has a significant limitationit can only gauge the accuracy of certain kinds of assemblers at one time, thereby excluding comparisons among the range of sequencing methods currently being employed. Many of these methods, where the original FRCurve failed, are becoming highly popular, as they are specifically designed to work with the most established next-generation sequencing technologies and are able to perform some error correction and data compression. However, by doing so, they also discard the original signature of key statistical features (e.g., position and orientation of the reads used to generate the candidate sequence) that FRCurve needs for evaluation.

The work reported in PLOS One unveils a new method, FRCbam, which has the capability to evaluate a much wider class of assemblers. It does so by reverse engineering the latent structures that were obscured by error-correction and data compression; and it performs this operation rapidly by using efficient and scalable mapping algorithms.

Instead of assumption-ridden simulation or expensive auxiliary methods, FRCbam validates its analysis by examining a large ensemble of assemblers working on a large ensemble of genomes, selected from crowd-sourced competitions like GAGE and Assemblathons. This way, FRCbam can characterize the statistics that are expected and then validate any individual system with respect to it.

FRCbam and FRCurve are expected to be used routinely to rank and evaluate future genome projects. This method is currently employed to evaluate the sequence assembly of the Norway Spruce, one of the largest genomes sequenced so farit is seven times longer than the human genome.

The studys authors were: Francesco Vezzi, a postdoctoral researcher in the School of Computer Science and Communication at Swedens KTH Royal Institute of Technology, Science for Life Laboratory; Giuseppe Narzisi, a researcher at Cold Spring Harbor Laboratorys Simons Center for Quantitative Biology; and Bud Mishra, a professor at NYUs Courant Institute of Mathematical Sciences who also holds appointments at Cold Spring Harbor Laboratory and NYU School of Medicine.

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Researchers Develop Tool To Evaluate Genome Sequencing Method

Jan. 2, 2013 Advances in bio-technologies and computer software have helped make genome sequencing much more common than in the past. But still in question are both the accuracy of different sequencing methods and the best ways to evaluate these efforts. Now, computer scientists have devised a tool to better measure the validity of genome sequencing.

The method, which is described in the journal PLOS One, allows for the evaluation of a wide range of genome sequencing procedures by tracking a small group of key statistical features in the basic structure of the assembled genome. Such sequence-assembly algorithm lays out the individual short reads (strings of DNA's four nucleic acid bases sampled from the target genome) to put together the complete genome sequence -- much like a complex jig-saw puzzle. The method uses techniques from statistical inference and learning theory to select the most significant features. Surprisingly, the method concludes that many features thought by human experts to be the most important were actually highly misleading.

The work was conducted by researchers at New York University's Courant Institute of Mathematical Sciences, NYU School of Medicine, Sweden's KTH Royal Institute of Technology, and Cold Spring Harbor Laboratory.

Current evaluation methods of genome sequencing are typically imprecise. They rely on what amounts to "crowd sourcing," with scientists weighing in on the accuracy of a sequencing method. Other evaluations use apples-to-oranges comparisons in making assessments, thus limiting their value.

In the PLOS One work, the researchers expanded upon an earlier system they created, Feature Response Curve (FRCurve), which offers a global picture of how genome-sequencing methods, or assemblers, are able to deal with different regions and different structures in a large complex genome. Specifically, it points out how an assembler might have traded off one kind of quality measure at the expense of another kind. For instance, it shows how aggressively a genome assembler might have tried to pull together a group of genes into a contiguous piece of the genome, while incorrectly rearranging their correct order and copy numbers.

However, FRCurve has a significant limitation -- it can only gauge the accuracy of certain kinds of assemblers at one time, thereby excluding comparisons among the range of sequencing methods currently being employed. Many of these methods, where the original FRCurve failed, are becoming highly popular, as they are specifically designed to work with the most established next-generation sequencing technologies and are able to perform some error correction and data compression. However, by doing so, they also discard the original signature of key statistical features (e.g., position and orientation of the reads used to generate the candidate sequence) that FRCurve needs for evaluation.

The work reported in PLOS One unveils a new method, FRCbam, which has the capability to evaluate a much wider class of assemblers. It does so by reverse engineering the latent structures that were obscured by error-correction and data compression; and it performs this operation rapidly by using efficient and scalable mapping algorithms.

Instead of assumption-ridden simulation or expensive auxiliary methods, FRCbam validates its analysis by examining a large ensemble of assemblers working on a large ensemble of genomes, selected from crowd-sourced competitions like GAGE and Assemblathons. This way, FRCbam can characterize the statistics that are expected and then validate any individual system with respect to it.

FRCbam and FRCurve are expected to be used routinely to rank and evaluate future genome projects. This method is currently employed to evaluate the sequence assembly of the Norway Spruce, one of the largest genomes sequenced so far -- it is seven times longer than the human genome.

The study's authors were: Francesco Vezzi, a postdoctoral researcher in the School of Computer Science and Communication at Sweden's KTH Royal Institute of Technology, Science for Life Laboratory; Giuseppe Narzisi, a researcher at Cold Spring Harbor Laboratory's Simons Center for Quantitative Biology; and Bud Mishra, a professor at NYU's Courant Institute of Mathematical Sciences who also holds appointments at Cold Spring Harbor Laboratory and NYU School of Medicine.

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Tool to evaluate genome sequencing method developed