Category Archives: Genome

Internal Drive Theory:Motivate your child to WANT to study This book documents 11 different motivation strategies, each inspired by an established stream of research in the field of Human Motivation. These strategies are designed to bring about Internal Drive Ignition(TM) in children. They form part of a larger basket of strategies that Dr Petunia Lee uses to ignite internal drive in children. Each research-inspired strategy is explained simply and illustrated with real-life anecdotes in order to paint the hows and whys of its use in vivid detail. Used together, these strategies may help families save money on tuition because a motivated child is half the battle won. It is hoped that these strategies will sweeten the lives of many children by enhancing the motivation skills of parents. This is a book worth buying because it brings gentleness and love back into motivation and shows that these two are far more effective motivators than fear, bribes and nagging. Successful use of these strategies also strengthens family relationships. The rewards from the use of these strategies will be reaped for many long years after the child has left school.

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MOTIVATION GENOME - Home

Being at the forefront of science and technology and promoting a healthy and attractive workplace are objectives integral to NYGCs mission. One important way NYGC strives to meet these objectives has been to design and maintain an environmentally responsible facility. Wherever possible, NYGC created systems, chose materials and supplies, and adopted approaches that lower its environmental impact and make NYGC a desirable place to work.

NYGCs multifaceted commitment to creating a green environment includes:

LEED Gold Facility NYGC has achieved LEED gold certification under the LEED for Commercial Interiors Rating System, from the U.S. Green Building Council, indicating that the facility has incorporated green elements in the categories of Sustainable Site selection, Water Efficiency, Energy and Atmosphere, Materials and Resources, Indoor Environmental Quality, and Innovation in Design. NYGCs facility is also located in a LEED for Core + Shell building, further exemplifying the synergies that the USGBC advocates.

Easy Access to Public Transportation NYGC is located in a dense urban area close to basic services and multiple subway lines. The 1, 6, A, C, E, N, Q, and R trains, as well as a number of bus lines are a short walking distance from the building. This proximity to public transportation decreases the need for automobiles, which in turn helps reduce air and noise pollution.

Bike and Runner Friendly For staff members who want to bike, run, or walk to work, NYGC has installed bicycle storage, showers, and changing facilities. Alternate ways of commuting reduce smog and air pollution, as well as traffic congestion, noise pollution, and the need for roadway and parking lot infrastructure.

Natural Light and Views With windows on each floor and no adjacent buildings on three sides of 101, almost every employee has the benefit of natural light as well as a direct view of the outdoors.

Outdoor Terraces NYGC has one landscaped roof terrace, and will eventually have a second, outside our 7th floor office floor. The gardens provide an inviting outdoor space where staff can take a break from lab benches and desks.

Water Efficiency Measures NYGCs low-flow plumbing fixtures and water-saving measures, when combined with the base buildings LEED Core & Shell measures, are expected to result in a 30-35% reduction in water use.

Lighting and Energy Efficiency NYGC has undertaken a number of measures to increase lighting and energy efficiency, including reducing light-power density, installing sub-meters to track energy consumption, and implementing an energy-efficient HVAC system and EnergySTAR-rated appliances such as refrigerators, printers, and computer monitors.

Single-Stream Recycling Program NYGC provides single-stream recycling with bins that hold paper, cardboard, glass, plastics, and metals together.

Environmentally Conscious Construction Program In building out a 170,000-square foot facility, NYGC took a number of steps in consideration of the environment:

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New York Genome Center About Us

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Genome - NRC Research Press

MGI Awarded $60M for New Centers for Complex Disease Genomics Network 4-Year Grant from NHGRI for Studies of Cardiovascular and Neuropsychiatric Diseases

Study Uncovers Hard-to-Detect Cancer Mutations Dr. Li Ding and her team developed a software tool for finding a certain type of genetic error that has been consistently missed by cancer genome studies and identified a large number of such events in critical cancer genes.

Reference Genomes Improvement Project MGI's commitment to enhancing the human reference genomes

MGI Hosts Hands-on Workshop Dr. Mitreva and her laboratory provide instruction for attendees on bioinformatics for helminth genomics

MGI Awarded Grant to Improve Human Reference Genome Resource Grant supports MGIs contributions to the Genome Reference Consortium to enhance the quality and breadth of the human genome reference assembly

Supporting Science Elizabeth H. and James S. McDonnell III provide an extraordinary gift to enable genomic research

The Cat's Meow MGI researchers and collaborators explore the cat genome, revealing clues to domestication.

McDonnell Genome Institute researchers make scientific double play Wesley Warren, PhD and Richard K. Wilson, PhD contribute to two separate cover stories in Science and Nature in the same week

Drug Gene Interaction Database Twin brothers develop an online database that matches disease genes with potential drugs.

McDonnell Genome Institute Researchers Remain on "Hottest Researchers" List Wilson, Mardis, Ding, Fulton on Hottest Researchers

Cancer Genomics Two new studies shed light on the genomics of two deadly forms of cancer.

Sample Prep Maze Ndonwi scans genomic DNA samples for an infant microbiome project.

Testing and Training Lisa Cook tests a new sample preparation protocol.

McDonnell Genome Institute Tours Outreach member, Latricia Wallace, leads a tour of the McDonnell Genome Institute.

Outreach Event Students present posters at an Outreach event for DNA Day.

Preparing to Sequence Preparing a fluorescent gel during library construction.

Software Development Staff developers and software engineers work on the latest analysis pipelines.

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McDonnell Genome Institute

GenomeNet Database Resources DBGET: Integrated Database Retrieval System DBGET search LinkDB search SPARQL endpoint available KEGG: Kyoto Encyclopedia of Genes and Genomes KEGG2 - Table of contents KEGG PATHWAY - Systems information: pathways KEGG BRITE - Systems information: ontologies KEGG Organisms - Organism-specific entry points KEGG GENES - Genomic information KEGG LIGAND - Chemical information KEGG MEDICUS - Health information KEGG MGENES: Metagenome gene catalogs Virus-Host DB: Hosts of sequenced viruses New! Reaction Ontology: Reaction classifications varDB: Antigenic variation database Community Databases CYORF - Cyanobacteria annotation database BSORF - Bacillus subtilis genome database EXPRESSION - Gene expression profile database GenomeNet Bioinformatics Tools Sequence Analysis BLAST / FASTA - Sequence similarity search MOTIF - Sequence motif search CLUSTALW / MAFFT / PRRN - Multiple alignment RAxML / FastTree - Phylogenetic analysis New! Genome Analysis OC Viewer - KEGG ortholog clusters Updated! REST service is available KAAS - KEGG automatic annotation server Updated! MAPLE 2.1 - Functionome evaluator Updated! EGassembler - EST consensus contigs GENIES - Gene network prediction DINIES - Drug-target network prediction Chemical Analysis SIMCOMP / SUBCOMP - Chemical structure search REST service is available KCaM - Glycan structure search PathComp - Possible reaction path computation PathSearch - Similar reaction path search PathPred - Reaction pathway prediction E-zyme - Enzymatic reaction prediction

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GenomeNet

Learn more about our philanthropic partnerships: the Carlos Slim Center for Health Research, the Klarman Cell Observatory, and the Stanley Center for Psychiatric Research

The Stanley Centers Guoping Feng discusses research showing how different mutations in the gene Shank3 may contribute to autism, schizophrenia

Correlations in basal gene expression and cell sensitivity data reveal insights into mechanisms of action for potential cancer drugs

The Broad Institute and the MIT Department of Biology seek applications for a tenure-track faculty position

Call for applications for the Broad Fellows program, an exciting new opportunity for extraordinary early-career scientists who are innovating at the intersection of biomedical and quantitative science

Wherever your interests lie, data from across the Broad can be accessed from this starting point

We are always looking for new team members to help us tackle important problems at the cutting edge of science

Read the latest highlights from the Broad scientific community

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Whitehead Institute/MIT Center for Genome Research

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NRC Research Press

The genome of an organism is the whole of its hereditary information encoded in its DNA (or, for some viruses, RNA). This includes both the genes and the non-coding sequences of the DNA. The term was coined in 1920.[1]

Winkler's definition, in translation, runs:

However, no single haploid chromosome set defines even the DNA of a species, because of the huge variety of alleles carried by a population. Even a diploid individual carries genetic variety. For that reason Dobzhansky preferred "set of chromosomes",[3] and the definition now must be broader than Winklers' definition. The genome of a haploid chromosome set is merely a sample of the total genetic variety of a species.

The term 'genome' can be applied specifically to mean the complete set of nuclear DNA (the 'nuclear genome') but can also be used of organelles that contain their own DNA, as with the mitochondrial genome or the chloroplast genome.

Note: The DNA from a single human cell has a length of ~1.8 m (but at a width of ~2.4 nanometers).

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Genome - Simple English Wikipedia, the free encyclopedia

DNA Sequencing Costs Data from the NHGRI Genome Sequencing Program (GSP) Overview

For many years, the National Human Genome Research Institute (NHGRI) has tracked the costs associated with DNA sequencing performed at the sequencing centers funded by the Institute. This information has served as an important benchmark for assessing improvements in DNA sequencing technologies and for establishing the DNA sequencing capacity of the NHGRI Genome Sequencing Program (GSP). Here, NHGRI provides an analysis of these data, which gives one view of the remarkable improvements in DNA sequencing technologies and data-production pipelines in recent years.

The cost-accounting data presented here are summarized relative to two metrics: (1) "Cost per Megabase of DNA Sequence" - the cost of determining one megabase (Mb; a million bases) of DNA sequence of a specified quality [see below]; (2) "Cost per Genome" - the cost of sequencing a human-sized genome. For each, a graph is provided showing the data since 2001; in addition, the actual numbers reflected by the graphs are provided in a summary table. NHGRI welcomes people to download these graphs and use them in their presentations and teaching materials. NHGRI plans to update these data on a regular basis.

To illustrate the nature of the reductions in DNA sequencing costs, each graph also shows hypothetical data reflecting Moore's Law, which describes a long-term trend in the computer hardware industry that involves the doubling of 'compute power' every two years (See: Moore's Law [wikipedia.org]). Technology improvements that 'keep up' with Moore's Law are widely regarded to be doing exceedingly well, making it useful for comparison.

In both graphs, note: (1) the use a logarithmic scale on the Y axis; and (2) the sudden and profound out-pacing of Moore's Law beginning in January 2008. The latter represents the time when the sequencing centers transitioned from Sanger-based (dideoxy chain termination sequencing) to 'second generation' (or 'next-generation') DNA sequencing technologies. Additional details about these graphs are provided below.

These data, however, do not capture all of the costs associated with the NHGRI Large-Scale Genome Sequencing Program. The sequencing centers perform a number of additional activities whose costs are not appropriate to include when calculating costs for production-oriented DNA sequencing. In other words, NHGRI makes a distinction between 'production' activities and 'non-production' activities. Production activities are essential to the routine generation of large amounts of quality DNA sequence data that are made available in public databases; the costs associated with production DNA sequencing are summarized here and depicted on the two graphs. Additional information about the other activities performed by the sequencing centers is provided below.

The expenditures included in each category were established based on discussions between NHGRI staff and sequencing center personnel.

For the two graphs ("Cost per Megabase of DNA Sequence" and "Cost per Genome"), the following 'production' costs are accounted for:

In the case of costs covered by significant subsidies to a sequencing center (e.g., a grantee institution providing funds for purchasing large equipment), NHGRI has attempted to appropriately account for such costs in these analyses.

The costs associated with the following 'non-production' activities are not reflected in the two graphs:

In both graphs, the data from 2001 through October 2007 represent the costs of generating DNA sequence using Sanger-based chemistries and capillary-based instruments ('first generation' sequencing platforms). Beginning in January 2008, the data represent the costs of generating DNA sequence using 'second-generation' (or 'next-generation') sequencing platforms. The change in instruments represents the rapid evolution of DNA sequencing technologies that has occurred in recent years.

For the Sanger-based sequence data, the cost accounting reflects the generation of bases with a minimum quality score of Phred20 (or Q20), which represents an error probability of 1 % and is an accepted community standard for a high-quality base. For sequence data generated with second-generation sequencing platforms, there is not yet a single accepted measure of accuracy; each manufacturer provides quality scores that are, at this time, accepted by the NHGRI sequencing centers as equivalent to or greater than Q20.

In the "Cost per Megabase of DNA Sequence" graph, the data reflect the cost of generating raw, unassembled sequence data; no adjustment was made for data generated using different instruments despite significant differences in the sequence read lengths. In contrast, the "Cost per Genome" graph does take these differences into account since sequence read length influences the ability to generate an assembled genome sequence.

The "Cost per Genome" graph was generated using the same underlying data as that used to generate the "Cost per Megabase of DNA Sequence" graph; the former thus reflects an estimate of the cost of sequencing a human-sized genome rather than the actual costs for specific genome-sequencing projects.

To calculate the cost for sequencing a genome, one needs to know the size of that genome and the required 'sequence coverage' (i.e., 'sequence redundancy') to generate a high-quality assembly of the genome given the specific sequencing platform being used. For generating the "Cost per Genome" graph, the assumed genome size was 3,000 Mb (i.e., the size of a human genome). The assumed sequence coverage needed differed among sequencing platforms, depending on the average sequence read length for that platform.

For data since January 2008 (representing data generated using 'second-generation' sequencing platforms), the "Cost per Genome" graph reflects projects involving the 're-sequencing' of the human genome, where an available reference human genome sequence is available to serve as a backbone for downstream data analyses. The required 'sequence coverage' would be greater for sequencing genomes for which no reference genome sequence is available.

See: http://www.genome.gov/10001691

Mardis E. A decade's perspective on DNA sequencing technology. Nature, 470: 198-203. 2011. [PubMed]

Metzker M. Sequencing technologies - the next generation. Nature Genetics, 11: 31-46. 2010. [PubMed]

Stein L. The case for cloud computing in genome informatics. Genome Biology, 11: 207-213. 2010. [PubMed]

Human genome at ten: the sequence explosion. Nature, 464: 670-671. 2010. [PubMed]

Wetterstrand KA. DNA Sequencing Costs: Data from the NHGRI Genome Sequencing Program (GSP) Available at: http://www.genome.gov/sequencingcosts. Accessed [date of access].

Kris Wetterstrand, M.S. Scientific Liaison to the Director for Extramural Activities National Human Genome Research Institute, NIH Phone: 301-435-5543 E-mail: wettersk@mail.nih.gov

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Last Updated: October 2, 2015

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DNA Sequencing Costs - Genome.gov | National Human Genome ...

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Genome Journal - NRC Research Press