Posts Tagged ‘Illumina’

Personalized Cardiovascular Genetic Medicine at Partners HealthCare and Harvard Medical School

Posted in Atherogenic Processes & Pathology, Biological Networks, Biomarkers & Medical Diagnostics, Cardiomyopathy, Cardiovascular Pharmacogenomics, Cardiovascular Research, Cell Biology, Chemical Biology and its relations to Metabolic Disease, Chemical Genetics, Computational Biology/Systems and Bioinformatics, Congenital Heart Disease, Electrophysiology, Frontiers in Cardiology and Cardiovascular Disorders, Genome Biology, Genomic Testing: Methodology for Diagnosis, HTN, Medical and Population Genetics, Molecular Genetics & Pharmaceutical, Myocardial Metabolism, Origins of Cardiovascular Disease, Personalized and Precision Medicine & Genomic Research, Pharmacogenomics, Pharmacotherapy of Cardiovascular Disease, Population Health Management, Proteomics, Regulated Clinical Trials: Design, Methods, Components and IRB related issues, Technology Transfer: Biotech and Pharmaceutical, Translational Research, Translational Science, tagged Aviva Lev-Ari, Children's Hospital Boston, DNA, DNA Sequencing, genetics, Harvard Medical School, Human Genome Project, Illumina, Partners HealthCare, Personalized medicine on February 25, 2013| 3 Comments »

Personalized Cardiovascular Genetic Medicine at Partners HealthCare and Harvard Medical School

Curator: Aviva Lev-Ari, PhD, RN

UPDATED on 5/4/2015

Goes to Clinic @MGH: Clinically validated versions of Exome Sequencing and Analysis using Broad-developed methods like Hybrid Capture, the Genome Analysis Toolkit (GATK), and MuTect

http://pharmaceuticalintelligence.com/2015/05/04/goes-to-clinic-mgh-clinically-validated-versions-of-exome-sequencing-and-analysis-using-broad-developed-methods-like-hybrid-capture-the-genome-analysis-toolkit-gatk-and-mutect/

Center for Personalized Genetic Medicine, Partners HealthCare and Harvard Medical School

The Partners HealthCare Center for Personalized Genetic Medicine offers technologies and technical support for the research activities of Partners investigators. Our objective is to help investigators advance their research programs and to provide the highest quality service, technical expertise, and leading technologies for genomics research. Our goal is to broaden the access to these technologies while offering the best customer service in the most cost conscious and time efficient manner possible.

We are organized into four principal service areas:

• sequence analysis,
• genotyping,
• expression analysis, and
• bioprocessing/sample management

Our platforms include next generation sequencing with Illumina HiSeq2000 and GA ii analyzers as well as Sanger sequencing using ABI 3730 XL sequence analyzers. Targeted custom genotyping is offered using Sequenom and Illumina GoldenGate panels as well as GWAS scale projects using Illumina Infinium and DNA methylation analysis using Illumina bead arrays. Expression analysis is available with capabilities for processing total RNA on either Affymetrix or Illumina arrays.

Through services from our BioSample Services Facility (BSF) and Partners Biorepository for Medical Discovery (PBMD) teams we provide a research platform for handling samples in a standardized manner to provide consistency from sample to sample. The BSF is able to assist investigators to configure projects utilizing your own samples or coupled with the PCPGM-PBMD we are able to support the integration of cohorts of samples selected from the PBMD into analysis on our genomics platforms.

 http://pcpgm.partners.org/research-services

DNA Sequencing

The DNA Sequencing Group at the Partners HealthCare Center for Personalized Genetic Medicine has a strong history of producing high quality, dependable, and informative results for collaborators and clients. The DNA Sequencing Group participated in the Human Genome Project, building the STS-Based BAC map for Human Chromosome 12, and providing Chromosome 12 tiling path clones to the Baylor Human Genome Sequencing Center for sequencing.

The group sequenced 113 BACs for the Mouse Genome Project, contributing 24 megabases of finished mouse sequences to the published Mouse Genome, as well as providing draft sequences for unique strains of several bacterial genomes, including Pseudomonas aeruginosa, and Vibrio cholerae. More recently, the group participated in identifying mutations linked to numerous diseases, either in collaborations or by providing client laboratories with full service resequencing and analysis.

Services by Project Goals

Mutation Identification via Resequencing

This facility provides full-service resequencing of regions of interest in one or more genomic DNAs, including the following:

• Discussion of the scope of the project and a cost quote
• Identification of genes in the region of interest as needed, with the Investigator
• Primer design using our automated system, to amplify desired regions
• Primer ordering
• QC of the primers on DNA standards, if required
• PCR amplification of DNA provided by Investigator
• PCR clean-up
• Sequencing reactions
• Sequencing reaction clean-up
• Sequence application to the ABI 3730 XL Analyzer
• Chromatograms are made available to Investigator over web (GIGPAD)
• Data assembly and analysis using Phred Phrap and PolyPhred
• One round of repeats and redesign if necessary
• Report of variations found throughout sequence
• Trouble shooting for 100% coverage if desired

Research Services

• Fee-for-service sequencing
• Fragment analysis / genotyping (Microsatellite Instability)

Technology Development

• New technology testing and development
• Collaborative Protocol development
• Beta-test site for instrumentation and software

Clinical Diagnosis

• Diagnostic test development
• Sequencing for clinical diagnostics group

Genomic Sequencing Projects

• Human
• Rodent
• Bacteria

http://pcpgm.partners.org/research-services/sequencing

Advancing Translational Genomics through Personalized Medicine Projects

The mission of the Partners HealthCare Center for Personalized Genetic Medicine (PCPGM) is to utilize genetics and genomics to promote and implement personalized medicine in caring for patients throughout the Partners HealthCare system and in health care nationally and globally.

The Personalized Medicine Project program was developed to support the clinical research efforts of junior Partners HealthCare investigators for translational genetics and genomic projects to advance personalized medicine.  The goal of this program is to identify biological markers that can be used as potential predictive tests.  This will be accomplished by:

• Leveraging the Partners HealthCare Research Patient Data Registry (RPDR) and the Partners Biorepository for Medical Discovery (PBMD), centralized locations where Partners HealthCare patient data and/or samples are stored.
• Identifying novel biological markers or new uses for existing markers.
• Focusing on tests that could:
  • improve diagnostic sensitivity or specificity;
  • further stratify patient groups with a given diagnosis;
  • predict improved clinical outcomes; or
  • assist with selection of therapies or methods to manage disease.

http://pcpgm.partners.org/biorepository/pmprojectsrfp

Harvard Medical School Genetics Training Program

The Harvard Medical School (HMS) Genetics Training Program is one of the oldest and largest programs in the country. It was founded by Drs. John Littlefield at the Massachusetts General Hospital and Park Gerald at Children’s Hospital Boston in the early 1970’s. The program has trained scientists and clinicians who have become leaders in academic genetics, and has supported investigators who have made major contributions to the clinical practice of genetics and genetics research.

The HMS Genetics Training Program is accredited by the ABMG in all areas of training – Clinical Genetics, Biochemical Genetics, Cytogenetics, and Molecular Genetics. This provides an opportunity for our trainees to become active candidates for board certification in a discipline(s) of medical genetics in addition to receiving laboratory training. The training laboratories and clinics of the program are centered at HMS and its affiliated institutions including Brigham and Women’s Hospital (BWH), the HMS Department of Genetics, Beth Israel Deaconess Medical Center (BIDMC), Children’s Hospital Boston (CHB), Dana Farber Cancer Institute (DFCI), and Massachusetts General Hospital (MGH). The HMS Genetics Training Program provides trainees the opportunity to take advantage of the extraordinarily rich academic environment offered at HMS and its affiliated institutions as well as the greater Boston scientific community.

 http://pcpgm.partners.org/educational-programs/harvard-medical-school-genetics-training-program

Cardiovascular Research Center @MGH

The Cardiovascular Research Center was founded in 1990, and occupies over 30,000 sq. ft. of laboratory space in both the Charlestown Navy Yard and the Richard B. Simches Research Building. Dr. Mark Fishman, now president of the Novartis Institutes for Biomedical Research, directed the Center from 1990 until 2002. From 2002-2005, Dr. Kenneth Bloch served as Interim Director and then in June 2005, the Massachusetts General Hospital welcomed Dr. Kenneth Chien as the new scientific director of the Cardiovascular Research Center. Prior to his MGH appointments, Dr. Chien directed the Institute for Molecular Medicine at the University of California at San Diego. An internationally recognized biologist specializing in cardiovascular science, he is a true pioneer in developing new therapeutic strategies to prevent the onset and progression of heart failure. Dr. Chien served as director until 2012.

Cardiovascular Research Center investigators have made many groundbreaking discoveries. Among these include:

• first identification of progenitor cells in the heart
• cloning of the first vertebrate cell death genes
• knocking out the genes that produce nitric oxide (NO), showing the importance of this molecule to atherosclerosis and stroke
• clinical use of NO to treat patients with pulmonary hypertension
• development of gene and cell transfer approaches to treat heart failure
• performance of the first large-scale genetic screen in a vertebrate (the zebrafish)
• identification of genes critical to cardiac pacemaking, rhythm, contractile function, and normal heart patterning
• discovery of a new methylase gene responsible for altering DNA structure during an individual’s lifetime

The Cardiovascular Research Center has taken great pride in the training of scientists with MDs and/or PhDs, as well as graduate students from a variety of Boston area institutions.

The Cardiovascular Research Center has two locations, one in the Charlestown Navy Yard and the other on the main campus’s Charles River Plaza complex in the Richard Simches Research Center.

Both the Simches and Navy Yard sites offer state of the art facilities, including tissue culture rooms, warm and cold rooms, histology rooms, autoclave facilities, hot labs, scope rooms and conference rooms. The Navy Yard lab has a topnotch zebrafish facility that is utilized by many scientists both inside and outside the Center, and a transgenic mouse core for both knock-ins and knock-outs. The Navy Yard facilities also contain echocardiogram equipment, specialized microscopes equipped with video capability for making movies, as well as a confocal microscope available to the Center researchers. The Simches lab houses the CVRC Stem Cell Biology + Therapy program, including a dedicated facility for human ES cell based technology, run by Dr. Chad Cowan, and future plans for high throughput screening facility to allow chemical screening in ESX cell based systems. Other cores available to researchers include a Cell Sorting and Flow Cytometry lab and a DNA sequencing core.

The Cardiology Laboratory for Integrative Physiology & Imaging lab is dedicated to large animal studies. An in house interventional cardiologist specializing in large animals performs the surgeries. In addition there are technicians that assist in the daily operations of the lab and can assist in experiment design and project implementation. This lab specializes in large animal imaging, CAT scans and catheter base manipulations. There is also an MRI imaging facility housed in the lab.

http://www2.massgeneral.org/cvrc/about.html

Genomics and Cardiovascular Medicine @MGH

Translational Medicine: Genomics and Proteomics @MGH The goal of the Translational Medicine Program is to harness the rich clinical cardiovascular population at the Massachusetts General Hospital to identify and validate novel genomic determinants of cardiovascular disease. Our goal is not to capture the entire cohort of cardiovascular patients presenting to Massachusetts General Hospital, but rather to focus our efforts on extremely well-phenotyped human models that are unique to cardiovascular disease. Of particular interest are “perturbational” studies in humans (e.g., cardiac exercise testing) that elicit robust phenotypes in affected individuals to serve as the springboard for analyses that span from genomics to proteomics and biochemical profiling. The Translational Medicine Program will involve a multidisciplinary group of investigators who contribute expertise in cardiovascular basic science, clinical cardiology, genetic/genomic epidemiology, bioinformatics, imaging, pathology, as well as clinical chemistry and mass spectrometry. While the Program in Translational Medicine will be physically located at the Massachusetts General Hospital Main Campus, the effort will leverage ongoing interdisciplinary collaborations with investigators at the Framingham Heart Study, the Broad Institute of M.I.T., Harvard University, and Harvard Medical School. Our goals are to:• Identify specific unmet needs in cardiovascular biomarker and pathway discovery (e.g., genomic markers of subclinical premature coronary artery disease, serum biomarkers of myocardial ischemia)• Match cutting-edge technologies with our unique patient cohorts for “first in man” studies• Establish the infrastructure necessary to phenotype patients with the targeted condition (from plasma samples, RNA, DNA, imaging, etc.) and enroll sufficiently sized cohort(s) with the requisite power to validate novel biomarkers.• Establish scientifically high priority research projects to target for independent funding.• Ultimately, develop novel therapeutic interventions.While efforts in translational investigation are already underway, this program will identify synergies between ongoing studies and catalyze new opportunities. Several of the ongoing projects that are anticipated to serve as cornerstones of this effort include:Proteomics and Metabolomics Studies (PI: Gerszten , Wang) Recent advances in proteomic and metabolic profiling technologies have enhanced the feasibility of high throughput patient screening for the diagnosis of disease states. Small biochemicals and proteins are the end result of the entire chain of regulatory changes that occur in response to physiological stressors, disease processes, or drug therapy. In addition to serving as biomarkers, both circulating metabolites and proteins participate as regulatory signals, such as in the control of blood pressure. Our ongoing studies have helped pioneer the application of novel mass spectrometry and liquid chromatography techniques to plasma analysis. In parallel with the profiling efforts, we have developed statistical software for functional pathway trend analysis and used it to demonstrate significant coordinate changes in specific pathways. Such analysis allows us to gain insight into the functionally relevant cellular mechanisms contributing to disease pathways and increases the likelihood that prospective biomarkers will be validated in other patient cohorts. Support for this effort would be synergistic with ongoing funding, including the recent appointment and support for Dr. Gerszten to lead a metabolomics initiative at the Broad Institute.Cardiovascular Genetics and Genomics Studies (PIs: Kathiresan, Newton-Cheh,Wang, and O’Donnell) Through the Human Genome Project and the International Haplotype Map project, researchers now have available the complete human genome sequence, a nearly complete set of common single nucleotide polymorphisms (SNPs), and a map of the patterns of correlation (“linkage disequilibrium”) among SNPs. Research on a large-scale is now possible to define associations of common, complex human cardiovascular diseases —such as myocardial infarction and sudden cardiac death—with genetic variants using candidate gene and genome-wide association studies, gene sequencing, and family-based linkage studies. Specific diseases and traits being studied by CVRC researchers include early-onset myocardial infarction, sudden cardiac death, blood lipids, blood pressure, electrocardiographic QT interval and blood hemostatic factor levels. These studies draw clinical material from the Massachusetts General Hospital and from collaborations with population-based epidemiologic cohorts such as the Framingham Heart Study. Like the metabolomics/proteomics work, these efforts build on the technologic and scientific expertise at the Broad Institute. Specifically, CVRC researchers leverage the Broad Institute’s expertise in large-scale genotyping, genomics, and statistical genetics. The collaboration between the Massachusetts General Hospital, the Framingham Heart Study, and the Broad Institute brings together resources that are unique to each institution to identify genes related to complex cardiovascular traits and to ultimately impact human health.Chemical Biology Program (PIs: Peterson and Shaw) Dr. Peterson’s group has championed the zebrafish as a tool for drug discovery. The zebrafish has become a widely used model organism because of its fecundity, its morphological and physiological similarity to mammals, the existence of many genomic tools and the ease with which large, phenotype-based screens can be performed. Because of these attributes, the zebrafish also provides opportunities to accelerate the process of drug discovery. By combining the scale and throughput of in vitro screens with the physiological complexity of animal studies, the zebrafish promises to contribute to several aspects of the drug development process, including target identification, disease modeling, lead discovery and toxicology. The Program in Translational Medicine will specifically support efforts to test novel pro-angiogenic factors (discovered as suppressors of the “gridlock” phenotype in zebrafish) on human cells such as circulating endothelial precursors.Dr. Shaw’s group is studying the cellular effects of human disease mutations in patient samples, by perturbing cells with a panel of thousands of drugs, and asking whether mutant versus wild-type cells react differently to a given biochemical (reminiscent of a genetic interaction screen). Dr. Shaw has demonstrated the feasibility of this approach using lymphoblast cell lines from a family affected by a monogenic form of diabetes (MODY1), and shown that glucocorticoid signaling differs between affected vs. unaffected patients. Because his studies incorporate the use of FDA-approved drugs, he can quickly identify both potentially “druggable” disease pathways as well as novel therapeutic agents. Further validation of these efforts in other monogenic disorders, such as LDL-receptor deficient patients is planned next. Ultimately this work will be extended to studies in complex genetic diseases.Director: Rob Gerszten, MDPrincipal Investigators: • Farouc Jaffer, MD, PhD • Sekar Kathiresan, MD • Chris Newton-Cheh, MD, MPH • Randall Peterson, PhD • Stanley Shaw, MD, PhD • Thomas Wang, MD

Genetic Basis of Cardiomyopathy

Original gene identification for Arrhythmogenic Right Ventricular Dysplasia/Cardiomyopathy, Autosomal Dominant

McNally E, MacLeod H, Dellefave L. Arrhythmogenic Right Ventricular Dysplasia/Cardiomyopathy, Autosomal Dominant. 2005 Apr 18 [Updated 2009 Oct 13]. In: Pagon RA, Bird TD, Dolan CR, et al., editors. GeneReviews™ [Internet]. Seattle (WA): University of Washington, Seattle; 1993-.

Summary

Disease characteristics. Autosomal dominant arrhythmogenic right ventricular dysplasia/cardiomyopathy (ARVD/C) is characterized by progressive fibrofatty replacement of the myocardium that predisposes to ventricular tachycardia and sudden death in young individuals and athletes. It primarily affects the right ventricle; with time, it may also involve the left ventricle. The presentation of disease is highly variable even within families, and affected individuals may not meet established clinical criteria. The mean age at diagnosis is 31 years (±13; range: 4-64 years).

Available from:

http://www.ncbi.nlm.nih.gov/books/NBK1131/

Pan Cardiomyopathy Panel

@the Center for Personalized Genetic Medicine of Partners HealthCare and Harvard Medical School

The Pan Cardiomyopathy (PCM) Panel contains 51 cardiomyopathy genes including Titin (TTN), which encodes the largest human protein. This panel covers genes associated with HCM, DCM, RCM, LVNC, ARVC and CPVT and uses a combination of Next Generation Sequencing technology and conventional Sanger sequencing.

For illustrative reference, click to see one of our images or diagrams. Genes on Pan Cardiomyopathy Panels, Disease-Gene Associations, Gene Cellular Location.

Please select on the disease to read more: HCM,DCM, ARVC/CPVT, or LVNC.

Current Tests:

Pan Cardiomyopathy Panel – 51 genes

• HCM Panel – 18 genes^§
• DCM Panel – 27 genes^§
• ARVC/CPVT Panel – 8 genes^§
• LVNC Panel – 10 genes^§

^§Optional reflex to remaining genes

Storage Cardiomyopathy – please select a disease to learn more

• Unexplained Hypertrophy Panel (LAMP2 & PRKAG2)
• Fabry Disease – GLA Gene Sequencing
• Transthyretin Amyloidosis – TTR Gene Sequencing

For any other single gene tests, please call the LMM at 617-768-8499 or lmm@partners.org.

For Variant Classification Rules – Lab for Molecular Medicine (LMM)

http://pcpgm.partners.org/sites/default/files/LMM/Resources/LMM_VariantClassification_05.26.11.pdf 

For LMM Reference Sequences

http://pcpgm.partners.org/sites/default/files/LMM/Resources/LMMRefSeq-2.20.13.pdf

When to order which panel?

The Pan Cardiomyopathy panel may shorten the “testing odyssey” when a clear diagnosis has not been established. However, because many genes have not yet been associated with more than one cardiomyopathy, interpretation of novel variants may be more difficult when they are found in a gene that is not (yet) known to cause the patient’s cardiomyopathy. Please note: We are expecting an increase in “variants of unknown significance” and recommend careful consideration of the following factors when deciding whether to order the full panel or the disease specific sub-panels. The Pan Cardiomyopathy Panel may be best suited for patients who have already exhausted current testing options or whose clinical diagnosis is not yet clear. It may also be a good first line test for patients who have a family history where the number of living affected relatives would allow segregation analysis to establish or rule out pathogenicity for “variants of unknown significance (VUSs)”. Finally, the patient’s personal preferences should be considered as VUSs can cause anxiety.

Disease Backgrounds

Hypertrophic cardiomyopathy (HCM) is characterized by unexplained left ventricular hypertrophy (LVH) in a non-dilated ventricle. With a prevalence estimated to be ~1/500 in the general population, HCM is the most common monogenic cardiac disorder. To date, over 1000 variants have been identified in genes causative of HCM, most of which affect the sarcomere, the contractile unit of the cardiac muscle. In addition, defects in genes involved in storage diseases, such as LAMP2, PRKAG2 and GLA, typically cause systemic disease but may also result in predominant cardiac manifestations, which can mimic hypertrophic cardiomyopathy (HCM). For additional information about HCM, please visit GeneReviews. 

Dilated cardiomyopathy (DCM) is characterized by ventricular chamber enlargement and systolic dysfunction with normal left ventricular wall thickness. The estimated prevalence of DCM is 1/2,500 and about 20-35% of cases have a family history showing a predominantly autosomal mode of inheritance. To date, over 40 genes have been demonstrated to cause DCM, encoding proteins involved in the sarcomere, Z-disk, nuclear lamina, intermediate filaments and the dystrophin-associated glycoprotein complex. Variants in some genes cause additional abnormalities: LMNA variants are frequently found in DCM that occurs with progressive conduction system disease. Variants in the TAZ gene cause Barth syndrome, an X-linked cardioskeletal myopathy in infants. In addition, variants in several genes (including LMNA, DES, SGCD, TCAP and EMD) can cause DCM in conjunction with skeletal myopathy.  For additional information about DCM, please visit GeneReviews.

Arrhythmogenic Right Ventricular Cardiomyopathy (ARVC) is estimated to affect approximately 1/5,000 individuals in the general population, about half of which have a family history. The disease is characterized by replacement of myocytes by fatty or fibrofatty tissue, mainly in the right ventricle. The resulting manifestations are broad and include ventricular tachyarrhythmias and sudden death in young individuals and athletes. ARVC is typically inherited in an autosomal dominant fashion with incomplete penetrance and variable expressivity and to date, 5 ARVC genes (DSP, DSC2, DSG2, PKP2, TMEM43) have been identified, all but one (TMEM43) encode components of the desmosome. For more information about ARVC, please visit GeneReviews.

Catecholaminergic polymorphic ventricular tachycardia (CPVT) is typically characterized by exercise induced syncope due to ventricular tachycardia in individuals without structural heart disease. Two CPVT genes are known to date (RYR2 – autosomal dominant; CASQ2 – autosomal recessive). For more information about CPVT, please visit GeneReviews.

Left ventricular noncompaction (LVNC) has recently been established as a specific type of cardiomyopathy and is characterized by a spongy appearance of the left ventricular myocardium, resulting from an arrest in normal cardiac development. LVNC can be found in isolation or in association with other cardiomyopathies (HCM, DCM) as well as congenital cardiac abnormalities. The population prevalence is not known but LVNC is reported in ~0.014% of echocardiograms. LVNC is often familial and the genetic spectrum is beginning to emerge although it is not yet well defined. LVNC genes reported to date include ACTC, DTNA, LDB3, MYBPC3, MYH7, TAZ, and TNNT2 (Montserrat 2007, Klaassen 2008; Kaneda 2007, Zaragoza 2007; reviewed in: Maron 2006, Finsterer 2009). For more information about LVNC, please visit OMIM.org.

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For any additional information, please contact us at 617-768-8500 or lmm@partners.org.

SOURCE:

http://pcpgm.partners.org/lmm/tests/cardiomyopathy

Genes: 51 genesMethodology: A combination of next generation sequencing technology and Sanger sequencingAnalytical Sensitivity:Substitutions: 100% (95%CI=98.5-100)Small InDels: 95% (95%CI=83-99)Clinical Sensitivity: See below.Additional Links: Genetic Basis of Cardiomyopathy Booklet http://pcpgm.partners.org/sites/default/files/LMM/Resources/LMM_Cardiomyopathy_Booklet_2011.pdf

Cardiomyopathy

	Price	TAT	CPT Codes
Pan Cardiomyopathy Panel (51 Genes)  –  lmPCM-pnlAv2_L
	$3,950	8-12 wks	81479
HCM Panel (18 Genes)  –  lmPCM-pnlB_L
	$3,200	8-12 wks	81479
DCM Panel (27 Genes)  –  lmPCM-pnlCv2_L
	$3,850	8-12 wks	81479
ARVC/CPVT Panel (8 Genes)  –  lmPCM-pnlD_L
	$3,000	8-12 wks	81479
LVNC Panel (10 Genes)  –  lmPCM-pnlE_L
	$3,200	8-12 wks	81479
Remaining Pan Cardiomyopathy Genes (HCM Reflex)  –  lmPCM-pnlFv2_L
	$2,000	8-12 wks	81479
Remaining Pan Cardiomyopathy Genes (DCM Reflex)  –  lmPCM-pnlGv2_L
	$2,000	8-12 wks	81479
Remaining Pan Cardiomyopathy Genes (ARVC/CPVT Reflex)  –  lmPCM-pnlHv2_L
	$2,000	8-12 wks	81479
Remaining Pan Cardiomyopathy Genes (LVNC Reflex)  –  lmPCM-pnlIv2_L
	$2,000	8-12 wks	81479
Remaining Pan Cardiomyopathy Genes (Version 1 Reflex) – lmPCM-pnlL_L
	$750	8-12 wks	81479
Unexplained Cardiac Hypertrophy Panel (2 genes)  –  lmUCH-pnlA_L
	$1,500	3 wks	81479
ABCC9 Gene Sequencing  –  lmABCC9-a_L
	$1,800	3 wks	81479
ACTC Gene Sequencing  –  lmACTC-a_L
	$700	3 wks	81405
ACTN2 Gene Sequencing  –  lmACTN2-a_L
	$1,500	3 wks	81479
CSRP3 Gene Sequencing  –  lmCSRP3-a_L
	$900	3 wks	81479
CTF1 Gene Sequencing  –  lmCTF1-a_L
	$800	3 wks	81479
DES Gene Sequencing  –  lmDES-a_L
	$750	3 wks	81479
DSC2 Gene Sequencing  –  lmDSC2-a_L
	$1,150	3 wks	81479
DSG2 Gene Sequencing  –  lmDSG2-a_L
	$1,075	3 wks	81479
DSP Gene Sequencing  –  lmDSP-a_L
	$1,700	3 wks	81479
DTNA Gene Sequencing – lmDTNA-a_L
	$1,500	5-6 wks	81479
EMD Gene Sequencing  –  lmEMD-a_L
	$450	3 wks	81479
GLA Gene Sequencing  –  lmGLA-a_L
	$700	3 wks	81405
LAMP2 Gene Sequencing  –  lmLAMP2-a_L
	$700	3 wks	81405
LDB3 Gene Sequencing  –  lmLDB3-a_L
	$950	3 wks	81406
LMNA Gene Sequencing  –  lmLMNA-a_L
	$700	3 wks	81406
MYBPC3 Gene Sequencing  –  lmMYBPC3-a_L
	$1,500	3 wks	81407
MYH7 Gene Sequencing  –  lmMYH7-a_L
	$1,700	3 wks	81407
MYL2 Gene Sequencing  –  lmMYL2-a_L
	$700	3 wks	81405
MYL3 Gene Sequencing  –  lmMYL3-a_L
	$700	3 wks	81405
PKP2 Gene Sequencing  –  lmPKP2-a_L
	$1,500	3 wks	81479
PLN Gene Sequencing  –  lmPLN-a_L
	$400	3 wks	81479
PRKAG2 Gene Sequencing  –  lmPRKAG2-a_L
	$1,000	3 wks	81406
SCN5A Gene Sequencing – lmSCN5A-a_L
	$1,700	5-6 wks	81407
SGCD Gene Sequencing  –  lmSGCD-a_L
	$1,100	3 wks	81405
TAZ Gene Sequencing  –  lmTAZ-a_L
	$700	3 wks	81406
TCAP Gene Sequencing  –  lmTCAP-a_L
	$700	3 wks	81479
TMEM43 Gene Sequencing  –  lmTMEM43-a_L
	$700	3 wks	81479
TNNI3 Gene Sequencing  –  lmTNNI3-a_L
	$700	3 wks	81405
TNNT2 Gene Sequencing  –  lmTNNT2-a_L
	$1,000	3 wks	81406
TPM1 Gene Sequencing  –  lmTPM1-a_L
	$700	3 wks	81405
TTN Gene Sequencing  –  lmTTN-a_L
	$3,000	8-12 wks	81479
TTR Gene Sequencing – lmTTR-a_L
	$485	3 wks	81404
VCL Gene Sequencing  –  lmVCL-a_L
	$1,500	3 wks	81479

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Congenital Heart Disease/Defects

	Price	TAT	CPT Codes
Congenital Heart Disease Panel A (GATA4, NKX2-5, JAG1)  –  lmCHD-pnlA_L
	$1,300	4 wks	81479
ELN (Elastin) Gene Sequencing  –  lmELN-a_L
	$1,300	4 wks	81479
GATA4 Gene Sequencing  –  lmGATA4-a_L
	$750	3 wks	81479
JAG1 Gene Sequencing  –  lmJAG1-a_L
	$1,100	3 wks	81407
NKX2-5 Gene Sequencing  –  lmNKX2-5-a_L
	$600	3 wks	81479

SOURCE:

http://pcpgm.partners.org/lmm/ordering/prices-CPTcodes_revised#Cardio_Header

Lakdawala NK, Funke BH, Baxter S, Cirino A, Roberts AE, Judge DP, Johnson N, Mendelsohn NJ, Morel C, Care M, Chung WK, Jones C, Psychogios A, Duffy E, Rehm HL, White E, Seidman JG, Seidman CE, Ho CY.  Genetic Testing for Dilated Cardiomyopathy in Clinical Practice. J Card Fail 2012, In press.

Neri PM, Pollard SE, Volk LA, Newmark L, Varugheese M, Baxter S, Aronson SJ, Rehm HL, Bates DW. Usability of a Novel Clinician Interface for Genetic Results. J Biomed Informatics. 2012. In press.

Genomics @Brigham and Women’s Hospital and Harvard Medical School  

The goal of The Cardiovascular Genome Unit (TCGU) is to foster interdisciplinary interaction between clinical investigators and scientists to comprehensively explore the era of human genomic research. In particular, our aim would be to identify, categorize and characterize the genes and genetic pathways of the vascular and cardiac tissues of the cardiovascular system during oncogenesis, normal function and the pathogenesis of cardiovascular diseases.

    The Cardiovascular Genome Unit is responsible for indexing gene expression, profiling gene expression, identifying SNPs and generation of protein profiles from a wide variety of tissues representative of various anatomical regions as well as developmental and pathological stages in the cardiovascular system. This information resource emphasizes on cardiovascular disease and should aid in the discovery of disease causing genes, diagnostic and prognostic markers, drug targets, protein therapeutics and improved therapeutic strategies for cardiovascular disease.

    Our laboratory is the curator of a genome-based resource for molecular cardiovascular medicine consisting of over 52,000 ESTs generated from nine heart and artery libraries, representing different developmental stages and disease states (Liew et al 1994, Hwang et al 1997, Dempsey et al 2000). 

    This comprehensive catalogue of cardiac and hematopoietic genes is an unmined molecular resource for microarray analysis and a genetic gold mine for the discovery of genes that may play a role in cardiovascular disorders. In order to exploit this raw data, we propose to develop cDNA microarrays consisting of known and novel sequence-tagged genes. The arrayed clones provide an excellent substrate for expression profiling of cardiovascular disease, for example heart failure or ischemic heart disease, leading the potential discovery of diagnostic as well as prognostic markers.

    In order to accomplish the goals of the center, several cutting edge technologies are being employed.

The human cardiovascular research component of our labs.

One of the most efficient and effective strategies for the identification genes is the Expressed Sequence Tag (EST) approach.  In this approach, randomly selected cDNA clones are subjected to automated sequencing (PCR or plasmid templates) to generate a partial sequence from either the 5’- or 3’-end termed an EST.  This method allows for large-scale gene tagging and indexing from any tissue- or cell-type of interest.  A comprehensive cardiovascular gene index could be developed using a variety of cardiovascular tissues representing different anatomical, developmental and pathological states.

Comparing transcript profiles between different development or disease states is a powerful way to gain insight into the genetic changes underlying these events.  This is especially important when looking at complex systems, such as in development or disease (e.g. hypertension or atherosclerosis).  There are several unique approaches to this problem, several of which are:

a)      EST profile Comparison– After the production of a significant number of ESTs from 2 or more libraries, the frequencies of ESTs can be compared to identify those genes which are differentially expressed.     However, normalized or subtracted cDNA libraries cannot be used for this and this method is most effective for finding large differences in expression.

 

b)      cDNA Microarray Hybridization– The recent introduction of the cDNA microarray, a technology capable of analyzing the expression of thousands of genes simultaneously in a single experiment, may  provide one of the best ways to delineate gene expression patterns.  In the cDNA microarray, cDNA clones are spotted onto a glass slide matrix and hybridized with fluorescently labeled cDNA probes derived from total RNA pools of test and reference cells or tissues.  The signal intensity for each probe is quantified and any differences between the two samples becomes readily apparent.  Thus, the genetic changes underlying the phenotype of study can be identified at the level of a single gene. 

 

c)      Identification of Single Nucleotide Polymorphisms– SNPs are single-base heritable variations in the genome which occur once in approximately 1000 bases in the human genome and occur at a frequency of >1% in the human population.  SNPs provide an important genetic resource useful for disease gene discovery. including the identification of disease susceptible genes.  SNPs can be identified through comparison of EST sequences, DNA hybridization strategies and direct sequencing of genomic DNA.  The generation of a SNP database for genes expressed in the cardiovascular system will provide a valuable resource to aid in disease gene discovery. 

 

d)      Quantitative determination of expressed genes– the up- and down- regulated genes are crucial to the phenotypic expression of any given cell.  The frequency of gene expressed in development or disease state can be obtained from an EST approach using cDNA libraries as well as its intensity detected using microarrays.  Such results can be verified through RT-PCR analysis from the tissue samples.  A high through-put analysis of 96 samples can be performed by real-time PCR analyses.

Using our 10,000 element “CardioChip”, we elucidated over 100 differentially expressed genes in end-stage heart failure resulting from dilated cardiomyopathy. The results were published in

Am J Pathol. 2002 June; 160(6): 2035–2043.

Global Gene Expression Profiling of End-Stage Dilated Cardiomyopathy Using a Human Cardiovascular-Based cDNA Microarray

J. David Barrans,^*† Paul D. Allen,^‡ Dimitrios Stamatiou,^* Victor J. Dzau,^* and Choong-Chin Liew^*†

From Cardiovascular Genome Unit^*, the Department of Medicine, and the Department of Anesthesiology,^‡Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts; and the Department of Laboratory Medicine and Pathobiology,^†University of Toronto, Toronto, Ontario, Canada

Abstract

To obtain a genomic portrait of heart failure derived from end-stage dilated cardiomyopathy (DCM), we explored expression analysis using the CardioChip, a nonredundant 10,848-element human cardiovascular-based expressed sequence tag glass slide cDNA microarray constructed in-house. RNA was extracted from the left ventricular free wall of seven patients undergoing transplantation, and five nonfailing heart samples. Cy3- and Cy5-labeled (and reverse dye-labeled) cDNA probes were synthesized from individual diseased or nonfailing adult heart RNA, and hybridized to the array. More than 100 transcripts were consistently differentially expressed in DCM >1.5-fold (versus pooled nonfailing heart,P < 0.05). Atrial natriuretic peptide was found to be up-regulated in DCM (19-fold compared to nonfailing, P < 0.05), as well as numerous sarcomeric and cytoskeletal proteins (eg, cardiac troponin, tropomyosin), stress response proteins (eg, HSP 40, HSP 70), and transcription/translation regulators (eg, CCAAT box binding factor, eIF-1AY). Down-regulation was most prominently observed with cell-signaling channels and mediators, particularly those involved in Ca²⁺ pathways (Ca²⁺/calmodulin-dependent kinase, inositol 1,4,5-trisphosphate receptor, SERCA). Most intriguing was the co-expression of several novel, cardiac-enriched expressed sequence tags. Quantitative real-time reverse transcriptase-polymerase chain reaction of a selection of these clones verified expression. Our study provides a preliminary molecular profile of DCM using the largest human heart-specific cDNA microarray to date.

Dilated cardiomyopathy (DCM) is characterized clinically by left ventricular dilatation, wall thinning, and homogeneous dysfunction of the myocardium leading to congestive heart failure. Genetically, DCM seems to evolve through primary mutations in the genes of the sarcomeric proteins. ¹ However, recent evidence suggests that, despite distinct pathways leading to divergent endpoint phenotypes of each disease, there may exist some overlapping genetic modifiers leading to a conversion of one to the other. ² How this occurs is under question; to understand this, a better knowledge of the molecular pathways and intermediary regulators is required.

Global analysis of gene expression has proven to be a fruitful means of examining the overall molecular portrait of a particular event as well as seeking out novel candidate transcripts that may play a role in formulating the phenotype or genotype of interest. By using this strategy, multiple genes and pathways in complex disorders can be visualized simultaneously, allowing for a feasible platform from which to investigate new and interesting genes. Using expressed sequence tag technology, our laboratory has generated a compendium of genes expressed in the human cardiovascular system, with the ultimate goal of assembling the intricacies of development and of disease, particularly the pathways leading to heart failure. ³ Through a computer-based in silico strategy, we have been able to identify—in a large scale—both known and previously unsuspected genetic modulators contributing to the growth of the myocardium from fetal through adult, and from normal to a perturbed hypertrophic phenotype. In contrast a gene-by-gene approach in elucidating the genes and mechanisms involved is time-consuming and cumbersome.

Recently, microarray technology has been used as a means of large-scale screening of vast numbers of genes—if not whole genomes—that possess differential expression in two distinct conditions. Although new and exciting developments have arisen in such fields as cancer ⁴ and yeast, ⁵ advances in understanding the complexity of cardiovascular disease, ⁶ specifically DCM, have been limited. One recent study examined gene expression in two failing hearts using oligo-based arrays. ⁷ Although the GeneChip^® (Affymetrix, Santa Clara, CA) offers a carefully controlled systematic method of analysis, its current lack of user flexibility in its design hinders novel gene discovery currently available in tissue-specific arrays. Our laboratory has taken advantage of our vast previously acquired resources and has constructed what we believe to be the first ever custom-made cardiovascular-based cDNA microarray, which we term the “CardioChip.” ⁸ Its practicality and flexibility has allowed us to conceptualize the molecular events surrounding end-stage heart failure.

This report describes the most informative cDNA microarray-based analysis of end-stage heart failure derived from DCM currently available. Although we believe we have effectively demonstrated reproducibility and reliability of our technology (both for the entire array and for a selection of genes located on it), a larger n from our population would enhance the validity of our conclusions. Certainly, there exists no homogeneous heart failure genotype, especially among only seven DCM patients. Nonetheless, we have demonstrated a common expression pattern among our set of samples, from both microarray and QRT-PCR analysis. We are also limited by the genes (both in number and identity) present on this array. Although we are currently unable to spot every gene and gene cluster on our CardioChip, we have tried to draw from a diverse assortment of genes and gene pathways, both known and unknown. It must be emphasized that this investigation is not exhaustive; by no means does it attempt to fully characterize the molecular basis of heart failure. Its intention is to provide a preliminary portrait of global gene expression in complex cardiovascular disease using cDNA microarray and QRT-PCR technology, and to highlight the effectiveness of our ever-evolving platform for gene discovery. With even more patient samples and a CardioChip toward completeness, we will be in a better position to reap the important benefits from this initial work and expand our body of knowledge.

Am J Pathol. 2002 June; 160(6): 2035–2043.

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Padova; 1988. P 11-17.

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Dempsey A, Liew CC. Genes involved in normal cardiac development. In: Sheridan DJ, editor. Left Ventricular Hypertrophy. London: Churchill Communications Europe Ltd; 1998: p. 61-70.

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Liew, CC. Expressed Sequence Tags. In: Encyclopedia of Molecular Medicine, Ed: T. Creighton, John Wiley and Son, New York. 2001

Hwang J-J, Dzau V and Liew CC. Genomics and thePathophysiology of Heart Failure. In: Current Cardiology Reports; Current Science Inc; 2001: Vol 3: 198-207.

 http://issuu.com/pcpgm_lmm/docs/cmbig_lmmcardio/16

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Genetic Basis of Complex Human Diseases: Dan Koboldt’s Advice to Next-Generation Sequencing Neophytes

Posted in Biological Networks, Gene Regulation and Evolution, Chemical Genetics, Computational Biology/Systems and Bioinformatics, Genome Biology, Genomic Testing: Methodology for Diagnosis, Medical and Population Genetics, Personalized and Precision Medicine & Genomic Research, Population Health Management, Genetics & Pharmaceutical, Proteomics, Statistical Methods for Research Evaluation, Technology Transfer: Biotech and Pharmaceutical, tagged Biology, DNA Sequencing, Genome Institute, Illumina, Washington University in St. Louis, Washington University School of Medicine on February 20, 2013| 1 Comment »

Reporter: Aviva Lev-Ari, PhD, RN

Word Cloud by Daniel Menzin

UPDATED 3/27/2013

The Exome is Not Enough

March 27, 2013

Dan Koboldt at MassGenomics explains why exome sequencing often fails to identify causal variants, even in Mendelian disorders — “the very plausible possibility that a noncoding functional variant is responsible.”

Koboldt, the analysis manager in the human genetics group at the Genome Institute at Washington University, says that researchers shouldn’t overlook the importance of noncoding functional variants, which require a suite of technologies to detect, including RNA-seq, ChiP-seq, DNAse sequencing and footprinting, bisulfite sequencing, and chromosome conformation capture.

“These types of experiments generate a wealth of data about regulatory activity in genomes,” he says. “While studying each of these independently is certainly informative, integrative analysis will be required to elucidate how all of these different regulatory mechanisms work together.”

While this effort will require “robust statistical models, substantial computing resources, and productive collaboration among research groups, the end result “will be a far more complete understanding of how the genome works,” he says.

 

SOURCE:

http://www.genomeweb.com//node/1209591?hq_e=el&hq_m=1544811&hq_l=3&hq_v=e1df6f3681

Dan Koboldt works as a staff scientist in the Human Genetics group of the Genome Institute at Washington University in St. Louis. There, he works with scientists, physicians, programmers, and data analysts to understand the genetic basis of complex human diseases such as cancer, vision disorders, and metabolic syndromes through next-gen sequencing analysis. He received bachelor’s degrees in Computer Science and French from the University of Missouri-Columbia, and a master’s degree in Biology fromWashington University.

Dan has worked in the field of human genetics since 2003, when he joined the lab of Raymond E. Miller, which played a role in the International HapMap Project and later the genetic map of C. briggsae, a model organism related to C. elegans.

Disclaimer: The views expressed on this site, including blog posts and static pages, do not necessarily reflect the opinions of the Genome Institute at Washington University, the Washington University School of Medicine, or Washington University in St. Louis.

Before diving in with both feet, next-generation sequencing neophytes might want to take a gander at a post by Dan Koboldt at MassGenomics where he describes his 10 commandments for good next-gen sequencing.

In his post, Koboldt breaks up his instructions into four categories: analysis, publications, data sharing and submissions, and research ethics and cost.

His list includes some oft repeated warnings. For example, he cautions against reinventing the wheel when it comes to developing analysis software, and, for pity’s sake, don’t invent any more words that end in “ome” or “omics.”

Some other no-no’s, according to Koboldt, include publishing results before they’ve been vetted properly, testing new methods on simulated data only, and taking “unfair advantage of submitted data.”

He also admonishes newcomers to think a little bit about the cost of analysis without which “your sequencing data, your $1,000 genome, is about as useful as a chocolate teapot,” and to have a care for the privacy of their study participants’ samples and data.

 http://www.genomeweb.com//node/1193731?hq_e=el&hq_m=1504568&hq_l=3&hq_v=df86f8764d

Ten Commandments for Next-Gen Sequencing

Just as the reach of next-generation sequencing has continued to grow — in both research and clinical realms — so too has the community of NGS users.  Some have been around since the early days. The days of 454 and Solexa sequencing. Since then, the field has matured at an astonishing pace. Many standards were established to help everyone make sense of this flood of data. The recent democratization of sequencing has made next-gen sequencing available to just about anyone.

And yet, there have been growing pains. With great power comes great responsibility. To help some of the newcomers into the field, I’ve drafted these ten commandments for next-gen sequencing.

NGS Analysis

1. Thou shalt not reinvent the wheel. In spite of rapid technological advances, NGS is not a new field. Most of the current “workhorse” technologies have been on the market for a couple of years or more. As such, we have a plethora of short read aligners, de novo assemblers, variant callers, and other tools already. Even so, there is a great temptation for bioinformaticians to write their own “custom scripts” to perform these tasks. There’s a new “Applications Note” every day with some tool that claims to do something new or better.

Can you really write an aligner that’s better than BWA? More importantly, do we need one? Unless you have some compelling reason to develop something new (as we did when we developed SomaticSniper and VarScan), take advantage of what’s already out there.

2. Thou shalt not coin any new term ending with “ome” or “omics”. We have enough of these already, to the point where it’s getting ridiculous. Genome, transcriptome, and proteome are obvious applications of this nomenclature. Epigenome, sure. But the metabolome, interactome, and various other “ome” words are starting to detract from the naming system. The ones we need have already been coined. Don’t give in to the temptation.

3. Thou shall follow thy field’s conventions for jargon. Technical terms, acronyms, and abbreviations are inherent to research. We need them both for precision and brevity. When we get into trouble is when people feel the need to create their own acronyms when a suitable one already exists. Is there a significant difference between next-generation sequencing (NGS), high-throughput sequencing (HTS), and massively parallel sequencing (MPS)?

Widely accepted terms provide something of a standard, and they should be used whenever possible. Insertion/deletion variants are indels, not InDels or INDELs DIPs. Structural variants are SVs, not SVars or GVs. We don’t need any more acronyms!

NGS Publications

These commandments address behaviors that get on my nerves, both as a blogger and a peer reviewer.

4. Thou shalt not publish by press release. This is a disturbing trend that seems to happen more and more frequently in our field: the announcement of “discoveries” before they have been accepted for publication. Peer review is the required vetting process for scientific research. Yes, it takes time and yes, your competitors are probably on the verge of the same discovery. That doesn’t mean you get to skip ahead and claim credit by putting out a press release.

There are already examples of how this can come back to bite you. When the reviewers trash your manuscript, or (gasp) you learn that a mistake was made, it looks bad. It reflects poorly on the researchers and the institution, both in the field and in the eyes of the public.

5. Thou shalt not rely only on simulated data. Often when I read a paper on a new method or algorithm, they showcase it using simulated data. This often serves a noble purpose, such as knowing the “correct” answer and demonstrating that your approach can find it. Even so, you’d better apply it to some real data too. Simulations simply can’t replicate the true randomness of nature and the crap-that-can-go-wrong reality of next-gen sequencing. There’s plenty of freely available data out there; go get some of it.

6. Thou shalt obtain enough samples. One consequence of the rapid growth of our field (and accompanying drop in sequencing costs) is that small sample numbers no longer impress anyone. They don’t impress me, and they certainly don’t impress the statisticians upstairs. The novelty of exome or even whole-genome sequencing has long worn off. Now, high-profile studies must back their findings with statistically significant results, and that usually means finding a cohort of hundreds (or thousands) of patients with which to extend your findings.

This new reality may not be entirely bad news, because it surely will foster collaboration between groups that might otherwise not be able to publish individually.

Data Sharing and Submissions

7. Thou shalt withhold no data. With some exceptions, sequencing datasets are meant to be shared. Certain institutions, such as large-scale sequencing centers in the U.S., are mandated by their funding agencies to deposit data generated using public funds on a timely basis following its generation. Since the usual deposition site is dbGaP, this means that IRB approvals and dbGaP certification letters must be in hand before sequencing can begin.

Any researchers who plan to publish their findings based on sequencing datasets will have to submit them to public datasets before publication.This is not optional. It is not “something we should do when we get around to it after the paper goes out.” It is required to reproduce the work, so it should really be done before a manuscript is submitted. Consider this excerpt from Nature‘s publication guidelines:

Data sets must be made freely available to readers from the date of publication, and must be provided to editors and peer-reviewers at submission, for the purposes of evaluating the manuscript.

For the following types of data set, submission to a community-endorsed, public repository is mandatory. Accession numbers must be provided in the paper.

The policies go on to list various types of sequencing data:

• DNA and RNA sequences
• DNA sequencing data (traces for capillary electrophoresis and short reads for next-generation sequencing)
• Deep sequencing data
• Epitopes, functional domains, genetic markers, or haplotypes.

Every journal should have a similar policy; most top-tier journals already do. Editors and referees need to enforce this submission requirement by rejecting any manuscripts that do not include the submission accession numbers.

8. Thou shalt not take unfair advantage of submitted data. Many investigators are concerned about data sharing (especially when mandated upon generation, not publication) from fear of being scooped. This is a valid concern. When you submit your data to a public repository, others can find it and (if they meet the requirements) use it. Personally, I think most of these fears are not justified — I mean, have you ever tried to get data out of dbGaP? The time it takes for someone to find, request, obtain, and use submitted data should allow the producers of the data to write it up.

Large-scale efforts to which substantial resources have been devoted — such as the Cancer Genome Atlas — have additional safeguards in place. Their data use policy states that, for a given cancer type, submitted data can’t be used until the “marker paper” has been published. This is a good rule of thumb for the NGS community, and something that journal editors (and referees) haven’t always enforced.

Just because you can scoop someone doesn’t mean that you should. It’s not only bad karma, but bad for your reputation. Scientists have long memories. They will likely review your manuscript or grant proposal sometime in the future. When that happens, you want to be the person who took the high road.

Research Ethics and Cost

9. Thou shalt not discount the cost of analysis. It’s true that since the advent of NGS technology, the cost of sequencing has plummeted. The cost of analysis, however, has not. And making sense of genomic data — alignment, quality control, variant calling, annotation, interpretation — is a daunting task indeed. It takes computational resources as well as expertise. This infrastructure is not free; in fact, it can be more expensive than the sequencing itself. 

Without analysis, your sequencing data, your $1,000 genome, is about as useful as a chocolate teapot.

10. Thou shalt honor thy patients and their samples. Earlier this month, I wrote about how supposedly anonymous individuals from the CEPH collection were identified using a combination of genetic markers and online databases. It is a simple fact that we can no longer guarantee a sequenced sample’s anonymity. That simple fact, combined with our growing ability to interpret the possible consequences of an individual genome, means a great deal of risk for study volunteers.

We must safeguard the privacy of study participants — and find ways to protect them from privacy violations and/or discrimination — if we want their continued cooperation.

This means obtaining good consent documents and ensuring that they’re all correct before sequencing begins. It also means adhering to the data use policies those consents specify. As I’ve written before, samples are the new commodity in our field. Anyone can rent time on a sequencer. If you don’t make an effort to treat your samples right, someone else will.

Related Posts:

• Science and Science Fiction It occurred to me recently, while refereeing a manuscript, that publication in research is a lot more forgiving than publication in fiction. I’d just read …
• Identifying Samples from Genomic Data In last week’s issue of Science, Melissa Gymrek and colleagues from the lab of Yaniv Erlich (Whitehead) report a method for the triangulation the identity …
• Human Genetics Challenges in an Era of Cheap Sequencing Next-generation sequencing promises to reach unprecedented levels of throughput this year, driving down the cost of sequencing dramatically. Somewhere between the GridION, the Ion Proton, …
• Ethical Challenges for Clinical Genome Sequencing The past decade has been a turbulent one for the ream of genetic and genomic research. Since the completion of the human genome and beginning …

SOURCE:

http://massgenomics.org/2013/02/ngs-commandments.html

Dan Koboldt’s Publications

Bose R, Kavuri SM, Searleman AC, Shen W, Shen D, Koboldt DC, Monsey J, Goel N, Aronson AB, Li S, Ma CX, Ding L, Mardis ER, & Ellis MJ (2013).Activating HER2 mtations in HER2 gene amplification negative breast cancer. Cancer discovery PMID: 23220880

The 1000 Genomes Project Consortium (2012). An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56-65. DOI: 10.1038/nature11632

Cancer Genome Atlas Network (2012). Comprehensive molecular portraits of human breast tumours. Nature, 490 (7418), 61-70 PMID:23000897

Ellis MJ, Ding L, Shen D, Luo J, Suman VJ, Wallis JW, Van Tine BA, Hoog J, Goiffon RJ, Goldstein TC, Ng S, Lin L, Crowder R, Snider J, Ballman K, Weber J, Chen K, Koboldt DC, Kandoth C, Schierding WS, McMichael JF, Miller CA, Lu C, Harris CC, McLellan MD, Wendl MC, DeSchryver K, Allred DC, Esserman L, Unzeitig G, Margenthaler J, Babiera GV, Marcom PK, Guenther JM, Leitch M, Hunt K, Olson J, Tao Y, Maher CA, Fulton LL, Fulton RS, Harrison M, Oberkfell B, Du F, Demeter R, Vickery TL, Elhammali A, Piwnica-Worms H, McDonald S, Watson M, Dooling DJ, Ota D, Chang LW, Bose R, Ley TJ, Piwnica-Worms D, Stuart JM, Wilson RK, & Mardis ER (2012). Whole-genome analysis informs breast cancer response to aromatase inhibition. Nature, 486 (7403), 353-60 PMID: 22722193

Welch JS, Ley TJ, Link DC, Miller CA, Larson DE, Koboldt DC, Wartman LD, Lamprecht TL, Liu F, Xia J, Kandoth C, Fulton RS, McLellan MD, Dooling DJ, Wallis JW, Chen K, Harris CC, Schmidt HK, Kalicki-Veizer JM, Lu C, Zhang Q, Lin L, O’Laughlin MD, McMichael JF, Delehaunty KD, Fulton LA, Magrini VJ, McGrath SD, Demeter RT, Vickery TL, Hundal J, Cook LL, Swift GW, Reed JP, Alldredge PA, Wylie TN, Walker JR, Watson MA, Heath SE, Shannon WD, Varghese N, Nagarajan R, Payton JE, Baty JD, Kulkarni S, Klco JM, Tomasson MH, Westervelt P, Walter MJ, Graubert TA, DiPersio JF, Ding L, Mardis ER, & Wilson RK (2012). The origin and evolution of mutations in acute myeloid leukemia. Cell, 150 (2), 264-78 PMID: 22817890

Cancer Genome Atlas Network (2012). Comprehensive molecular characterization of human colon and rectal cancer. Nature, 487(7407), 330-7 PMID: 22810696

Dees ND, Zhang Q, Kandoth C, Wendl MC, Schierding W, Koboldt DC, Mooney TB, Callaway MB, Dooling D, Mardis ER, Wilson RK, & Ding L (2012). MuSiC: identifying mutational significance in cancer genomes.Genome research, 22 (8), 1589-98 PMID: 22759861

Walter MJ, Shen D, Ding L, Shao J, Koboldt DC, Chen K, Larson DE, McLellan MD, Dooling D, Abbott R, Fulton R, Magrini V, Schmidt H, Kalicki-Veizer J, O’Laughlin M, Fan X, Grillot M, Witowski S, Heath S, Frater JL, Eades W, Tomasson M, Westervelt P, DiPersio JF, Link DC, Mardis ER, Ley TJ, Wilson RK, & Graubert TA (2012). Clonal architecture of secondary acute myeloid leukemia. The New England journal of medicine, 366(12), 1090-8 PMID: 22417201

Matsushita H, Vesely MD, Koboldt DC, Rickert CG, Uppaluri R, Magrini VJ, Arthur CD, White JM, Chen YS, Shea LK, Hundal J, Wendl MC, Demeter R, Wylie T, Allison JP, Smyth MJ, Old LJ, Mardis ER, & Schreiber RD (2012).Cancer exome analysis reveals a T-cell-dependent mechanism of cancer immunoediting. Nature, 482 (7385), 400-4 PMID: 22318521

Koboldt DC, Zhang Q, Larson DE, Shen D, McLellan MD, Lin L, Miller CA, Mardis ER, Ding L, & Wilson RK (2012). VarScan 2: Somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Research PMID: 22300766

Koboldt DC, Larson DE, Chen K, Ding L, & Wilson RK (2012). Massively parallel sequencing approaches for characterization of structural variation. Methods in molecular biology (Clifton, N.J.), 838, 369-84 PMID:22228022

Graubert TA, Shen D, Ding L, Okeyo-Owuor T, Lunn CL, Shao J, Krysiak K, Harris CC, Koboldt DC, Larson DE, McLellan MD, Dooling DJ, Abbott RM, Fulton RS, Schmidt H, Kalicki-Veizer J, O’Laughlin M, Grillot M, Baty J, Heath S, Frater JL, Nasim T, Link DC, Tomasson MH, Westervelt P, DiPersio JF, Mardis ER, Ley TJ, Wilson RK, & Walter MJ (2011). Recurrent mutations in the U2AF1 splicing factor in myelodysplastic syndromes. Nature genetics, 44 (1), 53-7 PMID: 22158538

Larson DE, Harris CC, Chen K, Koboldt DC, Abbott TE, Dooling DJ, Ley TJ, Mardis ER, Wilson RK, & Ding L. (2011). SomaticSniper: Identification of Somatic Point Mutations in Whole Genome Sequencing Data.Bioinformatics, Online : doi: 10.1093/bioinformatics/btr665

Cancer Genome Atlas Research Network (2011). Integrated genomic analyses of ovarian carcinoma. Nature, 474 (7353), 609-15 PMID:21720365

Marth GT, Yu F, Indap AR, Garimella K, et al & the 1000 Genomes Project (2011). The functional spectrum of low-frequency coding variation.Genome biology, 12 (9) PMID: 21917140

Ross JA, Koboldt DC, Staisch JE, Chamberlin HM, Gupta BP, Miller RD, Baird SE, & Haag ES (2011). Caenorhabditis briggsae recombinant inbred line genotypes reveal inter-strain incompatibility and the evolution of recombination. PLoS genetics, 7 (7) PMID: 21779179

Bowne SJ, Humphries MM, Sullivan LS, Kenna PF, Tam LC, Kiang AS, Campbell M, Weinstock GM, Koboldt DC, Ding L, Fulton RS, Sodergren EJ, et al (2011). A dominant mutation in RPE65 identified by whole-exome sequencing causes retinitis pigmentosa with choroidal involvement. European journal of human genetics : EJHG, 19 (10) PMID:21938004

Link DC, Schuettpelz LG, Shen D, Wang J, Walter MJ, Kulkarni S, Payton JE, Ivanovich J, Goodfellow PJ, Le Beau M, Koboldt DC, Dooling DJ, Fulton RS, et al (2011). Identification of a novel TP53 cancer susceptibility mutation through whole-genome sequencing of a patient with therapy-related AML. JAMA : the journal of the American Medical Association, 305 (15), 1568-76 PMID: 21505135

Ley T, Ding L, Walter M, McLellan M, Lamprecht T, Larson D, Kandoth C, Payton J, Baty J, Welch J, Harris C, Lichti C, Townsend R, Fulton R, Dooling D, Koboldt D, et al. (2010). DNMT3A Mutations in Acute Myeloid Leukemia
New England Journal of Medicine DOI: 10.1056/NEJMoa1005143

Ding L, Wendl MC, Koboldt DC, & Mardis ER (2010). Analysis of next-generation genomic data in cancer: accomplishments and challenges. Human Molecular Genetics, 19 (R2):R188-96. PMID:20843826

Sudmant PH, Kitzman JO, Antonacci F, Alkan C, Malig M, Tsalenko A, Sampas N, Bruhn L, Shendure J, 1000 Genomes Project, & Eichler EE (2010). Diversity of human copy number variation and multicopy genes. Science (New York, N.Y.), 330 (6004), 641-6 PMID: 21030649

The 1000 Genomes Project Consortium (2010). A map of human genome variation from population-scale sequencing. Nature, 467(7319), 1061-1073 DOI: 10.1038/nature09534

Bowne SJ, Sullivan LS, Koboldt DC, Ding L, Fulton R, Abbott RM, Sodergren EJ, Birch DG, Wheaton DH, Heckenlively JR, Liu Q, Pierce EA, Weinstock GM, & Daiger SP (2010). Identification of Disease-Causing Mutations in Autosomal Dominant Retinitis Pigmentosa (adRP) Using Next-Generation DNA Sequencing. Investigative ophthalmology & visual science PMID: 20861475

Fehniger, T., Wylie, T., Germino, E., Leong, J., Magrini, V., Koul, S., Keppel, C., Schneider, S., Koboldt, D., Sullivan, R., Heinz, M., Crosby, S., Nagarajan, R., Ramsingh, G., Link, D., Ley, T., & Mardis, E. (2010). Next-generation sequencing identifies the natural killer cell microRNA transcriptome Genome Research DOI: 10.1101/gr.107995.110

Ramsingh G, Koboldt DC, Trissal M, Chiappinelli KB, Wylie T, Koul S, Chang LW, Nagarajan R, Fehniger TA, Goodfellow P, Magrini V, Wilson RK, Ding L, Ley TJ, Mardis ER, & Link DC (2010). Complete characterization of the microRNAome in a patient with acute myeloid leukemia. BloodPMID: 20876853

Koboldt DC, Ding L, Mardis ER & Wilson RK. (2010). Challenges of sequencing human genomes. Briefings in Bioinformatics DOI:10.1093/bib/bbq016

Ding L, Ellis MJ, Li S, Larson DE, Chen K, Wallis JW, Harris CC, McLellan MD, Fulton RS, Fulton LL, Abbott RM, Hoog J, Dooling DJ, Koboldt DC, et al. (2010). Genome remodelling in a basal-like breast cancer metastasis and xenograft. Nature, 464 (7291), 999-1005 PMID:20393555

Koboldt DC and Miller RD (2010). Identification of polymorphic markers for genetic mapping. Genomics: Essential Methods, In Press.

Koboldt DC, Staisch J, Thillainathan B, Haines K, Baird SE, Chamberlin HM, Haag ES, Miller RD, & Gupta BP (2010). A toolkit for rapid gene mapping in the nematode Caenorhabditis briggsae. BMC genomics, 11 (1) PMID: 20385026

Voora D, Koboldt DC, King CR, Lenzini PA, Eby CS, Porche-Sorbet R, Deych E, Crankshaw M, Milligan PE, McLeod HL, Patel SR, Cavallari LH, Ridker PM, Grice GR, Miller RD, & Gage BF (2010). A polymorphism in the VKORC1 regulator calumenin predicts higher warfarin dose requirements in African Americans. Clinical pharmacology and therapeutics, 87 (4), 445-51 PMID: 20200517

Zhang Q, Ding L, Larson DE, Koboldt DC, McLellan MD, Chen K, Shi X, Kraja A, et al (2009). CMDS: a population-based method for identifying recurrent DNA copy number aberrations in cancer from high-resolution data. Bioinformatics (Oxford, England) PMID: 20031968

Mardis ER, Ding L, Dooling DJ, Larson DE, McLellan MD, Chen K, Koboldt DC, et al (2009). Recurring mutations found by sequencing an acute myeloid leukemia genome. The New England journal of medicine, 361(11), 1058-66 PMID: 19657110

Koboldt DC, Chen K, Wylie T, Larson DE, McLellan MD, Mardis ER, Weinstock GM, Wilson RK, & Ding L (2009). VarScan: variant detection in massively parallel sequencing of individual and pooled samples.Bioinformatics (Oxford, England), 25 (17), 2283-5 PMID: 19542151

Ley TJ, Mardis ER, Ding L, Fulton B, McLellan MD, Chen K, Dooling D, Dunford-Shore BH, McGrath S, Hickenbotham M, Cook L, Abbott R, Larson DE, Koboldt DC, et al (2008). DNA sequencing of a cytogenetically normal acute myeloid leukaemia genome. Nature, 456 (7218), 66-72 PMID: 18987736

Ding L, Getz G, Wheeler DA, Mardis ER, McLellan MD, Cibulskis K, Sougnez C, et al (2008). Somatic mutations affect key pathways in lung adenocarcinoma. Nature, 455 (7216), 1069-75 PMID: 18948947

Cancer Genome Atlas Research Network (2008). Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature, 455 (7216), 1061-8 PMID: 18772890

International HapMap Consortium (2007). A second generation human haplotype map of over 3.1 million SNPs. Nature, 449 (7164), 851-61 PMID: 17943122

Sabeti PC, Varilly P, Fry B, et al (2007). Genome-wide detection and characterization of positive selection in human populations. Nature, 449 (7164), 913-8 PMID: 17943131

Hillier LW, Miller RD, Baird SE, Chinwalla A, Fulton LA, Koboldt DC, & Waterston RH (2007). Comparison of C. elegans and C. briggsaegenome sequences reveals extensive conservation of chromosome organization and synteny. PLoS biology, 5 (7) PMID: 17608563

Stanley SL Jr, Frey SE, Taillon-Miller P, Guo J, Miller RD, Koboldt DC, Elashoff M, Christensen R, Saccone NL, & Belshe RB (2007). The immunogenetics of smallpox vaccination. The Journal of infectious diseases, 196 (2), 212-9 PMID: 17570108

Koboldt DC, Miller RD, & Kwok PY (2006). Distribution of human SNPs and its effect on high-throughput genotyping. Human mutation, 27(3), 249-54 PMID: 16425292

The International HapMap Consortium (2005). A haplotype map of the human genome. Nature, 437 (7063), 1299-1320 PMID: 16255080

Miller RD, Phillips MS, et al (2005). High-density single-nucleotide polymorphism maps of the human genome. Genomics, 86 (2), 117-26 PMID: 15961272

Other Writing by Dan Koboldt

Dan Koboldt is also the author of Get Your Baby to Sleep, a resource to help new parents whose baby won’t sleep with advice on establishing healthy baby sleep habits and handling baby sleep problems. He contributes to The Best of Twins and In Search of Whitetails blogs as well.

How would you like to start your own blog? See this guide to building a blog or website in 20 minutes. It walks you through setting up a site with open-source WordPress software, which happens to be what runs Massgenomics.

SOURCE:

 http://massgenomics.org/about

Other related articles on this Open Access Online Scientific Journal:

“Genome in a Bottle”: NIST’s new metrics for Clinical Human Genome Sequencing “Genome in a Bottle”: NIST’s new metrics for Clinical Human Genome Sequencing

http://pharmaceuticalintelligence.com/2012/09/06/genome-in-a-bottle-nists-new-metrics-for-clinical-human-genome-sequencing/

DNA – The Next-Generation Storage Media for Digital Information

http://pharmaceuticalintelligence.com/2012/08/27/dna-the-next-generation-storage-media-for-digital-information/

How Genome Sequencing is Revolutionizing Clinical Diagnostics

http://pharmaceuticalintelligence.com/2012/08/20/how-genome-sequencing-is-revolutionizing-clinical-diagnostics/

NGS Market: Trends and Development for Genotype-Phenotype Associations Research

http://pharmaceuticalintelligence.com/2013/02/19/ngs-market-trends-and-development-for-genotype-phenotype-associations/

What is the Future for Genomics in Clinical Medicine?

http://pharmaceuticalintelligence.com/2013/02/17/what-is-the-future-for-genomics-in-clinical-medicine/

Genomically Guided Treatment after CLIA Approval: to be offered by Weill Cornell Precision Medicine Institute

http://pharmaceuticalintelligence.com/2013/02/06/genomically-guided-treatment-after-clia-approval-to-be-offered-by-weill-cornell-precision-medicine-institute/

Inaugural Genomics in Medicine – The Conference Program, 2/11-12/2013, San Francisco, CA

http://pharmaceuticalintelligence.com/2013/02/04/inaugural-genomics-in-medicine-the-conference-program-211-122013-san-francisco-ca/

GSK for Personalized Medicine using Cancer Drugs needs Alacris systems biology model to determine the in silico effect of the inhibitor in its “virtual clinical trial”

http://pharmaceuticalintelligence.com/2012/11/14/gsk-for-personalized-medicine-using-cancer-drugs-needs-alacris-systems-biology-model-to-determine-the-in-silico-effect-of-the-inhibitor-in-its-virtual-clinical-trial/

arrayMap: Genomic Feature Mining of Cancer Entities of Copy Number Abnormalities (CNAs) Data

http://pharmaceuticalintelligence.com/2012/11/01/arraymap-genomic-feature-mining-of-cancer-entities-of-copy-number-abnormalities-cnas-data/

NGS Cardiovascular Diagnostics: Long-QT Genes Sequenced – A Potential Replacement for Molecular Pathology

http://pharmaceuticalintelligence.com/2012/10/01/ngs-cardiovascular-diagnostics-long-qt-genes-sequenced-a-potential-replacement-for-molecular-pathology/

Speeding Up Genome Analysis: MIT Algorithms for Direct Computation on Compressed Genomic Datasets

http://pharmaceuticalintelligence.com/2012/09/18/speeding-up-genome-analysis-mit-algorithms-for-direct-computation-on-compressed-genomic-datasets/

Clinical Genetics, Personalized Medicine, Molecular Diagnostics, Consumer-targeted DNA – Consumer Genetics Conference (CGC) – October 3-5, 2012, Seaport Hotel, Boston, MA

http://pharmaceuticalintelligence.com/2012/09/06/clinical-genetics-personalized-medicine-molecular-diagnostics-consumer-targeted-dna-consumer-genetics-conference-cgc-october-3-5-2012-seaport-hotel-boston-ma/

“CRACKING THE CODE OF HUMAN LIFE: The Birth of BioInformatics & Computational Genomics” lays the manifold multivariate systems analytical tools that has moved the science forward to a groung that ensures clinical application.

http://pharmaceuticalintelligence.com/2013/02/13/cracking-the-code-of-human-life-the-birth-of-bioinformatics-and-computational-genomics/

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NGS Market: Trends and Development for Genotype-Phenotype Associations Research

Posted in Biological Networks, Gene Regulation and Evolution, Chemical Genetics, Computational Biology/Systems and Bioinformatics, Genome Biology, International Global Work in Pharmaceutical, Interviews with Scientific Leaders, Medical and Population Genetics, Personalized and Precision Medicine & Genomic Research, Pharmaceutical R&D Investment, Pharmacogenomics, Proteomics, Scientist: Career considerations, Statistical Methods for Research Evaluation, tagged CLC, CLC Bio, DNA Sequencing, HUGO Gene Nomenclature Committee, Illumina, UCSC Genome Browser on February 19, 2013| Leave a Comment »

Curator: Aviva Lev-Ari, PhD, RN

Dr. M. Michael Barmada, Associate Professor at Center for Computational Genetics, University of Pittsburgh, tells about how the hot topic of the times now – genetics – has challenged the computational resources across the University:

• Everybody has got a different focus – some are doing
• DNA-sequencing, some
• RNA-sequencing, some
• ChiP-seq, some
• methylation studies, and everybody wants to use the latest hottest technologies.

Associate Professor at Center for Computational Genetics at University of Pittsburgh, Dr. M. Michael Barmada

http://www.clcbio.com/blog/computational-resources-for-ngs-research-at-university-of-pittsburgh/

CLC bio Annual Survey Results

http://www.clcbio.com/wp-content/uploads/2013/02/annual_survey_clcbio.pdf?utm_source=Survey2012&utm_medium=CLC

CLC Bio has published the results of a survey of researchers in the next-generation sequencing market to find out which sequencers and software are used the most.

The company says it received responses from 708 individuals in 73 countries.

Not surprisingly, they found that Sequencers

• Illumina’s HiSeq and MiSeq are the most used instruments with about 34.6 percent and 21.3 percent of respondents, respectively, stating that they use the systems. Meanwhile,
• Roche’s 454 sequencers got 21.2 percent of the votes and
• Life Technologies’ Ion Torrent Personal Genome Machine got 11.5 percent of the responses.

In terms of Bioinformatics tools, the

• UCSC Genome Browser has the most use, according to the survey, with 28.9 percent of respondents reporting that they use the program. Next in line is
• Ensembl tools and then – 26.9
• Bowtie with  23.4 percent of the votes, respectively.

Also worth noting is that NGS is being used primarily for

• whole-genome sequencing — 40.8 percent of the votes — followed by
• RNA-seq and — 40.1 percent
• de novo sequencing with  39.8 percent of the votes, respectively.

Of the 708 respondents, about 24.6 percent work in the US, according to CLC. Also,

• 73 percent of respondents work in academic research while
• 9 percent work in industry, another
• 9 percent in government, and
• 6 percent work in not-for-profit organizations, according to the survey.

http://www.genomeweb.com//node/1192936?hq_e=el&hq_m=1502990&hq_l=2&hq_v=bea4dc08f7

We believe MedQL has the potential to be an effective time saver for researchers working with variant prioritization, making it a promising new plugin for CLC Genomics Workbench. We’re excited to add BioQL’s technology for evidence-based downstream analysis of Next Generation Sequencing data to our products.

Director of Global Partner Relations at CLC bio, Mikael Flensborg

Using CLC Genomics Workbench, a common workflow to detect causative mutations in medical genomics involves read mapping and variant detection. The result is a list of candidate gene variants that differ from the reference genome. The MedQL plugin uses an evidence-based approach to prioritize these genes for functional studies and, thereby, allowing researchers to focus their efforts on the most promising candidates.

CLC BIO AND BIOQL RELEASE MEDICAL GENOMICS PLUGIN FOR GENOTYPE–PHENOTYPE ASSOCIATIONS

Aarhus, Denmark — November 7, 2012 — Today, CLC bio and the independent software vendor, BioQL, announced the release of the MedQL Variant Prioritizer plugin for CLC Genomics Workbench. The plugin connects with MedQL’s online database to prioritize a list of variants in gene regions based on their degree of association with a given phenotype.

The MedQL database contains more than 20 million articles from Medline, indexed using a dictionary of nearly 300,000 terms from authoritative ontologies such as the HUGO Gene Nomenclature Committee (HGNC), the Human Disease Ontology, and the Online Mendelian Inheritance in Man (OMIM).

CLC BIO

We’re the world’s leading bioinformatics software developers and the only ones providing an analysis platform where both desktop and server software are seamlessly integrated and optimized for best performance.

Our wide range of analyses are available both through a user-friendly graphical user-interface as well as through command-line, allowing scientists to choose their preferred interface.

By developing our own proprietary algorithms, based on published methods, we have successfully accelerated the data calculations to achieve remarkable improvements in speed over comparable solutions.

Our enterprise platform serves as the backbone of sequence analysis pipelines for a large number of the world’s most prominent research institutions. With around 2000 different organizations as our customers around the globe, including the ten biggest pharmaceutical companies in the world, we have established ourselves as the market-leader in sequence analysis software.

One of our key strategies is to be ‘cross-platform’, which means we support all the major next generation sequencing platforms as well as traditional Sanger-based sequencing, effectively giving our customers a one-stop-shop for their analysis needs across all sequencing platforms.

http://www.clcbio.com/corporate/about-clc-bio/

 Desktop software for Sequence Analysis based on an overall level of subjects.

FEATURES
Next Generation Sequencing analysis
Genomics
Transcriptomics (Gene expression features also available in CLC Main Workbench)
Epigenomics
RNA secondary structure
BLAST searches
Protein analyses
Primer design
Assembly of Sanger sequencing data
Molecular cloning
Pattern discovery and motif search
Nucleotide analyses
GenBank Entrez searches
Sequence alignment
Phylogenetic trees
Detailed history log
Batch processing
Customization of your workbenches

 http://www.clcbio.com/sequencing-analysis/overview-of-applications/

CLC Genomics Machine

Our turnkey solution, for small research labs. It includes CLC Genomics Server and CLC Genomics Workbench. Everything is preinstalled on a powerful desktop computer or server blade – ready to plug-in and run from the day it is delivered.

CLC Genomics Factory

Our turnkey solution for medium and large research labs that needs a complete IT infrastructure for their NGS data analysis.

USER-FRIENDLY BIOINFORMATICS

Our software is made for biologists by biologists, so it’s easy to analyze, visualize, and compare DNA, RNA, and Protein data, as well as run advanced workflows with large and complicated datasets.

J. CRAIG VENTER INSTITUTE EXTENDS CLC BIO SITE LICENSE THROUGH 2017

Aarhus, Denmark — January 8, 2013 — Today CLC bio, the global leader in commercial sequence analysis software, announced that the J. Craig Venter Institute (JCVI) has extended their site license agreement with CLC bio through 2017.

JCVI has been utilizing CLC bio’s enterprise platform since 2009 and currently uses it on more than 30 research grants, including their work as part of the Human Microbiome Project (HMP). The HMP is a National Institutes of Health-funded project to catalogue and characterize the microbes living in and on the human body. Recently, the HMP Consortium published a series of papers with results from this work in Nature and PLOSone. CLC’s bio software was used in the analysis of this work.

The complexity and diversity of our research projects necessitates unique tools to analyze these increasingly large data sets. In our pursuit of excellence we always test and employ the best available tools for our research projects. As such we’re happy to announce the extension of our site license with CLC bio through 2017.

Karen Nelson, Ph.D., President, JCVI

http://www.clcbio.com/news/jcvi-extends-site-license/

For us, it’s always very exciting to see the results of all the intriguing research that our customers are doing, and no less so, when JCVI published their papers on the HMP project this summer. JCVI was one of our first site license deals with a premier institution in the genomics research field, and we’re proud to announce it has been extended for another five years.

Thomas Knudsen, CEO, CLC bio

The original 4-year site license agreement between JCVI and CLC bio was signed in the summer of 2009, and has now been extended by another 5 years, through 2017. JCVI deploys CLC bio’s platform in an integrated environment across multiple geographical locations and together with international collaborators.

http://www.clcbio.com/news/jcvi-extends-site-license/ 

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FDA Warning for the Leader of Consumer Market for Personal DNA Sequencing: Part 4

Posted in Biomarkers & Medical Diagnostics, Cardiovascular Pharmacogenomics, Chemical Genetics, Computational Biology/Systems and Bioinformatics, Genome Biology, Genomic Testing: Methodology for Diagnosis, Medical and Population Genetics, Metabolomics, Molecular Genetics & Pharmaceutical, Personalized and Precision Medicine & Genomic Research, Pharmaceutical R&D Investment, Pharmacogenomics, Population Health Management, Genetics & Pharmaceutical, Reproductive Andrology, Embryology, Genomic Endocrinology, Preimplantation Genetic Diagnosis and Reproductive Genomics, Stem Cells for Regenerative Medicine, Technology Transfer: Biotech and Pharmaceutical, tagged 23andMe, Anne Wojcicki, Cancer - General, DNA, DNA microarray, DNA Sequencing, Food and Drug Administration, Full genome sequencing, genetic testing, Genomic, Illumina, Margaret Hamburg, mutation, Nucleic acid sequence on January 13, 2013| 9 Comments »

Consumer Market for Personal DNA Sequencing: Part 4

Reporter: Aviva Lev-Ari, PhD RN

Word Cloud by Daniel Menzin

This Part 4 of the series on Present and Future Frontier of Research in Genomics has been 

UPDATED on 12/6/2013

23andMe Suspends Health Interpretations

December 06, 2013

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Direct-to-consumer genetic testing company 23andMe hasstopped offering its health-related test to new customers, bringing it in line with a request from the US Food and Drug Administration.

In letter sent on Nov. 22, FDA said that 23andMe had not adequately responded to its concerns regarding the validity of their Personal Genome Service. The letter instructed 23andMe to “immediately discontinue marketing” the service until it receives authorization from the agency.

According to a post at the company’s blog from CEO Anne Wojcicki, 23andMe customers who purchased their kits on or after Nov. 22 “will not have access to health-related results.” They will, though, have access to ancestry information and their raw genetic data. Wojcicki notes that the customers may have access to the health interpretations in the future depending on FDA marketing authorization. Those customers are also being offered a refund.

Customers who purchased their kits before Nov. 22 will have access to all reports.

“We remain firmly committed to fulfilling our long-term mission to help people everywhere have access to their own genetic data and have the ability to use that information to improve their lives,” a notice at the 23andMe site says.

In a letter appearing in the Wall Street Journal earlier this week, FDA Commissioner Margaret Hamburg wrote that the agency “supports the development of innovative tests.” As an example, she pointed to its recent clearance of sequencing-based testsfrom Illumina.

She added that the agency also understands that some consumers do want to know more about their genomes and their genetic risk of disease, and that a DTC model would let consumers take an active role in their health.

“The agency’s desire to review these particular tests is solely to ensure that they are safe, do what they claim to do and that the results are communicated in a way that a consumer can understand,” Hamburg said.

In a statement, 23andMe’s Wojcicki says that the company remains committed to its ethos of allowing people access to their genetic information. “Our goal is to work cooperatively with the FDA to provide that opportunity in a way that clearly demonstrates the benefit to people and the validity of the science that underlies the test,” Wojcicki adds.

SOURCE

http://www.genomeweb.com/blog/23andme-suspends-health-interpretations

UPDATED on 11/27/2013

FDA Tells Google-Backed 23andMe to Halt DNA Test Service

VIEW VIDEO

http://www.bloomberg.com/news/2013-11-25/fda-tells-google-backed-23andme-to-halt-dna-test-service.html

FDA Letter to 23andME

23andMe, Inc. 11/22/13

Department of Health and Human Services	Public Health Service Food and Drug Administration
	10903 New Hampshire Avenue Silver Spring, MD 20993

Nov 22, 2013

Ann Wojcicki

CEO

23andMe, Inc.

1390 Shoreline Way

Mountain View, CA 94043

Document Number: GEN1300666

Re: Personal Genome Service (PGS)

WARNING LETTER

Dear Ms. Wojcicki,

The Food and Drug Administration (FDA) is sending you this letter because you are marketing the 23andMe Saliva Collection Kit and Personal Genome Service (PGS) without marketing clearance or approval in violation of the Federal Food, Drug and Cosmetic Act (the FD&C Act).

This product is a device within the meaning of section 201(h) of the FD&C Act, 21 U.S.C. 321(h), because it is intended for use in the diagnosis of disease or other conditions or in the cure, mitigation, treatment, or prevention of disease, or is intended to affect the structure or function of the body. For example, your company’s website at http://www.23andme.com/health (most recently viewed on November 6, 2013) markets the PGS for providing “health reports on 254 diseases and conditions,” including categories such as “carrier status,” “health risks,” and “drug response,” and specifically as a “first step in prevention” that enables users to “take steps toward mitigating serious diseases” such as diabetes, coronary heart disease, and breast cancer. Most of the intended uses for PGS listed on your website, a list that has grown over time, are medical device uses under section 201(h) of the FD&C Act. Most of these uses have not been classified and thus require premarket approval or de novo classification, as FDA has explained to you on numerous occasions.

Some of the uses for which PGS is intended are particularly concerning, such as assessments for BRCA-related genetic risk and drug responses (e.g., warfarin sensitivity, clopidogrel response, and 5-fluorouracil toxicity) because of the potential health consequences that could result from false positive or false negative assessments for high-risk indications such as these. For instance, if the BRCA-related risk assessment for breast or ovarian cancer reports a false positive, it could lead a patient to undergo prophylactic surgery, chemoprevention, intensive screening, or other morbidity-inducing actions, while a false negative could result in a failure to recognize an actual risk that may exist. Assessments for drug responses carry the risks that patients relying on such tests may begin to self-manage their treatments through dose changes or even abandon certain therapies depending on the outcome of the assessment. For example, false genotype results for your warfarin drug response test could have significant unreasonable risk of illness, injury, or death to the patient due to thrombosis or bleeding events that occur from treatment with a drug at a dose that does not provide the appropriately calibrated anticoagulant effect. These risks are typically mitigated by International Normalized Ratio (INR) management under a physician’s care. The risk of serious injury or death is known to be high when patients are either non-compliant or not properly dosed; combined with the risk that a direct-to-consumer test result may be used by a patient to self-manage, serious concerns are raised if test results are not adequately understood by patients or if incorrect test results are reported.

Your company submitted 510(k)s for PGS on July 2, 2012 and September 4, 2012, for several of these indications for use. However, to date, your company has failed to address the issues described during previous interactions with the Agency or provide the additional information identified in our September 13, 2012 letter for(b)(4) and in our November 20, 2012 letter for (b)(4), as required under 21 CFR 807.87(1). Consequently, the 510(k)s are considered withdrawn, see 21 C.F.R. 807.87(1), as we explained in our letters to you on March 12, 2013 and May 21, 2013.  To date, 23andMe has failed to provide adequate information to support a determination that the PGS is substantially equivalent to a legally marketed predicate for any of the uses for which you are marketing it; no other submission for the PGS device that you are marketing has been provided under section 510(k) of the Act, 21 U.S.C. § 360(k).

The Office of In Vitro Diagnostics and Radiological Health (OIR) has a long history of working with companies to help them come into compliance with the FD&C Act. Since July of 2009, we have been diligently working to help you comply with regulatory requirements regarding safety and effectiveness and obtain marketing authorization for your PGS device. FDA has spent significant time evaluating the intended uses of the PGS to determine whether certain uses might be appropriately classified into class II, thus requiring only 510(k) clearance or de novo classification and not PMA approval, and we have proposed modifications to the device’s labeling that could mitigate risks and render certain intended uses appropriate for de novo classification. Further, we provided ample detailed feedback to 23andMe regarding the types of data it needs to submit for the intended uses of the PGS.  As part of our interactions with you, including more than 14 face-to-face and teleconference meetings, hundreds of email exchanges, and dozens of written communications, we provided you with specific feedback on study protocols and clinical and analytical validation requirements, discussed potential classifications and regulatory pathways (including reasonable submission timelines), provided statistical advice, and discussed potential risk mitigation strategies. As discussed above, FDA is concerned about the public health consequences of inaccurate results from the PGS device; the main purpose of compliance with FDA’s regulatory requirements is to ensure that the tests work.

However, even after these many interactions with 23andMe, we still do not have any assurance that the firm has analytically or clinically validated the PGS for its intended uses, which have expanded from the uses that the firm identified in its submissions. In your letter dated January 9, 2013, you stated that the firm is “completing the additional analytical and clinical validations for the tests that have been submitted” and is “planning extensive labeling studies that will take several months to complete.” Thus, months after you submitted your 510(k)s and more than 5 years after you began marketing, you still had not completed some of the studies and had not even started other studies necessary to support a marketing submission for the PGS. It is now eleven months later, and you have yet to provide FDA with any new information about these tests.  You have not worked with us toward de novo classification, did not provide the additional information we requested necessary to complete review of your 510(k)s, and FDA has not received any communication from 23andMe since May. Instead, we have become aware that you have initiated new marketing campaigns, including television commercials that, together with an increasing list of indications, show that you plan to expand the PGS’s uses and consumer base without obtaining marketing authorization from FDA.

Therefore, 23andMe must immediately discontinue marketing the PGS until such time as it receives FDA marketing authorization for the device. The PGS is in class III under section 513(f) of the FD&C Act, 21 U.S.C. 360c(f). Because there is no approved application for premarket approval in effect pursuant to section 515(a) of the FD&C Act, 21 U.S.C. 360e(a), or an approved application for an investigational device exemption (IDE) under section 520(g) of the FD&C Act, 21 U.S.C. 360j(g), the PGS is adulterated under section 501(f)(1)(B) of the FD&C Act, 21 U.S.C. 351(f)(1)(B).  Additionally, the PGS is misbranded under section 502(o) of the Act, 21 U.S.C. § 352(o), because notice or other information respecting the device was not provided to FDA as required by section 510(k) of the Act, 21 U.S.C. § 360(k).

Please notify this office in writing within fifteen (15) working days from the date you receive this letter of the specific actions you have taken to address all issues noted above. Include documentation of the corrective actions you have taken. If your actions will occur over time, please include a timetable for implementation of those actions. If corrective actions cannot be completed within 15 working days, state the reason for the delay and the time within which the actions will be completed. Failure to take adequate corrective action may result in regulatory action being initiated by the Food and Drug Administration without further notice. These actions include, but are not limited to, seizure, injunction, and civil money penalties.

We have assigned a unique document number that is cited above. The requested information should reference this document number and should be submitted to:

James L. Woods, WO66-5688

Deputy Director

Patient Safety and Product Quality

Office of In vitro Diagnostics and Radiological Health

10903 New Hampshire Avenue

Silver Spring, MD 20993

If you have questions relating to this matter, please feel free to call Courtney Lias, Ph.D. at 301-796-5458, or log onto our web site at www.fda.gov for general information relating to FDA device requirements.

Sincerely yours,

/S/

Alberto Gutierrez

Director

Office of In vitro Diagnostics

and Radiological Health

 Center for Devices and Radiological Health

SOURCE

http://www.fda.gov/ICECI/EnforcementActions/WarningLetters/2013/ucm376296.htm

Cancer Diagnostics by Genomic Sequencing: ‘No’ to Sequencing Patient’s DNA, ‘No’ to Sequencing Patient’s Tumor, ‘Yes’ to focus on Gene Mutation Aberration & Analysis of Gene Abnormalities

Symposia

http://aaas.confex.com/aaas/2013/webprogram/start.html

Personal Genetics: An Intersection Between Science, Society, and Policy

Saturday, February 16, 2013: 8:30 AM-11:30 AM

Room 203 (Hynes Convention Center)

On 26 June 2000, scientists announced the completion of a rough draft of the human genome, the result of the $3 billion publicly funded Human Genome Project. In the decade since, the cost of genome sequencing has plummeted, coinciding with the development of deep sequencing technologies and allowing, for the first time, personalized genetic medicine. The advent of personal genetics has profound implications for society that are only beginning to be discussed, even as the technologies are rapidly maturing and entering the market. This symposium will focus on how the genomic revolution may affect our society in coming years and how best to reach out to the general public on these important issues. How has the promise of genomics, as stated early in the last decade, matched the reality we observe today? What are the new promises — and pitfalls — of genomics and personal genetics as of 2013? What are the ethical implications of easy and inexpensive human genome sequencing, particularly with regard to ownership and control of genomic datasets, and what stakeholder interests must be addressed? How can the scientific community engage with the public at large to improve understanding of the science behind these powerful new technologies? The symposium will comprise three 15-minute talks from representatives of relevant sectors (academia/education, journalism, and industry), followed by a 45-minute panel discussion with the speakers.

Organizer:

Peter Yang, Harvard University

Co-organizers:

Brenna Krieger, Harvard University

and Kevin Bonham, Harvard University

Discussant:

James Thornton, Harvard University

Speakers:

 

Ting Wu, Harvard University

Personal Genetics and Education

Mary Carmichael, Boston Globe

The Media and the Personal Genetics Revolution

Brian Naughton, 23andMe Inc.

Commercialization of Personal Genomics: Promise and Potential Pitfalls

Mira Irons, Children’s Hospital Boston

Personal Genomic Medicine: How Physicians Can Adapt to a Genomic World

Sheila Jasanoff, Harvard University

Citizenship and the Personal Genomics

Jonathan Gitlin, National Human Genome Research Institute

Personal Genomics and Science Policy

THIS IS A SERIES OF FOUR POINTS OF VIEW IN SUPPORT OF the Paradigm Shift in Human Genomics

How to Tailor Cancer Therapy to the particular Genetics of a patient’s Cancer

‘No’ to Sequencing Patient’s DNA, ‘No’ to Sequencing Patient’s Tumor, ‘Yes’ to focus on Gene Mutation Aberration & Analysis of Gene Abnormalities PRESENTED in the following FOUR PARTS. Recommended to be read in its entirety for completeness and arrival to the End Point of Present and Future Frontier of Research in Genomics

Part 1:

Research Paradigm Shift in Human Genomics – Predictive Biomarkers and Personalized Medicine

http://pharmaceuticalintelligence.com/2013/01/13/paradigm-shift-in-human-genomics-predictive-biomarkers-and-personalized-medicine-part-1/

Part 2:

LEADERS in the Competitive Space of Genome Sequencing of Genetic Mutations for Therapeutic Drug Selection in Cancer Personalized Treatment

http://pharmaceuticalintelligence.com/2013/01/13/leaders-in-genome-sequencing-of-genetic-mutations-for-therapeutic-drug-selection-in-cancer-personalized-treatment-part-2/

Part 3:

Personalized Medicine: An Institute Profile – Coriell Institute for Medical Research

http://pharmaceuticalintelligence.com/2013/01/13/personalized-medicine-an-institute-profile-coriell-institute-for-medical-research-part-3/

Part 4:

The Consumer Market for Personal DNA Sequencing

 

Part 4:

The Consumer Market for Personal DNA Sequencing

How does 23andMe genotype my DNA?

Technology and Standards

23andMe is a DNA analysis service providing information and tools for individuals to learn about and explore their DNA. We use the Illumina OmniExpress Plus Genotyping BeadChip (shown here). In addition to the variants already included on the chip by Illumina, we’ve included our own, customized set of variants relating to conditions and traits that are interesting. Technical information on the performance of the chip can be found on Illumina’s website.

All of the laboratory testing for 23andMe is done in a CLIA-certified laboratory.

Once our lab receives your sample, DNA is extracted from cheek cells preserved in your saliva. The lab then copies the DNA many times — a process called “amplification” — growing the tiny amount extracted from your saliva until there is enough to be genotyped.

In order to be genotyped, the amplified DNA is “cut” into smaller pieces, which are then applied to our DNA chip, a small glass slide with millions of microscopic “beads” on its surface (read more about this technology). Each bead is attached to a “probe”, a bit of DNA that matches one of the approximately one million genetic variants that we test. The cut pieces of your DNA stick to the matching DNA probes. A fluorescent signal on each probe provides information that can tell us which version of that genetic variant your DNA corresponds to.

Although the human genome is estimated to contain about 10-30 million genetic variants, many of them are correlated due to their proximity to each other. Thus, one genetic variant is often representative of many nearby variants, and the approximately one million variants on our genotyping chip provide very good coverage of common variation across the entire genome.

Our research team has also hand-picked tens of thousands of additional genetic variants linked to various conditions and traits in the scientific literature to analyze on our genotyping chip. As a result we can provide you with personal genetic information available only through 23andMe.

Genetics service 23andMe announced some new cash in the bank today with a $50 million raise from Yuri Milner, 23andMe CEO Anne Wojcicki, Google’s Sergey Brin (who also happens to be Wojcicki’s husband), New Enterprise Associates, MPM Capital, and Google Ventures.

With today’s new funding also comes the reduction of the price of its genome analysis service to $99. This isn’t special holiday pricing (as 23andMe has run repeatedly in the past) the company tells me, but rather what its normal pricing will be from now on.

This move is overdue, at least as far as 23andMe’s business model is concerned. Just yesterday TechCrunch Conference Chair Susan Hobbs told me she was waiting for another $99 pricing deal to buy the Personal Genome Analysis product. Sure 23andMe has experimented with various pricing models, including subscription, since its founding in 2007, but had been at an official and prohibitive $299 price point until today. It’s also apparently been rigorously beta-testing various price points in the past couple of weeks, at some point experimenting with some lower than $99.

For comparison, the company’s original pricing began at $999 and offered subscribers just 14 health and trait reports versus today’s 244 reports, as well as genetic ancestry information. Natera, Counsyl and Pathway Genomics are also in the genomics space, but they work by offering their services through doctors rather than direct to consumer.

Since the company’s launch five years ago, it’s had 180K civilians profile their DNA, and representative Catherine Afarian tells us that, post-price drop and funding, its goal is to reach a million customers in 2013. This is a supremely ambitious goal considering it wants to turn an average user acquisition rate of 36K per year into one of 820K in one year alone.

But Afarian isn’t fazed and brings up how the company once sold out 20k in $99 account inventory on something called “DNA Day.” “Once we can offer the service at $99 it means the average American will buy in,” she said.

That $299 was too pricey, according to Hobbs, but $99 might be just right. She said the $99 price point, which yes, is less than an iPhone, was the main factor in her decision to buy in. “23andMe is more ‘nice-to-know’ information rather than ‘need-to-know’ information. It’s nice to know your ancestry. It’s more of a need to know that you are predisposed genetically for a type of cancer, so that you may take precautionary measures,” she said, implying that the data given by 23andMe isn’t necessarily vital medical information, or actionable when it is. While 23andMe can give you indicators about certain disease risks, it doesn’t close the loop, as in tell you what to do to prevent these diseases.

“Its [utility] depends on your genetic data,” said Afarian when I asked her about the usefulness of the product. “If you’ve got a Factor 5 that puts you at risk for clotting, you might want to invest in anti-clotting socks. [And] there’s always something about themselves that people didn’t know.”

Hobbs said eventually that she wouldn’t buy it, but only because she was looking into more exact lineage information for her little girl, and you need a Y chromosome in all DNA tests to show paternal lineage. Afarian also countered this hesitation, saying that what makes 23andMe unique is that it’s not only looking at just your Y or your mitochondrial DNA, but also your autosomal DNA, which does show some patrilineal information for females who lack that precious Y.

While still sort of a novelty, the potential for 23andMe goes beyond lineage and hopefully that extra $50 million will go further than keeping the price low and into research. The company hopes that a million users will result in a giant database of 23andWe genetic info that can be used to spot trends, like which genes mean a higher risk of diabetes/cancer, etc. Which is great if it happens but for now remains a pipe dream for 23andMe/We.

http://techcrunch.com/2012/12/11/23andnotme/

12/13/2012 @ 5:23PM |6,471 views

What Is 23andMe Really Selling: The Moral Quandary At The Center Of The Personalized Genomics Revolution

This week, 23andme, the personalized genomics company founded by Anne Wojcicki, wife of Google co-founder Sergey Brin, got an influx of investment cash ($50 million). According to their press release, they are using the money to bring the cost of their genetic test down to $99 (it was previously $299) which, they hope, will inspire the masses to get tested.

So should the masses indulge?

I prefer a quantified self approach to this question. At the heart of the quantified self-movement lies a very simple idea: metrics make us better. For devotees, this means “self-tracking,” using everything from the Nike fuel band to the Narcissism Personality Index to gather large quantities of personal data and—the bigger idea—use that data to improve performance.

If you consider that performance suffers when health suffers then a genetic test can been seen as a kind of metric used to improve performance. This strikes me as the best way to evaluate this idea and leads us to ask the same question about personalized genomics that the quantified self movement asks about every other metric: will it improve performance.

Arguments rage all over the place on this one, but the short answer is that SNP tests—which is the kind of DNA scan 23andme relies upon— don’t tell us all that much (yet).  They analyze a million genes out of three billion total and the impact those million play in long term-health outcomes is still in dispute. For example, the nature/nurture split is normally viewed at 30/70—meaning environmental factors play a far more significant role in long-term health outcomes than genetics.

Moreover, all of the performance metrics used by the quantified self movement are used to for behavior modification—to drive self-improvement. Personalized genomics isn’t there yet. As Stanford University’s Nobel Prize-winning RNA researcher Andy Fire once told me, “if someone off the street is looking for pointers on how to live a healthier life, there’s nothing these tests will tell you besides basic physician advice like ‘eat right, don’t smoke and get plenty of exercise.’”

And even with more well-regarded SNP tests, like the ones that examine the BRCA 1 and 2 markers for breast cancer—which  . NYU Langone Medical Center bioethicist Arthur Caplan explains it like this, “Say you test positive for a breast cancer disposition—then what are you going to do? The only preventative step you can take is to chop off your breasts.”

So if prevention is not available the only thing left is fear and anxiety. Unfortunately, in the past few decades, there have been hundreds of studies linking stress to everything from immunological disorders to heart disease to periodonitic troubles. So while finding out you may be at risk for Parkinson’s may make you feel informed, that knowledge isn’t going to stop you from developing the disease—but the resulting stress may contribute to a host of other complications.

This brings up a different question: if personalized genomics can’t yet help us much and could possibly hurt us—where’s the upside?

Turns out there’s a big upside: Citizen science. SNP tests are not yet viable because we need more info. 23andme talks about the “power of one million people,” meaning, if one million take these tests then the resulting genetic database could lead to big research breakthroughs and these could lead to all sorts of health/performance improvements.

This is what 23andme is really selling for $99 bucks a pop—a crowdsourced shot at unraveling a few more DNA mysteries.

And this also means that the question at the heart of the personalized genomics industry is not about metrics at all—it’s about morals: Should I risk my health for the greater good?

http://www.forbes.com/sites/stevenkotler/2012/12/13/what-is-23andme-really-selling-the-moral-quandary-at-the-center-of-the-personalized-genomics-revolution/

You can browse your data for all of the variants we test using the Browse Raw Data feature, or download your data here.

before you buy (59) »

What unexpected things might I learn?

How does 23andMe genotype my DNA?

Can I use the saliva collection kit for infants and toddlers?

getting started (20) »

When and how do I get my data?

How do I collect saliva samples?

How long will it take for my sample to reach the lab?

account/profile settings (20) »

Which Ancestry setting in My Profile should I choose?

How do I use Browse Raw Data?

What do the options under the “Account” link in the upper right-hand corner control?

product features (145) »

I know that a particular person is my relative. What’s the probability that we share a sufficient amount of DNA to be detected by Relative Finder?

What is the average percent DNA shared for different types of cousins?

How does Relative Finder estimate the Predicted Relationship?

research initiatives (8) »

What do I get in return for taking surveys?

What is your research goal?

What is 23andMe Research?

https://customercare.23andme.com/categories/20021003-faqs

https://customercare.23andme.com/home

REFERENCES

http://www.foundationmedicine.com/diagnostics-publications.php

http://www.coriell.org/media-center/publications

Http://www.coriell.org/assets/pdfs/gronowski_etal_coriellinstitute_clinicalchemistry2011_humantissuesinresearch.pdf

http://scholar.google.com/scholar?start=10&q=Gene+Mutation+Aberration+%26+Analysis+of+Gene+Abnormalities&hl=en&as_sdt=0,22&as_vis=1

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Genome and Genetics: Resources @Stanford, @MIT, @NIH’s NCBCS

Posted in Computational Biology/Systems and Bioinformatics, Genome Biology, Genomic Testing: Methodology for Diagnosis, Molecular Genetics & Pharmaceutical, Population Health Management, Genetics & Pharmaceutical, tagged Aviva Lev-Ari, Biochemistry and Molecular Biology, Bioinformatics, Biology, DNA, Illumina, Nucleic acid sequence, Online Services, Population genetics, RNA, Sequence alignment on September 18, 2012| 5 Comments »

Curator: Aviva Lev-Ari, PhD, RN

Population Genetics

HAPAA: a tool for ancestral haploblock reconstruction. Specifically, given the genotype  (for instance, as derived by an Illumina genotyping array) of an individual of admixed ancestry, find the source population for each segment of the individual’s genome.

Protein Interaction Networks

A tool for aligning multiple global protein interaction networks; Graemlin also supports search for homology between a query module of proteins and a database of interaction networks.

Machine Learning

CONTRA: Conditionally trained models for sequence analysis. SeeCONTRAlign, a protein sequence aligner with very high accuracy, especially in twilight alignments. See CONTRAfold, an RNA secondary structure prediction tool. Stay tuned for more…

RNA Structure Prediction

CONTRAfold: Prediction of RNA secondary structure with a Conditional Log-Linear model that relies on automatically trained parameters, rather than on a physics-based energy model of RNA folding.

Protein Alignment

CONTRAlign: A protein sequence aligner that users can optionally train on feature sets such as secondary structure and solvent accessibility; see the CONTRA project above.

A protein multiple sequence aligner that exhibits high accuracy on popular benchmarks.

A protein multiple aligner that automatically finds domain structures of sequences with shuffled and repeated domain architectures.

Motif Finding

MotifCut: a non-parametric graph-based motif finding algorithm. MotifScan: a non-parametric method for representing motifs and scanning DNA sequences for known motifs.

CompareProspector: motif finding with Gibbs sampling & alignment.

Genomic Alignment

Stanford ENCODE: Multiple Alignments of 1% of the Human genome.

Typhon: BLAST-like sequence search to a multiple alignments database.

LAGAN: tools for genomic alignment. These include the MLAGAN multiple alignment tool, and Shuffle-LAGAN for alignment with rearrangements.

Microarray Analysis

Application of Independent Component Analysis (ICA) to microarrays.

Researchers Hope New Database Becomes Universal Cancer Genomics Tool

1. Mike Martin

Swiss scientists hope that a new online database called “arrayMap” will bring cancer genomics to the desktop, laptop, and tablet computers of pathologists and researchers everywhere.

The database combines genomic information from three sources: large repositories such as the NCBI Gene Expression Omnibus (GEO) and Cancer Genome Atlas (CGA); journal literature; and submissions from individual investigators. It incorporates more than 42,000 genomic copy number arrays—normal and abnormal DNA comparisons—from 195 cancer types.

“arrayMap includes a wider range of human cancer copy number samples than any single repository,” said principal investigator Michael Baudis, M.D. Ease of access, visualization, and data manipulation, he added, are top priorities in its ongoing development.

A product of the University of Zurich Institute for Molecular Life Sciences, where Baudis researches bioinformatics and oncogenomics, arrayMap illustrates the importance of copy number abnormalities (CNA)—dysfunctional DNA gains or losses that visibly lengthen or shorten certain chromosomes—in the diagnosis, staging, and treatment of various malignancies.

“I have this particular tumor type—are there any CNAs in it that can tell me anything about prognosis or treatment?” said Michael Rossi, Ph.D., director of the Winship Cancer Institute cancer genomics program at the Emory University School of Medicine in Atlanta. “Data mining tools like arrayMap are incredibly useful to help answer such questions.”

 http://jnci.oxfordjournals.org/content/104/14/1045

arrayMap – genomic arrays for copy number profiling in human cancer

arrayMap is a curated reference database and bioinformatics resource targeting copy number profiling data in human cancer. The arrayMap database provides an entry point for meta-analysis and systems level data integration of high-resolution oncogenomic CNA data. The current data reflects:

• 42875 genomic copy number arrays
• 634 experimental series
• 256 array platforms
• 197 ICD-O cancer entities
• 480 publications (Pubmed entries)

For the majority of the samples, probe level visualization as well as customized data representation facilitate gene level and genome wide data review. Results from multi-case selections can be connected to downstream data analysis and visualization tools, as we provide through our Progenetix project.

arrayMap is developed by the group “Theoretical Cytogenetics and Oncogenomics” at the Institute of Molecular Life Sciences of the University of Zurich.

ARRAYMAP NEWS

2012-08-29: arrayMap featured at the Journal of the National Cancer Institute

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2012-08-25: New Progenetix article published at PLoS ONE

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2012-07-25: Renal cell carcinoma paper published at BMC Cancer

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More News …

These tools were developed for our research projects. You are welcome to try them out, but there is only sparse documentation. If more support and/or custom analysis is needed, please contact Michael Baudis regarding a collaborative project.

http://www.arraymap.org/cgi-bin/amHome.cgi

MIT: A New Approach Uses Compression to Speed Up Genome Analysis

Public-Domain Computing Resources

• Structural Bioinformatics 
• Genomics 
• Systems Biology 
• Other

Structural Bioinformatics

The BetaWrap program detects the right-handed parallel beta-helix super-secondary structural motif in primary amino acid sequences by using beta-strand interactions learned from non-beta-helix structures.

Wrap-and-pack detects beta-trefoils in protein sequences by using both pairwise beta-strand interactions and 3-D energetic packing information

The BetaWrapPro program predicts right-handed beta-helices and beta-trefoils by using both sequence profiles and pairwise beta-strand interactions, and returns coordinates for the structure.

The MSARi program indentifies conserved RNA secondary structure in non-coding RNA genes and mRNAs by searching multiple sequence alignments of a large set of candidate catalogs for correlated arrangements of reverse-complementary regions

The Paircoil2 program predicts coiled-coil domains in protein sequences by using pairwise residue correlations obtained from a coiled-coil database. The original Paircoil program is still available for use.

The MultiCoil program predicts the location of coiled-coil regions in amino acid sequences and classifies the predictions as dimeric or trimeric. An updated version, Multicoil2, will soon be available.

The LearnCoil Histidase Kinase program uses an iterative learning algorithm to detect possible coiled-coil domains in histidase kinase receptors.

The LearnCoil-VMF program uses an iterative learning algorithm to detect coiled-coil-like regions in viral membrane-fusion proteins.

The Trilogy program discovers novel sequence-structure patterns in proteins by exhaustively searching through three-residue motifs using both sequence and structure information.

The ChainTweak program efficiently samples from the neighborhood of a given base configuration by iteratively modifying a conformation using a dihedral angle representation.

The TreePack program uses a tree-decomposition based algorithm to solve the side-chain packing problem more efficiently. This algorithm is more efficient than SCWRL 3.0 while maintaining the same level of accuracy.

PartiFold: Ensemble prediction of transmembrane protein structures. Using statistical mechanics principles, partiFold computes residue contact probabilities and sample super-secondary structures from sequence only.

tFolder: Prediction of beta sheet folding pathways. Predict a coarse grained representation of the folding pathway of beta sheet proteins in a couple of minutes.

RNAmutants: Algorithms for exploring the RNA mutational landscape.Predict the effect of mutations on structures and reciprocally the influence of structures on mutations. A tool for molecular evolution studies and RNA design.

AmyloidMutants is a statistical mechanics approach for de novo prediction and analysis of wild-type and mutant amyloid structures. Based on the premise of protein mutational landscapes, AmyloidMutants energetically quantifies the effects of sequence mutation on fibril conformation and stability.

Genomics

GLASS aligns large orthologous genomic regions using an iterative global alignment system. Rosetta identifies genes based on conservation of exonic features in sequences aligned by GLASS.

RNAiCut – Automated Detection of Significant Genes from Functional Genomic Screens.

MinoTar – Predict microRNA Targets in Coding Sequence.

Systems Biology

The Struct2Net program predicts protein-protein interactions (PPI) by integrating structure-based information with other functional annotations, e.g. GO, co-expression and co-localization etc. The structure-based protein interaction prediction is conducted using a protein threading server RAPTOR plus logistic regression.

IsoRank is an algorithm for global alignment of multiple protein-protein interaction (PPI) networks. The intuition is that a protein in one PPI network is a good match for a protein in another network if the former’s neighbors are good matches for the latter’s neighbors.

Other

t-sample is an online algorithm for time-series experiments that allows an experimenter to determine which biological samples should be hybridized to arrays to recover expression profiles within a given error bound.

http://people.csail.mit.edu/bab/computing_new.html#systems

Compressive genomics

• Po-Ru Loh,
• Michael Baym
• & Bonnie Berger

http://www.nature.com/nbt/journal/v30/n7/abs/nbt.2241.html

Nature Biotechnology 30, 627–630 (2012) doi:10.1038/nbt.2241

Published online 10 July 2012

STANFORD UNIVERSITY: Resources

BMIR is committed to the development of research tools as part of its goal to provide reusable, computational building blocks to facilitate the development of a vast array of systems. Some of these resources are described below.

Resources

The National Center for Biomedical Ontology (NCBO)

The National Center for Biomedical Ontology is a consortium of leading biologists, clinicians, informaticians, and ontologists who develop innovative technology and methods that allow scientists to create, disseminate, and manage biomedical information and knowledge in machine-processable form.

visit site

Protégé

Protégé is a free, open-source platform that provides its community of more than 80,000 users with a suite of tools to construct domain models and knowledge-based applications with ontologies.

visit site

PharmGKB

PharmGKB curates information that establishes knowledge about the relationships among drugs, diseases and genes, including their variations and gene products. Our mission is to catalyze pharmacogenomics research.

visit site

Simbios

About Simbios

Simbios, the National NIH Center for Physics-based Simulation of Biological Structures is devoted to helping biomedical researchers understand biological form and function. It provides infrastructure, software, and training to assist users as they create novel drugs, synthetic tissues, medical devices, and surgical interventions.

Simbios scientists investigate structure-function studies on a wide scale of biology – from molecules to organisms, and are currently focusing on challenging biological problems in RNA folding, myosin dynamics, neuromuscular biomechanics and cardiovascular dynamics.

visit site

 http://bmir.stanford.edu/resources/

Stanford BioMedical Informatics Research (BMIR) – Publications by Project

There are 8 publications for the project “Genomic Nosology for Medicine (GNOMED)”.

BMIR-2009-1362

Identifying compartment-specific non-HLA targets after renal transplantation by integrating transcriptome and ‘‘antibodyome’’ measures

L. Li, P. Wadia, M. Sarwal, N. Kambham, T. Sigdel, D. B. Miklos, R. Chen, M. Naesens, A. J. Butte

PNAS, 106, 11, 4148-4153. Published in 2009

BMIR-2008-1338

Using SNOMED-CT For Translational Genomics Data Integration

J. Dudley, D. P. Chen, A. J. Butte

Ronald Cornet, Kent Spackman (eds.): Representing and sharing knowledge using SNOMED. Proceedings of the 3rd International Conference on Knowledge Rep, Pheonix (AZ), USA, CEUR Workshop Proceedings, ISSN 1613-0073, online CEUR-WS.org/Vol-410/, 91-96. Published in 2008

BMIR-2008-1303

The Ultimate Model Organism

A. J. Butte

Science, 320, 5874, 325-327. Published in 2008

BMIR-2008-1293

Novel Integration of Hopsital Electronic Medical Records and Gene Expression Measurements to Identify Genetic Markers of Maturation

D. P. Chen, S. C. Weber, P. S. Constantinou, T. A. Ferris, H. J. Lowe, A. J. Butte

Pacific Symposium on Biocomputing, Big Island, Hawaii, 13, 243-254. Published in 2008

BMIR-2008-1292

Enabling Integrative Genomic Analysis of High-Impact Human Diseases through Text Mining

J. Dudley, A. J. Butte

Pacific Symposium on Biocomputing, Big Island, Hawaii, 13, 580-591. Published in 2008

BMIR-2007-1297

Methodologies for Extracting Functional Pharmacogenomic Experiments from International Repository

Y. Lin, A. P. Chiang, P. Yao, R. Chen, A. J. Butte, R. S. Lin

AMIA Annual Symposium, Chicago, IL, 463-467. Published in 2007

BMIR-2007-1296

Clinical Arrays of Laboratory Measures, or “Clinarrays”, Built from an Electronic Health Record Enable Disease Subtyping by Severity

D. P. Chen, S. C. Weber, P. S. Constantinou, T. A. Ferris, H. J. Lowe, A. J. Butte

AMIA Annual Symposium, Chicago, IL, 115-119. Published in 2007

BMIR-2006-1232

Finding Disease-Related Genomic Experiments Within an International Repository: First Steps in Translational Bioinformatics

A. J. Butte, R. Chen

Annual Symposium of the American Medical Informatics Association, Washington, D.C., 106-10. Published in 2006

http://bmir.stanford.edu/publications/project.php/genomic_nosology_for_medicine_gnomed

Featured Publications

BMIR-2011-1468

The National Center for Biomedical Ontology

M. A. Musen, N. F. Noy, C. G. Chute, M. A. Storey, B. Smith, N. H. Shah

. Published in 2011

BMIR-2009-1378

Prototyping a Biomedical Ontology Recommender Service

C. Jonquet, N. H. Shah, M. A. Musen

Bio-Ontologies: Knowledge in Biology, SIG, ISMB ECCB 2009, Stockholm, Sweden. Published in 2009

BMIR-2009-1376

Translational bioinformatics applications in genome medicine

A. J. Butte

Genome Medicine, 1, 6, 64. Published in 2009

BMIR-2009-1362

Identifying compartment-specific non-HLA targets after renal transplantation by integrating transcriptome and ‘‘antibodyome’’ measures

L. Li, P. Wadia, M. Sarwal, N. Kambham, T. Sigdel, D. B. Miklos, R. Chen, M. Naesens, A. J. Butte

PNAS, 106, 11, 4148-4153. Published in 2009

BMIR-2009-1361

Technology for Building Intelligent Systems: From Psychology to Engineering

M. A. Musen

Modeling Complex Systems, Bill Shuart, Will Spaulding and Jeffrey Poland, U Nebraska P, Lincoln, Nebraska, Vol 52 of the Nebraska Symposium on Motivation, 145-184. Published in 2009

BMIR-2009-1358

Software-Engineering Challenges of Building and Deploying Reusable Problem Solvers

M. J. O’Connor, C. I. Nyulas, A. Okhmatovskaia, D. Buckeridge, S. W. Tu, M. A. Musen

Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 24, 3. Published in 2009

BMIR-2009-1355

Data-Driven Methods to Discover Molecular Determinants of Serious Adverse Drug Events

A. P. Chiang, A. J. Butte

Clinical Pharmacology and Therapeutics, 28 January 2009, Advance online publication, doi:10.1038/clpt.2008.274. Published in 2009

BMIR-2009-1318

Knowledge-Data Integration for Temporal Reasoning in a Clinical Trial System

M. J. O’Connor, R. D. Shankar, D. B. Parrish, A. K. Das

International Journal of Medical Informatics, 78, Suppl. 1, S77-S85. Published in 2009

BMIR-2008-1353

GeneChaser: Identifying all biological and clinical conditions in which genes of interest are differentially expressed

R. Chen, R. Mallelwar, A. Thosar, S. Venkatasubrahmanyam, A. J. Butte

BMC Bioinformatics, 9, 1, 548. (doi:10.1186/1471-2105-9-548). Published in 2008

BMIR-2008-1346

FitSNPs: highly differentially expressed genes are more likely to have variants associated with disease

R. Chen, A. A. Morgan, J. Dudley, A. M. Deshpande, L. Li, K. Kodama, A. P. Chiang, A. J. Butte

Genome Biology, 9, 12, R170 (doi:10.1186/gb-2008-9-12-r170). Published in 2008

BMIR-2008-1341

Translational Bioinformatics: Coming of Age

A. J. Butte

Journal of the American Medical Informatics Association, JAMIA, 15, 6, 709-14. Published in 2008

BMIR-2008-1329

An Ontology-Driven Framework for Deploying JADE Agent Systems

C. I. Nyulas, M. J. O’Connor, S. W. Tu, A. Okhmatovskaia, D. Buckeridge, M. A. Musen

IEEE/WIC/ACM International Conference on Intelligent Agent Technology, Sydney, Australia, 2, 573-577. Published in 2008

BMIR-2008-1322

Understanding Detection Performance in Public Health Surveillance: Modeling Aberrancy-Detection Algorithms

D. Buckeridge, A. Okhmatovskaia, S. W. Tu, C. I. Nyulas, M. J. O’Connor, M. A. Musen

Journal of the American Medical Informatics Association, 15, 6, 760-769. Published in 2008

BMIR-2008-1319

Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer’s Disease

K. S. Supekar, V. Menon, M. A. Musen, D. L. Rubin, M. Greicius

Public Library of Science-Computational Biology., PLoS Computational Biology, June 2008. Published in 2008

BMIR-2008-1315

Medical Imaging on the Semantic Web: Annotation and Image Markup

D. L. Rubin, P. Mongkolwat, V. Kleper, K. S. Supekar, D. S. Channin

AAAI Spring Symposium Series, Semantic Scientific Knowledge Integration, Stanford. Published in 2008

BMIR-2008-1303

The Ultimate Model Organism

A. J. Butte

Science, 320, 5874, 325-327. Published in 2008

BMIR-2008-1298

BioPortal: A Web Portal to Biomedical Ontologies

D. L. Rubin, D. de Abreu Moreira, P. P. Kanjamala, M. A. Musen

AAAI Spring Symposium Series, Symbiotic Relationships between Semantic Web and Knowledge Engineering, Stanford University, (in press). Published in 2008

BMIR-2007-1295

AILUN: reannotating gene expression data automatically

R. Chen, L. Li, A. J. Butte

Nature Methods, 4, 11, 879. Published in 2007

BMIR-2007-1281

Evaluation and Integration of 49 Genome-wide Experiments and the Prediction of Previously Unknown Obesity-related Genes

S. B. English, A. J. Butte

Bioinformatics, Epub. Published in 2007

BMIR-2007-1261

Protege: A Tool for Managing and Using Terminology in Radiology Applications

D. L. Rubin, N. F. Noy, M. A. Musen

Journal of Digital Imaging, J Digit Imaging. Published in 2007

BMIR-2007-1244

Efficiently Querying Relational Databases using OWL and SWRL

M. J. O’Connor, R. D. Shankar, S. W. Tu, C. I. Nyulas, A. K. Das, M. A. Musen

The First International Conference on Web Reasoning and Rule Systems, Innsbruck, Austria, Springer, LNCS 4524, 361-363. Published in 2007

BMIR-2006-1090

Creation and implications of a phenome-genome network

A. J. Butte, I. S. Kohane

Nature Biotechnology, 24, 1, 55 – 62. Published in 2006

http://bmir.stanford.edu/publications/

NATIONAL CENTERS FOR BIOMEDICAL COMPUTING

National Center for Simulation of Biological Structures (SimBioS) at Stanford University

National Center for the Multiscale Analysis of Genomic and Cellular Networks (MAGNet) at Columbia University

National Alliance for Medical Image Computing (NA-MIC) at Brigham and Women’s Hospital, Boston, MA

Integrating Biology and the Bedside (I2B2) at Brigham and Women’s Hospital, Boston, MA

National Center for Biomedical Ontology (NCBO) at Stanford University

Integrate Data for Analysis, Anonymization, and Sharing (IDASH) at the University of California, San Diego

https://commonfund.nih.gov/bioinformatics/

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