Development of FDA MicroDB: A Regulatory-Grade Microbial Reference DatabaseNathan Olson
"Development of FDA MicroDB: A Regulatory-Grade
Microbial Reference Database" presentation at the Standards for Pathogen Identification via NGS (SPIN) workshop hosted by the National Institute for Standards and Technology October 2014 by Heike Sichtig, PhD from the FDA and Luke Tallon from IGS UMSOM.
Development of FDA MicroDB: A Regulatory-Grade Microbial Reference DatabaseNathan Olson
"Development of FDA MicroDB: A Regulatory-Grade
Microbial Reference Database" presentation at the Standards for Pathogen Identification via NGS (SPIN) workshop hosted by the National Institute for Standards and Technology October 2014 by Heike Sichtig, PhD from the FDA and Luke Tallon from IGS UMSOM.
Comparing Retrieval Effectiveness of Alternative Content Segmentation Methods...Maria Eskevich
We present an exploratory study of the retrieval of semi-professional user-generated Internet video. The study is based on the MediaEval 2011 Rich Speech Retrieval (RSR) task for which the dataset was taken from the Internet sharing platform blip.tv, and search queries associated with specific speech acts occurring in the video. We compare results from three participant groups using: automatic speech recognition system transcript (ASR), metadata manually assigned to each video by the user who uploaded it, and their combination. RSR 2011 was a known-item search for a single manually identified ideal jump-in point in the video for each query where playback should begin. Retrieval effectiveness is measured using the MRR and mGAP metrics.
Using different transcript segmentation methods the participants tried to maximize the rank of the relevant item and to locate the nearest match to the ideal jump-in point. Results indicate that best overall results are obtained for topically homogeneous segments which have a strong overlap with the relevant region associated with the jump-in point, and that use of metadata can be beneficial when segments are unfocused or cover more than one topic.
The Clinical Significance of Transcript Alignment DiscrepanciesReece Hart
Gene transcripts are the lens through which we understand variants that are identified by genome sequencing, reported in scientific literature, and communicated on clinical reports. An accurate, shared representation of transcripts is essential to communicating variants reliably. This talk presents observations of significant discrepancies between sources of transcripts that will lead to discrepancies in the clinical interpretation of variants, and tools that we have released to contend with these complexities.
BIOBASE, the leader in data annotation and curation for genomics, took part in the Genome Informatics Alliance 2012: Logistics meeting in Oregon, and had an opportunity to present on trends in annotation of genomic data.
Examining gene expression and methylation with next gen sequencingStephen Turner
Slides on RNA-seq and methylation studies using next-gen sequencing given at the University of Miami Hussman Institute for Human Genomics "Genetic Analysis of Complex Human Diseases" course in 2012 (http://hihg.med.miami.edu/educational-programs/analysis-of-complex-human-diseases/genetic-analysis-of-complex-human-diseases/)
Next Generation Sequencing for Identification and Subtyping of Foodborne Pat...Nathan Olson
"Next Generation Sequencing for Identification and Subtyping of Foodborne Pathogens" presentation at the Standards for Pathogen Identification via NGS (SPIN) workshop hosted by the National Institute for Standards and Technology October 2014 by Rebecca Lindsey, PhD from Enteric Diseases Laboratory Branch of the CDC.
About the Webinar: Genomic testing has already become commonplace in oncology, but exponential growth in more comprehensive genomic tests, other innovative tests and testing approaches in oncology, as well as a number of other therapeutic areas is expected in the coming years. With the emergence of more complex, more expensive, and more promising tests, policymakers and healthcare providers may be challenged to provide these to patients at the pace of innovation. Don Husereau will describe what conditions are necessary for equitable access to advanced innovative testing, how major Canadian provinces are doing, and what more needs to be done in the coming years to benefit all patients.
Comparing Retrieval Effectiveness of Alternative Content Segmentation Methods...Maria Eskevich
We present an exploratory study of the retrieval of semi-professional user-generated Internet video. The study is based on the MediaEval 2011 Rich Speech Retrieval (RSR) task for which the dataset was taken from the Internet sharing platform blip.tv, and search queries associated with specific speech acts occurring in the video. We compare results from three participant groups using: automatic speech recognition system transcript (ASR), metadata manually assigned to each video by the user who uploaded it, and their combination. RSR 2011 was a known-item search for a single manually identified ideal jump-in point in the video for each query where playback should begin. Retrieval effectiveness is measured using the MRR and mGAP metrics.
Using different transcript segmentation methods the participants tried to maximize the rank of the relevant item and to locate the nearest match to the ideal jump-in point. Results indicate that best overall results are obtained for topically homogeneous segments which have a strong overlap with the relevant region associated with the jump-in point, and that use of metadata can be beneficial when segments are unfocused or cover more than one topic.
The Clinical Significance of Transcript Alignment DiscrepanciesReece Hart
Gene transcripts are the lens through which we understand variants that are identified by genome sequencing, reported in scientific literature, and communicated on clinical reports. An accurate, shared representation of transcripts is essential to communicating variants reliably. This talk presents observations of significant discrepancies between sources of transcripts that will lead to discrepancies in the clinical interpretation of variants, and tools that we have released to contend with these complexities.
BIOBASE, the leader in data annotation and curation for genomics, took part in the Genome Informatics Alliance 2012: Logistics meeting in Oregon, and had an opportunity to present on trends in annotation of genomic data.
Examining gene expression and methylation with next gen sequencingStephen Turner
Slides on RNA-seq and methylation studies using next-gen sequencing given at the University of Miami Hussman Institute for Human Genomics "Genetic Analysis of Complex Human Diseases" course in 2012 (http://hihg.med.miami.edu/educational-programs/analysis-of-complex-human-diseases/genetic-analysis-of-complex-human-diseases/)
Next Generation Sequencing for Identification and Subtyping of Foodborne Pat...Nathan Olson
"Next Generation Sequencing for Identification and Subtyping of Foodborne Pathogens" presentation at the Standards for Pathogen Identification via NGS (SPIN) workshop hosted by the National Institute for Standards and Technology October 2014 by Rebecca Lindsey, PhD from Enteric Diseases Laboratory Branch of the CDC.
About the Webinar: Genomic testing has already become commonplace in oncology, but exponential growth in more comprehensive genomic tests, other innovative tests and testing approaches in oncology, as well as a number of other therapeutic areas is expected in the coming years. With the emergence of more complex, more expensive, and more promising tests, policymakers and healthcare providers may be challenged to provide these to patients at the pace of innovation. Don Husereau will describe what conditions are necessary for equitable access to advanced innovative testing, how major Canadian provinces are doing, and what more needs to be done in the coming years to benefit all patients.
*Watch the video at the end of the presentation
Seminar led by Dr. Xavier de la Cruz, ICREA Research Professor. Head of the Translational Bioinformatics in Neuroscience group of VHIR, at VHIR (22nd November 2012).
Content: The need to identify the pathological character of mutations may arise in different contexts in biomedical research. However, the methods available to address this problem essentially depend on the number of cases under analysis. When we work with only a few mutations we can use an artisan-like approach, where all information available on protein sequence, structure and function is manually retrieved and studied. However, when we need to characterize many variants, as can be the case in exome projects, faster methods are required to assess their pathogenicity. In my talk I will illustrate the principles underlying these two approaches with examples from the study of Fabry disease mutations, resulting from our collaborative work at the VHIR.
CLARITY BPA: a Novel Approach to study EDCsDES Daughter
by the Collaborative on Health and the Environment
On this call Retha Newbold, MS, Researcher Emeritus, National Toxicology Program, National Institute of Environmental Health Sciences, discussed the program called “The Consortium Linking Academic and Regulatory Insights on the Toxicity of Bisphenol A (CLARITY-BPA)” which is an interagency agreement, conducted under the auspices of the National Toxicology Program (NTP), between The National Institute of Environmental Health Sciences (NIEHS) supported grantees, the staff of the Division of the National Toxicology Program (DNTP) at NIH/NIEHS, and the Food and Drug Administration at the National Center for Toxicological Research (FDA/NCTR). The goals of the consortium are to enhance the utility of a perinatal 2-year GLP chronic toxicity study on BPA for regulatory decision-making by incorporating a wide range of doses and some additional disease-related endpoints that are not usually covered.
To this end, 12 NIEHS grantees are studying hypothesis-driven mechanisms by investigating specific endpoints that maybe altered by BPA including behavioral/neuroendocrine, immune function, cardiac, reproductive tract, cancer, thyroid, and other organ systems. This consortium is unique in that it combines the knowledge and skills of the NTP staff with experts from the academic field who are covering more mechanistic studies. Although this program focuses on BPA, it may provide an example of how to better study effects of other endocrine disrupting chemicals especially since numerous organ systems may be involved.
Sources: http://www.healthandenvironment.org/partnership_calls/14639
Personalized Medicine and the Omics Revolution by Professor Mike SnyderThe Hive
Personalized medicine is expected to benefit from the combination of genomic information with the global monitoring of molecular components and physiological states. To ascertain whether this can be achieved, we determined the whole genome sequence of an individual at high accuracy and performed an integrated Personal Omics Profiling (iPOP) analysis, combining genomic, transcriptomic, proteomic, metabolomic, and autoantibodyomic information, over a 38-month period that included healthy and two virally infected states. Our iPOP analysis of blood components revealed extensive, dynamic and broad changes in diverse molecular components and biological pathways across healthy and disease conditions. Importantly, genomic information was also used to estimate medical risks, including Type 2 Diabetes, whose onset was observed during the course of our study. Our study demonstrates that longitudinal personal omics profiling can relate genomic information to global functional omics activity for physiological and medical interpretation of healthy and disease states.
Meet the speaker, Professor Michael Snyder (Stanford):
Michael Snyder is the Stanford Ascherman Professor, Chair of Genetics and the Director of the Center of Genomics and Personalized Medicine. He received his Ph.D. from the California Institute of Technology and postdoctoral training at Stanford University. He is a leader in the field of functional genomics and proteomics, and one of the major participants of the ENCODE project. His laboratory study was the first to perform a large-scale functional genomics project in any organism, and has launched many technologies in genomics and proteomics. These including the development of proteome chips, high resolution tiling arrays for the entire human genome, methods for global mapping of transcription factor binding sites (ChIP-chip now replaced by ChIP-seq), paired end sequencing for mapping of structural variation in eukaryotes, de novo genome sequencing of genomes using high throughput technologies and RNA-Seq. These technologies have been used for characterizing genomes, proteomes and regulatory networks. Seminal findings from the Snyder laboratory include; the discovery that much more of the human genome is transcribed and contains regulatory information than was previously appreciated, and a high diversity of transcription factor binding occurs both between and within species. He has also combined different state-of–the-art omics technologies to perform the first longitudinal detailed integrative personal omics profile (iPOP) of person and used this to assess disease risk and monitor disease states for personalized medicine. He is a co-founder of several biotechnology companies including; Protometrix (now part of Life Technologies), Affomix (now part of Illumina), Excelix, and Personalis, and he presently serves on the board of a number of companies.
In the late Fall and Winter of 2018, the Pistoia Alliance in cooperation with Elsevier and charitable organizations Cures within Reach and Mission: Cure ran a datathon aiming to find drugs suitable for treatment of childhood chronic pancreatitis, a rare disease that causes extreme suffering. The datathon resulted in identification of four candidate compounds in a short time frame of just under three months. In this webinar our speakers discuss the technologies that made this leap possible
Similar to HVP Critical Assessment of Genome Interpretation (20)
1. ca·gey ˈkā-jē adjective
1: hesitant about committing oneself;
2a: wary of being trapped or deceived;
2b: marked by cleverness
CAGI (ˈkā-jē)
Critical Assessment of Genome Interpretation
A community experiment to evaluate phenotype prediction
Reece Hart (with Steven Brenner and John Moult)
QB3 / Center for Computational Biology
UC Berkeley
reece@berkeley.edu
Human Variome Project Meeting
Paris 2010-05-12
2. The Significance of
“Variants of Uncertain Significance”
“VUS – Variant of uncertain significance. A variation
in a genetic sequence whose association with
disease risk is unknown. Also called variant of
uncertain significance, variant of unknown
significance, and unclassified variant.”
http://www.cancer.gov/cancertopics/genetics-terms-alphalist
2
3. The long tail of rare diseases.
“A rare disease typically affects a patient
population estimated at fewer than 200,000 in
the U.S. There are more than 6,000 rare
diseases known today and they affect an
estimated 25 million persons in the U.S.”
NIH Office of Rare Diseases Research
http://rarediseases.info.nih.gov/
3
4. Interpretation of Unclassified Variants
a sampling of responses from genetic counselors
➢ Routinely used ➢ Selectively used
● dbSNP ● PharmGKB
● OMIM ● LSDBs
● GeneReviews ● Domain prediction
● PolyPhen ● Structure impact
● SIFT analysis
● PubMed ● Homology
● Mailing lists
4
5. Genome Variant Impact Prediction Tools
an incomplete list
Program URL
Align-GVGD http://agvgd.iarc.fr/
AutoMute http://proteins.gmu.edu/automute/
CUPSAT http://cupsat.tu-bs.de/
Dmutant http://sparks.informatics.iupui.edu/hzhou/mutation.html
nsSNPAnalyzer http://snpanalyzer.uthsc.edu/
PantherPSEC http://www.pantherdb.org/tools/csnpScoreForm.jsp
PhD-SNP http://gpcr.biocomp.unibo.it/~emidio/PhD-SNP/PhD-SNP.htm
Pmut http://mmb2.pcb.ub.es:8080/PMut/
PolyPhen http://coot.embl.de/PolyPhen/
SIFT http://sift.jcvi.org/
SNAP http://cubic.bioc.columbia.edu/services/snap/
SNP Function Pred. http://www.ensembl.org/ [N.B. login required]
SNPinfo / FuncPred http://snpinfo.niehs.nih.gov/snpfunc.htm
SNPs3D http://snps3d.org/
UMD-predictor http://www.umd.be/
5
6. Current methods are the tip of the iceberg.
m
C
protein non-protein repeats indels epigenetics
transcripts transcripts
~99%
~1%
6
7. Objectively Assessing Computational Predictions
➢ CASP – Structure prediction
➢ CAPRI – Protein-ligand docking
➢ EGASP – Encode Gene Annotation
➢ RGASP – RNA-Seq mapping
➢ DREAM – network model assessment
Data Acquisition
Publication
The Prediction Window
~1-12 months when unpublished
high-quality data are available
7
8. CAGI – Critical Assessment of Genome Interpretation
A community assessment of the state-of-the-art in phenotype prediction.
➢ Follow the successful critical
assessment framework:
● Solicit pre-publication genotype-
phenotype associations
● Provide genomic data to predictors
and collect their predictions
● Assess predictions against revealed
annotations, mechanisms, and
phenotypes
8
9. Sample Prediction Categories
Molecular Cellular Organismal
A A A
T T T
MTHFR mutants – Breast Cancer – PGP100 –
Yeast growth Segregation of rare Unpublished
rates with various variants among phenotypes from
MTHFR mutations 2500 cases and PGP100 project.
and [folate]. controls.
(Jasper Rine) (Sean Tavtigian) (George Church)
Please contact us if you have pre-publication genotype-phenotype
association data.
9
10. Census of Molecular Mechanisms
possible mechanisms of variant impact for WTCCC SNVs
Wellcome Trust Case Control Consortium Nature. 2007;447(7145):661-78. 10
11. Contributors, Predictors, Assessors
an incomplete list of participants
Gad Getz Sean Tavtigian Rachel Karchin Jasper Rine
Pauline Ng Marc Greenblatt Mauno Vihinen George Church
11
12. Sample CAGI Timeline
Dates are for illustration – exact dates have not been set.
05-24
05-31
06-07
06-14
06-21
06-28
08-23
08-30
09-06
09-13
09-20
09-27
11-22
11-29
12-06
12-13
12-20
12-27
05-03
05-10
05-17
07-05
07-12
07-19
07-26
08-02
08-09
08-16
10-04
10-11
10-18
10-25
11-01
11-08
11-15
01-03
01-10
01-17
01-24
01-31
Data Gathering
Prediction Season
Assessment
Key Dates
▲ finalize data sources ▲ workshop
▲ release prospectus / rules
▲ open participant registration
12
13. CAGI Summary
➢ CAGI will:
● objectively assess phenotype prediction methods
● inform future research directions
● introduce researchers in diverse fields
➢ CAGI is being planned for the end of 2010
or early 2011.
➢ Now seeking data contributors, assessors,
and predictors.
➢ Feedback is sought! reece@berkeley.edu
➢ See http://genomecommons.org/cagi for more
information. 13
16. Program in Translational Genomics
Rasmus Nielseno
Michael I. Jordan
Ian Holmes
Kimmen Sjölander
Yun Song
Monty Slatkin
Terry Speed
Steven Hart
Reece Brenner Sandrine Dudoit Robert Nussbaum Mark van der Laan
Plant & Mol. Biology
Chief Scientist Biostatistics Chief, Medical Genetics Richard Karp
UC Berkeley
UC Berkeley UC Berkeley UCSF Bernd Sturmfels
Steven Evans
Elizabeth Purdom
Haiyan Huang
Peter Bickel
Susan Marqusee
Michael Eisen
Lisa Barcellos
Rachel Brem
Tom Alber
Jasper Rine Lior Pachter Bernie Lo
Genetics, Genomics & Dev Mathematics Director, Medical Ethics
Chair, Computational Biology Mol., Cell, Biol Department of Medicine
UC Berkeley UC Berkeley UCSF
16