The Genome in a Bottle Consortium is developing well-characterized reference genomes and methods to assess confidence in whole genome variant calls. They have generated data from multiple sequencing technologies for several reference genomes, including NA12878. They are developing integrated variant call sets and evaluating structural variants. The consortium is also working with the Global Alliance for Genomics and Health on benchmarking tools and metrics to evaluate variant caller performance.
Presentation by Justin Zook at GRC/GIAB ASHG 2017 workshop "Getting the most from the reference assembly and reference materials" on benchmarks for indels and structural variants.
Clinical labs need to be able to process samples down to a shortlist of variants and publish a professional report. Two common clinical applications for genetic tests include Cancer Gene Panels and Whole Exome Trios. Using VarSeq and VSReports, we will demonstrate how easy it is to go from a variant file created by a secondary analysis pipeline containing unfiltered variants to a report containing information for variants of interest. Along the way, we will discuss tips and tricks and answer frequently asked questions to help you get the most out of your data!
This webcast will present a thorough overview of VarSeq's support for clinics:
Cancer Gene Panel:
- Variant, Region and Sample Quality Assurance
- Filtering to variants in targeted cancer genes relevant to the tumor type
- Summarizing variants in a clinical report
Whole Exome Trio:
- Variant Quality Assurance
- Filtering to variants matching several inheritance patterns
- Summarizing variants in a clinical report
Two Clinical Workflows - From Unfiltered Variants to a Clinical ReportGolden Helix Inc
Clinical labs need to be able to process samples down to a short list of variants and publish a professional report. Two common clinical applications for genetic tests include Cancer Gene Panels and Whole Exome Trios. Using VarSeq and VSReports, we will demonstrate how easy it is to go from a variant file created by a secondary analysis pipeline containing unfiltered variants to a report containing information for variants of interest. Along the way we will discuss tips and tricks and answer frequently asked questions to help you get the most out of your data!
A workshop is intended for those who are interested in and are in the planning stages of conducting an RNA-Seq experiment. Topics to be discussed will include:
* Experimental Design of RNA-Seq experiment
* Sample preparation, best practices
* High throughput sequencing basics and choices
* Cost estimation
* Differential Gene Expression Analysis
* Data cleanup and quality assurance
* Mapping your data
* Assigning reads to genes and counting
* Analysis of differentially expressed genes
* Downstream analysis/visualizations and tables
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
Couples presenting to the infertility clinic- Do they really have infertility...Sujoy Dasgupta
Dr Sujoy Dasgupta presented the study on "Couples presenting to the infertility clinic- Do they really have infertility? – The unexplored stories of non-consummation" in the 13th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2024) at Manila on 24 May, 2024.
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
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Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
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Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
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NYSORA Guideline
2 Case Reports of Gastric Ultrasound
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists
Giab ashg webinar 160224
1. Genome in a Bottle Consortium
February 24, 2016
Reference Materials for Human Genome
Sequencing
Justin Zook, Ph.D and Marc Salit, Ph.D.
National Institute of Standards and Technology
2. Outline
• Genome in a Bottle
(GIAB) products
• Current and future work
• Best practices for using
GIAB products to
benchmark variant calls
• Genome in a Bottle
– Open consortium to
develop well-
characterized genomes
for benchmarking
– 100-150 public, private,
and academic
participants at
workshops
3. GIAB Scope
• The Genome in a Bottle Consortium is
developing the reference materials, reference
methods, and reference data needed to assess
confidence in human whole genome variant
calls.
• Priority is authoritative characterization of
human genomes.
GIAB steering committee, Aug 2015
4. Well-characterized, stable RMs
• Obtain metrics for
validation, QC, QA, PT
• Determine sources and
types of bias/error
• Learn to resolve difficult
structural variants
• Improve reference
genome assembly
• Optimization
• Enable regulated
applications
5. Analytical Performance
• Use well-characterized
genomic DNA reference
materials to benchmark
performance
• Tools to facilitate their
use
– With the Global Alliance
Data Working Group
Benchmarking Team
Sample
gDNA isolation
Library Prep
Sequencing
Alignment/Mapping
Variant Calling
Confidence Estimates
Downstream Analysis
genericmeasurementprocess
6. High-confidence SNP/indel calls
• Methods to develop
SNP/indel call set
described in manuscript
• Broad and quick
adoption of call set for
benchmarking
– struck nerve
Zook et al., Nature Biotechnology, 2014.
7. Candidate NIST Reference Materials
Genome PGP ID Coriell ID NIST ID NIST RM #
CEPH
Mother/Daugh
ter
N/A GM12878 HG001 RM8398
AJ Son huAA53E0 GM24385 HG002 RM8391
(son)/RM8392
(trio)
AJ Father hu6E4515 GM24149 HG003 RM8392 (trio)
AJ Mother hu8E87A9 GM24143 HG004 RM8392 (trio)
Asian Son hu91BD69 GM24631 HG005 RM8393
Asian Father huCA017E GM24694 N/A N/A
Asian Mother hu38168C GM24695 N/A N/A
Note: RMs 8391 to 8393 are planned for release by end of Q2 2016
8. Dataset AJ Son AJ Parents Chinese son Chinese
parents
NA12878
Illumina Paired-
end
X X X X X
Illumina Long
Mate pair
X X X X X
Illumina
“moleculo”
X X X X X
Complete
Genomics
X X X X X
Complete
Genomics LFR
X X X
Ion exome
X X X X
BioNano
X X X X
10X
X X X
PacBio
X X X
SOLiD single end
X X X
Illumina exome
X X X X
Oxford
Nanopore
X
10. Data Release:
Real-time, Open, Public Release
Individual Datasets
• Uploaded to GIAB FTP site
as data are collected
• Includes raw reads, aligned
reads, and
variant/reference calls
• 12 datasets described in
bioRxiv paper
• Develop SNP, indel, and
homozygous reference calls
similar to NA12878
• Developing methods to
form high-confidence calls
for difficult variant types
and regions
• Released calls are versioned
• Preliminary call-sets will be
made available to be
critiqued
Integrated High-confidence Calls
11. SNP/Indel Integration Method Update
• Implementing refined integration methods
– Developed so others can readily reproduce results
– Consistent results for all GIAB genomes
– Simpler process taking advantage of best practices
for each technology
• Validating with released NA12878 RM data
– Preliminary comparisons show minor changes
• Application to PGP trios
– Plan to analyze AJ trio by Q2 2016
– Release of NIST RMs in Q2 2016
– Develop calls for GRCh38
12. Proposed approach to form high-
confidence SV (and non-SV) calls
Generate Candidate Calls
Compare/evaluate calls using
Parliament/MetaSV/svclassify/others?;
manual inspection
Integrate new and revised calls; manual
inspection
Combine integrated calls; manual inspection;
targeted experimental validation?
Aug/Dec 2015
Aug 2015-Jan 2016
Planning in
Jan-Feb 2016
Feb 2016 and
beyond
13. Preliminary comparisons of 17 Deletion Callsets
Sensitivity to calls in 2 technologies
NOTE: These are preliminary comparisons of data under active development and likely
different from true sensitivity of callers
14. Preliminary comparisons of 17 Deletion Callsets
Difference between predicted size and median predicted size
NOTE: These are preliminary comparisons of data under active development and likely
different from true size accuracy
15. Preliminary comparisons of 17 Deletion Callsets
Number of unique calls
NOTE: These are preliminary comparisons of data under active development without
filtering and unique calls may be correct
16. GeT-RM Browser from NCBI and CDC
• http://www.ncbi.nlm.nih.gov/variation/tools/get-rm/
• Allows visualization of data underlying call each call
17. Global Alliance for Genomics and Health
Benchmarking Task Team
Progress:
• Initial version of standardized definitions for
performance metrics like TP, FP, and FN.
• Continued development of sophisticated benchmarking
tools
– vcfeval – Len Trigg
– hap.py – Peter Krusche
– vgraph – Kevin Jacobs
• Standardized intermediate and final file formats
• Standardized bed files with difficult genome contexts for
stratification
• github.com/ga4gh/benchmarking-tools
18. Proposed Performance Metrics
Definitions
• Define TP/FP/FN/TN in 4 ways depending on
required stringency of match:
• Loose match: TP if within x-bp of a true variant
• Allelle match: TP if ALT allele matches
• Genotype match: TP if genotype and ALT allele
match
• Phasing match: TP if genotype, ALT allele, and
phasing with nearby variants all match
• True negatives are difficult to define because
an infinite number of potential alleles exist
19. Approaches to Benchmarking Variant
Calling
• Well-characterized whole genome Reference
Materials
• Many samples characterized in clinically relevant
regions
• Synthetic DNA spike-ins
• Cell lines with engineered mutations
• Simulated reads
• Modified real reads
• Modified reference genomes
• Confirming results found in real samples over
time
20. Challenges in Benchmarking
Small Variant Calling
• It is difficult to do robust benchmarking of tests designed to
detect many analytes (e.g., many variants)
• Easiest to benchmark only within high-confidence bed file,
but…
• Benchmark calls/regions tend to be biased towards easier
variants and regions
– Some clinical tests are enriched for difficult sites
• Challenges with benchmarking complex variants near
boundaries of high-confidence regions
• Always manually inspect a subset of FPs/FNs
• Stratification by variant type and region is important
• Always calculate confidence intervals on performance
metrics
22. Acknowledgments
• FDA
• Many members of
Genome in a
Bottle
–New members
welcome!
–Sign up on website
for email
newsletters
GIAB Steering Committee
– Marc Salit
– Justin Zook
– David Mittelman
– Andrew Grupe
– Michael Eberle
– Steve Sherry
– Deanna Church
– Francisco De La Vega
– Christian Olsen
– Monica Basehore
– Lisa Kalman
– Christopher Mason
– Elizabeth Mansfield
– Liz Kerrigan
– Leming Shi
– Melvin Limson
– Alexander Wait Zaranek
– Nils Homer
– Fiona Hyland
– Steve Lincoln
– Don Baldwin
– Robyn Temple-Smolkin
– Chunlin Xiao
– Kara Norman
– Luke Hickey
23. For More Information
www.genomeinabottle.org - sign up for general GIAB and Analysis
Team google group emails
github.com/genome-in-a-bottle – Guide to GIAB data & ftp
www.slideshare.net/genomeinabottle
www.ncbi.nlm.nih.gov/variation/tools/get-rm/ - Get-RM Browser
Data: http://biorxiv.org/content/early/2015/09/15/026468
Global Alliance Benchmarking Team
– https://github.com/ga4gh/benchmarking-tools
Twice yearly public workshops
– Winter at Stanford University, California, USA
– Summer at NIST, Maryland, USA
Justin Zook: jzook@nist.gov
Marc Salit: salit@nist.gov