Benchmarking variant calls is challenging but important for evaluating sequencing and analysis methods. The GA4GH Benchmarking Team has developed standardized tools using Genome in a Bottle reference samples to robustly benchmark variant calls, including SNPs and indels. Their tools provide stratified performance metrics in different genomic contexts to better understand accuracy. Ongoing work focuses on more difficult variants like indels and structural variants. Standardized benchmarking allows fair comparison of methods and helps improve variant detection.
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.
GenoThreat / GenoGUARD -- open source biosecurity solution for the gene synth...madalladam
DNA sequence screening software that implements the best match method recommended by the federal government.
Publication: Adam L et al, Strengths and limitations of the federal guidance on synthetic DNA, Nature Biotechnology (2011) 29, 208–210 doi:10.1038/nbt.1802
US Department of Health and Human Services voluntary guidelines “Screening Framework Guidance for Synthetic Double-Stranded DNA Providers” November 2009.
Software: http://sourceforge.net/projects/genothreat/
Prix Galien International 2024 Forum ProgramLevi Shapiro
June 20, 2024, Prix Galien International and Jerusalem Ethics Forum in ROME. Detailed agenda including panels:
- ADVANCES IN CARDIOLOGY: A NEW PARADIGM IS COMING
- WOMEN’S HEALTH: FERTILITY PRESERVATION
- WHAT’S NEW IN THE TREATMENT OF INFECTIOUS,
ONCOLOGICAL AND INFLAMMATORY SKIN DISEASES?
- ARTIFICIAL INTELLIGENCE AND ETHICS
- GENE THERAPY
- BEYOND BORDERS: GLOBAL INITIATIVES FOR DEMOCRATIZING LIFE SCIENCE TECHNOLOGIES AND PROMOTING ACCESS TO HEALTHCARE
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- Prix Galien International Awards Ceremony
MANAGEMENT OF ATRIOVENTRICULAR CONDUCTION BLOCK.pdfJim Jacob Roy
Cardiac conduction defects can occur due to various causes.
Atrioventricular conduction blocks ( AV blocks ) are classified into 3 types.
This document describes the acute management of AV block.
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
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These lecture slides, by Dr Sidra Arshad, offer a quick overview of physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar leads (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
Report Back from SGO 2024: What’s the Latest in Cervical Cancer?bkling
Are you curious about what’s new in cervical cancer research or unsure what the findings mean? Join Dr. Emily Ko, a gynecologic oncologist at Penn Medicine, to learn about the latest updates from the Society of Gynecologic Oncology (SGO) 2024 Annual Meeting on Women’s Cancer. Dr. Ko will discuss what the research presented at the conference means for you and answer your questions about the new developments.
Anti ulcer drugs and their Advance pharmacology ||
Anti-ulcer drugs are medications used to prevent and treat ulcers in the stomach and upper part of the small intestine (duodenal ulcers). These ulcers are often caused by an imbalance between stomach acid and the mucosal lining, which protects the stomach lining.
||Scope: Overview of various classes of anti-ulcer drugs, their mechanisms of action, indications, side effects, and clinical considerations.
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TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
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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The prostate is an exocrine gland of the male mammalian reproductive system
It is a walnut-sized gland that forms part of the male reproductive system and is located in front of the rectum and just below the urinary bladder
Function is to store and secrete a clear, slightly alkaline fluid that constitutes 10-30% of the volume of the seminal fluid that along with the spermatozoa, constitutes semen
A healthy human prostate measures (4cm-vertical, by 3cm-horizontal, 2cm ant-post ).
It surrounds the urethra just below the urinary bladder. It has anterior, median, posterior and two lateral lobes
It’s work is regulated by androgens which are responsible for male sex characteristics
Generalised disease of the prostate due to hormonal derangement which leads to non malignant enlargement of the gland (increase in the number of epithelial cells and stromal tissue)to cause compression of the urethra leading to symptoms (LUTS
171114 best practices for benchmarking variant calls justin
1. Best practices for benchmarking variant calls
Justin Zook and the GA4GH Benchmarking Team
NIST Genome-Scale Measurements Group
Joint Initiative for Metrology in Biology (JIMB)
Genome in a Bottle Consortium
November 14, 2017
2. Take-home Messages
• Benchmarking variant calls is easy to do incorrectly
• The GA4GH Benchmarking Team has developed a set of public
tools for robust, standardized benchmarking of variant calls
• Benchmarking results should be interpreted critically
• Ongoing work on difficult variants and regions
3. Why are we doing this work?
• Technologies evolving rapidly
• Different sequencing and
bioinformatics methods give
different results
• Now have concordance in easy
regions, but not in difficult
regions
• Challenge:
– How do we benchmark variants in a
6 billion base-pair genome?
O’Rawe et al, Genome Medicine, 2013
https://doi.org/10.1186/gm432
4. Genome in a Bottle Consortium
Authoritative Characterization of Human Genomes
Sample
gDNA isolation
Library Prep
Sequencing
Alignment/Mapping
Variant Calling
Confidence Estimates
Downstream Analysis
• gDNA reference materials to
evaluate performance
• established consortium to
develop reference materials,
data, methods, performance
metrics
genericmeasurementprocess
www.slideshare.net/genomeinabottle
5. Bringing Principles of Metrology
to the Genome
• Reference materials
– DNA in a tube you can buy from
NIST
• Extensive state-of-the-art
characterization
– arbitrated “gold standard” calls for
SNPs, small indels
• “Upgradable” as technology
develops
• PGP genomes suitable for
commercial derived products
• Developing benchmarking tools
and software
– with GA4GH
• Samples being used to develop
and demonstrate new technology
6. Benchmarking the GIAB benchmarks
• Compare high-confidence calls to
other callsets and manually
inspect subset of differences
– vs. pedigree-based calls
– vs. common pipelines
– Trio analysis
• When benchmarking a new
callset against ours, most
putative FPs/FNs should actually
be FPs/FNs
8. Evolution of high-confidence calls
Calls
HC
Regions HC Calls
HC
indels
Concordant
with PG
NIST-
only in
beds
PG-only
in beds PG-only
Variants
Phased
v2.19 2.22 Gb 3153247 352937 3030703 87 404 1018795 0.3%
v3.2.2 2.53 Gb 3512990 335594 3391783 57 52 657715 3.9%
v3.3 2.57 Gb 3566076 358753 3441361 40 60 608137 8.8%
v3.3.2 2.58 Gb 3691156 487841 3529641 47 61 469202 99.6%
5-7
errors
in NIST
1-7
errors
in NIST
~2 FPs and ~2 FNs per million NIST variants in PG and NIST bed files
9. Global Alliance for Genomics and Health Benchmarking Task
Team
• Developed standardized
definitions for performance
metrics like TP, FP, and FN.
• Developing sophisticated
benchmarking tools
• Integrated into a single framework
with standardized inputs and
outputs
• Standardized bed files with
difficult genome contexts for
stratification
https://github.com/ga4gh/benchmarking-tools
Variant types can change when decomposing
or recomposing variants:
Complex variant:
chr1 201586350 CTCTCTCTCT CA
DEL + SNP:
chr1 201586350 CTCTCTCTCT C
chr1 201586359 T A
Credit: Peter Krusche, Illumina
GA4GH Benchmarking Team
10. Why are definitions important?
Challenges
• Genotype comparisons don’t naturally
fall into 2 categories as required for
sensitivity, precision, and specificity
• Sometimes variants are partially called
and/or partially filtered
• Clustered variants can be counted
individually or as a single complex
event
• How should filtered variants or “no-
call” sites be treated?
Example cases
• Truth is a heterozygous SNP but vcf has
a homozygous SNP
– 1 FP, 1 FN, and 1 Genotype mismatch
• Truth is an indel but vcf has a SNP at
same position
– 1 FP, 1 FN, and 1 allele mismatch
• Truth is a deletion + SNP but vcf has
the deletion only
– 1 TP and 1 FN, or 1 FP and 1-2 FNs,
depending on representations and
comparison method
12. Comparison methods affect performance metrics
• Some callers are affected by the comparison method more than
others
–Biggest effect from clustering nearby variants
13. GA4GH Reference Implementation
Truth VCF
Query VCF
Comparison Engine
vcfeval / vgraph / xcmp /
bcftools / ...
VCF-I
Quantification
quantify / hap.py
Stratification BED
files
Confident Call
Regions
VCF-R
Counts / ROCs
HTML Report e.g. for
precisionFDA
16. FN rates high in some tandem repeats
1x0.3x 10x3x 30x
11to50bp51to200bp
2bp unit repeat
3bp unit repeat
4bp unit repeat
2bp unit repeat
3bp unit repeat
4bp unit repeat
FN rate vs. average
17. Benchmarking stats can be difficult to interpret
Example: FN SNPs in coding regions
RefSeq Coding Regions
• Studies often focus on variants in
coding regions
• We look at FN SNP rates for bwa-GATK
using the decoy
SNP benchmarking stats vs. PG and 3.3.2
• 97.98% sensitivity vs. PG
– FNs predominately in low MQ and/or
segmental duplication regions
– ~80% of FNs supported by long or linked
reads
• 99.96% sensitivity vs. NISTv3.3.2
– 62x lower FN rate than vs PG
• As always, true sensitivity is unknown
18. Benchmarking stats can be difficult to interpret
Example: FN SNPs in coding regions
RefSeq Coding Regions
• Studies often focus on variants in
coding regions
• We look at FN SNP rates for bwa-GATK
using the decoy
SNP benchmarking stats vs. PG and 3.3.2
• 97.98% sensitivity vs. PG
– FNs predominately in low MQ and/or
segmental duplication regions
– ~80% of FNs supported by long or linked
reads
• 99.96% sensitivity vs. NISTv3.3.2
– 62x lower FN rate than vs PG
• As always, true sensitivity is unknown
True accuracy is hard to
estimate, especially in
difficult regions
19. Benchmarking against each GIAB genome
Genome Type Subset 100% -
recall
100% - precision Recall Precision Fraction of calls
outside high-conf
bed
HG001 SNP all 0.0277 0.1274 0.9997 0.9987 0.1653
HG002 SNP all 0.0664 0.1342 0.9993 0.9987 0.1910
HG003 SNP all 0.0625 0.1489 0.9994 0.9985 0.1967
HG004 SNP all 0.0633 0.1592 0.9994 0.9984 0.1975
HG005 SNP all 0.1175 0.0870 0.9988 0.9991 0.1834
HG001 SNP notinalldifficultregions 0.0096 0.0783 0.9999 0.9992 0.0491
HG002 SNP notinalldifficultregions 0.0102 0.0576 0.9999 0.9994 0.0864
HG003 SNP notinalldifficultregions 0.0128 0.0819 0.9999 0.9992 0.0864
HG004 SNP notinalldifficultregions 0.0102 0.0860 0.9999 0.9991 0.0854
HG005 SNP notinalldifficultregions 0.0931 0.0541 0.9991 0.9995 0.0664
HG001 INDEL all 0.8354 0.7458 0.9916 0.9925 0.4485
HG002 INDEL all 0.8271 0.7016 0.9917 0.9930 0.4547
HG003 INDEL all 0.7546 0.6523 0.9925 0.9935 0.4632
HG004 INDEL all 0.7345 0.6390 0.9927 0.9936 0.4592
HG005 INDEL all 0.9840 0.7418 0.9902 0.9926 0.4850
HG001 INDEL notinalldifficultregions 0.0551 0.1475 0.9994 0.9985 0.1927
HG002 INDEL notinalldifficultregions 0.0497 0.0893 0.9995 0.9991 0.2208
HG003 INDEL notinalldifficultregions 0.0508 0.1627 0.9995 0.9984 0.2229
HG004 INDEL notinalldifficultregions 0.0496 0.1307 0.9995 0.9987 0.2190
HG005 INDEL notinalldifficultregions 0.1182 0.1535 0.9988 0.9985 0.2049
20. 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
21. Challenges in Benchmarking 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
• Can you predict your performance for clinical variants of
interest based on sequencing reference samples?
22. Best Practices for Benchmarking
Benchmark sets Use benchmark sets with both high-confidence variant calls as well as high-confidence regions, so that both false negatives and
false positives can be assessed.
Stringency of
variant comparison
Determine whether it is important that the genotype match exactly, only the allele matches, or the call just needs to be near the
true variant.
Variant comparison
tools
Use sophisticated variant comparison engines such as vcfeval, xcmp, or varmatch that are able to determine if different
representations of the same variant are consistent with the benchmark call. Subsetting by high-confidence regions and, if
desired, targeted regions, should only be done after comparison to avoid problems comparing variants with differing
representations.
Manual curation Manually curate alignments, ideally from multiple data types, around at least a subset of putative false positive and false negative
calls in order to ensure they are truly errors in the user’s callset and to understand the cause(s) of errors. Report back to
benchmark set developers any potential errors found in the benchmark set (e.g., using https://goo.gl/forms/ECbjHY7nhz0hrCR52
for GIAB).
Interpretation of
metrics
All performance metrics should only be interpreted with respect to the limitations of the variants and regions in the benchmark
set. Performance metrics are likely to be lower for more difficult variant types and regions that are not fully represented in the
benchmark set, such as those in repetitive or difficult-to-map regions. When comparing methods, method 1 may perform better
in the high-confidence regions, but method 2 may perform better for more difficult variants outside the high-confidence regions.
Stratification Overall performance metrics can be useful, but for many applications it is important to assess performance for particular variant
types and genome contexts. Performance often varies significantly across variant types and genome contexts, and stratification
allows users to understand this. In addition, stratification allows users to see if some variant types and genome contexts of
interest are not sufficiently represented.
Confidence
Intervals
Confidence intervals for performance metrics such as precision and recall should be calculated. This is particularly critical for the
smaller numbers of variants found when benchmarking in targeted regions and/or less common stratified variant types and
regions.
23. Ongoing and Future Work
• Characterizing difficult variants and regions
– Large indels and structural variants
– Tandem repeats and homopolymers
– Difficult to map regions
– Complex variants
• New germline samples
– Additional ancestries
• Tumor/normal cell lines
– Developing IRB protocol for broadly-consented samples
24. Acknowledgements
• NIST/JIMB
– Marc Salit
– Jenny McDaniel
– Lindsay Vang
– David Catoe
– Lesley Chapman
• Genome in a Bottle Consortium
• GA4GH Benchmarking Team
• FDA
25. 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://www.nature.com/articles/sdata201625
Global Alliance Benchmarking Team
– https://github.com/ga4gh/benchmarking-tools
– Web-based implementation at precision.fda.gov
Public workshops
– Next workshop Jan 25-26, 2018 at Stanford University, CA, USA
NIST/JIMB postdoc opportunities available!
Justin Zook: jzook@nist.gov
Marc Salit: salit@nist.gov