The document discusses Genome in a Bottle (GIAB) and its efforts to characterize human genomes and provide reference materials and benchmarks to evaluate genome sequencing and variant calling. Specifically, it summarizes how GIAB has characterized 7 human genomes, provides extensive public sequencing data for benchmarking, and is now using linked and long reads to expand the small variant benchmark set, develop a structural variant benchmark, and perform diploid assembly of difficult regions. It also shows how new benchmarks that include more difficult regions have revealed errors in previous benchmarks and reduced performance metrics for variant calling tools.
Presentation at 2019 ASHG GRC/GIAB workshop describing history of the human reference genome, current curation efforts and future plans, and the relationship of all 3 to efforts to produce a human pan-genome.
Linkage and QTL mapping Populations and Association mapping population.
F2, Immortalized F2, Backcross (BC), Near isogenic lines (NIL), RIL, Double haploids(DH), Nested Association mapping (NAM), MAGIC and Interconnected populations.
Genomics research and discovery has led to a large increase of reported single nucleotide polymorphisms (SNPs). From 2006 to 2017, the number of refSNPs in the NCBI dbSNP database has increased 13-fold. Many polymorphisms can be linked to disease susceptibility and responses to chemical therapies. Other polymorphisms are used as trait identifiers in livestock and plants. Being able to inexpensively and accurately determine the genotype in high-throughput fashion, with low sample input is a critical need in current, large-scale screening efforts. In this presentation, we present a novel, probe-based, PCR genotyping solution that possesses the universal cycling conditions, strong signal generation, and benchtop reaction stability needed for high-throughput screening. We also present the mechanism and unique technical advantages of using the rhAmp SNP Genotyping System, and we will illustrate how easy it is to generate high quality genotyping data.
Presentation at 2019 ASHG GRC/GIAB workshop describing history of the human reference genome, current curation efforts and future plans, and the relationship of all 3 to efforts to produce a human pan-genome.
Linkage and QTL mapping Populations and Association mapping population.
F2, Immortalized F2, Backcross (BC), Near isogenic lines (NIL), RIL, Double haploids(DH), Nested Association mapping (NAM), MAGIC and Interconnected populations.
Genomics research and discovery has led to a large increase of reported single nucleotide polymorphisms (SNPs). From 2006 to 2017, the number of refSNPs in the NCBI dbSNP database has increased 13-fold. Many polymorphisms can be linked to disease susceptibility and responses to chemical therapies. Other polymorphisms are used as trait identifiers in livestock and plants. Being able to inexpensively and accurately determine the genotype in high-throughput fashion, with low sample input is a critical need in current, large-scale screening efforts. In this presentation, we present a novel, probe-based, PCR genotyping solution that possesses the universal cycling conditions, strong signal generation, and benchtop reaction stability needed for high-throughput screening. We also present the mechanism and unique technical advantages of using the rhAmp SNP Genotyping System, and we will illustrate how easy it is to generate high quality genotyping data.
Next-Generation Sequencing an Intro to Tech and Applications: NGS Tech Overvi...QIAGEN
This slidedeck provides a technical overview of DNA/RNA preprocessing, template preparation, sequencing and data analysis. It covers the applications for NGS technologies, including guidelines for how to select the technology that will best address your biological question.
Platform presentation at ASHG 2019 describing recent updates to the human reference genome assembly (GRCh38) and future plans with relevance to pan-genomic representations.
Using VarSeq to Improve Variant Analysis Research WorkflowsGolden Helix Inc
In this webinar presentation, we will review workflow strategies for quality control and analysis of DNA sequence variants using the VarSeq software package from Golden Helix. VarSeq is a powerful platform for analysis of DNA sequence variants in clinical and translational research settings. VarSeq provides researchers with easy access to curated public databases of variant annotation information, and also enables users to incorporate their own local databases or downloaded information about variants and genomic regions.
Команда Data Phoenix Events приглашает всех, 17 августа в 19:00, на первый вебинар из серии "The A-Z of Data", который будет посвящен MLOps. В рамках вводного вебинара, мы рассмотрим, что такое MLOps, основные принципы и практики, лучшие инструменты и возможные архитектуры. Мы начнем с простого жизненного цикла разработки ML решений и закончим сложным, максимально автоматизированным, циклом, который нам позволяет реализовать MLOps.
https://dataphoenix.info/the-a-z-of-data/
https://dataphoenix.info/the-a-z-of-data-introduction-to-mlops/
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
This slide deck uses case studies and scientific publications to highlight how Life Technologies platforms and products are used in plant genetic analysis applications such as plant genome sequencing, SNP genotyping, marker assisted selection, GMO detection, plant genetic engineering, plant gene expression, and plant nucleic acid isolation.
Life Technologies is committed to providing instruments, reagents, and technologies for Plant Sciences and Genomic Applications that will lead the way to remarkable agricultural discoveries—everything from improved crops that feed more people to sustainable biofuels that keep things moving.
Paired-end alignments in sequence graphsChirag Jain
Graph based non-linear reference structures such as variation graphs and colored de Bruijn graphs enable incorporation of full genomic diversity within a population. However, transitioning from a simple string-based reference to graphs requires addressing many computational challenges, one of which concerns accurately mapping sequencing read sets to graphs. Paired-end Illumina sequencing is a commonly used sequencing platform in genomics, where the paired-end distance constraints allow disambiguation of repeats. Many recent works have explored provably good index-based and alignment-based strategies for mapping individual reads to graphs. However, validating distance constraints efficiently over graphs is not trivial, and existing sequence to graph mappers rely on heuristics. We introduce a mathematical formulation of the problem, and provide a new algorithm to solve it exactly. We take advantage of the high sparsity of reference graphs, and use sparse matrix- matrix multiplications (SpGEMM) to build an index which can be queried efficiently by a mapping algorithm for validating the distance constraints. Effectiveness of the algorithm is demonstrated using real reference graphs, including a human MHC variation graph, and a pan-genome de-Bruijn graph built using genomes of 20 B. anthracis strains. While the one-time indexing time can vary from a few minutes to a few hours using our algorithm, answering a million distance queries takes less than a second.
Introduction:
Proposed by Meuwissen et al. (2001)
GS is a specialized form of MAS, in which information from genotype data on marker alleles covering the entire genome forms the basis of selection.
The effects associated with all the marker loci, irrespective of whether the effects are significant or not, covering the entire genome are estimated.
The marker effect estimates are used to calculate the genomic estimated breeding values (GEBVs) of different individuals/lines, which form the basis of selection.
Why to go for genomic selection:
Marker-assisted selection (MAS) is well-suited for handling oligogenes and quantitative trait loci (QTLs) with large effects but not for minor QTLs.
MARS attempts to take into account small effect QTLs by combining trait phenotype data with marker genotype data into a combined selection index.
Based on markers showing significant association with the trait(s) and for this reason has been criticized as inefficient
The genomic selection (GS) scheme was to rectify the deficiency of MAS and MARS schemes. The GS scheme utilizes information from genome-wide marker data whether or not their associations with the concerned trait(s) are significant.
GEBV: GenomicEstimated Breeding Values-
The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection
Calculated on a single individual basis
Gene-assisted genomic selection:
A GS model that uses information about prior known QTLs, the targeted QTLs were accumulated in much higher frequencies than when the standard ridge regression was used
The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection
Calculated on a single individual basis
Population used:
Training population: used for training of the GS model and for obtaining estimates of the marker-associated effects needed for estimation of GEBVs of individuals/lines in the breeding population.
Breeding population: the population subjected to GS for achieving the desired improvement and isolation of superior lines for use as new varieties/parents of new improved hybrids.
Training population-
large enough: must be representative of the breeding population: max. trait variance with marker : by cluster analysis
should have either equal or comparable LD, LD decay rates with breeding populations
Updated by including individuals/lines from the breeding population
Training more than one generation
Low colinearity between markers is needed since high colinearity tends to reduce prediction accuracy of certain GS models. (colinearity disturbed by recombination)
This is a lecture for Bio4025, a graduate class at Washington University in St. Louis. Some slides are derived from Julin Maloof (University of California, Davis), some of which were altered.
Next-Generation Sequencing an Intro to Tech and Applications: NGS Tech Overvi...QIAGEN
This slidedeck provides a technical overview of DNA/RNA preprocessing, template preparation, sequencing and data analysis. It covers the applications for NGS technologies, including guidelines for how to select the technology that will best address your biological question.
Platform presentation at ASHG 2019 describing recent updates to the human reference genome assembly (GRCh38) and future plans with relevance to pan-genomic representations.
Using VarSeq to Improve Variant Analysis Research WorkflowsGolden Helix Inc
In this webinar presentation, we will review workflow strategies for quality control and analysis of DNA sequence variants using the VarSeq software package from Golden Helix. VarSeq is a powerful platform for analysis of DNA sequence variants in clinical and translational research settings. VarSeq provides researchers with easy access to curated public databases of variant annotation information, and also enables users to incorporate their own local databases or downloaded information about variants and genomic regions.
Команда Data Phoenix Events приглашает всех, 17 августа в 19:00, на первый вебинар из серии "The A-Z of Data", который будет посвящен MLOps. В рамках вводного вебинара, мы рассмотрим, что такое MLOps, основные принципы и практики, лучшие инструменты и возможные архитектуры. Мы начнем с простого жизненного цикла разработки ML решений и закончим сложным, максимально автоматизированным, циклом, который нам позволяет реализовать MLOps.
https://dataphoenix.info/the-a-z-of-data/
https://dataphoenix.info/the-a-z-of-data-introduction-to-mlops/
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
This slide deck uses case studies and scientific publications to highlight how Life Technologies platforms and products are used in plant genetic analysis applications such as plant genome sequencing, SNP genotyping, marker assisted selection, GMO detection, plant genetic engineering, plant gene expression, and plant nucleic acid isolation.
Life Technologies is committed to providing instruments, reagents, and technologies for Plant Sciences and Genomic Applications that will lead the way to remarkable agricultural discoveries—everything from improved crops that feed more people to sustainable biofuels that keep things moving.
Paired-end alignments in sequence graphsChirag Jain
Graph based non-linear reference structures such as variation graphs and colored de Bruijn graphs enable incorporation of full genomic diversity within a population. However, transitioning from a simple string-based reference to graphs requires addressing many computational challenges, one of which concerns accurately mapping sequencing read sets to graphs. Paired-end Illumina sequencing is a commonly used sequencing platform in genomics, where the paired-end distance constraints allow disambiguation of repeats. Many recent works have explored provably good index-based and alignment-based strategies for mapping individual reads to graphs. However, validating distance constraints efficiently over graphs is not trivial, and existing sequence to graph mappers rely on heuristics. We introduce a mathematical formulation of the problem, and provide a new algorithm to solve it exactly. We take advantage of the high sparsity of reference graphs, and use sparse matrix- matrix multiplications (SpGEMM) to build an index which can be queried efficiently by a mapping algorithm for validating the distance constraints. Effectiveness of the algorithm is demonstrated using real reference graphs, including a human MHC variation graph, and a pan-genome de-Bruijn graph built using genomes of 20 B. anthracis strains. While the one-time indexing time can vary from a few minutes to a few hours using our algorithm, answering a million distance queries takes less than a second.
Introduction:
Proposed by Meuwissen et al. (2001)
GS is a specialized form of MAS, in which information from genotype data on marker alleles covering the entire genome forms the basis of selection.
The effects associated with all the marker loci, irrespective of whether the effects are significant or not, covering the entire genome are estimated.
The marker effect estimates are used to calculate the genomic estimated breeding values (GEBVs) of different individuals/lines, which form the basis of selection.
Why to go for genomic selection:
Marker-assisted selection (MAS) is well-suited for handling oligogenes and quantitative trait loci (QTLs) with large effects but not for minor QTLs.
MARS attempts to take into account small effect QTLs by combining trait phenotype data with marker genotype data into a combined selection index.
Based on markers showing significant association with the trait(s) and for this reason has been criticized as inefficient
The genomic selection (GS) scheme was to rectify the deficiency of MAS and MARS schemes. The GS scheme utilizes information from genome-wide marker data whether or not their associations with the concerned trait(s) are significant.
GEBV: GenomicEstimated Breeding Values-
The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection
Calculated on a single individual basis
Gene-assisted genomic selection:
A GS model that uses information about prior known QTLs, the targeted QTLs were accumulated in much higher frequencies than when the standard ridge regression was used
The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection
Calculated on a single individual basis
Population used:
Training population: used for training of the GS model and for obtaining estimates of the marker-associated effects needed for estimation of GEBVs of individuals/lines in the breeding population.
Breeding population: the population subjected to GS for achieving the desired improvement and isolation of superior lines for use as new varieties/parents of new improved hybrids.
Training population-
large enough: must be representative of the breeding population: max. trait variance with marker : by cluster analysis
should have either equal or comparable LD, LD decay rates with breeding populations
Updated by including individuals/lines from the breeding population
Training more than one generation
Low colinearity between markers is needed since high colinearity tends to reduce prediction accuracy of certain GS models. (colinearity disturbed by recombination)
This is a lecture for Bio4025, a graduate class at Washington University in St. Louis. Some slides are derived from Julin Maloof (University of California, Davis), some of which were altered.
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.
Using VarSeq to Improve Variant Analysis Research WorkflowsDelaina Hawkins
Many questions must be answered when analyzing DNA sequence variants: How do I determine which variants are potentially deleterious? Is the sequencing quality sufficient? How do I prioritize the results? Which annotation sources may help answer my research question?
In this webinar presentation, we will review workflow strategies for quality control and analysis of DNA sequence variants using the VarSeq software package from Golden Helix. VarSeq is a powerful platform for analysis of DNA sequence variants in clinical and translational research settings. VarSeq provides researchers with easy access to curated public databases of variant annotation information, and also enables users to incorporate their own local databases or downloaded information about variants and genomic regions.
The presentation will include interactive demonstrations using VarSeq to analyze variants found by exome sequencing of an extended family with a complex disease. We will review strategies for assessing variant quality, applying genomic annotations, incorporating custom annotation sources, and creating variant filters in VarSeq. We will also demonstrate the PhoRank gene ranking algorithm and its application for prioritizing variants.
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
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
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
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
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
- ETHICAL CHALLENGES IN LIFE SCIENCES
- Prix Galien International Awards Ceremony
Pulmonary Thromboembolism - etilogy, types, medical- Surgical and nursing man...VarunMahajani
Disruption of blood supply to lung alveoli due to blockage of one or more pulmonary blood vessels is called as Pulmonary thromboembolism. In this presentation we will discuss its causes, types and its management in depth.
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.
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.
MANAGEMENT OF ATRIOVENTRICULAR CONDUCTION BLOCK.pdf
Genome in a Bottle - Towards new benchmarks for the “dark matter” of the human genome 190502
1. May 2, 2019
Genome in a Bottle: Towards
new benchmarks for the “dark
matter” of the human genome
2. What’s
Genome
in a
Bottle?
• Authoritative Characterization of Human
Genomes
– enduring commitment to resource availability
• Samples
• Data
– widely available open resources
– all data made available without embargo
• Enable technology and tool-building with benchmark
samples and methods for…
– development
– optimization
– demonstration
• Germline samples available now
• Developing capacity for somatic sample development
3. GIAB has characterized 7 human genomes
• Pilot genome
– NA12878
• PGP Human Genomes
– Ashkenazi Jewish son
– Ashkenazi Jewish trio
– Chinese son
• Parents also characterized
National I nstituteof S tandards & Technology
Report of I nvestigation
Reference Material 8391
Human DNA for Whole-Genome Variant Assessment
(Son of Eastern European Ashkenazim Jewish Ancestry)
This Reference Material (RM) is intended for validation, optimization, and process evaluation purposes. It consists
of a male whole human genome sample of Eastern European Ashkenazim Jewish ancestry, and it can be used to assess
performance of variant calling from genome sequencing. A unit of RM 8391 consists of a vial containing human
genomic DNA extracted from a single large growth of human lymphoblastoid cell line GM24385 from the Coriell
Institute for Medical Research (Camden, NJ). The vial contains approximately 10 µg of genomic DNA, with the peak
of the nominal length distribution longer than 48.5 kb, as referenced by Lambda DNA, and the DNA is in TE buffer
(10 mM TRIS, 1 mM EDTA, pH 8.0).
This material is intended for assessing performance of human genome sequencing variant calling by obtaining
estimates of true positives, false positives, true negatives, and false negatives. Sequencing applications could include
whole genome sequencing, whole exome sequencing, and more targeted sequencing such as gene panels. This
genomic DNA is intended to be analyzed in the same way as any other sample a lab would process and analyze
extracted DNA. Because the RM is extracted DNA, it is not useful for assessing pre-analytical steps such as DNA
extraction, but it does challenge sequencing library preparation, sequencing machines, and the bioinformatics steps of
mapping, alignment, and variant calling. This RM is not intended to assess subsequent bioinformatics steps such as
functional or clinical interpretation.
Information Values: Information values are provided for single nucleotide polymorphisms (SNPs), small insertions
and deletions (indels), and homozygous reference genotypes for approximately 88 % of the genome, using methods
similar to described in reference 1. An information value is considered to be a value that will be of interest and use to
the RM user, but insufficient information is available to assess the uncertainty associated with the value. We describe
and disseminate our best, most confident, estimate of the genotypes using the data and methods currently available.
These data and genomic characterizations will be maintained over time as new data accrue and measurement and
informatics methods become available. The information values are given as a variant call file (vcf) that contains the
high-confidence SNPs and small indels, as well as a tab-delimited “bed” file that describes the regions that are called
high-confidence. Information values cannot be used to establish metrological traceability. The files referenced in this
report are available at the Genome in a Bottle ftp site hosted by the National Center for Biotechnology Information
(NCBI). The Genome in a Bottle ftp site for the high-confidence vcf and high confidence regions is:
New!
5. Best Practices for Benchmarking Small Variants
https://github.com/ga4gh/benchmarking-tools
Paper: https://rdcu.be/bqpDT https://precision.fda.gov/
Describe public
“Truth” VCFs
with confident
regions
Enable
stratification of
performance in
difficult regions
Tools to compare
different
representations of
complex variants Standardized
VCF-I output of
comparison
tools
Standardized
output formats for
performance
metrics
Web-based interface for
performance metrics
Standardized
definitions of
performance metrics
based on matching
stringency
6. Best practice #1: Account for
different representations
Representation 1
CAAG
CAAAG
REF 1 CA C 0/1
Representation 2
Representation 3
REF 2 AA A 0/1
REF 3 AA A 0/1
CAAG
CAAAG
CAAG
CAAAG
CHROM POS REF ALT GT
(a)
Representation 1
REF 1 A C 0|1
Representation 2 REF 1 AAC CGG 0/1
REF 2 A G 0|1
REF 3 C G 0|1
(b)
CGG
AACREF:
CGG
AAC
Representation 1
Representation 2
ATGCREF:
ATCTGTGC
REF 1 A ATC 0|1
REF 3 G GTG 0|1
REF 1 A ATCTG 0/1
(c)
ATGC
ATCTGTGC
Representation 1
Representation 2
Representation 3
GCG
GCCC
REF:
REF:
GCG
GCCCREF:
GCG
GCCCREF:
Representation 4
GCG
GCCCREF:
REF 1 GCCC GCG 0/1
REF 3 CC G 0/1
REF 4 C G 0|1
REF 1 GC G 0|1
REF 3 C G 0|1
REF 4 C <DEL> 0|1
(d)
REF:
REF:
ALT:
REF:
ALT:
REF:
ALT:
ALT:
ALT:
ALT:
ALT:
ALT:
ALT:
ALT:
ALT:
• Complex variants are often represented in different
ways
• Normalization can help, but not always
• Phasing of nearby variants can affect interpretation
7. Best practice #2:
Stratify by variant type
and genome context
• Performance metrics can
be very different for
different variant types
and genome contexts
• GA4GH tools enable very
granular stratification
• Also can see what the
benchmark excludes
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
8. Best practice #3:
Manually curate FPs
and FNs
• Helps to understand what
is causing errors
• Sometimes, putative FPs
and FNs are errors in the
benchmark set
https://doi.org/10.1101/581264
10. Now using linked and long reads for
difficult variants and regions
GIAB Public Data
• Linked Reads
– 10x Genomics
– Complete Genomics/BGI stLFR
• Long Reads
– PacBio Continuous Long Reads
– PacBio Circular Consensus Seq
– Oxford Nanopore “ultralong”
GIAB Use Cases
• Expand small variant
benchmark
• Develop structural variant
benchmark
• Diploid assembly of difficult
regions like MHC
11. Linked Reads
• Short reads, but
barcodes give long
range information
>100kb
• Most useful for:
– Phasing variants & reads
– Difficult-to-map regions
– De novo assembly
https://dx.doi.org/10.1038%2Fnbt.3432
12. PACBIO CIRCULAR CONSENSUS SEQUENCING (CCS)
Double-stranded DNA
Ligate adapters
Anneal primer and bind
DNA polymerase
Sequence
Generate
consensus HiFi read
Subreads
(passes)
Subread errors
Passes
5 10 15 200
30
0
10
20
40
50
Accuracy(Phred)
Wenger, Peluso, et al. (2018). bioRxiv. doi:10.1101/519025
Read accuracy improves
with more passes
13. 15X Coverage by reads > 100Kb
Oxford Nanopore Can Produce “Ultralong” Reads
15. Long+Linked Reads expand small
variant benchmark set
Benchmark includes more bases, variants, and segmental duplications in v4⍺
v3.3.2 v4⍺ In v4⍺ not in
v3.3.2
In v3.3.2 not in
v4⍺
Base pairs
covered
2,358,060,765 2,572,421,057 225,990,474 11,630,182
Percent of
GRCh37 covered
87.84% 95.82% 8.42% 0.43%
SNPs 3,046,933 3,432,698 385,765 25,219
Indels 465,670 537,035 71,365 15,382
Base pairs in
Segmental
Duplications
13,722,546 116,687,703 103,466,431 501,274
16. Small variant performance metrics
decrease vs. new benchmark
Comparison of Illumina GATK4 VCF against benchmark sets
• SNP FN rate increases by a factor of 10
– almost entirely due to new benchmark variants in difficult to
map regions (lowmap) and segmental duplications (segdups)
Subset v3.3.2 Recall v4⍺ Recall v3.3.2 Precision v4⍺ Precision
All SNPs 0.9995 0.9914 0.9981 0.9941
Lowmap 100 bp 0.9799 0.7911 0.9623 0.8582
Lowmap 250 bp no mismatch 0.9474 0.4916 0.8911 0.7171
Segdups 0.9982 0.9103 0.9910 0.9014
19. 50 to 1000 bp
Alu
Alu
1kbp to 10kbp
LINE
LINE
Discovery: 498876 (296761 unique) calls >=50bp and 1157458 (521360
unique) calls >=20bp discovered in 30+ sequence-resolved callsets from 4
technologies for AJ Trio
Compare SVs: 128715 sequence-resolved SV calls >=50bp after clustering
sequence changes within 20% edit distance in trio
Discovery Support: 30062 SVs with 2+ techs or 5+ callers predicting
sequences <20% different or BioNano/Nabsys support in trio
Evaluate/genotype: 19748 SVs with consensus variant
genotype from svviz in son
Filter complex: 12745 SVs not within
1kb of another SV
Regions: 9641 SVs inside
2.66 Gbp benchmark
regions supported by
diploid assembly
v0.6
tinyurl.com/GIABSV06
20. Support from long reads Support from short reads
Fraction of reads supporting SV Fraction of reads supporting SV
Het Hom Het Hom
Het Hom Het Hom
Het Hom
Het Hom
Het Hom
Het Hom
Reads support benchmark SV genotypes
22. High Mendelian Genotype Concordance
Father 0/0 0/0 0/0 0/1 0/1 0/1 1/1 1/1 1/1
Son | Mother 0/0 0/1 1/1 0/0 0/1 1/1 0/0 0/1 1/1
0/1 14 1185 417 1143 1119 462 416 522 12
1/1 0 0 0 0 449 444 2 431 2748
Trio Mendelian genotype violation rate
28/9392 = 0.3%
(Excludes X/Y and sites with no GT in a parent)
Also, >627/635 genotypes concordant with crowd-sourced manual curations
23. Our benchmark sets are useful in evaluating SVs
from multiple technologies
Goal: When comparing any callset
to our vcf within the bed, most
putative FPs and FNs should be
errors in the tested callset
github.com/spiralgenetics/truvari
github.com/nhansen/SVanalyzer
24. Resolve MHC regions from
HG002
https://github.com/NCBI-Hackathons/TheHumanPangenome/tree/master/MHC
Justin Wenger, Justin Zook, Mikko Rautiainen, Jason Chin, Tobias Marschall, Qian Zeng,
Erik Garrison, Shilpa Garg
Mar. 25-27, UCSC, The Human Pangenomics Hackathon
25. Goals
• Make the best haplotype-correct
assemblies for the MHC regions of
HG002 from all available data
• Fewest gaps
• Correct phasing for both SNPs and
SVs
• Provide the best genomic sequences
for future GIAB SNP and SV
benchmark for this complicated but
medically important region
26. MHC in GRCh37 / HG002 Assembly
ONT
CCS
10X VCF
Falcon / Peregrine
HLA-ASM
seqwish + odgiGraphAligner
Error corrected ONT
reads
Heterozygous SNPs
WhatsHap
ONT
CCS
Haplotype binned reads
Compare to HLA-Typing Results
DV VCF
10X VCF Heterozygous SNPs
DV VCF
Github: phasing-notes.md
Github: assembly directory
CCS
ONT for gap filling
Identify ONT reads filling
in
regions missed by
PacBio CCS reads
+
FALCON EC module
MHC Diploid assembly process
27. Preliminary MHC Diploid Assembly Results
MHC region MHC region
Haplotype II
(3 contigs spanning the region)
Haplotype I
(2 contigs spanning the region)
A loop in the assembly
graph
Missing Sequence?
28. Open consent enables secondary reference samples to
meet specific clinical needs
• >50 products now available
based on broadly-consented,
well-characterized GIAB PGP cell
lines
• Genomic DNA + DNA spike-ins
• Clinical variants
• Somatic variants
• Difficult variants
• Clinical matrix (FFPE)
• Circulating tumor DNA
• Stem cells (iPSCs)
• Genome editing
• …
29. The road
ahead... 2019
Integration pipeline
development for small and
structural variants
Manuscripts for small and
structural variants
2020
Difficult large variants
Somatic sample development
Germline samples from new
ancestries
Diploid assembly
2021+
Somatic integration pipeline
Somatic structural variation
Large segmental duplications
Centromere/ telomere
...
30. Acknowledgment of many GIAB contributors
Government
Clinical Laboratories Academic Laboratories
Bioinformatics developers
NGS technology developers
Reference samples
* Funders
*
*
31. For More Information
www.genomeinabottle.org - sign up for general GIAB and Analysis Team google group
GIAB slides, including 2019 Workshop slides: www.slideshare.net/genomeinabottle
Public, Unembargoed Data:
– http://www.nature.com/articles/sdata201625
– ftp://ftp-trace.ncbi.nlm.nih.gov/giab/
– github.com/genome-in-a-bottle
Global Alliance Benchmarking Team
– https://github.com/ga4gh/benchmarking-tools
– Web-based implementation at precision.fda.gov
– Best Practices at https://rdcu.be/bqpDT
Public workshops
– Next workshop planned for April 1-2, 2020 at Stanford University, CA, USA
Justin Zook: jzook@nist.gov
NIST postdoc
opportunities
available!
Diploid assembly,
cancer genomes,
other ‘omics, …