GRC Workshop at Churchill College on Sep 21, 2014. This is Aaron Quinlan's talk on issues with representing variants in the full assembly, with suggestions for VCF modifications for handling variant calls on the alts.
GRC Workshop at Churchill College on Sep 21, 2014. This is Aaron Quinlan's talk on issues with representing variants in the full assembly, with suggestions for VCF modifications for handling variant calls on the alts.
Presentation by Valerie Schneider discussing Genome Reference Consortium (GRC) plans for the mouse and zebrafish reference genome assemblies, presented at the 2016 meeting of the The Allied Genetic Conference (TAGC). Includes description of resources at the National Center for Biotechnology Information (NCBI) for working with reference genome assemblies.
Real-time quantitative PCR (qPCR) is a preferred platform for high throughput gene expression profiling, where large numbers of samples are characterized for hundreds of expression markers. Technically, the qPCR measurements are performed in the same way as when classical qPCR is used to analyze only a few targets per sample, but the higher throughput introduces additional sources of potential confounding variation that must be controlled for. In this presentation, Dr Kubista describes how high throughput qPCR profiling studies are designed. He covers assay optimization and validation, sample quality testing, and how to merge multi-plate measurements into a common analysis. Dr Kubista also discusses how to cost-effectively measure and compensate for background due to genomic DNA.
Presentation by Valerie Schneider discussing Genome Reference Consortium (GRC) plans for the mouse and zebrafish reference genome assemblies, presented at the 2016 meeting of the The Allied Genetic Conference (TAGC). Includes description of resources at the National Center for Biotechnology Information (NCBI) for working with reference genome assemblies.
Real-time quantitative PCR (qPCR) is a preferred platform for high throughput gene expression profiling, where large numbers of samples are characterized for hundreds of expression markers. Technically, the qPCR measurements are performed in the same way as when classical qPCR is used to analyze only a few targets per sample, but the higher throughput introduces additional sources of potential confounding variation that must be controlled for. In this presentation, Dr Kubista describes how high throughput qPCR profiling studies are designed. He covers assay optimization and validation, sample quality testing, and how to merge multi-plate measurements into a common analysis. Dr Kubista also discusses how to cost-effectively measure and compensate for background due to genomic DNA.
Introducing data analysis: reads to resultsAGRF_Ltd
AGRF in conjunction with EMBL Australia recently organised a workshop at Monash University Clayton. This workshop was targeted at beginners and biologists who are new to analysing Next-Gen Sequencing data. The workshop also aimed to provide users with a snapshot of bioinformatics and data analysis tips on how to begin to analyse project data. Introduction data analysis: reads to results was presented by Dr. Torsten Seemann from the Victorian Bioinformatics Consortium at Monash University, Clayton.
Presented: 1st August 2012
Characterization of Novel ctDNA Reference Materials Developed using the Genom...Thermo Fisher Scientific
Liquid biopsy diagnostic technologies have revolutionized cancer testing and therapeutic monitoring. Non-invasive sample collection removes the need for invasive and dangerous biopsies to diagnose cancer and monitor therapeutic efficacy. As liquid biopsy technologies become more sensitive, screening for early detection of cancer DNA using a blood test could become routine clinical practice. However, such technologies cannot be developed without high quality reference materials. In this study, ctDNA reference materials using the NIST Genome in a Bottle GM24385 cell line DNA were developed in a human plasma-EDTA matrix. The allelic frequency (AF), size and stability of the materials were analyzed.
Snippy - Rapid bacterial variant calling - UK - tue 5 may 2015Torsten Seemann
Using Snippy to call variants in bacterial short read datasets via alignment to reference, and then using these alignments to produce core SNP alignments for phylogenomics.
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.
Matrix Factorizations at Scale: a Comparison of Scientific Data Analytics on ...Databricks
Explore the trade-offs of performing linear algebra for data analysis and machine learning using Apache Spark, compared to traditional C and MPI implementations on HPC platforms. Apache Spark is designed for data analytics on cluster computing platforms with access to local disks and is optimized for data-parallel tasks.
This session will examine three widely-used and important matrix factorizations: NMF (for physical plausibility), PCA (for its ubiquity) and CX (for data interpretability). Learn how these methods are applied to terabyte-sized problems in particle physics, climate modeling and bioimaging, as use cases where interpretable analytics is of interest. The data matrices are tall-and-skinny, which enable the algorithms to map conveniently into Spark’s data-parallel model. We perform scaling experiments on up to 1600 Cray XC40 nodes, describe the sources of slowdowns and provide tuning guidance to obtain high performance. Based on joint work with Alex Gittens and many others.
Presentation at IMGC 2019 workshop describing the latest improvements to the mouse reference genome assembly and analyses performed in preparation for the next release of the mouse genome assembly (GRCm39).
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.
Platform presentation at ASHG 2019 describing recent updates to the human reference genome assembly (GRCh38) and future plans with relevance to pan-genomic representations.
Presentation at 2019 ASHG GRC/GIAB workshop describing goals and progress of the telomere-to-telomere consortium to generate a genome assembly that provides representation of all sequences, including repetitive regions.
Presentation at 2019 ASHG GRC/GIAB workshop describing features and recent updates to the vg toolkit, including examples of comparisons to other methods used for alignment and variant detection.
Presentation at 2019 ASHG GRC/GIAB workshop describing recent updates to the MANE project, which aims to provide matched annotation from RefSeq and GENCODE.
Presentation at PanGenomics in the Cloud Hackathon, run by NCBI at UCSC (https://ncbiinsights.ncbi.nlm.nih.gov/2019/02/06/pangenomics-cloud-hackathon-march-2019/). Presents points to consider about the adoption of a pangenome reference, emphasizing aspects for long-term data management and wide-spread adoption.
Presentation by Benedict Paten at GRC/GIAB ASHG 2017 workshop "Getting the most from the reference assembly and reference materials" on updates to the human reference assembly, GRCh38.
Presentation by Valerie Schneider at GRC/GIAB ASHG 2017 workshop "Getting the most from the reference assembly and reference materials" on updates to the human reference assembly, GRCh38.
Presentation by Tina Graves-Lindsay at GRC/GIAB ASHG 2017 workshop "Getting the most from the reference assembly and reference materials" on production of reference grade assemblies for various human populations.
Presentation by Fritz Sedlazeck at GRC/GIAB ASHG 2017 workshop "Getting the most from the reference assembly and reference materials" on characterizing human structural variation.
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.
Presentation by Karen Miga at GRC/GIAB ASHG 2017 workshop "Getting the most from the reference assembly and reference materials" on centromere assemblies.
Basavarajeeyam is an important text for ayurvedic physician belonging to andhra pradehs. It is a popular compendium in various parts of our country as well as in andhra pradesh. The content of the text was presented in sanskrit and telugu language (Bilingual). One of the most famous book in ayurvedic pharmaceutics and therapeutics. This book contains 25 chapters called as prakaranas. Many rasaoushadis were explained, pioneer of dhatu druti, nadi pareeksha, mutra pareeksha etc. Belongs to the period of 15-16 century. New diseases like upadamsha, phiranga rogas are explained.
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
Muktapishti is a traditional Ayurvedic preparation made from Shoditha Mukta (Purified Pearl), is believed to help regulate thyroid function and reduce symptoms of hyperthyroidism due to its cooling and balancing properties. Clinical evidence on its efficacy remains limited, necessitating further research to validate its therapeutic benefits.
Antimicrobial stewardship to prevent antimicrobial resistanceGovindRankawat1
India is among the nations with the highest burden of bacterial infections.
India is one of the largest consumers of antibiotics worldwide.
India carries one of the largest burdens of drug‑resistant pathogens worldwide.
Highest burden of multidrug‑resistant tuberculosis,
Alarmingly high resistance among Gram‑negative and Gram‑positive bacteria even to newer antimicrobials such as carbapenems.
NDM‑1 ( New Delhi Metallo Beta lactamase 1, an enzyme which inactivates majority of Beta lactam antibiotics including carbapenems) was reported in 2008
- Video recording of this lecture in English language: https://youtu.be/kqbnxVAZs-0
- Video recording of this lecture in Arabic language: https://youtu.be/SINlygW1Mpc
- 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
share - Lions, tigers, AI and health misinformation, oh my!.pptxTina Purnat
• Pitfalls and pivots needed to use AI effectively in public health
• Evidence-based strategies to address health misinformation effectively
• Building trust with communities online and offline
• Equipping health professionals to address questions, concerns and health misinformation
• Assessing risk and mitigating harm from adverse health narratives in communities, health workforce and health system
Integrating Ayurveda into Parkinson’s Management: A Holistic ApproachAyurveda ForAll
Explore the benefits of combining Ayurveda with conventional Parkinson's treatments. Learn how a holistic approach can manage symptoms, enhance well-being, and balance body energies. Discover the steps to safely integrate Ayurvedic practices into your Parkinson’s care plan, including expert guidance on diet, herbal remedies, and lifestyle modifications.
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.
Local Advanced Lung Cancer: Artificial Intelligence, Synergetics, Complex Sys...Oleg Kshivets
Overall life span (LS) was 1671.7±1721.6 days and cumulative 5YS reached 62.4%, 10 years – 50.4%, 20 years – 44.6%. 94 LCP lived more than 5 years without cancer (LS=2958.6±1723.6 days), 22 – more than 10 years (LS=5571±1841.8 days). 67 LCP died because of LC (LS=471.9±344 days). AT significantly improved 5YS (68% vs. 53.7%) (P=0.028 by log-rank test). Cox modeling displayed that 5YS of LCP significantly depended on: N0-N12, T3-4, blood cell circuit, cell ratio factors (ratio between cancer cells-CC and blood cells subpopulations), LC cell dynamics, recalcification time, heparin tolerance, prothrombin index, protein, AT, procedure type (P=0.000-0.031). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and N0-12 (rank=1), thrombocytes/CC (rank=2), segmented neutrophils/CC (3), eosinophils/CC (4), erythrocytes/CC (5), healthy cells/CC (6), lymphocytes/CC (7), stick neutrophils/CC (8), leucocytes/CC (9), monocytes/CC (10). Correct prediction of 5YS was 100% by neural networks computing (error=0.000; area under ROC curve=1.0).
These lecture slides, by Dr Sidra Arshad, offer a quick overview of the 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 lead (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
6. Describe the flow of current around the heart during the cardiac cycle
7. Discuss the placement and polarity of the leads of electrocardiograph
8. Describe the normal electrocardiograms recorded from the limb leads and explain the physiological basis of the different records that are obtained
9. Define mean electrical vector (axis) of the heart and give the normal range
10. Define the mean QRS vector
11. Describe the axes of leads (hexagonal reference system)
12. Comprehend the vectorial analysis of the normal ECG
13. Determine the mean electrical axis of the ventricular QRS and appreciate the mean axis deviation
14. Explain the concepts of current of injury, J point, and their significance
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. Chapter 3, Cardiology Explained, https://www.ncbi.nlm.nih.gov/books/NBK2214/
7. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
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.
Graph and assembly strategies for the MHC and ribosomal DNA regions
1. Graph and assembly strategies for the
MHC and ribosomal DNA regions
Alexander Dilthey
2. The MHC is the zebrafish of the genome!
(model region)
3. PRGs – Population Reference Graphs
• Simple: acyclic, directed (sub-class of general variation graphs)
• Usually built from MSA, preserve gap positions
(i.e. global homology between input sequences).
• Generative model: Recombination
• Ploidy well-defined (0, 1, 2)
TA CT A G
C
C
_
_
A
TA
A
4. Outline
• Quick recap:
What we know about the utility of graph genome approaches
• New results:
Haplotyping in hypervariable regions (HLA)
Pseudo graph alignment
• De novo assembly of ribosomal DNA
5. In most of the MHC, single-reference
approaches work just fine…
Numberofkmers(millions)
4.55.0
PGFreference Platypus PRG-Viterbi PRG-Mapped
kmersrecovered
kmersnot recovered
+ long-read validation with consistent results (not shown)
Dilthey et al., Nature Genetics 2015
6. … graph genomes outperform in the most
complex sub-region of the MHC …
Dilthey et al., Nature Genetics 2015
7. … remaining problems driven by incomplete
input haplotypes + algorithmics.
Aligned kmers
Chromotype position (kb)
Readposition(kb)
0 10 20
0
2
4
6
Incomplete input haplotypes:
Large uncharacterized inversion
Algorithmics:
Incorrect HLA haplotyping.
Dilthey et al., Nature Genetics 2015
8. HLA haplotyping
• Hypothesis: Whole-genome sequencing data contains the information
necessary for accurate HLA typing
• “HLA typing” HLA gene exon sequences
• HLA class I: exons 2 and 3
• HLA class II: exon 2
• Challenge: align reads to the right gene – homology hell.
• Proper read-to-graph alignment instead of k-Mers.
9. Class I exon homology
Exon 2 Exon 3
HLA-A 3284 alleles
HLA-B 4077 alleles
HLA-C 2799 alleles
10. Approach: deep PRG + mapping
Exonic MSA
T*01:01 _ _ A C G T A C T _ _
T*01:02 C A A C A T A C T _ _
T*01:03 _ _ A C G C G C T _ _
T*01:04 _ _ A T C C G C T A C
T*01:05 _ _ A T C C C C T _ _
T*01:06 _ _ _ C C T A C T _ _
Genomic MSA
T*01:01 A G C A _ _ A C G T A C T _ _ C C T A
T*01:02 A C C A C A A C A T A C T _ _ C C T A
T*01:04 _ T T A _ _ A T C C G C T A C C C T A
8 xMHC reference haplotypes
PGF (with T*01:01) A C T A G C A _ _ A C G T A C T _ _ C C T A T G A
MANN (with T*01:04) T T T _ T T A _ _ A T C C G C T A C C C T A T G A
1) Gene-only PRG – 46 (pseudo) genes, mostly HLA
|--NNN--| |--NNN--|Gene 1 Gene 2 Gene 3
Padding UTR Exon 1 Intron 1 Exon 2 UTR Padding
Numberofreferencesequences
Region covered by 'genomic' sequences
2) Varying numbers of input sequences across PRG
3) Use hierarchical MSA approach to combine in
11. Approach: deep PRG + mapping
Level 1
CA
_ _
C T
C
CC
G
AAligned read
2 3 4 5 6 7
A _ TATA _ C
198 9 10 11 12 13 14 15 16 17 18 25 26
C AGTATC
20 21 22 23 24
TC
TC
T T
A
_
A _
A G
C
T
C
T
T
C T
ATA
C
C {G, C}T
C
G
CA
A
_ _
A
4) Seed-and-extend paired-end mapping to PRG
5) Likelihood-based inference: maximize L( aligned reads | HLA types )
(independently per locus)
12. High-quality WGS data enables gold-standard
accuracy
(of note: 2/3 original discrepancies with validation data were errors in the validation data!)
16. Conclusion (intermediate)
• If the input sequencing data is „good enough“, we manage near-
perfect haplotyping in the genome‘s most polymorphic region
• Effective fragment length likely the most important factor
• Not-so-good sequencing data: joint haplotyping + alignment
(i.e. alignment location is not independent of inferred haplotype)
• Read mapping implementation SLOW
25. Read error vs variation
… from whole-genome data?
Long reads de Bruijn graph Technology!
6% > 50k
26. Summary
• Variation graphs are worth the effort – at least in highly complex regions.
• Evidence: MHC „model system“
+ overall improvement of Genome inference accuracy
+ complex-locus haplotyping
• Incorporate LD?
• Middle ground between full graph alignment and linear sequence
alignment?
• Ribosomal DNA – let me know if you‘re also interested!
27. Acknowledgements
NIH
Adam Phillippy
Sergey Koren
Brian Walenz
Jung-Hyun Kim
Vladimir Larionov
Oxford
Gil McVean
Zam Iqbal
Alexander Mentzer
Histogenetics
Nezih Cereb
UCSF/Nantes
Pierre-Antoine Gourraud
GSK
Matt Nelson
Charles Cox