This lecture introduces next-generation sequencing and its applications in biomedical research. It discusses how next-gen sequencing is transforming genetic disease diagnosis and personalized medicine. The lecture covers sequencing workflows including read alignment, variant calling, and annotation. It also describes different sequencing experiments like whole genome, exome, RNA-seq, and ChIP-seq. Finally, it discusses how next-gen sequencing is advancing research into genetic diseases and cancer genomics.
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.
Avances en genética. Utilidad de la NGS y la bioinformática.BBK Innova Sarea
27 Octubre 2014. Presentación de Pablo Lapunzina, Director del Instituto de Medicina Genética Médica y Molecular (INGEMM), de IDIPAZ y de CEBERER, en la "Jornada Avances en Genética y Tecnología Social. La experiencia de la Fundación Síndrome de Dravet ".
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.
Avances en genética. Utilidad de la NGS y la bioinformática.BBK Innova Sarea
27 Octubre 2014. Presentación de Pablo Lapunzina, Director del Instituto de Medicina Genética Médica y Molecular (INGEMM), de IDIPAZ y de CEBERER, en la "Jornada Avances en Genética y Tecnología Social. La experiencia de la Fundación Síndrome de Dravet ".
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.
CRISPR Gene Editing Congress, 25-27 February 2015 in Boston, MADiane McKenna
Key industry leaders will gather at the inaugural CRISPR Precision Gene Editing Congress with an ultimate purpose of addressing the importance of overcoming specificity, efficiency and delivery challenges associated with the CRISPR/Cas9 system. Pioneers will showcase the expanding biomedical and therapeutic potential of gene editing tools for drug discovery and development.
A Genome Sequence Analysis System Built with HypertableDATAVERSITY
Deep genome sequencing has revolutionized the fields of biology and medicine. Since January 2008, the capacity to generate sequence data has increased exponentially, far outpacing Moore's Law. The emergence of scalable NoSQL database technologies has made the analysis of this vast amount of sequence data not only feasible, but cost effective.
The University of California at San Francisco UCSF-Abbott Viral Detection and Discovery Center, led by director Charles Chiu, MD, PhD, Taylor Sittler, MD and the Hypertable development team have embarked upon a project to build a scalable software platform to facilitate deep sequencing analysis in diagnostic microbiology, transcriptomic analysis, and clinical / environmental metagenomics, areas for which existing commercial and academic solutions are sorely lacking. Doug Judd, the original creator of Hypertable, will present an overview of this genome sequencing analysis system. The presentation will cover the following topics:
Rationale for choosing NoSQL
Schema design
Sources and description of input data
Algorithms for generating and querying lookup tables
Table sizes and compression ratios
Lessons learned during system deployment
The key considerations of crispr genome editingChris Thorne
While CRISPR is simple to use, widely applicable and often highly efficient, there are a number of things to keep in mind to maximise experimental success. Here's what we recommend...
The CRISPR/Cas9 system has emerged as one of the leading tools for modifying genomes of organisms ranging from E. coli to humans. Additionally, the simple gene targeting mechanism of CRISPR technology has been modified and adapted to other applications that include gene regulation, detection of intercellular trafficking, and pathogen detection. With a wealth of methods for introducing Cas9 and gRNAs into cells, it can be challenging to decide where to start. In this presentation, Dr Adam Clore describes the CRISPR mechanism and some of the most prominent uses for CRISPR, along with methods where IDT technologies can assist scientists in designing, testing, and executing a variety of CRISPR-mediated experiments. For more informaton, visit: http://www.idtdna.com/crispr
Achieve improved variant detection in single cell sequencing infographicQIAGEN
High sequencing depth may increase the sensitivity of variant detection for bulk samples, but it has not proven appropriate for single cell sequencing. What’s more, it makes whole genome sequencing prohibitively expensive.
For variant detection in rare cells, such as circulating tumor cells, Zhang et al. recently presented a brilliant way to overcome these challenges: low depth sequencing of multiple single cells and census-based variant detection. For your convenience, we’ve summarized the concept in a new infographic.
Presentation for teaching faculty about resources, data, issues, and strategies for including personal genomics in the classroom, within the context of precision medicine as an overarching theme.
RefSeq curation in-depth. Examples of targeted transcript and protein curation, presented at the 8th International Biocuration conference (April, 2015).
An Introduction to Crispr Genome EditingChris Thorne
In this short presentation, I make a case for doing genome editing vs some of the approaches that have gone before, describe some of the tools available, and the focus on CRISPR-Cas9, what it is, where it's come from and how it works.
Genome Editing Comes of Age; CRISPR, rAAV and the new landscape of molecular ...Candy Smellie
Information is no longer a bottleneck, emphasis is shifting to the ‘what does it all mean’
In a translational context we hope that by answering that question we will be able to is to characterise the genetics that drive disease, and indeed develop drugs and diagnostics that are personalised to patients.
Genome editing provides the link between the information here, and this outcome here, by allowing scientists to recapitulate specific genetic alterations in any gene in any living tissue to probe function, develop disease models and identify therapeutic strategies. So, not only do we now have unparalleled access to genetic information, but we now have the tools to most accuartely understand what this genetic information – with genome editing allowing us to explore the genetic drivers of disease in physiological models.
AAV is a single-stranded, linear DNA virus with a a 4.7 kb genome which for the purpose of genome editing is replaced almost in entirety with the targeting vector sequence (except for the iTRs)
It is in effect a highly effective DNA delivery mechanism
After entry of the vector into the cell, target-specific homologous DNA is believed to activate and recruit HR-dependent repair factors can induce HR at rates approximately 1,000 times greater than plasmid based double stranded DNA vectors, but the mechanism by which it achieves this is still largely unknown
By including a selection cassette can select for cells that have integrated the targeting vector, and then screen for clones which have undergone targeted insetion rather than random integration, which will generally be around 1%.
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.
CRISPR Gene Editing Congress, 25-27 February 2015 in Boston, MADiane McKenna
Key industry leaders will gather at the inaugural CRISPR Precision Gene Editing Congress with an ultimate purpose of addressing the importance of overcoming specificity, efficiency and delivery challenges associated with the CRISPR/Cas9 system. Pioneers will showcase the expanding biomedical and therapeutic potential of gene editing tools for drug discovery and development.
A Genome Sequence Analysis System Built with HypertableDATAVERSITY
Deep genome sequencing has revolutionized the fields of biology and medicine. Since January 2008, the capacity to generate sequence data has increased exponentially, far outpacing Moore's Law. The emergence of scalable NoSQL database technologies has made the analysis of this vast amount of sequence data not only feasible, but cost effective.
The University of California at San Francisco UCSF-Abbott Viral Detection and Discovery Center, led by director Charles Chiu, MD, PhD, Taylor Sittler, MD and the Hypertable development team have embarked upon a project to build a scalable software platform to facilitate deep sequencing analysis in diagnostic microbiology, transcriptomic analysis, and clinical / environmental metagenomics, areas for which existing commercial and academic solutions are sorely lacking. Doug Judd, the original creator of Hypertable, will present an overview of this genome sequencing analysis system. The presentation will cover the following topics:
Rationale for choosing NoSQL
Schema design
Sources and description of input data
Algorithms for generating and querying lookup tables
Table sizes and compression ratios
Lessons learned during system deployment
The key considerations of crispr genome editingChris Thorne
While CRISPR is simple to use, widely applicable and often highly efficient, there are a number of things to keep in mind to maximise experimental success. Here's what we recommend...
The CRISPR/Cas9 system has emerged as one of the leading tools for modifying genomes of organisms ranging from E. coli to humans. Additionally, the simple gene targeting mechanism of CRISPR technology has been modified and adapted to other applications that include gene regulation, detection of intercellular trafficking, and pathogen detection. With a wealth of methods for introducing Cas9 and gRNAs into cells, it can be challenging to decide where to start. In this presentation, Dr Adam Clore describes the CRISPR mechanism and some of the most prominent uses for CRISPR, along with methods where IDT technologies can assist scientists in designing, testing, and executing a variety of CRISPR-mediated experiments. For more informaton, visit: http://www.idtdna.com/crispr
Achieve improved variant detection in single cell sequencing infographicQIAGEN
High sequencing depth may increase the sensitivity of variant detection for bulk samples, but it has not proven appropriate for single cell sequencing. What’s more, it makes whole genome sequencing prohibitively expensive.
For variant detection in rare cells, such as circulating tumor cells, Zhang et al. recently presented a brilliant way to overcome these challenges: low depth sequencing of multiple single cells and census-based variant detection. For your convenience, we’ve summarized the concept in a new infographic.
Presentation for teaching faculty about resources, data, issues, and strategies for including personal genomics in the classroom, within the context of precision medicine as an overarching theme.
RefSeq curation in-depth. Examples of targeted transcript and protein curation, presented at the 8th International Biocuration conference (April, 2015).
An Introduction to Crispr Genome EditingChris Thorne
In this short presentation, I make a case for doing genome editing vs some of the approaches that have gone before, describe some of the tools available, and the focus on CRISPR-Cas9, what it is, where it's come from and how it works.
Genome Editing Comes of Age; CRISPR, rAAV and the new landscape of molecular ...Candy Smellie
Information is no longer a bottleneck, emphasis is shifting to the ‘what does it all mean’
In a translational context we hope that by answering that question we will be able to is to characterise the genetics that drive disease, and indeed develop drugs and diagnostics that are personalised to patients.
Genome editing provides the link between the information here, and this outcome here, by allowing scientists to recapitulate specific genetic alterations in any gene in any living tissue to probe function, develop disease models and identify therapeutic strategies. So, not only do we now have unparalleled access to genetic information, but we now have the tools to most accuartely understand what this genetic information – with genome editing allowing us to explore the genetic drivers of disease in physiological models.
AAV is a single-stranded, linear DNA virus with a a 4.7 kb genome which for the purpose of genome editing is replaced almost in entirety with the targeting vector sequence (except for the iTRs)
It is in effect a highly effective DNA delivery mechanism
After entry of the vector into the cell, target-specific homologous DNA is believed to activate and recruit HR-dependent repair factors can induce HR at rates approximately 1,000 times greater than plasmid based double stranded DNA vectors, but the mechanism by which it achieves this is still largely unknown
By including a selection cassette can select for cells that have integrated the targeting vector, and then screen for clones which have undergone targeted insetion rather than random integration, which will generally be around 1%.
Next-generation sequencing and quality control: An Introduction (2016)Sebastian Schmeier
This lecture is part is an introductory bioinformatics workshop. It gives a background to what sequencing is, what the results of a sequencing experiment are, how to assess the quality of a sequencing run, what error sources exist and how to deal with errors. The accompanying websites are available at http://sschmeier.com/bioinf-workshop/
Bioinformatics tools for the diagnostic laboratory - T.Seemann - Antimicrobi...Torsten Seemann
"Bioinformatics tools for the diagnostic laboratory" presented at the Australian Society for Antimicrobials 2016 annual conference in Melbourne Australia. Slides are aimed at a biological / pathology / clinican audience. Some material has been re-imagined from Nick Loman's ECCMID 2015 talk.
Predictive Analytics of Cell Types Using Single Cell Gene Expression ProfilesAli Al Hamadani
Conducted domain independent predictive analysis pipeline using R for cell type predictions. Applied many predictive analytics models, and machine learning techniques.
Open pacbiomodelorgpaper j_landolin_20150121Jane Landolin
Jane Ladolin's slides on Open Data Paper (http://www.nature.com/articles/sdata201445) presented at Balti and Bioinformatics virtual meeting on Jan. 21st 2015. (http://bit.ly/1KYGxr4)
Moving Towards a Validated High Throughput Sequencing Solution for Human Iden...Thermo Fisher Scientific
Presented by Jennifer D. Churchill, PhD during a special Lunch and Learn session during the American Academy of Forensic Sciences (AAFS) 67th annual conference, February 2015. / Conclusions
• Robust panels of identity and ancestry SNPs
• Robust STR panel
• Whole genome mtDNA sequencing
• Highly informative
• Sensitive
• Quantitative – scaling comparison
• Low density chip is not necessarily a bad chip
• Wide range of density can still yield high quality data
• Based on results continue development and validation
Intended for a mixed/general audience of Clinicians, Business Interests, and Research Scientists. No audio, however the event was recorded and posted to youtube by Genome Atlantic: http://www.youtube.com/watch?v=FLVjwOngu-Q I
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
Normal Labour/ Stages of Labour/ Mechanism of Labour
2015 Bioc4010 lecture1and2
1. Next-Generation Sequence Analysis
for Biomedical Applications
BIOC 4010/5010
Lecture 1
Dr. Dan Gaston
Postdoctoral Fellow Department of Pathology
Dr. Karen Bedard Lab
3. Overview: Lecture 1
• Why Next-Gen Sequencing Matters
• What is Next-Gen Sequencing
• Bioinformatics Workflows
• Types of Next-Gen Experiments
• Working with the Human Genome
• Slides available on slideshare:
– http://www.slideshare.net/DanGaston
12. Major Areas in Human Disease
Genomics
• Complex Disease
– Genome Wide Association Studies (GWAS)
• Mendelian Disease
– Whole Genome/Exome Sequencing
– Transcriptomics
– Genetic Linkage – Sanger Sequencing
• Cancer
– Tumour Genomics
– Transcriptomics
13. Traditional Diagnosis of Genetic
Disease
• Genetic Counselors/Physicians order
individual testing of genes based on patient
phenotype
• For rare diseases or unusual phenotypes may
run tens to hundreds of tests
• …..EXPENSIVE (Easily thousands of dollars)
14. Next Generation Diagnosis of Genetic
Disease
• NGS-Based Targeted Sequencing Panels
• Clinical Exome
• Clinical Genome
20. Human Genomics: More Power!
• $5,000 - $10,000 to sequence whole genome
– Dropping towards $1000 for sequencing only
• ~$1000 to sequence only protein-coding
portion (exome, later)
21. Clinical Genomics
• Rapid diagnosis of genetic disease in NICU cases
• Quicker and cheaper than sequential genetic
testing (traditional method)
24. Personalized Medicine: Oncology
Tumour Sample
DNA
Non-Tumour
Sample
DNA
Databases and
Annotations
Sequence
Tumour
Specific
Mutations
Tumour
Classification
Drugs
34. FastQ Quality Scores
Quality Score (Q) Probability of incorrect base call Base call accuracy
10 1 in 10 90%
20 1 in 100 99%
30 1 in 1000 99.90%
40 1 in 10000 99.99%
50 1 in 100000 100.00%
Q = -10 log10 P
36. General Genomics Workflow
Quality Control of Raw
Data
Raw Data
Analysis
Alignment to reference
genome
Whole Genome
Mapping
Detection of genetic variation
(SNPs, Indels, SV)
Variant Calling
Linking variants to biological
information
Annotation
37. Find the Location of Each Read in the
Genome
• Problems:
– Short sequence
38. Find the Location of Each Read in the
Genome
• Problems:
– Short sequence
– Millions of short sequences
39. Find the Location of Each Read in the
Genome
• Problems:
– Short sequence
– Millions of short sequences
– Big genome
40. Find the Location of Each Read in the
Genome
• Problems:
– Short sequence
– Millions of short sequences
– Big genome
– Mismatches
• Polymorphisms
• Sequencing errors
41. Find the Location of Each Read in the
Genome
• Problems:
– Short sequence
– Millions of short sequences
– Big genome
– Mismatches
• Polymorphisms
• Sequencing errors
– Insertions and deletions
42. Find the Location of Each Read in the
Genome
• Problems:
– Short sequence
– Millions of short sequences
– Big genome
– Mismatches
• Polymorphisms
• Sequencing errors
– Insertions and deletions
– May be processing many (100’s) of individuals
44. Short Read Mapping: Brute Force
Method (Stupid)
Simple conceptually: Compare each query k-mer to all k-
mers of genome
Scales with size of the genome and the reads (Not
particularly well)
Genome = AGCATGCTGCAGTCATGCTTAGGCTA
Read = GCT
45. Solution
Index the Reference Genome
Indexing the reference is like constructing a phone
book, quickly move towards the relevant portion of the
genome and ignore the rest.
46. Suffix Array
Split genome into all suffixes (substrings) and sort
alphabetically
Allows query to be searched against an alphabetical
reference, skipping 96% of the genome
Ex: banana$
banana$ $
anana$ a$
nana$ ana$
ana$ anana$
na$ banana$
a$ nana$
$ na$
47. Short Read Alignment: Binary Search
• Searching the index efficiently is still a
problem…
Index # Sequence Pos Pos
1 ACAGATTACC… 6
2 ACC… 13
3 AGATTACC… 8
4 ATTACAGATTACC… 3
5 ATTACC… 10
6 C… 15
7 CAGATTACC… 7
8 CC… 14
9 GATTACAGATTACC… 2
10 GATTACC… 9
11 TACAGATTACC… 5
12 TACC… 12
13 TGATTACAGATTACC… 1
14 TTACAGATTACC… 4
15 TTACC… 11
Search for GATTACA…
48. Short Read Alignment: Binary Search
• Searching the index efficiently is still a
problem…
Index # Sequence Pos Pos
1 ACAGATTACC… 6
2 ACC… 13
3 AGATTACC… 8
4 ATTACAGATTACC… 3
5 ATTACC… 10
6 C… 15
7 CAGATTACC… 7
8 CC… 14
9 GATTACAGATTACC… 2
10 GATTACC… 9
11 TACAGATTACC… 5
12 TACC… 12
13 TGATTACAGATTACC… 1
14 TTACAGATTACC… 4
15 TTACC… 11
Search for GATTACA…
49. Short Read Alignment: Binary Search
• Searching the index efficiently is still a
problem…
Index # Sequence Pos Pos
1 ACAGATTACC… 6
2 ACC… 13
3 AGATTACC… 8
4 ATTACAGATTACC… 3
5 ATTACC… 10
6 C… 15
7 CAGATTACC… 7
8 CC… 14
9 GATTACAGATTACC… 2
10 GATTACC… 9
11 TACAGATTACC… 5
12 TACC… 12
13 TGATTACAGATTACC… 1
14 TTACAGATTACC… 4
15 TTACC… 11
Search for GATTACA…
50. Short Read Alignment: Binary Search
• Searching the index efficiently is still a
problem…
Index # Sequence Pos Pos
1 ACAGATTACC… 6
2 ACC… 13
3 AGATTACC… 8
4 ATTACAGATTACC… 3
5 ATTACC… 10
6 C… 15
7 CAGATTACC… 7
8 CC… 14
9 GATTACAGATTACC… 2
10 GATTACC… 9
11 TACAGATTACC… 5
12 TACC… 12
13 TGATTACAGATTACC… 1
14 TTACAGATTACC… 4
15 TTACC… 11
Search for GATTACA…
51. Short Read Alignment: Binary Search
• Searching the index efficiently is still a
problem…
Index # Sequence Pos Pos
1 ACAGATTACC… 6
2 ACC… 13
3 AGATTACC… 8
4 ATTACAGATTACC… 3
5 ATTACC… 10
6 C… 15
7 CAGATTACC… 7
8 CC… 14
9 GATTACAGATTACC… 2
10 GATTACC… 9
11 TACAGATTACC… 5
12 TACC… 12
13 TGATTACAGATTACC… 1
14 TTACAGATTACC… 4
15 TTACC… 11
Search for GATTACA…
52. Binary Search
• Initialize search range to entire list
– mid = (hi+lo)/2; middle = suffix[mid]
– if query matches middle: done
– else if query < middle: pick low range
– else if query > middle: pick hi range
• Repeat until done or empty range
53. Applied to Human Genome
• In practice simple methods of indexing the
genome can create very large data structures
– Suffix Array: > 12 GB
• Solution: Apply complex procedures that allow
you to index and compress the data:
– Burrows-Wheeler Transform
– FM-Index
55. Burrows-Wheeler Transform
• Similar in many ways to creation of Suffix
Array
BANANA$
BANANA$
ANANA$B
NANA$BA
ANA$BAN
NA$BANA
A$BANAN
$BANANA
Circular
Permutation
56. Burrows-Wheeler Transform
• Similar in many ways to creation of Suffix
Array
BANANA$
BANANA$
ANANA$B
NANA$BA
ANA$BAN
NA$BANA
A$BANAN
$BANANA
$BANANA
A$BANAN
ANA$BAN
ANANA$B
BANANA$
NA$BANA
NANA$BA
Lexicographical
Sort
57. Burrows-Wheeler Transform
• Similar in many ways to creation of Suffix
Array
BANANA$Burrows-Wheeler
Matrix
$BANANA
A$BANAN
ANA$BAN
ANANA$B
BANANA$
NA$BANA
NANA$BA
58. Burrows-Wheeler Transform
• Similar in many ways to creation of Suffix
Array
BANANA$
$BANANA
A$BANAN
ANA$BAN
ANANA$B
BANANA$
NA$BANA
NANA$BA
59. Burrows-Wheeler Transform
• Similar in many ways to creation of Suffix
Array
BANANA$
T(string) = ANNB$AA
Transformed String:
Compressible and Reversible
$BANANA
A$BANAN
ANA$BAN
ANANA$B
BANANA$
NA$BANA
NANA$BA
60. Burrows-Wheeler Transform
• Similar in many ways to creation of Suffix
Array
BANANA$
T(string) = ANNB$AA
Suffix Array
$BANANA
A$BANAN
ANA$BAN
ANANA$B
BANANA$
NA$BANA
NANA$BA
6
5
3
1
0
4
2
61. Burrows-Wheeler Transform
• Similar in many ways to creation of Suffix
Array
BANANA$
TT(string) = ANNB$AA
FM-Index
$BANANA
A$BANAN
ANA$BAN
ANANA$B
BANANA$
NA$BANA
NANA$BA
6
5
3
1
0
4
2
6, 5, 3, 1, 0, 4, 2
+
+
Character Count Tables
62. Short Read Aligners
• BLAT: BLAST-Like Alignment Tool
• MAQ: First to take in to account quality scores
• Bowtie: One of the first to use BWT, ungapped
alignment only
• BWA: One of the first to use BWT. First gapped
BWT, incredibly fast and memory efficient
• Bowtie2: Allows indels
• SOAP, SOAP2: Also use BWT
• … and many more
65. Transcriptomics: RNA-Seq
• Sequence the actively transcribed genes in a
cell line or tissue
– Only about 20% of genes are transcribed in
particular cell types
• Two types:
– Poly-A selection
– Total RNA + ribodepletion
• Many experimental questions can be
addressed
70. RNA-Seq
• Important to take in to account biological
variability. A sample of cells is a mixed population
– Replicates!
• Not suited for discovering polymorphisms due to
higher error rates introduced by reverse
transcription step (RNA -> cDNA)
• High false positive rates for fusion gene discovery,
novel exons, when low expression levels
80. Genetic Variation
• dbSNP (NCBI) build 142
– Catalogs Single Nucleotide Variants (SNV)
– 365 Million Submitted
– 113 Million Validated
– 54 Million in Genes
– 36 Million With Frequency in Populations
• 50-80% of mutations involved in inherited
disease caused by SNVs
– May be an overestimate due to lack of knowledge
81. SNP vs SNV
• Technically a polymorphism is a variation that
doesn’t cause disease and is common in a
population
• What is common?
– Greater than 5% in a population a typical
definition
– Definition for rare ranges from < 0.1% to < 1.0%
83. Frequency of Polymorphisms:
Common vs Rare
• Mendelian disorders are caused by rare
variation, < 1% frequency in the relevant
population
• Leverage large projects aimed at assessing
genetic diversity in populations around the
world
85. Exome Sequencing Project
• Multi-Institutional
• Total possible patient pool of > 250,000
individuals, well phenotyped
– Includes healthy individuals and diseased
• Currently 6700 exomes sequenced
– 4420 European descent
– 2312 African American
• 1.2 million coding variations
– Most extremely rare/unique
– Many population specific
87. Other Resources and Projects
• Exome Aggregation Consortium: 60,000
Exomes
• Personal Genome Project (Ongoing)
• 100,000 Genomes Project (UK, Ongoing)
• BGI (Announced, China): 1 Million Genomes
• Precision Medicine Initiative (US, Announced):
1 Million Genomes
88. Population Matters
• Most variations in protein-coding genes
occurred fairly recently (last 20,000 years)
– Adaptation to agriculture and diet changes,
pathogen exposure and urban living
91. Population Matters
• Most variations in protein-coding genes occurred
fairly recently (last 20,000 years)
– Adaptation to agriculture and diet changes, pathogen
exposure and urban living
• Monogenic diseases have different prevalence in
different populations
– Cystic fibrosis in European population
– Hereditary Hemochromatosis in Northern Europeans
– Tay-Sachs in Ashkenazi Jews
– Sickle-Cell Anemia in Sub-Saharan African populations
95. Genotype Calling: Determining the
Type of Needle, The Absurdly Simple
Way (Stupid)
ATCCTGATTCGGTGAACGTTATCGACGATCCGATCGA
CGGTGAACGTTATCGACGATCCGATCGAACTGTCAGC
GGTGAACGTTATCGACGTTCCGATCGAACTGTCAGCG
TGAACGTTATCGACGTTCCGATCGAACTGTCAGCGGC
TGAACGTTATCGACGTTCCGATCGAACTGTCAGCGGC
TGAACGTTATCGACGTTCCGATCGAACTGTCAGCGGC
GTTATCGACGATCCGATCGAACTGTCAGCGGCAAGCT
TTATCGACGATCCGATCGAACTGTCAGCGGCAAGCT
ATCCTGATTCGGTGAACGTTATCGACGATCCGATCGAACTGTCAGCGGCAAGCTGATCGATCGATCGATGCTAGTG
TTATCGACGATCCGATCGAACTGTCAGCGGCAAGCT
TCGACGATCCGATCGAACTGTCAGCGGCAAGCTGAT
TTCCGATCGAACTGTCAGCGGCAAGCTGATCGATCGA
ATCCTGATTCGGTGAACGTTATCGACGATCCGATCGA
reference genome
Read depth at base: 10 T: 4 A: 6
Genotype: Heterozygous A/T
96. Genotype Calling: The Absurdly Simple
Way (Stupid)
• Doesn’t account for sequencing error
• Doesn’t account for sequencing bias
• Doesn’t count for bias in short-read mapping
process
• Doesn’t account for mapping error
• Doesn’t consider any external source of
information regarding populations or known
genetic variations
97. Genotype Calling: The Absurdly Simple
Way (Slightly less Stupid)
• Algorithm:
– Count all aligned bases that pass quality threshold
(e.g. >Q20)
– If #reads with alternative base > lower bound (20%)
and < upper bound (80%) call heterozygous alt
– Else if > upper bound call homozygous alternative
– Else call homozygous reference
• …But what about base qualities for more than
keeping reads?
101. What’s Missing
• No estimate of the confidence (stats) of
variant and genotype calls
102. What’s Missing
• No estimate of the confidence (stats) of
variant and genotype calls
• Doesn’t account robustly for known sources of
error
103. What’s Missing
• No estimate of the confidence (stats) of
variant and genotype calls
• Doesn’t account robustly for known sources of
error
• Doesn’t make use of any sources of external
information
104. What’s Missing
• No estimate of the confidence (stats) of
variant and genotype calls
• Doesn’t account robustly for known sources of
error
• Doesn’t make use of any sources of external
information
• Doesn’t include base qualities
107. Improved Genotype Calling: Prior
Probability
• Known Polymorphic Site?
– Allele Frequencies
• Global rate of polymorphisms
• Other samples
• Substitution Type
108. Substitution Type
• Transition:
– Purine to Purine (A to G)
– Pyrimidine to Pyrimidine (C to T)
• Transversion
– Purine to Pyrimidine
• Transition/Transversion ratio
– Transitions 2x as common (Genome Wide)
– 4x when looking only at exons
– Random Error: 0.5
110. Prior Probability Example
A C G T
A 3.33x10-4 1.11x10-7 6.67x10-4 1.11x10-7
C 8.33x10-5 1.67x10-4 2.78x10-8
G 0.9985 1.67x10-4
T 8.33x10-5
Assume:
Heterozygous SNP Rate of 0.001
Homozygous SNP Rate of 0.0005
Reference: G
Transition/Transversion Ratio: 2
111. Improved Genotype Calling: Error
Rates
Predicted Base
A C G T
Actual
Base
A - 57.7 17.1 25.2
C 34.9 - 11.3 53.9
G 31.9 5.1 - 63.0
T 45.9 22.1 32.0 -
If a base was miscalled, what is it most likely to be called
as instead?
113. Decisions and Trade-Offs
• Option 1: Use stringent program options for
calling variants and hard filtering early to
produce only highly-confident call set.
114. Decisions and Trade-Offs
• Option 1: Use stringent program options for
calling variants and hard filtering early to
produce only highly-confident call set.
– Pro: Few false positives
– Con: Will miss real variants
115. Decisions and Trade-Offs
• Option 1: Use stringent program options for
calling variants and hard filtering early to
produce only highly-confident call set.
– Pro: Few false positives
– Con: Will miss real variants
• Option 2: Use less stringent (but reasonable)
options and filtering. Produce high-confidence
call set. Progressive filtering at later stage
116. Decisions and Trade-Offs
• Option 1: Use stringent program options for
calling variants and hard filtering early to produce
only highly-confident call set.
– Pro: Few false positives
– Con: Will miss real variants
• Option 2: Use less stringent (but reasonable)
options and filtering. Produce high-confidence
call set. Progressive filtering at later stage
– Pro: Won’t miss real variants
– Con: Many more false positives
117. Decisions and Trade-Offs
• Option 1: Use stringent program options for
calling variants and hard filtering early to produce
only highly-confident call set.
– Pro: Few false positives
– Con: Will miss real variants
• Option 2: Use less stringent (but reasonable)
options and filtering. Produce high-confidence
call set. Progressive filtering at later stage
– Con: False positives
– Pro: Won’t miss real variants
118. How Good Are My Calls?
• How many called SNPs?
– Human average of 1 heterozygous SNP / 1000
bases
• Fraction of variants already in dbSNP
– ~90%
• Transition/Transversion ratio
– Transitions 2x as common
• 3x when looking only at exons
121. Discovering Genetic Variants Causing
Mendelian Disease
4 million genetic variants
2 million associated with
protein-coding genes
10,000 possibly
of disease
causing type
1500 <1%
frequency in
population
Single Causal
Genetic Variant
122. If a problem cannot be
solved, enlarge it.
--Dwight D. Eisenhower
Supreme Commander Allied Forces:
Second World War
34th President USA
132. Example: SIFT Algorithm
Input Query
Sequence
Psi-BLAST
Homologs
Alignment
Multiple
Sequence
Alignment
Multiple
Sequence
Alignment
PSSM
Normalize
By most
frequent AA
Score
133. Prediction Take-Away
The more conserved a site is the more likely
any substitution is to be deleterious
However: Current methods have pretty poor
performance, not suitable for clinical-level
diagnosis
135. Classifying Genetic Variants
4 million
variants
Intronic
Unknown Splice Site
Potential
Disease Causing
Exonic
Amino Acid
Changing
Known Genetic
Disease Variant
Stop Loss / Stop
Gain
Missense
Mutation
Known
Polymorphism in
Population
Silent Mutation Splice Site
Potential
Disease Causing
Intergenic
138. Annotating Genes and Variants
• Is variant in a known protein-coding gene?
– What does the gene do?
– What molecular pathways?
– What protein-protein interactions?
– What tissues is it expressed in?
– When in development?
4 million genetic variants
2 million associated with
protein-coding genes
10,000 possibly
of disease
causing type
1500 <1%
frequency in
population
144. IGNITE Project: Local Controls
• IGNITE: Tasked with studying rare monogenic
diseases identified in Atlantic Canada
• Atlantic Canada harbours several non-
represented population groups and sub-
groups…
145. IGNITE Project: Local Controls
• IGNITE: Tasked with studying rare monogenic
diseases identified in Atlantic Canada
• Atlantic Canada harbours several non-
represented population groups and sub-
groups…
– Acadians
– Native American
– Non-Acadian/European Descent
146. Population Frequency
• Mendelian disorders are rare
• If variation is in database, is it associated with
disease?
• Causal variation also needs to be rare
– Cutoff somewhere in the < 0.1 - < 1% range
– Should appear rarely or not at all in local controls
– Track with disease in family members under study
150. Brain Calcification
• 84 genes in chromosome 5 region
• No likely homozygous or compound heterozygous
variants within region shared between two
patients
• 29 genes with at least one targeted region with
little or no sequencing coverage
• Many only lacked coverage in 5’ and 3’ UTRs
• Collaborators performed statistical tests for
possibly copy-number variations of targeted
regions using exome sequencing data