The document discusses the Human Genome Project which aimed to determine the entire DNA sequence of the human genome consisting of 3 billion base pairs. As of June 2004, over 1000 genome projects were underway including completed genomes of various organisms ranging from small bacteria to humans. The human genome was sequenced in a collaborative effort between research groups around the world and was completed in 2003, finding that the human genome contains approximately 20,000-25,000 protein-coding genes.
The Human Genome Project (HGP) was an international scientific research project with the goal of determining the base pairs that make up human DNA, and of identifying and mapping all of the genes of the human genome from both a physical and a functional standpoint.
What is bioinformatics?
About human genome
Human genome project
Aim of human genome project
History
Sequencing Strategy
Benefits of Human Genome Project research
Disadvantages of human genome project
Conclusion
References
The Human Genome Project (HGP) was an international scientific research project with the goal of determining the base pairs that make up human DNA, and of identifying and mapping all of the genes of the human genome from both a physical and a functional standpoint.
What is bioinformatics?
About human genome
Human genome project
Aim of human genome project
History
Sequencing Strategy
Benefits of Human Genome Project research
Disadvantages of human genome project
Conclusion
References
Human Genome Project (HGP) was an international scientific research project with the goal of determining the base pairs that make up human DNA, and of identifying and mapping all of the genes of the human genome from both a physical and a functional
Human Genome Project (HGP)
Main objectives Human Genome Project (HGP)
Goals for the HGP
Medical Implications
Applications of HGP
Timeline of HGP
Technical aspects in HGP
Mapping strategies
Sequencing strategies
. Shotgun sequencing method
Sanger sequencing method
Outcomes of HGP
this is done by me and my team mates of Wayamba University Sri Lanka for our project.From now we decided to allow download this file.I would be greatful if you could send your comments..
And I'm willing to help you in similar works.I'm in final year of my degree(.BSc Biotechnology)..
pubudu_gokarella@yahoo.com
After sequencing of the genome has been done, the first thing that comes to mind is "Where are the genes?". Genome annotation is the process of attaching information to the biological sequences. It is an active area of research and it would help scientists a lot to undergo with their wet lab projects once they know the coding parts of a genome.
Genomics, Transcriptomics, Proteomics, Metabolomics - Basic concepts for clin...Prasenjit Mitra
This set of slides gives an overview regarding the various omics technologies available and how they can be used for improvement in clinical setting or research
HGP was conceived in 1984 & officially begun in earnest in October 1990.
HGP is a large multicentric, international collaborative venture, the main aim of which is to determine the nucleotide sequence of the entire human nuclear genome.
In 1997, United States established the National Human Genome Research Institute (NHGRI).
The HGP was an international research groups from six countries- USA, UK, France, Germany, Japan and China, & several laboratories and a large no. of scientists and technicians from various disciplines.
MT115 Precision Medicine: Integrating genomics to enable better patient outcomesDell EMC World
"The emergence of genomics and real-time screening is helping to transform the practice of medicine as we know it today. New technologies present improved ways to tackle health issues and what was once thought to be “untouchable” due to cost, timing or resources, is now achievable through genetic screenings and genome sequencing.
During this session, we will explore:
1. The benefits of incorporating a genomics strategy early in lifeline
2. The Precision Medicine Initiative – how does this help? Does this encourage more people to get genetic screenings?
3. What’s involved in a genetic screening
"
Human Genome Project (HGP) was an international scientific research project with the goal of determining the base pairs that make up human DNA, and of identifying and mapping all of the genes of the human genome from both a physical and a functional
Human Genome Project (HGP)
Main objectives Human Genome Project (HGP)
Goals for the HGP
Medical Implications
Applications of HGP
Timeline of HGP
Technical aspects in HGP
Mapping strategies
Sequencing strategies
. Shotgun sequencing method
Sanger sequencing method
Outcomes of HGP
this is done by me and my team mates of Wayamba University Sri Lanka for our project.From now we decided to allow download this file.I would be greatful if you could send your comments..
And I'm willing to help you in similar works.I'm in final year of my degree(.BSc Biotechnology)..
pubudu_gokarella@yahoo.com
After sequencing of the genome has been done, the first thing that comes to mind is "Where are the genes?". Genome annotation is the process of attaching information to the biological sequences. It is an active area of research and it would help scientists a lot to undergo with their wet lab projects once they know the coding parts of a genome.
Genomics, Transcriptomics, Proteomics, Metabolomics - Basic concepts for clin...Prasenjit Mitra
This set of slides gives an overview regarding the various omics technologies available and how they can be used for improvement in clinical setting or research
HGP was conceived in 1984 & officially begun in earnest in October 1990.
HGP is a large multicentric, international collaborative venture, the main aim of which is to determine the nucleotide sequence of the entire human nuclear genome.
In 1997, United States established the National Human Genome Research Institute (NHGRI).
The HGP was an international research groups from six countries- USA, UK, France, Germany, Japan and China, & several laboratories and a large no. of scientists and technicians from various disciplines.
MT115 Precision Medicine: Integrating genomics to enable better patient outcomesDell EMC World
"The emergence of genomics and real-time screening is helping to transform the practice of medicine as we know it today. New technologies present improved ways to tackle health issues and what was once thought to be “untouchable” due to cost, timing or resources, is now achievable through genetic screenings and genome sequencing.
During this session, we will explore:
1. The benefits of incorporating a genomics strategy early in lifeline
2. The Precision Medicine Initiative – how does this help? Does this encourage more people to get genetic screenings?
3. What’s involved in a genetic screening
"
Comment procéder pour traquer les marqueurs génétiques de résistance aux artemisinines et autres nouvelles molécules antipaludiques ? - Conférence de la 5e édition du Cours international « Atelier Paludisme » - Carol HOPKINS SIBLEY - University of Washington Seattle, USA - sibley@u.washington.edu
Genome project of Human and methods of sequencing human genome; Genome project of Rice and its post genome sequencing era; Arabidopsis genome project: Why Rice and Arabidopsis chosen for genome project?
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
To Graph or Not to Graph Knowledge Graph Architectures and LLMs
L14 human genome
1. Human Genome Project Determine the entire sequence of the human genome. 3 billion base pairs Problem: It’s really big!
2. Genome Sequencing As of 6/ 25/ 04 1128 genome projects: 199 complete (includes 28 eukaryotes) 508 prokaryotic genomes in progress 421 eukaryotic genomes in progress smallest: archaebacterium Nanoarchaeum equitans 500 kb Bacillus anthracis (anthrax) 5228 kb S. cerivisiae (yeast) 12,069 kb Arabidopsis thaliana 115,428 kb Drosophila melanogaster (fruit fly) 137,000 kb Anopheles gambiae (malaria mosquito) 278,000 kb Oryza sativa (rice) 420,000 kb Mus musculus (mouse) 2,493,000 kb Homo sapiens (human) 2,900,000 kb http:// www. genomesonline. org/ 1980 - $10/bp 2001 - $0.1 / bp S. cerevisiae 200x H. sapiens 200x A. dubia
3.
4. Human Genome Project timeline E. coli Drosophila C. elegans Yeast NRC Recommends HGP U.S. HGP Begins 1990 1995 2000 Human Gene Map (16,000 genes) Human Gene Map (30,181 genes) Goal for Human Genetic Map Exceeded Physical Map Covers 98% of Genome Pilot Human Sequencing Begins Full-Scale Human Sequencing Begins Human draft Phil Hieter
5. Completion of the genome 4-5 coverage 9x coverage 99.99 % acc GenBank entries double every 18 months “ Working Draft” “ Complete”
6. Completion of the genome The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers approximately 99% of the euchromatic genome and is accurate to an error rate of approximately 1 event per 100,000 bases. Human genome seems to encode only 20,000-25,000 protein-coding genes International Human Genome Sequencing Consortium . Finishing the euchromatic sequence of the human genome. Nature 2004 Oct 21;431(7011):931-45.
7. Institutes that produced 85 % of the sequence 1. Whitehead Institute for Biomedical Research , Center for Genome Research, Cambridge, MA 2. The Sanger Centre , Cambridge, UK 3. Washington University Genome Sequencing Center , St. Louis, MI 4. US Department of Energy , JGI, Walnut Creek, CA 5. Baylor College of Medicine Human Genome Sequencing Center , Houston, TX Countries: USA, UK, Japan, Germany, China, France
8. Genome Sequencing Genome: 3 Gb Cut genome into large pieces Clone into BACs: 100 kb Order based on sequence features ( markers ) = mapping Cut again Sequence AGAACAGGACGTATGTGGT TGTGGTTTTCTACTCC CTACTCCTGTGTT TTGTAAGTGAGAACA Assemble each BAC … TTGTAAGTGAGAACAGGACGTATGTGGTTTTCTACTCCTGTGTT… Assemble entire sequence
9.
10.
11.
12. What does the sequence mean? TCACAATTTAGACATCTAGTCTTCCACTTAAGCATATTTAGATTGTTTCCAGTTTTCAGCTTTTATGACTAAATCTTCTAAAATTGTTTTTCCCTAAATGTATATTTTAATTTGTCTCAGGAGTAGAATTTCTGAGTCATAAAGCGGTCATATGTATAAATTTTAGGTGCCTCATAGCTCTTCAAATAGTCATCCCATTTTATACATCCAGGCAATATATGAGAGTTCTTGGTGCTCCACATCTTAGCTAGGATTTGATGTCAACCAGTCTCTTTAATTTAGATATTCTAGTACATACAAAATAATACCTCAGTGTAACCTCTGTTTGTATTTCCCTTGATTAACTGATGCTGAGCACATCTTCATGTGCTTATTGACCATTAATTAGTCTTATTTGTTAAATGTCTCAAATATTTTATACAGTTTTACATTGTGTTATTCATTTTTTAAAAAATTCATTTTAGGTTATATGTATGTGTGTGTCAAAGTGTGTGTACATCTATTTGATATATGTATGTCTATATATTCTGGATACCATCTCTGTTTCATGCATTGCATATATATTTGCCTATTTAGTGGTTTATCTTTTCATTTTCTTTTGGTATCTTTTCATTAGAAATGTTATTTATTTTGAGTAAGTAACATTTAATATATTCTGTAACATTTAATGAATCATTTTATGTTATGTTTAGTATTAAATTTCTGAAAACATTCTATGTATTCTACTAGAATTGTCATAATTTTATCTTTTATATACATTGATATTTTTATGTCAAATATGTAGGTATGTGATATTATGCACATGGTTTTAATTCAGTTAATTGTTCTTCCAGATGTTTGTACCATTCCAACATCATTTAAATCATTAAATGAAAAGCCTTTCCTTACTAGCTAGCCAGCTTTGAAAATCCATTCATAGGGTTTGTGTTAATATATTTTTGTTCTTTTTTTTCCTTTCTACTGATCTCTTTATATTAATACCTACTGTGGCTTTATATGAAGTCATGGAATAATACGTAGTAAGCCCTCTAACACTGTTCTGTTACTGTTGTTATTGTTTTCTCAGGGTACTTTGAAATATTCGAGATTTTATTATTTTTTAGTAGCCTAGATTTCAAGATTGTTTTGACGATCAATTTTTGAATCAATTGTCAATATTTTTAGTAATAAAATGATGATTTTTGATTGGAAATACATTAAATCTATAAGCCAAATTGGAGATTATTGATATATTAACAAAAATGAGTTTTCCAGTCCATGAATGTATGCACATTATAAAATTCATTCTTAAGTATGTCATTTTTTAAGTTTTAGTTTCAGCAGTATATGTTTGTTACATAGGTAAACTCCTGTCATGGGGGTTAGTTGTACAGGTTATTTTATCATCCAGGCATAAAGCCCAGTACCCAGTAGTTATCTTTTCTGCTCCTCTCCCTCCTGTCACCCTCCACTCTCAAGTAGACCCCAGTTTCTGTTGTTCTCTTCTTTGCATTAATGACTTCTCATCATTTAGATTGCACTTGTAAGTGAGAACAGGACGTATGTGGTTTTCTACTCCTGTGTTAGTTTGCTAAGGATAACCACCTCCATCTCCATCCATGTTCCCACAAAAGACATGATCTCCTTTTTTATGGCTGCATATTATTCCATGGTATATATGTACCACATTTTCTTTATCCAATCTGTCATTGATGGACATTTAGGTTGTTTCCACATCATTGCCGTTGTAAATACTGCTGCAGTGAATATTCGTGTGTATGTCTTTATGGTAGAATGATTTATATTCCTCTGGGTATATTTCCAAGTAATGGGATGGTTGGGTCAAATGGTAATTCTGCTTTTAGCTTTTTGAGGAATTGCCATATTGCCTTTCACAACGGTTGAACTAATTTATACTCCCAAGAGTGTATAAGTTGTTCCTTTTTCTCTGCAACCTCGACATCACCTGTTATTTATGACTTTTATATAATAGCCATTCTGCTGGTCTGAGATGGTATCTCATTATGATTTTGATTTGCATTTCTCTAATGCTCAGTGATATTGAGCTTGGCTGCATATATGTCTTCTTTTAAAAATATCTGTTCATGTCCTTTGCCTAATTTATAACGGGGTTGTTTGTTTTTCTCTTGTAAATTTGTTTAAGTTCCTTATAGATTCTAGGTATTAAACCTTTTTTCAGAGGCGTGGCTTGCAAATATTTTCTCCCATTCTATAGGTTGTCTGTTTATTCTGTTGATAGTTTCCCTTGCTGTGCAGAAGCTCTTAACTTTAATTAGATCCGACTTGTCAATTTTTGCTTTGGTCGCAATTGCTTTTGATGTTATTGTCGTGAAATCTTTGCTAGTTCTTAGGTCCAGGATGATATTGCCCAAGTTGTCTTCCAGGGCTTTTATAATTTTGGATTTTACATTTAAGTCTTAATATATTTATTAAATTTGTTAGGGTTTCAGGATACAAGGACAATATAGCAGCAAACAATGTAAAAGTAAAATCTGAAAAATAATAGAAAACAGTTTAATTGAACACTTTACCATTATGTAATGCCCTTCTTTGTCTTTCCTGATCTTTGTTGGTTTGAAGTTCAAAAAAGACAAACTTAATGGTACAATAGGTATTGTAGATTTCAGGACTTTCTGTATAAAATATTTTGTATATATGAATAGATCATTTTTTATTTCCAGTCTTTAAACATTTTCTTAACATTTTCTTCTATTGCTTCACTTCACTCGCTAGGACCATCAGGACAGTGTTGAACAGAAATTGTCAGACTGATCATCACAACTTTTTCTAGATTTTAGAAGGAAATTTTTCTTTATTTCAACATAAAGCAGCATGTTAATGCCAAGTTTTAATATGTGTTATCAGATTGAAATTTTTTTGTATATTTCTACATTACCAAGAATTTTTAGCAAGAGTTTTTGTTGAGTTTTAATTTAAAAATCATTTGTTAATTTCATCTGATTTTTTTATTTCTCTTTTTACCTTAAGAGATTAAACTGACTACAGATTGAATATAAACAAACAAACAAACAAACAAAAACTCTAAAATGCTGTGGATCAACACCACTTAGTAATTTGTATACTTGGATTCAATTTGCTGAAATTTTGTTAGACATTTTTGCGTCGATATTTATGAGGGATGTTGATCTGTAAAAGTATTAAAATGCCTTTGACAGATTTTGATAGCAGTGTTATTCTGGCCTAATAAATCAAACTGAGGTATGATCCTTCCTTTTCTATTTCTTAATAGCATTTTTAAAATTGGTGGTTTTTTCCTTCCTTAGTGAAATTTACCAGCAAAGTAACAGGCCTTATATTTCTCTTGTGGAAATATTTTAATTTCAAATTAATGGTATTTTGTTCTTGTAGGGTGGTAATTTTCTCTGTGTTTGGTCTTAATGGACTCTTAGCTGATCACCCAGTTACTCAGCGAGGTCTCTTCACTCTGGAAGAGCTGGAACTCCAGTGTGTTTTAGTGCAGCATGACCACGGGTATTACCGTTCAACATTTAGGCTTTATCAGTGATAACTATTTGTCCTCATGGAGTTTTTGCCGCTGGGCCTACACAGTTTAGGCTTCAGCTTAGAACACATAATGAATTCTTATGCAGATTTCTGCCCACCTTTGACCTTTCATGATTTCCTCTTCTTGGGTAAGCTGCCTTATTAATCTGATACACTTCAGCAGTCCAGAACTACACTCTTTCCCTTCTCTGCTCTTGGAGATGACTCTTTTGTCTGAGATTCACTTTGCTGTGCTGAAAAAGAAAAGTGCTTCAAGGAAGATACCAAGGAAAATCACAGGGCTCATTTATGTATTTCTCTTCTTTCAAGGACTACAGCTTTGTGTTGCCTATGTTCAATTTCTGAAAATAATTAGAGCATATATACTCTGTGTGAGAAGGCAAATCCAGACAGTTAGTTTGTATGACTAGAAGCAGAAGTCTACATGGAGAATTTTACTTAACTGTGTTATAGTTTCTTTAATTATTTCAAGAGTATGTTTAATGTTCCACAGATCTCATTCTATAAATCTTTATCATCTTAGAGCTCTGATACTATTTAGAATTACTATTCCTTCAAATAAGAGATTAGAAACAGGGTTATATTTGGGGTAGGTTGACTTACTTTTCTGGGAACCAAAGCATATTAAATTGACCAGTTTTAACACACTTCTATGTATGCACAAAGATATATATTTACATTCTGCAAAATCATTCTTTCCTTTTTGAATTTGAAAAGGATCTTTGGTATACAGATATTCAATAGCCAGCCTGAAGATTCATTTGAATTCATTTAATGTTTAGATTCACTACATGAAATGATCCAGAAGAGAGTACTCAAATATAAGTATCTATAACGATGGAAATATACATCTCCACTGCCCAAGATGGTAGTCATGAGTCAATATTGATCATGTGAGACGTGGCAAGTGTTACTCAGGGTCTCAATATTTAAATGTATTAAGCTTTAATTAATGTAAATTTGAATTTAGCAAAACATGTATAGCTTGTGGTTACTGTTTTATTCAGTGCCAATATAGAACATTTCCATGATTACAGAAAGTTATCTTAGAATACTCAGTTCTGGACTATTTTATCTGGCTAAATTAAATGTTAAAATATTACAAATTCATCTTCAGGCTGGCTGTTGAATATTTTTATAGCAAAAGTCATTTATAAATTTAAAACTCAAATAATTATCTTTTTCAATATGTAAAATATGTCTTTACATATTCTACTCCCTTCTTACATACATATTCTGATGTAACATAGGTATTCTCTTATTCATGCACACTGAAATGACAACATAAATAATTTTACTAAGTGTCACCATATAAAAAACTTTGAACAAAATCAGATTATATCACTGTGGATATTTCTATTTTGAACTAACTTAGATGATAATTTTAATCTATATCCTAGATGAACTTTAAATCAATAAAATCTCTCAATGGTGTTATAAATCTCAAGCCATTAGCCACTGATTATCCCATTTTTATTCTTTTCATATTAATTTTATTGCCATGTATGAATGCTGTAGCATCCATGTTTAAATACTAGTTAACAAAATGCACTGGCATCAGATACAATAAGGATGAAATGAGATATAATTAGGACTCTGGTAACACACATAAAATTGGAAAGATACCCTGAAATTCAAGCCAAGAAGATATTTATCCAGCTTATTTTATTTTGAGACAGAGTCTTGCTCTCTCACTCAGGCTGGAGTGCAGTGGACCATTCTAGGCTCGCTCCAACCTCTGTCTCCCAAATTGAAGTAATTCTCGTGCCTCAATCTCCCGAGTAGCTGGGATTACAGGCATGTGTCACCAAGCCTGGCTGATTTTTGTAGTTTTAGTAGAGACGGGGTTTCACCATGATGGCCAGGCTGGTCTTGAACTCCTGGCCTCAAGTGACTGGAACACCTCGGCCTCCTAAAGTGCTGGGATTACAGACGAGAGCCACTGAACAGCTTTGATCCAACTTATTTGGATGAATGAGTTACATATTTTACATTAAATCTGTTATTGTGATAATTCTTCATGTTATTTTCCATGTATAGATTTATATATAATGTAATTTTAATTTTTTTTCACCGGAGAGTATAAACAACAATTATTTTATAAACAGGATAATAAAAATAAGACAAAAATTGTTGAAATGTCTTCATTTGACTACTAACTTTTTACATGTTTGTTACTTTGAAGCTGTTATCAATACTTGTGATGTATTACAATTAAGTAAAGATTTAAAGATGCCATTTTTAACTTATTATGACACAAAGTCTATAAATTCTTATATTTTGAGATTTGTATTTAAATAACTTGTGAAATTTAATTTTAAAATAAAATTTCTTCTATGGATTGGTCTTCAATCGAGGCATAAAAAGGAATATAACAGTGTGGCACTATAACTTCTATATTGAATTTCTATATTATTTAACACAATTATAATTTTGCTAATGAATTGTAATGTTTTTAAAAAGCTAGGTGAATTTTATTAAATTCATTACATGGCGATAACACAGAGAAAACATTTTGGGGATTCTTTTAAAATGGTATGTACAAAAGCTTAAAAGTTGTTATGTAGTGGCAGAGATAAAAAAGTAAAACAAAAAAAAGCTTAAAAGTTTGCTTTACTATTTATAGGCTCATAAGTGTAAGTGTGCCAGAAAATGAAAAAGAAAGGAGAGAAATTATAAATAACTGTGTGGAAAACACAGATAAAGCATAAAGATAGAATATAAAGATAGAAGCATTTTAATATGAGGCAGTGATGGCTTTTTGAAGAATCCCAACTAAGGACCTACTTTTAGTTAATAAATAATATGTTTCTAATCCCTATATTGTCCACAGCAACCTTTTTAGGACATGGAGCAGTGACTATGAGTGCCAGAAGGCAAGAGTAGAAGCAATTGTAAAATCATGAACACTAGTTTGTAAAATCCTCACTGAGATATAATATCTGTTTGCCTCTACCTTAGAATTATTAATGTCTTGAGGGCTGGGA A very small piece of chromosome 21
13.
14. What’s in a genome? Genes (i. e., protein coding) But. . . only <2% of the human genome encodes proteins Other than protein coding genes, what is there? • genes for noncoding RNAs (rRNA, tRNA, miRNAs, etc.) • structural sequences (scaffold attachment regions) R egulatory sequences • “ junk” (including transposons, retroviral insertions, etc.)
15.
16.
17.
18.
19.
20.
21. Chimpanzee Sequencing and Analysis Consortium . Initial sequence of the chimpanzee genome and comparison with the human genome. Nature 2005 Sep 1;437(7055):69-87. Thirty-five million single-nucleotide changes, five million insertion/deletion events, and various chromosomal rearrangements. 98,6 % identitity to human genome sequence Differences in gene/exon structures
22. Apparent differences between humans and great apes in the incidence or severity of medically important conditions (excluding differences explained by obvious anatomical differences ). Medical Condition Humans Great Apes Definite HIV progression to AIDS Common Very rare Influenza A symptomatology Moderate to severe Mild Hepatitis B/C late complications Moderate to severe Mild P. falciparum malaria Susceptible Resistant Menopause Universal Rare Likely E. coli K99 gastroenteritis Resistant Sensitive? Alzheimer’s disease pathology Complete Incomplete Coronary atherosclerosis Common Uncommon Epithelial cancers Common Rare