HMD_Sequencing_KIBGE_KCHI.pptx
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DNA sequencing
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HMD_Sequencing_KIBGE_KCHI.pptx
- 1. By Dr.Abdul Hameed Chief Scientific Officer IBGE, Islamabad, Pakistan
- 2. DNA SEQUENCING • DNA sequencing is the process of determining the precise order of nucleotides within a DNA molecule. • It includes any method or technology that is used to determine the order of the four bases—adenine, guanine, cytosine, and thymine—in a strand of DNA.
- 3. DNA SEQUENCING METHODS Historically there are two main methods of DNA sequencing 1. Maxam and Gilbert method 2. Sanger method
- 4. •A. M. Maxam and W.Gilbert-1977 •Chemical Sequencing •Treatment of DNA with certain Chemicals DNA cuts into Fragments Monitoring of sequences •MAXAM & GILBERT METHOD
- 6. • Most common approach used for DNA sequencing . • Invented by Frederick Sanger - 1977 • Nobel prize - 1980 • Also termed as Chain Termination or Dideoxy method SANGER METHOD
- 7. •SANGER METHOD • The chain termination reaction • Dideoxynucleotide triphosphates (ddNTPs) chain terminators •havig an H on the 3’C of the ribose sugar (normally OH found in dNTPs) • ssDNA addition of dNTPs elongation • ssDNA addition of ddNTPs elongation stops
- 12. Fluorescent Dyes • Fluorescent dyes are multicyclic molecules that absorb and emit fluorescent light at specific wavelengths. • Examples are fluorescein and rhodamine derivatives. • For sequencing applications, these molecules can be covalently linked to nucleotides.
- 13. AC GT The fragments are distinguished by size and “color.” •Dye Terminator Sequencing • A distinct dye or “color” is used for each of the four ddNTP. • Since the terminating nucleotides can be distinguished by color, all four reactions can be performed in a single tube. A T G T
- 16. ABI_Sequencing Analysis 5.2 Software
- 17. MEGA7.0.26
- 18. •The Human Genome Project • First draft genome of human in 2001, final 2004 • Estimated costs $3 billion, time 13 years • Used Sanger Sequencing • Today: Illumina: 1 week, 9500$ Exome: 6 weeks*, $1000 Towards 1000$ genome Setia Pramana 18
- 19. •The Human Genome Project • The draft sequence of the HGP was imperfect because of the incomplete coverage of many regions – a huge number of gaps • The IHGSC published a ‘finished’ version of the human genome sequence in 2004 and the HGP was then deemed to be ‘complete’ 19
- 20. •The Human Genome Project • This ‘finished’ version of the genome achieved almost complete coverage of all the regions and also significantly reduced the number of gaps to 341 from the initial hundreds of thousands • Initiated a new era in the study of genetic variation and the functional characterization of the human genome 20
- 21. •Next (second) Generation Sequencing • New technologies allowing the massive production of tens of millions of short sequencing fragments. Thus, it is also called: “Massively parallel sequencing” • These techniques could be used to • deal with similar problems than microarrays, • but also with many other. • They raised the promise of personalized medicine 21
- 22. NGS • The advent of high-throughput sequencing technologies has initiated the ‘personal genome sequencing’ era for both normal and cancer genomes • Large-scale international projects such as the 1000 Genomes Project and the International Cancer Genome Consortium 22
- 23. NGS • NGS technologies have been on the market only since 2004 • Have now largely replaced Sanger sequencing technologies (owing to the ultra-high-throughput production/hundreds gigabases) • Ability to simultaneously sequence millions of DNA fragments - massively parallel sequencing technologies 23
- 24. •NGS • Reduced sequencing costs significantly, making large-scale or WGS studies much more affordable Setia Pramana 24
- 27. •Bioinformatics Challenges of NGS Setia Pramana 27
- 28. Sequencing has gotten Cheaper and Faster Cost of one human genome • HGP $ 3 billion (13 yrs) •2004: $ 30,000,000 •2008: $100,000 •2010: $ 30,000 •2011: $10,000 •2012-13: $7,000 •2014: $4,000 (~1 week) •???: $1,000 The Race for the $1,000 Genome
- 29. equencing) Cost is Getting Cheaper • Reduced sequencing costs significantly, making large-scale or WGS studies much more affordable Setia Pramana 29
- 31. •Huge Data Storage and HPC Demand
- 32. •NGS Challenges • Highest cost is (almost) not the sequencing but storage and analysis. • A standard human (30-40x) whole genome sequencing would create 100 Gb of data • Extreme data size causes problems • Just transferring and storing the data • Standard comparisons fail (N*N) • Standard tools can not be used • Think in fast and parallel programs Setia Pramana 32
- 33. •Bioinformatics Challenges of NGS • Need for large amount of CPU power - Informatics groups must manage compute clusters -Challenges in parallelizing existing software or redesign of algorithms to work in a parallel environment - Another level of software complexity and challenges to interoperability Setia Pramana 33
- 34. •Bioinformatics Challenges of NGS • VERY large text files (~10 million lines long) - Can’t do ‘business as usual’with familiar tools such as Perl/Python. - Impossible memory usage and execution time - Impossible to browse for problems • Need sequence Quality filtering Setia Pramana 34
- 35. •Data Management Issues • Raw data are large. How long should be kept? • Processed data are manageable for most people • 20 million reads (50bp) ~1Gb • More of an issue for a facility: HiSeq recommends 32 CPU cores, each with 4GB RAM • Certain studies much more data intensive than other • Whole genome sequencing 30X coverage genome pair (tumor/normal) ~500 GB 50 genome pairs ~ 25 TB Setia Pramana 35
- 36. •Data Management • Primary data usually discarded soon after run • Secondary and tertiary data maintained on fast access disk during analysis, then moved to slower access disk afterward
- 38. •Big Collaboration • Need Collaborative expertise (human intelligence and intuition) are required for meaning and interpretation (Bergeron 2002) • Including on-demand communication & sharing of protocols, electronic resources, data, and findings among the stakeholders • Collaboration with other Big DATA sources: National Registers, BPJS, Hospitals, etc.
- 39. •Summary • Challenges: • Still expensive • Lack of Infrastructure (in developing countries) • Lack of skilled personal on Bioinformatics • Need (large scale) collaborations • Integrate different technologies and system • Making it all clinically relevant Setia Pramana 39