HMD_Sequencing_KIBGE_KCHI.pptx
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DNA sequencing is the process of determining the precise order of nucleotides in a DNA molecule. There are two main historical methods - the Maxam-Gilbert chemical method from 1977 and the Sanger dideoxy chain termination method from 1977, which is still commonly used. Next generation sequencing uses massively parallel sequencing to produce millions of short DNA fragments at once, reducing costs significantly. However, next generation sequencing produces huge amounts of data that presents challenges for data storage, analysis, and interpretation.
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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 basesadenine, guanine, cytosine, and thyminein 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 3C 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) - Cant do business as usualwith 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