Introduc1on and History

Transcription

1 Genome Sequencing

2 Introduc1on and History

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4 Sample Prepara1on

5 Sample Prepara1on Fragments

6 Sample Prepara1on Fragments Sequencing Next Genera1on Sequencing (NGS) GTGTGTG GTGTGTG GGGGTGTG Reads

7 Sample Prepara1on Fragments Sequencing Reads ssembly GTGTGTG GTGTGTG GGGGTGTG GTGTGTGGTTGGG on1gs

8 Sample Prepara1on Fragments Sequencing Reads ssembly on1gs nalysis

9 Reference Genome 9

10 De novo vs. Re- sequencing De novo assembly ( from the beginning ) implies that you have no prior knowledge of the genome. Re- sequencing assembly assumes you have a copy of the reference genome (that has been verified to a certain degree). The programs that work for re- sequencing will not work for de novo.

11 De novo vs. Re- sequencing

12 Sample Prepara1on Re-sequencing (LOS, Shrimp) requires 15x to 30x coverage. nything less and re-sequencing programs will not produce results or produce questionable results. Fragments

13 Sample Prepara1on De-novo assembly requires higher coverage. t least 30x but upwards to 100x s coverage. Most de novo assemblers require paired-end data. Fragments

14 Sample Prepara1on Fragments Sequencing Reads Our focus for today s lecture: 1. omparison of sequencing plasorms 2. Details of sample prepara1on 3. Defini1ons and terminologies concerning data and sequencing plasorms ssembly on1gs nalysis

15 History and Background

16 Landmarks in Sequencing Efficiency (bp/person/ year) Year Event 1870 Miescher: Discovers DN 1940 very: Proposes DN as Gene1c Material 1953 Watson & rick: Double Helix Structure of DN Holley: transfer RN from Yeast 1, Maxam & Gilbert: "DN sequencing by chemical degrada1on Sanger: DN sequencing with chain- termina1ng inhibitors 15, Messing and his colleagues developed shotgun sequencing method 25, BI markets the first sequencing plasorm, BI 370

17 Landmarks in Sequencing Efficiency (bp/person/year) Year Event 50, NIH begins large- scale sequencing bacteria genomes. 200, raig Venture and Hamilton Smith at the Ins1tute for Genomic Research (TIGR) published the first complete genome of a free- living organism in Science. This marks the first use of whole- genome shotgun sequencing, elimina1ng the need for ini1al mapping efforts drai of the human genome was published in Science drai of the human genome was published in Nature. 50,000, Life Sciences comes out with a pyrosequencing machine. 100,000, Next genera1on sequencing machines arrive. Huge 2011 Oxford Nanopore: 600 Million base pairs per hour.

18 Robert Holley and team in 1965 Watson and rick Messing: World s most- cited scien1st Francis and ollins: Private Human Genome project.

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22 Next- Gen Sequencing PlaSorms 454/Roche GS- 20/FLX (2005) PacBio RS ( ) 3 rd genera1on? Illumina HISeq (2007)

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24 omparison of PlaSorms Technology Reads per run verage Read Length bp per run Types of errors 454 (Roche) 400, bp 70 Million Subs1tu1on SoLID (BI) Million 35bp 1 Billion Illumina HISeq 150 Million bp 15 Billion Subs1tu1on with exponen1al increase PacBio 45, bp 45 Million Inser1ons and dele1ons \

25 Sequencing Methods and Terminology

26 Sanger Sequencing The key principle of the Sanger method was the dideoxynucleo1de triphosphates (ddntps) as DN chain terminators. These ddntps will also be radioac1vely for detec1on in automated sequencing machines. Posi1ves: longer reads (600 to 1000 bp). Nega1ves: poor coverage (6x), expensive, inaccurate. S1ll commonly used for small scale sequencing.

27 Sanger Sequencing Video

28 Sanger Sequencing DN target sample SHER

29 Sanger Sequencing DN target sample T G SHER T G lose each fragment many times. T G T G

30 Sanger Sequencing DN target sample SHER T T G T G T G T G G 30

31 Sanger Sequencing DN polymerase T G Primer

32 Sanger Sequencing DN polymerase T G Primer Primer T G DN polymerase

33 Sanger Sequencing T T T G G Primer DN polymerase T G

34 Sanger Sequencing T T T G G G Primer DN polymerase T G

35 Sanger Sequencing T T T G G G G Primer T G

36 Sanger Sequencing T T T G G Primer G G T G

37 Sanger Sequencing Primer G G G G T T G T G T T

38 Sanger Sequencing T T T G G Primer

39 Sanger Sequencing T T T G G Primer on1nue un1l all strands of DN have undergone this reac1on. If you choose the reagents correctly then you should have all possible - terminated strands; resul1ng in sequences of varying lengths.

40 Sanger Sequencing

41 Sanger Sequencing In the radioac1ve gel, the longer DN fragments move to the bopom and the shorter ones move to the top. ierward the sequence can be read off by going from top to bopom.

42 hallenges Requires a lot of space and >me: you need a place to run the reac1on, and then you need a gel to determine the length of the DN You could only run perhaps a hundred of these reac1ons at any one 1m There are 3 billion base pairs of DN in the human genome, meaning about 6 million 500- base pair fragments of DN Nonetheless it was s1ll used to come up with the first copy of the human genome 42

43 elera Sequencing (2001) 300 BI DN sequencing plasorms 50 produc1on staff 20,000 square feet of wet lab space 1 million dollars / year for electrical service 10 million dollars in reagents Total cost of human genome: 2.7 Billion dollars

44 elera Sequencing (2001) 300 BI DN sequencing plasorms 50 produc1on staff 20,000 square feet of wet lab space 1 million dollars / year for electrical service 10 million dollars in reagents urrent cost of human genome: < 10,000 $

45 Second/Next Genera1on Sequencing Second genera1on sequencing techniques overcome the restric1ons by finding ways to sequence the DN without having to move it around. You s1ck the bit of DN you want to sequence in a liple dot, called a cluster, and you do the sequencing there; as a result, you can pack many millions of clusters into one machine.

46 Sequencing a strand of DN while keeping it held in place is tricky, and requires a lot of cleverness.

47 Illumina Sequencing: Video

48 Steps in Illumina sequencing Sample prep: size select fragments, add adapters to ensure the fragments ligate to the flow cell (1 to 5 days) ligate adapters 48

49 Steps in Illumina sequencing luster genera1on on flow cell Why do we need clusters? 49

50 flow cell contains 8 lanes Each lane contains three columns of 1les 20K to 30K clusters Each column contains 100 1les Each 1le is imaged four 1mes per cycle, which is one image per base

51 We mul1ply up the template stand, i.e. the bit of DN that we are sequencing, and s1ck on a few bases of adaptor sequence ; this sequence s1cks on to complementary bits of DN stuck to a surface, which holds the DN in place while we sequence it:

52 Steps in Illumina sequencing Turn on the sequencing machine and wait (1 week) 52

53 We then flood the DN with RT- bases. We also add a polymerase enzyme, which incorporates the RT- base into the new strand that is complementary to the template strand:

54 We then wash away all the RT- bases, leaving just those that were incorporated into the new strand; we can read off what base this is by looking at the color of the dye:

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58 Illumina uses the modified version of Sanger sequencing called dye- terminator method. In a single Illumina machine we have hundreds of millions of these clusters; cameras look at all of these dots and record how they change color over 1me, allowing you to determine the sequence of bases of millions of bits of DN at once. Sequencing method is actually prepy inefficient, however, the machine is capable of sequencing millions of fragments of DN at once.

59 Inside the Illumina Machine 59