1. Sequencing 101
Readout

2. Original Sanger Sequencing with Radioactive Signal
Template (Crick)
very low concentration of ddNTPs compared to dNTPs
A nested series of DNA fragments ending in the base specified by the terminator-ddNTP
Watsons
Expose gel to x-ray film (to make an “auto-radiogram”)
Recombinant DNA: Genes and Genomes. 3rd Edition (Dec06). WH Freeman Press.

3. Wouldn’t it be great to run everything in one lane?
CAAGTGTCTTAAC
This is great but…
Wouldn’t it be great to run everything in one lane?
Save space and time, more efficient
Fluorescently label the ddNTPs so that they each appear a different color

4. Fluorescent Sanger Sequencing Trace
Lane signal
(Real fluorescent signals from a lane/capillary are much uglier than this).
A bunch of magic to boost signal/noise, correct for dye-effects, mobility differences, etc, generates the ‘final’ trace (for each capillary of the run)
Trace
Recombinant DNA: Genes and Genomes. 3rd Edition (Dec06). WH Freeman Press.

5. Sanger Base Calling Base Caller (Phred) ugly over here ugly over there
... N A G C G T T C C G C G ... A N N ...
Base Caller (Phred)
Quality score = -10 * log(probability of error)
For Q20, probability of error = 1/100
For Q99, probability of error ~10-10

6. Whole Genome Sequencing
Hierarchical Shotgun Approach
Genomic DNA
BAC library
Organized, Mapped Large Clone Contigs
(minimal tiling path)
Shotgun Clones
GCAATGAAATATGTTCTTGTAATTTAAGCTGACACTCCTAATTTAGCTCTTGTCCTCTACTGAGTCTACCTAATTATATGTATGGATTGACTTGG
AGCTCTTGTCCTCTACTGAGTCTACCTAATTATATGTATGGATTGACTTGGTGTTTTCTCTTTTTCTTAAATAGTAATGCAGAAAGCCTGGAGAGAGAG
Reads
GCAATGAAATATGTTCTTGTAATTTAAGCTGACACTCCTAATTTAGCTCTTGTCCTCTACTGAGTCTACCTAATTATATGTATGGATTGACTTGGTGTTTTCTCTTTTTCTTAAATAGTAATGCAGAAAGCCTGGAGAGAGAG
Assembly

7. De Novo Whole Genome Sequencing
Make millions of random clones: “Shotgunning”
Plasmid "backbone"
Insert
sequencing primer
"forward read"
"reverse read"

8. Assembly: Contigs and Supercontigs
"Supercontig" or "Scaffold"
"Contig"
Seq gap
NNNNNN
number of N's in sequence = estimated size

9. Why Different Insert Sizes are Useful
Longer (fosmid) mate pairs connect assembly pieces that are not connected by shorter (plasmid) mate pairs

10. 454 Sequencing Most viable “UHTS” method for de novo genome sequencing
Still sequencing by synthesis, but fundamentally different
Uses pyrosequencing (light produced as a by product of base extension)
No cloning required
Sequences ~1,000,000 reads simultaneously
Each read is ~400bp long
Takes ~10 hours for machine run

11. 454 Library Generation (2) Adaptor DNA adapter ends plus beads
~ 1 DNA molecule and 1 bead per droplet
Make water-in-oil emulsion
In brief, an in vitro–constructed adaptor flanked shotgun library (shown as gold and turquoise adaptors flanking unique inserts) is PCR amplified (that is, multi-template PCR, not multiplex PCR, as only a single primer pair is used, corresponding to the gold and turquoise adaptors) in the context of a water-in-oil emulsion. One of the PCR primers is tethered to the surface (5′-attached) of micron-scale beads that are also included in the reaction. A low template concentration results in most bead-containing compartments having either zero or one template molecule present. In productive emulsion compartments (where both a bead and template molecule is present), PCR amplicons are captured to the surface of the bead. After breaking the emulsion, beads bearing amplification products can be selectively enriched. Each clonally amplified bead will bear on its surface PCR products corresponding to amplification of a single molecule from the template library.
emPCR
Most beads with with multiple copies of individual DNA molecules

12. 454 Sequencing Process Beads loaded into wells Enzymes added
1) The emulsion is broken, the DNA strands are denatured, and beads carrying single-stranded DNA templates are enriched (not shown) and deposited into wells of a fiber-optic slide.
2) Smaller beads carrying immobilized enzymes required for a solid phase pyrophosphate sequencing reaction are deposited into each well. 3)
Sequencing gives off light

13. Pyrogram

14. <454 movie here>

15. Illumina Sequencing Technology Robust Reversible Terminator Chemistry Foundation5’ 3’ G T C A
DNA ( ug) T G C A G T
Sample preparation
Cluster growth
Single molecule array
Sequencing 1 2 3 7 8 9 4 5 6
Base calling
T G C T A C G A T …
Image acquisition

16. Sequencing 20 Microns 100 Microns 250+ Million Clusters Per Flow Cell
Purpose: To visually demonstrate sequencing by synthesis
Details: This is an actual cluster pattern from a flow cell (4-color overlay)
If you zoom in on one small section (the 20 micron box) you can follow each cluster through sequencing by synthesis
Conclusion: This is the visual set up to the following slide
20 Microns
100 Microns

17. Flow Cells and Imaging
Solexa: single 8-channel
Reagents flowed in here
Image for each channel is actually tiled (four images per panel, one for each color)
Out to waste

18. <Illumina movie here>

19. The numbers (March 2013) Platform Read Length Gb/run Time/run Cost/run
Error rate
454 1
~1 day
~$5-10k
~1%
Illumina HiSeq
~1 week
~$20-25k
~0.1%
Illumina MiSeq
1-2
~$1k
Ion Torrent
~2-3 hours
~$0.5-1k
Pac Bio
~$2k
~15%

20. What are the data?
Vendors are typically producing data in fastq, or cfastq format.
followed by a sequence Identifier
@SEQ_ID
GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTCAACTCACAGTTT +
!''*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>>CCCCCCC65
The sequence
‘+’, optionally followed by a sequence Identifier
The quality scores
Explain the quality scores – also, see wikipedia page on fastq format, and provide reference here

21. Example of Illumina SeqID
@HWUSI-EAS100R:6:73:941:1973#0/1
HWUSI-EAS100R
The unique instrument name 6
Flowcell lane 73
Tile number within the flowcell
941
'x'-coordinate of the cluster within the tile
1973
'y'-coordinate of the cluster within the tile #0
index number for a multiplexed sample (0 for no indexing) /1
the member of a pair, /1 or /2 (paired-end or mate-pair reads only)

22. Error profiles De-phasing Crosstalk Degradation De-phasing
(1) A strong correlation of the A and C intensities as well as of the G and T intensities due to similar emission spectra of the fluorophores and limited separation by the filters used.
(2) Dependence of the signal of a specific cycle by the signal of the cycles before and after, called phasing and pre-phasing respectively. Phasing and pre-phasing are caused by incomplete removal of the 3' terminators and fluorophores, sequences in the cluster missing an incorporation cycle, as well as by the incorporation of nucleotides without effective 3' terminators.
The Illumina base caller (Bustard) uses a so-called crosstalk matrix estimated from the first and second imaging cycle to orthogonalize the correlated channels. Further this matrix is used to scale the different intensities measured for each of the fluorophores. The estimation of the crosstalk matrix is based on the assumption that the four nucleotides are almost equally frequent in the library being sequenced. If the sample does not fulfill this assumption this estimate can be inaccurate and lead to incorrect base calling. Bustard estimates the phasing and pre-phasing as two channel- independent parameters from the increasing correlation of intensities in the first few cycles of the sequencing run. Using the crosstalk matrix and the two phasing parameters, it creates corrected intensity values and calls the base with the highest corrected intensity for each cluster and cycle. In the case of equal intensity values or small intensity differences an N is called.
De-phasing
Error profiles

23. Assessing Quality
Aberrant Solexa tiles. This figure displays three distinct types of errors that we have seen occur on Solexa tiles. The image was generated using plotQTile with the color-by category option. The tile data was drawn from tiles 129, 85, and 6 respectively – all produced from the same lane and run. The error depicted in 1b appears to be caused by a bubble in one of the reagents. This bubble increased the error rate, converting U0 reads into U1 reads. From further investigation we know this occurred during cycle 28. The error depicted in 1c is an area in which no reads at all occurred – possibly a problem with the DNA binding to the flow cell, or a smudge on the surface of the flow cell. The error depicted in 1a remains enigmatic.
Figure 2d was generated using the cycleplot function. It displays the mean QAG-score for cycles 1–32 on tile 47, and showcases the ability to detect the source of an error by decomposing the data according to cycle. Here we see the drop in average intensity that occurred during cycle 28.
The aggregate quality score (QAG-score) for a base call is the maximum Q-score minus the sum of the remaining three Q-scores.

24. Read Frequency Distribution
VecBase Screen
> gnl|uv|NGB :1-219 pCR4-TOPO multiple cloning site
Length=219
Score = 100 bits (50), Expect = 9e-19
Identities = 50/50 (100%), Gaps = 0/50 (0%)
Strand=Plus/Plus
Query 1 ATTAACCCTCACTAAAGGGACTAGTCCTGCAGGTTTAAACGAATTCGCCC 50
||||||||||||||||||||||||||||||||||||||||||||||||||
Sbjct 43 ATTAACCCTCACTAAAGGGACTAGTCCTGCAGGTTTAAACGAATTCGCCC 92
1. Trim trailing bad bases
2. Remove duplicate reads
QA: filtering

25. Alignment

26. Aligners differ in speed and quality

27. Mapping Short Reads
Many options; often a trade off between speed, resources and sensitivity.
Several open source projects to solve this problem, continually improving in speed and memory requirements.
New features being added all the time.
When dealing with short read data, make sure you have the very latest versions of the software you’re using, as some are updated frequently.
Unlike earlier-generation sequence alignment programs such as BLAST, which were designed in an environment that required alignments of protein sequences and searching though large databases to find homologous sequences, today’s short-read alignment programs are generally used for the alignment of DNA sequence from the species of interest to the reference genome assembly of that species. This difference, although it initially seems subtle, has several consequences to the final algorithm design and implementation, which include letting assumptions about the number of expected mismatches be driven by the species polymorphism rate and the technology error rate rather than by considerations of evolutionary substitutions.

28. Alignment Exhaustive searching in this manner is prohibitively slow
@HWI-EAS412_4:1:1:1376:380 AATGATACGGCGACCACCGAGATCTA
Exhaustive searching in this manner is prohibitively slow
Algorithms are needed to that will trade speed for sensitivity

29. Approaches to Short Read Alignment
Hash-Based mapping
Hashing of reads (E.g. bwa, Eland)
Hashing of genome (E.g. novoalign)
Indexing using Suffix Array/Burrows-Wheeler Transform (BWT) (E.g. bowtie, bwa)
The methods covered are (i) hash table–based implementations, in which the hash may be created using either the reference genome or the set of sequencing reads, and (ii) Burrows Wheeler transform (BWT)-based methods, which first create an efficient index of the reference genome assembly in a way that facilitates rapid searching in a low-memory footprint

30. How does the “hashing” work?
In general, a hash function simply converts a string to an integer.
The integer is then used as an index in an array, for fast look up.
The reads are “hashed”, using 6 different permutations of the first 28bp.
The genome is then looked through, in 28bp chunks, to see if they match, via the hash, to reads.
Read up more on hashing, and add some notes here.
Do they need to know this? No – however, it’s interesting (in my opinion), and it’s useful for bioinformaticians to think about algorithms.

31. The Burrows-Wheeler Transform

32. Bowtie Algorithm
@HWI-EAS412_4:1:1:1376:380 AATGATACGGCGACCACCGAGATCTA

33. Bowtie Algorithm
@HWI-EAS412_4:1:1:1376:380 AATGATACGGCGACCACCGAGATCTA

34. Bowtie Algorithm
@HWI-EAS412_4:1:1:1376:380 AATGATACGGCGACCACCGAGATCTA

35. Bowtie Algorithm
@HWI-EAS412_4:1:1:1376:380 AATGATACGGCGACCACCGAGATCTA

36. Bowtie Algorithm
@HWI-EAS412_4:1:1:1376:380 AATGATACGGCGACCACCGAGATCTA

37. Bowtie Algorithm
@HWI-EAS412_4:1:1:1376:380 AATGATACGGCGACCACCGAGATCTA

38. Bowtie Algorithm
@HWI-EAS412_4:1:1:1376:380 AATGATACGGCGACCACCGAGATCTA

39. Bowtie Algorithm
@HWI-EAS412_4:1:1:1376:380 AATGATACGGCGACCACCGAGATCTA

42. Comparison (old!) PC: 2.4 GHz Intel Core 2, 2 GB RAM
CPU time
Wall clock time
Reads
per
hour
Peak virtual memory footprint
Bowtie
speedup
Reads aligned (%)
Bowtie –v 2 (server)
15m:07s
15m:41s
33.8 M
1,149 MB -
67.4
SOAP (server)
91h:57m:35s
91h:47m:46s
0.08 M
13,619 MB
351x
67.3
Bowtie (PC)
16m:41s
17m:57s
29.5 M
1,353 MB
71.9
Maq (PC)
17h:46m:35s
17h:53m:07s
0.49 M
804 MB
59.8x
74.7
Bowtie (server)
17m:58s
18m:26s
28.8 M
Maq (server)
32h:56m:53s
32h:58m:39s
0.27 M
107x
PC: 2.4 GHz Intel Core 2, 2 GB RAM
Server: 2.4 GHz AMD Opteron, 32 GB RAM
Bowtie v0.9.6, Maq v0.6.6, SOAP v1.10
SOAP not run on PC due to memory constraints
Reads: FASTQ 8.84 M reads from 1000 Genomes (Acc: SRR001115)
Reference: Human (NCBI 36.3, contigs)

43. Assembly
Iverson et al. Science 3 February 2012: Vol. 335 no. 6068 pp

44. De novo assembly New methods are still actively developed
Short reads require long overlaps
e.g., nt reads must overlap by 20+nt
end-trimming helps to remove low quality bases.
Most de novo short read assemblers use a k-mer hashing based approach.
Known as “de Bruijn graph” assembly
Describes which short words follow which other short words

45. Sequence Hashing (k = 4) TAGTCGAGGCTTTAGATCCGATGAGGCTTTAGAGACAG
AGTCGAG CTTTAGA CGATGAG CTTTAGA
GTCGGG TTAGATC ATGAGGC GAGACAG
GAGGCTC ATCCGAT AGGCTTT GAGACAG
AGTCGAG TAGATCC ATGAGGC TAGAGAA
TAGTCGA CTTTAGA CCGATGA TTAGAGA
CGAGGCT AGATCCG TGAGGCT AGAGACA
TAGTCGA GCTTTAG TCCGATG GCTCTAG
TCGACGC GATCCGA GAGGCTT AGAGACA
TAGTCGA TTAGATC GATGAGG TTTAGAG
GTCGAGG TCTAGAT ATGAGGC TAGAGAC
AGGCTTT ATCCGAT AGGCTTT GAGACAG
AGTCGAG TTAGATT ATGAGGC AGAGACA
GGCTTTA TCCGATG TTTAGAG
CGAGGCT TAGATCC TGAGGCT GAGACAG
AGTCGAG TTTAGATC ATGAGGC TTAGAGA
GAGGCTT GATCCGA GAGGCTT GAGACAG
An example de Bruijn graph assembly for a short genomic sequence without polymorphism. Sequence at top represents the genome, which is then sampled using shotgun sequencing in base space with 7-bp reads (step 1). Some of the reads have errors (red).
Hashing (k = 4)

46. { Graph Building TAGTCGAGGCTTTAGATCCGATGAGGCTTTAGAGACAG
TAGT AGTC GTCG TCGA CGAG GAGG AGGC GGCT GCTT AGAG GAGA AGAC GACA ACAG
(3x) (7x) (9x) (10x) (8x) (16x) (16x) (16x) (11x) (9x) (12x) (9x) (8x) (5x) 
CTTC TTCA TCAG CAGA
(1x) (2x) (2x) (1x)
CTTT TTTA TTAG TAGA
(8x) (8x) (12x) (16x)
CGAC GACG ACGC
(1x) (1x) (1x)
TGAG ATGA GATG CGAT CCGA TCCG ATCC GATC AGAT
(9x) (8x) (5x) (6x) (7x) (7x) (7x) (8x) (8x)
GATT
(1x)
AGAA {
In step 2, the k-mers in the reads (4-mers in this example) are collected into nodes and the coverage at each node is recorded. There are continuous linear stretches within the graph, and the sequencing errors create distinctive, low-coverage features through out the graph
TAGTCGAGGCTTTAGATCCGATGAGGCTTTAGAGACAG

47. { { Simplification of Linear Stretches
TAGT AGTC GTCG TCGA CGAG GAGG AGGC GGCT GCTT AGAG GAGA AGAC GACA ACAG
(3x) (7x) (9x) (10x) (8x) (16x) (16x) (16x) (11x) (9x) (12x) (9x) (8x) (5x) 
CTTC TTCA TCAG CAGA
(1x) (2x) (2x) (1x)
CTTT TTTA TTAG TAGA
(8x) (8x) (12x) (16x)
CGAC GACG ACGC
(1x) (1x) (1x)
TGAG ATGA GATG CGAT CCGA TCCG ATCC GATC AGAT
(9x) (8x) (5x) (6x) (7x) (7x) (7x) (8x) (8x)
GATT
(1x)
AGAA {
Simplification of Linear Stretches
TAGTCGA CGAG
CGACGC
GCTCTAG
GCTTTAG
GATCCGATGAG
AGAT
AGAA  {
GATT
GAGGCT
TAGA AGAGA AGACAG
In step 3, the graph is simplified to combine nodes that are associated with the continuous linear stretches into single, larger nodes of various k-mer sizes.

48. { { Tips Bubble Error (tip and bubble) removal
TAGTCGA CGAG
CGACGC
GCTCTAG
GCTTTAG
GATCCGATGAG
AGAT
AGAA  {
GATT
GAGGCT
TAGA AGAGA AGACAG
Tips
Bubble {
TAGTCGAG GAGGCTTTAGA AGAGACAG 
AGATCCGATGAG
Error (tip and bubble) removal
In step 4, error correction removes the tips and bubbles that result from sequencing errors and creates a final graph structure that accurately and completely describes in the original genome sequence.
TAGTCGAGGCTTTAGATCCGATGAGGCTTTAGAGACAG

49. De novo Assembler performance: the Assemblathon
From Genome10K/UCSD
Roughly annual, latest 21 participants, 3 genomes
Assembly size for bird genome (budgie)
NG50: if all scaffold lengths are summed from longest to the
shortest, this is the length at which the sum length accounts for 50% of total sequence.
“NG50” calculated in genome size, “N50” in assembly size.
Higher is better (most sequence in long scaffolds), but it does not account for error!
Scaffold size
% scaffolds

50. De novo Assembler performance: the Assemblathon
Assembly metrics for bird genome (budgie)

51. Other assembly considerations: computational demands
The time taken to run an assembly can vary by >100x.
The memory required for assembly can be 16G, 32G, or more.
No one right answer – depends on technology, amount of data, quality of data, GC%, computational resources, desired outcome…

52. http://saaientist. blogspot
Chimeras, Rearrangements, the problem of reference genomes
Second level of QA
Mismatched paired end reads

53. 222 appshttp://seqanswers.com/wiki/SEQanswers
The standard tools

54. Illumina announces HiSeq 2000
The Future
Illumina announces HiSeq 2000
+ Better dynamic range, higher resolution- Library construction, machine acquisition, downstream processing
2010