1. High Throughput Sequencing
Xiaole Shirley Liu
STAT115, STAT215, BIO298, BIST520
Guest lecturer: Wei Li

2. About me Wei Li, research fellow at DFCI
Studied high-throughput sequencing algorithms shortly after HTS comes out (2009)
Transcript reconstruction algorithms from high-throughput RNA sequencing data (RNA-seq): IsoInfer/IsoLasso/CEM
CRISPR/Cas9 screening algorithms: design, analysis (MAGeCK/MAGeCK-VISPR)

3. Why high-throughput sequencing?
High-throughput sequencing/HTS/Next-generation sequencing/NGS
2-3 orders of magnitude faster/cheaper/higher data throughput compared with “first generation”
Huge applications in academia/industry

4. First generation: Sanger sequencing
Frederick Sanger: the 3rd person overall to win two Nobel prizes

5. First Generation
Sanger Sequencing: 384 * 1kb / 3 hours

6. Sanger sequencing materials
Sanger sequencing uses DNA elongation to “read” sequences
dNTPs: required for normal elongation process
ddNTPs: missing oxygen bond, will stop the synthesis
dideoxyNTP, di=two, deoxy=remove oxygen

7. Sanger sequencing setup
4 tubes, each test tube has deoxyA,G,C,T
In addition each also has ONE of the 4 ddNTP

8. What happens if you have both dATP and ddATP?
The synthesis stops whenever you encounter “T”

14. Sequencing in 2001
[Enter any extra notes here; leave the item ID line at the bottom]
Avitage Item ID: {{CE8AAEAA-A22F-47FE-A1F8-66CBC3CDB6FC}}

15. Sequencing in 2007
[Enter any extra notes here; leave the item ID line at the bottom]
Avitage Item ID: {{010D7619-E070-4F7B-BC AA639C8D}}

16. Second Generation Massively parallel sequencing by synthesis
Many different technologies: Illumina, 454, SOLiD, Helicos, etc
Illumina: HiSeq, MiSeq, NextSeq
1-16 samples
25M-4B reads
30-300bp
1-8 days
15GB-1TB output
Moving targets

17. Illumina Cluster Generation
Amplify sequenced fragments in place on the flow cell
Can sequence from both the pink and purple adapters (Paired-end seq)
Can multiplex many samples / lane

21. Illumina Sequencing process
1. Incorporate all 4 nucleotides, each label with a different dye
2. Wash, 4-color imaging
4. Repeat cycles
3. Cleave dye and terminating groups, wash

22. Illumina Sequencing
Cycle 1 2 3 4 5 6

23. Third Generation Single molecule sequencing: no amp
Fewer but much longer reads
Good for sequencing long reads, but not for read count applications, technology still in developmenthttp://

24. High Throughput Sequencing
Big (data), fast (speed), cheap (cost), flexible (applications)
Cost reduces faster than Moore’s law: Bioinformatic analyses become bottleneck!

25. High Throughput Sequencing Data Analysis

26. FASTQ File Format Quality score using ASCII (higher -> better)
Sequence ID, sequence
Quality ID, quality score
Quality score using ASCII (higher -> better)
@HWI-EAS305:1:1:1:991#0/1
GCTGGAGGTTCAGGCTGGCCGGATTTAAACGTAT
+HWI-EAS305:1:1:1:991#0/1
MVXUWVRKTWWULRQQMMWWBBBBBBBBBBBBBB
@HWI-EAS305:1:1:1:201#0/1
AAGACAAAGATGTGCTTTCTAAATCTGCACTAAT
+HWI-EAS305:1:1:1:201#0/1
PXX[[[[XTXYXTTWYYY[XXWWW[TMTVXWBBB

27. FASTQC: Sequencing Quality
Good quality!
Poor quality!

28. Read Mapping
Mapping hundreds of millions of reads back to the reference genome is CPU and RAM intensive and slow
Read quality decreases with length (small single nucleotide mismatches or indels)
Most mappers allow ~2 mismatches within first 30bp (4 ^ 28 could still uniquely identify most 30bp sequences in a 3GB genome), slower when allowing indels
Mapping output: SAM (BAM) or BED

29. Read mapping algorithms
Spaced seed alignment
Burrows-Wheeler
Suffix tree

30. Spaced seed alignment
Tags and tag-sized pieces of reference are cut into small “seeds.”
Pairs of spaced seeds are stored in an index.
Look up spaced seeds for each tag.
For each “hit,” confirm the remaining positions.
Report results to the user.

31. BW alignment

32. Burrows-Wheeler Store entire reference genome.
Align tag base by base from the end.
When tag is traversed, all active locations are reported.
If no match is found, then back up and try a substitution.
Trapnell & Salzberg, Nat Biotech 2009

33. Burrows-Wheeler Transform
Reversible permutation used originally in compression
Once BWT(T) is built, all else shown here is discarded
First col can be derived by sorting the last col T
(query sequence)
BWT(T)
Encoding for
compression
gc$ac
Burrows
Wheeler
Matrix
Last column
Burrows M, Wheeler DJ: A block sorting lossless data compression algorithm. Digital Equipment Corporation, Palo Alto, CA 1994, Technical Report 124; 1994
Slides from Ben Langmead

34. Burrows-Wheeler Transform
Property that makes BWT(T) reversible is “LF Mapping”
ith occurrence of a character in Last column is same text occurrence as the ith occurrence in First column
Rank: 2
(2nd ‘a’ in First column)
BWT(T) T
Rank: 2
(2nd ‘a’ in Last column)
Burrows Wheeler
Matrix
Slides modified from Ben Langmead

35. BWT: How to reconstruct T from BWT(T)?
To recreate T from BWT(T), repeatedly apply rule:
T = BWT[ LF(i) ] + T; i = LF(i)
Where LF(i) maps row i to row whose first character corresponds to i’s last per LF Mapping
Final T
Slides from Ben Langmead

36. BWT: How to reconstruct T from BWT(T)?
To recreate T from BWT(T), repeatedly apply rule:
T = BWT[ LF(i) ] + T; i = LF(i)
Where LF(i) maps row i to row whose first character corresponds to i’s last per LF Mapping
LF(i)=7; the first ‘g’
BWT[LF(i)]=‘c’; the second last character is ‘c’;
i=LF(i)=7
i=1; this is the last character of T
The first and last columns are known
Slides from Ben Langmead

37. BWT: How to reconstruct T from BWT(T)?
To recreate T from BWT(T), repeatedly apply rule:
T = BWT[ LF(i) ] + T; i = LF(i)
Where LF(i) maps row i to row whose first character corresponds to i’s last per LF Mapping
LF(i)=6; the second ‘c’
BWT[LF(i)]=‘a’; the 3rd last character is a’;
i=LF(i)=6
i=7; this is the second last character of T
Slides from Ben Langmead

38. BWT: How to reconstruct T from BWT(T)?
To recreate T from BWT(T), repeatedly apply rule:
T = BWT[ LF(i) ] + T; i = LF(i)
Where LF(i) maps row i to row whose first character corresponds to i’s last per LF Mapping
Final T
Slides from Ben Langmead

39. BWT: How To Do Exact Matching?
To match Q in T using BWT(T), repeatedly apply rule:
top = LF(top, qc); bot = LF(bot, qc)
Where qc is the next character in Q (right-to-left) and LF(i, qc) maps row i to the row whose first character corresponds to i’s last character as if it were qc
Slides from Ben Langmead

40. BWT: How To Do Exact Matching?
To match Q in T using BWT(T), repeatedly apply rule:
top = LF(top, qc); bot = LF(bot, qc)
Where qc is the next character in Q (right-to-left) and LF(i, qc) maps row i to the row whose first character corresponds to i’s last character as if it were qc
qc=‘a’
top=LF(5,’a’)=3
bot=LF(6,’a’)=4
qc=‘c’
top=5
The last character of row 5,6 is ‘a’
bot=6
Slides from Ben Langmead

41. BWT: How To Do Exact Matching?
To match Q in T using BWT(T), repeatedly apply rule:
top = LF(top, qc); bot = LF(bot, qc)
Where qc is the next character in Q (right-to-left) and LF(i, qc) maps row i to the row whose first character corresponds to i’s last character as if it were qc
qc=‘a’
top=LF(3,’a’)=2
bot=LF(4,’a’)=2
The last character of row 3,4 is ‘a’,’$’
Slides from Ben Langmead

42. Exact Matching with FM Index
In progressive rounds, top & bot delimit the range of rows beginning with progressively longer suffixes of Q (from right to left)
If range becomes empty the query suffix (and therefore the query) does not occur in the text
If no match, instead of giving up, try to “backtrack” to a previous position and try a different base (mismatch, much slower)
Slides from Ben Langmead

43. STAR Alignment Suffix Tree
Very fast and accuracy for mapping PE-seq and high read counts
O(n) time to build
O(mlogn) time to
search

44. Suffix tree (Example)
Let s=abab, a suffix tree of s is a compressed trie of all suffixes of s=abab$ $ { $ b$
ab$
bab$
abab$ } a b $

45. Mapped Seq Files Mapped SAM
HWUSI-EAS366_0112:6:1:1298:18828#0/1    16      chr9            255     38M     *       0       0       TACAATATGTCTTTATTTGAGATATGGATTTTAGGCCG  Y\]bc^dab\[_UU`^`LbTUT\ccLbbYaY`cWLYW^  XA:i:1  MD:Z:3C30T3     NM:i:2
HWUSI-EAS366_0112:6:1:1257:18819#0/1    4       *       0       0       *       *       0       0       AGACCACATGAAGCTCAAGAAGAAGGAAGACAAAAGTG  ece^dddT\cT^c`a`ccdK\c^^__]Yb\_cKS^_W\  XM:i:1
HWUSI-EAS366_0112:6:1:1315:19529#0/1    16      chr9           255     38M     *       0       0       GCACTCAAGGGTACAGGAAAAGGGTCAGAAGTGTGGCC  ^c_Yc\Lcb`bbYdTa\dd\`dda`cdd\Y\ddd^cT`  XA:i:0  MD:Z:38 NM:i:0
chr
chr
Mapped SAM
Map: 0 OK, 4 unmapped, 16 mapped reverse strand
Sequence, quality score
XA (mapper-specific)
MD: mismatch info: 3 match, then C ref, 30 match, then T ref, 3 match
NM: number of mismatch
BAM: binary SAM format
Mapped BED
Chr, start, end, strand

46. Mapping Statistics Terms
Mappable locations: reads that can find match to A location in the genome
Uniquely mapped reads: reads that can find match to A SINGLE location in the genome
Repeat sequences in the genome, length-dependent
Uniquely mapped locations: number of unique locations hit by uniquely mapped reads
Redundancy: potential PCR amplification bias

47. Summary Sequencing technologies Sequence quality assessment
1st, 2nd, 3rd generation
Sequence quality assessment
FASTQC
Read mapping
Spaced seed
BWA: Borrows Wheeler transformation, LF mapping
STAR: Suffix Tree, fast
SAM / BAM format