1. Massive Parallel Sequencing
Kun Huang, PhD
Department of Biomedical Informatics
OSU CCC Bioinformatics Shared Resources

2. Introduction High throughput sequencing – a new paradigm Applications
Solexa
SOLiD
454/Roche Genome Sequencer
Applications
Genome sequencing
microRNA screening
Gene expression
ChIP-seq
Genome-worth of sequence, terabytes of data

3. What is ChIP-Sequencing?
ChIP-Sequencing is a new frontier technology to analyze protein interactions with DNA.
ChIP-Seq
Combination of chromatin immunoprecipitation (ChIP) with ultra high-throughput massively parallel sequencing
Allow mapping of protein–DNA interactions in-vivo on a genome scale

4. Workflow of
ChIP-Seq
Mardis, E.R. Nat. Methods 4, (2007)

5. Workflow of
ChIP-Seq

6. ChIP-seq Challenges: Millions of segments Mapping to genome
Visualization
Peak detection
Data normalization …

8. Johnson et al, 2007
ChIP-Seq technology is used to understand in vivo binding of the neuron-restrictive silencer factor (NRSF)
Results are compared to known binding sites
ChIP-Seq signals are strongly agree with the existing knowledge
Sharp resolution of binding position
New noncanonical NRSF binding motifs are identified

10. Robertson et al, 2007
ChIP-Seq technology used to study genome-wide profiles of STAT1 DNA association
STAT1 targets in interferon-γ-stimulated and unstimulated human HeLA S3 cells are compared
The performance of ChIP-Seq is compared to the alternative protein-DNA interaction methods of ChIP-PCR and ChIP-chip.
41,582 and 11,004 putative STAT-1 binding regions are identified in stimulated and unstimulated cells respectively.

11. Why ChIP-Sequencing?
Current microarray and ChIP-ChIP designs require knowing sequence of interest as a promoter, enhancer, or RNA-coding domain.
Lower cost
Less work in ChIP-Seq
Higher accuracy
Alterations in transcription-factor binding in response to environmental stimuli can be evaluated for the entire genome in a single experiment.

12. Bioinformatics

13. Sequencers Solexa (Illumina) 454 Life Sciences (Roche Diagnostics)
1 GB of sequences in a single run
35 bases in length
454 Life Sciences (Roche Diagnostics)
25-50 MB of sequences in a single run
Up to 500 bases in length
SOLiD (Applied Biosystems)
6 GB of sequences in a single run

14. Illumina Genome Analysis System
8 lanes
100 tiles per lane

15. Sequencing

16. Sequencer Output
Quality Scores
Sequence Files

17. Sequence Files
~10 million sequences per lane
~500 MB files

18. Quality Score Files
Quality scores describe the confidence of bases in each read
Solexa pipeline assigns a quality score to the four possible nucleotides for each sequenced base
9 million sequences (500MB file)  ~6.5GB quality score file

19. Bioinformatics Challenges
Rapid mapping of these short sequence reads to the reference genome
Visualize mapping results
Thousand of enriched regions
Peak analysis
Peak detection
Finding exact binding sites
Compare results of different experiments
Normalization
Statistical tests

20. Mapping of Short Oligonucleotides to the Reference Genome
Mapping Methods
Need to allow mismatches and gaps
SNP locations
Sequencing errors
Reading errors
Indexing and hashing
genome
oligonucleotide reads
Use of quality scores
Use of SNP knowledge
Performance
Partitioning the genome or sequence reads

21. Mapping Methods: Indexing the Genome
Fast sequence similarity search algorithms (like BLAST)
Not specifically designed for mapping millions of query sequences
Take very long time
e.g. 2 days to map half million sequences to 70MB reference genome (using BLAST)
Indexing the genome is memory expensive

23. SOAP (Li et al, 2008)
Both reads and reference genome are converted to numeric data type using 2-bits-per-base coding
Load reference genome into memory
For human genome, 14GB RAM required for storing reference sequences and index tables
300(gapped) to 1200(ungapped) times faster than BLAST

24. SOAP (Li et al, 2008) 2 mismatches or 1-3bp continuous gap
Errors accumulate during the sequencing process
Much higher number of sequencing errors at the 3’-end (sometimes make the reads unalignable to the reference genome)
Iteratively trim several basepairs at the 3’-end and redo the alignment
Improve sensitivity

25. Mapping Methods: Indexing the Oligonucleotide Reads
ELAND (Cox, unpublished)
“Efficient Large-Scale Alignment of Nucleotide Databases” (Solexa Ltd.)
SeqMap (Jiang, 2008)
“Mapping massive amount of oligonucleotides to the genome”
RMAP (Smith, 2008)
“Using quality scores and longer reads improves accuracy of Solexa read mapping”
MAQ (Li, 2008)
“Mapping short DNA sequencing reads and calling variants using mapping quality scores”

26. Mapping Algorithm (2 mismatches)
GATGCATTGCTATGCCTCCCAGTCCGCAACTTCACG
GATGCATTG CTATGCCTC CCAGTCCGC AACTTCACG seeds
GATGCATTG
CTATGCCTC
CCAGTCCGC
AACTTCACG
Exact match
Genome
Indexed table of exactly matching seeds
Approximate search around the exactly matching seeds

27. Mapping Algorithm (2 mismatches)
Partition reads into 4 seeds {A,B,C,D}
At least 2 seed must map with no mismatches
Scan genome to identify locations where the seeds match exactly
6 possible combinations of the seeds to search
{AB, CD, AC, BD, AD, BC}
6 scans to find all candidates
Do approximate matching around the exactly-matching seeds.
Determine all targets for the reads
Ins/del can be incorporated
The reads are indexed and hashed before scanning genome
Bit operations are used to accelerate mapping
Each nt encoded into 2-bits

28. ELAND (Cox, unpublished)
Commercial sequence mapping program comes with Solexa machine
Allow at most 2 mismatches
Map sequences up to 32 nt in length
All sequences have to be same length

31. RMAP (Smith et al, 2008) Improve mapping accuracy
Possible sequencing errors at 3’-ends of longer reads
Base-call quality scores
Use of base-call quality scores
Quality cutoff
High quality positions are checked for mismatces
Low quality positions always induce a match
Quality control step eliminates reads with too many low quality positions
Allow any number of mismatches

32. Mapped to a unique location
Map to reference
genome
Mapped to a unique location
Mapped to multiple locations
No mapping
Low quality
7.2 M
1.8 M
2.5 M
0.5 M
12 M
3 M
Quality
filter

34. Bioinformatics Challenges
Rapid mapping of these short sequence reads to the reference genome
Visualize mapping results
Thousand of enriched regions
Peak analysis
Peak detection
Finding exact binding sites
Compare results of different experiments
Normalization
Statistical tests

35. Visualization BED files are build to summarize mapping results
BED files can be easily visualized in Genome Browser

36. Visualization: Genome Browser
Robertson, G. et al. Nat. Methods 4, (2007)

37. Visualization: Custom
300 kb region from mouse ES cells
Mikkelsen,T.S. et al. Nature 448, (2007)

38. Visualization
Huang, 2008 (unpublished)

39. Huang, 2008 (unpublished)

40. Bioinformatics Challenges
Rapid mapping of these short sequence reads to the reference genome
Visualize mapping results
Thousand of enriched regions
Peak analysis
Peak detection
Finding exact binding sites
Compare results of different experiments
Normalization
Statistical tests

41. Peak Analysis Peak Detection
ChIP-Peak Analysis Module (Swiss Institute of Bioinformatics)
ChIPSeq Peak Finder (Wold Lab, Caltech)

44. Peak Analysis Finding Exact Binding Site
Determining the exact binding sites from short reads generated from ChIP-Seq experiments
SISSRs (Site Identification from Short Sequence Reads) (Jothi 2008)
MACS (Model-based Analysis of ChIP-Seq) (Zhang et al, 2008)

45. Bioinformatics Challenges
Rapid mapping of these short sequence reads to the reference genome
Visualize mapping results
Thousand of enriched regions
Peak analysis
Peak detection
Finding exact binding sites
Compare results of different experiments
Normalization
Statistical tests

46. Compare Samples
Huang, 2008 (unpublished)

47. Compare Samples Fold change
HPeak: An HMM-based algorithm for defining read-enriched regions from massive parallel sequencing data
Xu et al, 2008
Advanced statistics

48. QUESTIONS?