1. Next Generation Sequencing

2. Sequencing techniques
ChIP-seq
MBD-seq (MIRA-seq)
BS-seq
RNA-seq
miRNA-seq

3. ChIP-seq
ChIP-Seq is a new frontier technology to analyze in vivo protein-DNA interactions.
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)

7. The advantages of ChIP-seq
Current microarray and ChIP-ChIP designs require knowing sequence of interest as a promoter, enhancer, or RNA-coding domain.
Lower cost
Higher resolution
Higher accuracy
Alterations in transcription-factor binding in response to environmental stimuli can be evaluated for the entire genome in a single experiment.

8. Sequencers Solexa (Illumina) 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

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

10. Sequencing

11. Sequencer Output
Quality Scores
Sequence Files

12. Sequence Files
10-40 million reads per lane
~500 MB files

13. 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

14. 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

15. 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

16. 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

18. SOAP (Li et al, 2008) 2 mismatches or 1-3bp continuous gap
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
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

19. 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”

20. 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

21. 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

24. 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

25. 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

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

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

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

30. Screen shot for ZNF263 peaks
Frietze et al JBC 2010

31. ChIP-seq peak analysis programs
SISSRs (Site Identification from Short Sequence Reads): Jothi et al. NAR, 2008.
MACS (Model-based Analysis of ChIP-Seq): Zhang et al, Genome Biology, 2008.
QuEST (Genome-wide analysis of transcription factor binding sites based on ChIP–seq data): Valouev, A. et al. Nature Methods, 2008.
PeakSeq (PeakSeq enables systematic scoring of ChIP–seq experiments relative to controls): Rozowsky, J. et al. Nature Biotech
FindPeaks (FindPeaks 3.1: a tool for identifying areas of enrichment from massively parallel short-read sequencing technology.): Fejes, A .P. et al. Bioinformatisc, 2008.
Hpeak (An HMM-based algorithm for defining read-enriched regions from massive parallel sequencing data): Xu et al, Bioinformatics, 2008.

32. MBD-seq (MIRA-seq)
The MBD methyl-CpG binding domain-based (MBDCap) technology to capture the methylation sites. Double stranded methylated DNA fragments can be detected. It is sensitive to different methylation densities
Genome-wide sequencing technology was used to get the sequence of each short fragment.
The sequenced read was mapped to human genome to find the locations.

33. BALM – High resolution program for MBD-seq
Methylated CpG
Unmethylated CpG
Fragmentation
MBD2 enrichment
Elution
500mM
1000mM
2000mM
Sequencing and Alignment
BALM analysis
Tags mapped to
forward strand
reverse strand
BALM 1
BALM 2
Mixture model
Scan genome for
signal enriched regions
Estimate parameters of Bi-asymmetric-Laplace (MLE)
Measure tags distribution
around target sites
Yes
Initial scan enriched region
using tag shifting method
Set t > 0, s = 1
s = t No
Decompose the mixture model using Expectation Maximization (EM)
s = s + 1
Define hypermethylated regions and methylation score for each CpG dinucleotides
Tags distribution
BALM
Unenriched
input
Lan et al, PLoS ONE, 2011, 6:e22226

34. Application on MBD-seq data (MCF7)

35. BS-seq
BS-seq: genomic DNA is treated with sodium bisulphite (BS) to convert cytosine, but not methylcytosine, to uracil, and subsequent high- throughput sequencing.
Truly single-base resolution

36. RNA-seq
RNA-Seq is a new approach to transcriptome profiling that uses deep-sequencing technologies.
Studies using this method have already altered our view of the extent and complexity of eukaryotic transcriptomes. RNA-Seq also provides a far more precise measurement of levels of transcripts and their isoforms than other methods.

37. RNA-seq protocol

38. The advantages of RNA-seq
Single base resolution
High throughput
Low background noise
Ability to distinguish different isoforms and alleic expression
Relatively low cost