ChIP Sequencing BMI/IBGP 730

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ChIP Sequencing BMI/IBGP 730

• Victor Jin, Ph.D.
• (Slides from Dr. H. Gulcin Ozer)
• Department of Biomedical Informatics

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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 proteinDNA interactions in-vivo
  on a genome scale

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Workflow of ChIP-Seq
Mardis, E.R. Nat. Methods 4, 613-614 (2007)
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Workflow of ChIP-Seq
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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

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Robertson et al, 2007

• ChIP-Seq technology used to study genome-wide
  profiles of STAT1 DNA association
• STAT1 targets in interferon-?-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.

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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.

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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
• 35 bases in length

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Illumina Genome Analysis System
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Sequencing
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Sequencer Output
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Sequence Files

• 10 million sequences per lane
• 500 MB files

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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

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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

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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

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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

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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

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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

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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

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Mapping Algorithm (2 mismatches)
GATGCATTGCTATGCCTCCCAGTCCGCAACTTCACG
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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

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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

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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

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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

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Visualization

• BED files are build to summarize mapping results
• BED files can be easily visualized in Genome
  Browser
• http//genome.ucsc.edu

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Visualization Genome Browser
Robertson, G. et al. Nat. Methods 4, 651-657
(2007)
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Visualization Custom
300 kb region from mouse ES cells
Mikkelsen,T.S. et al. Nature 448, 553-562 (2007)
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Visualization
Huang, 2008 (unpublished)
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Huang, 2008 (unpublished)
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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

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Peak Analysis

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

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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)

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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

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Compare Samples
Huang, 2008 (unpublished)
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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

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QUESTIONS?