signal processing for next gen sequencing data
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Peaks. DNAse I- seq. CHIP- seq. SIGNAL PROCESSING FOR NEXT-GEN SEQUENCING DATA. Gene models. RNA- seq. Binding sites. RIP/CLIP- seq. FAIRE- seq. Transcripts. The Power of Next-Gen Sequencing. Chromosome Conformation Capture. + More Experiments. DNA Genome Sequencing

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Presentation Transcript
signal processing for next gen sequencing data

Peaks

DNAse I-seq

CHIP-seq

SIGNAL PROCESSING FORNEXT-GEN SEQUENCING DATA

Gene models

RNA-seq

Binding sites

RIP/CLIP-seq

FAIRE-seq

Transcripts

the power of next gen sequencing
The Power of Next-Gen Sequencing

Chromosome Conformation Capture

+More Experiments

DNA

Genome Sequencing

Exome Sequencing

DNAse I hypersensitivity

CHART, CHirP, RAP

CHIP

CLIP, RIP

CRAC

PAR-CLIP

iCLIP

RNA

RNA-seq

Protein

next gen sequencing as signal data
Next-Gen Sequencing as Signal Data
  • Map reads (red) to the genome. Whole pieces of DNA are black.
  • Count # of reads mapping to each DNA base  signal

Rozowskyet al. 2009 Nature Biotech

read mapping
Read mapping
  • Problem: match up to a billion short sequence reads to the genome
  • Need sequence alignment algorithm faster than BLAST

Rozowskyet al. 2009 Nature Biotech

read mapping sequence alignment
Read mapping (sequence alignment)
  • Dynamic programming
    • Optimal, but SLOW
  • BLAST
    • Searches primarily for close matches, still too slow for high throughput sequence read mapping
  • Read mapping
    • Only want very close matches, must be super fast
index based s hort r ead m appers
Index-based short read mappers
  • Similar to BLAST
  • Map all genomic locations of all possible short sequences in a hash table
  • Check if read subsequences map to adjacent locations in the genome, allowing for up to 1 or 2 mismatches.
  • Very memory intensive!

Trapnell and Salzberg 2010, Slide adapted from Ray Auerbach

read alignment using burrows wheeler transform
Read Alignment using Burrows-Wheeler Transform
  • Used in Bowtie, the current most widely used read aligner
  • Described in Coursera course: Bioinformatics Algorithms (part 1, week 10)

Trapnell and Salzberg 2010, Slide adapted from Ray Auerbach

read mapping issues
Read mapping issues
  • Multiple mapping
  • Unmapped reads due to sequencing errors
  • VERY computationally expensive
    • Remapping data from The Cancer Genome Atlas consortium would take 6 CPU years1
  • Current methods use heuristics, and are not 100% accurate
  • These are open problems

1https://twitter.com/markgerstein/status/396658032169742336

outline
Outline
  • Finding enriched regions
    • CHIP-seq: peaks of protein binding
  • RNA-seq: going beyond enrichment
  • Comparing genome-wide signal information
  • Application: Predicting gene expression from transcription factor and histone modification binding
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