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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SIGNAL PROCESSING FOR NEXT-GEN SEQUENCING DATA

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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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