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SIGNAL PROCESSING FOR NEXT-GEN SEQUENCING DATA

Explore the forefront of next-generation sequencing with a focus on advanced methods such as DNase I-seq, ChIP-seq, RIP/CLIP-seq, and RNA-seq. This overview delves into modern signal processing techniques for mapping reads to the genome, addressing challenges like multiple mappings and sequencing errors. Learn how to identify protein binding peaks, analyze chromatin accessibility, and leverage high-throughput data to predict gene expression patterns. Enhance your understanding of genomic data and its applications in research and medicine.

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SIGNAL PROCESSING FOR NEXT-GEN SEQUENCING DATA

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  1. Peaks DNAse I-seq CHIP-seq SIGNAL PROCESSING FORNEXT-GEN SEQUENCING DATA Gene models RNA-seq Binding sites RIP/CLIP-seq FAIRE-seq Transcripts

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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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