How Computing Science Saved The Human Genome Project Athabasca Hall Sept. 9, 2013
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1 How Computing Science Saved The Human Genome Project 3-41 Athabasca Hall Sept. 9, 2013
2 The Human Genome Project
3 HGP Announcement June 26, 2000*** J. Craig Venter Francis Collins Bill Clinton Tony Blair
4 Most Significant Scientific Accomplishments of the Last 50 Years June 26, 2000 July 20, 1969
5 The Human Genome Project First efforts started in October 1990 Celera vs. NIH Two competing efforts (private vs. public) First Draft completed on June 26, 2000 Finished on May 18, 2006 ($3.8 billion) Used hundreds of machines and 1000s of scientists to sequence a total of 3,283,984,159 bases on 24 chromosomes
6 21,000 metabolite
7 DNA Structure & Bases
8 DNA Sequencing The Key to the Human Genome Project
9 Shotgun Sequencing Isolate ShearDNA Chromosome into Fragments Clone into Seq. Vectors Sequence
10 Multiplexed CE with Fluorescent detection ABI x700 bases
11 Shotgun Sequencing Sequence Chromatogram Send to Computer Assembled Sequence
12 HGP - Challenges Reading the DNA sequencer chromatograms (base calling) Putting millions of short reads together to assemble the genome (assembly) Identifying the genes from the DNA sequence (gene finding) Figuring out what each gene does
13 HGP - Challenges Reading the DNA sequencer chromatograms 35 billion base calls Putting millions of short reads together to assemble the genome piecing 35 million reads together Identifying the genes from the DNA sequence Finding 1% signal with >95% accuracy Figuring out what each gene does 20,000x100,000,000 comparisons
14 Biting Off Too Much
15 Computational Challenges Reading the DNA sequencer chromatograms 35 billion base calls Putting millions of short reads together to assemble the genome piecing 35 million reads together Identifying the genes from the DNA sequence Finding 1% signal with >95% accuracy Figuring out what each gene does 20,000x100,000,000 comparisons
16 Principles of DNA Sequencing
17 Principles of DNA Sequencing G T C A short G C A T G + + C long
18 Capillary Electrophoresis Separation by Electro-osmotic Flow
19 Multiplexed CE with Fluorescent detection ABI x700 bases
20 Base Calling Image processing Peak detection De-noising Peak deconvolution Signal analysis Reliability assessment 99.99% accurate for 35 billion base calls
21 Base Calling With Phred* READ QUALITY Bad Better Good Excellent
22 Base Calling - Result ATGTCACTGCAATTGATGTATAAATGGA! GTTAGACACTAGATCACATAGGAGTTTA! CGCTAAATGACAGATAGACA! GGGATATCTATAGATAGACACATAGCTCTCT! AATGACGACTAGCTGAGTAGATT! TTACGATCGATCGATATTACCGCGCGAAATAT! AGCTATGATGTCGAT! AGACTAGCTAGCTTCTCGGATATTAGA!
23 Shotgun Sequencing Sequence Chromatogram Send to Computer Assembled Sequence
24 Computational Challenges Reading the DNA sequencer chromatograms 35 billion base calls Putting millions of short reads together to assemble the genome piecing 35 million reads together Identifying the genes from the DNA sequence Finding 1% signal with >95% accuracy Figuring out what each gene does 20,000x100,000,000 comparisons
25 Sequence Assembly Reads Consensus ATGGCATTGCAA! TGGCATTGCAATTTG! AGATGGTATTG! GATGGCATTGCAA! GCATTGCAATTTGAC! ATGGCATTGCAATTT! AGATGGTATTGCAATTTG!! AGATGGCATTGCAATTTGAC!
26 Sequence Assembly (An Analogy) The DARPA Shredder Challenge
27 Dynamic Programming GAATTCAGTTA! GGATCGA!
28 Dynamic Programming GAATTCAGTTA! GGAT-C-G--A!
29 De Bruijn Graphs & Assembly
30 A Real Assembler
31 The Result >P12345 Human chromosome1 GATTACAGATTACAGATTACAGATTACAGATTACAG! ATTACAGATTACAGATTACAGATTACAGATTACAGA! TTACAGATTACAGATTACAGATTACAGATTACAGAT! TACAGATTAGAGATTACAGATTACAGATTACAGATT! ACAGATTACAGATTACAGATTACAGATTACAGATTA! CAGATTACAGATTACAGATTACAGATTACAGATTAC! AGATTACAGATTACAGATTACAGATTACAGATTACA! GATTACAGATTACAGATTACAGATTACAGATTACAG! ATTACAGATTACAGATTACAGATTACAGATTACAGA! TTACAGATTACAGATTACAGATTACAGATTACAGAT! TTGGC. And on for 150,000,000 bases
32 February, 2002 *
33 Computational Challenges Reading the DNA sequencer chromatograms 35 billion base calls Putting millions of short reads together to assemble the genome piecing 35 million reads together Identifying the genes from the DNA sequence Finding 1% signal with >95% accuracy Figuring out what each gene does 20,000x100,000,000 comparisons
34 Genome Sequence >P12345 Human chromosome1 GATTACAGATTACAGATTACAGATTACAGATTACAG! ATTACAGATTACAGATTACAGATTACAGATTACAGA! TTACAGATTACAGATTACAGATTACAGATTACAGAT! TACAGATTAGAGATTACAGATTACAGATTACAGATT! ACAGATTACAGATTACAGATTACAGATTACAGATTA! CAGATTACAGATTACAGATTACAGATTACAGATTAC! AGATTACAGATTACAGATTACAGATTACAGATTACA! GATTACAGATTACAGATTACAGATTACAGATTACAG! ATTACAGATTACAGATTACAGATTACAGATTACAGA! TTACAGATTACAGATTACAGATTACAGATTACAGAT! TTGGC. And on for 150,000,000 bases
35 Eukaryotic Gene Structure Transcribed Region exon 1 intron 1 exon 2 intron 2 exon3 Upstream Intergenic Region Start codon 5 UTR Stop codon 3 UTR Downstream Intergenic Region
36 Genome Sequence GATTACAGATTACAGATTACAGATTACAGATTACAG! ATTACAGATTACAGATTACAGATTACAGATTACAGA! TTACAGATTACAGATTACAGATTACAGATTACAGAT! TACAGATTAGAGATTACAGATTACAGATTACAGATT! ACAGATTACAGATTACAGATTACAGATTACAGATTA! CAGATTACAGATTACAGATTACAGATTACAGATTAC! AGATTACAGATTACAGATTACAGATTACAGATTACA! GATTACAGATTACAGATTACAGATTACAGATTACAG! ATTACAGATTACAGATTACAGATTACAGATTACAGA!! ATTACAGATTACAGATTACAATTAGAGATTACAGAT! TACAGATTACAGATTACAGATTACAGATTACAGATT! ACCAGATTACAGA!
37 Problem Similar to Speech or Text Recognition
38 Hidden Markov Models
39 HMM for Gene Finding
40 Genscan The Ultimate Gene Finder
41 How Well Do They Do? "Evaluation of gene finding programs" S. Rogic, A. K. Mackworth and B. F. F. Ouellette. Genome Research, 11: (2001).
42 Gene Prediction (Evaluation) Actual TP FP TN FN TP FN TN Predicted Sensitivity Measure of the % of false negative results (sn = means 0.4% false negatives) Specificity Measure of the % of false positive results Precision Measure of the % positive results Correlation Combined measure of sensitivity and specificity
43 Gene Prediction (Evaluation) Actual TP FP TN FN TP FN TN Predicted Sensitivity or Recall Sn=TP/(TP + FN) Specificity Sp=TN/(TN + FP) Precision Pr=TP/(TP + FP) Correlation CC=(TP*TN-FP*FN)/[(TP+FP)(TN+FN)(TP+FN)(TN+FP)] 0.5 This is a better way of evaluating
44 Computational Challenges Reading the DNA sequencer chromatograms 35 billion base calls Putting millions of short reads together to assemble the genome piecing 35 million reads together Identifying the genes from the DNA sequence Finding 1% signal with >95% accuracy Figuring out what each gene does 20,000x100,000,000 comparisons
45 A List of Genes >P12346 Gene 1 ATGTACAGATTACAGATTACAGATTACAGATTACAG ATTACAGATTACAGATTACAGATTACAGATTACAGA TTACAGATTACAGATTACAGATTACAGAT >P12347 Gene 2 ATGAGATTAGAGATTACAGATTACAGATTACAGATT ACAGATTACAGATTACAGATTACAGATTACAGATTA CAGATTACAGATTACAGATTACAGATTACAGATT >P12348 Gene 3 ATGTTACAGATTACAGATTACAGATTACAGATTACA GATTACAGATTACAGATTACAGATTACA...
46 What Biologists Want >P12346 Gene 1 Human hemoglobin alpha chain, transports oxygen, located on chromosome 14 p.12.1 >P12347 Gene 2 Human super oxide disumutase, removes oxygen radicals and prevents rapid aging, located on chromosome 14 p >P12348 Gene 3 Human hemoglobin beta chain, transports oxygen, located on chromosome 14 p.12.23
47 What Biologists Want Trick is to use sequence similarity or sequence matching and prior knowledge By 2005 millions of genes had already been characterized from other organisms Find the human genes that are similar to the already-characterized genes and assume they are pretty much the same Annotation by sequence homology Key is to do rapid sequence comparisons
48 Definitions by Similarity Query: Bananas Database Banana a yellow curved fruit Bandana a colorful kerchief Banal boring and obvious Banyan a fig that starts as an epiphyte Ananas genus name for pineapple
49 Dynamic Programming Too Slow GAATTCAGTTA! GGATCGA!
50 The BLAST Search Algorithm ,000X faster than DP methods
51 The BLAST Server
52 Computational Challenges Reading the DNA sequencer chromatograms Solved with Phred Putting millions of short reads together to assemble the genome Solved with Phusion Identifying the genes from the DNA sequence Solved with Genscan Figuring out what each gene does Solved with BLAST
53 Who Were the Real Heroes of The Human Genome Project? J. Craig Venter Francis Collins Bill Clinton Tony Blair
54
55 Questions? 3-41 Athabasca Hall
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