[Bejerano Fall11/12] 1 MW 11:00-12:15 in Beckman B302 Profs: Serafim Batzoglou, Gill Bejerano TAs: Aaron Wenger & Jim Notwell.

http://cs273a.stanford.edu [Bejerano Fall11/12] 1 MW 11:00-12:15 in Beckman B302 Profs: Serafim Batzoglou, Gill Bejerano TAs: Aaron Wenger & Jim Notwell

Goals http://cs273a.stanford.edu [Bejerano Fall11/12] 2

3 Goals Meet your genome (learn to surf, learn the surf) Understand genomic tools (theory, applications) DIY (pose questions, use tools, write code, get answers)

http://cs273a.stanford.edu [Bejerano Fall11/12] 4 Materials How is the class split between CS and BIO? We’ll have three Friday sessions starting this Friday 10am, in B-302: Bio Primer; Text Processing Primer; UCSC Genome Browser Primer. Strong programming background recommended (CS107 level) Homework (schedule on website): Two individual homework assignments (theory + practice), plus a group project. Instead of an exam we’ll have a milestone and a final poster session. Attendance is mandatory (for grade). You may skip 2 lectures without affecting your grade. Get on the mailing list! Reading Material: mostly journal papers

http://cs273a.stanford.edu [Bejerano Fall11/12] 5 Topics Topics will include: (1) genome sequencing: technologies, assembly, personalized sequencing (2) functional landscape: genes, regulatory modules, repeats, RNA genes, epigenetics (3) genomic contribution to human disease and disease susceptibility (4) genome evolution: evolutionary processes, comparative genomics, ultraconservation, exaptation As time permits, we may cover population genetics and personalized genomics, ancient DNA, metagenomics, or other current topics. Discuss difference from CS262 (Winter), CS374(Fall), Gene203(Fall)

Asides Biology is very complex. Beautiful truths can be gleaned with little knowledge. We’ll start with a bird’s eye view and gradually dive in. But we still won’t exhaust the depth of any single topic. Feedback always welcome. http://cs273a.stanford.edu [Bejerano Fall11/12] 6

Organism – Cell - Genome http://cs273a.stanford.edu [Bejerano Fall11/12] 7 10 13 different cells in an adult human. The cell is the basic unit of life. DNA = linear molecule inside the cell that carries instructions needed throughout the cell’s life ~ long string(s) over a small alphabet Alphabet of four (nucleotides/bases) {A,C,G,T} Strings of length 10 4 -10 11...ACGTACGACTGACTAGCATCGACTACGACTAGCAC... “instruction” Genome:

http://cs273a.stanford.edu [Bejerano Fall11/12] 8 One Cell, One Genome, One Replication Every cell holds a copy of all its DNA = its genome. The human body is made of ~10 13 cells. All originate from a single cell through repeated cell divisions. cell genome = all DNA chicken ≈ 10 13 copies (DNA) of egg (DNA) chicken egg cell division DNA strings = Chromosomes

http://cs273a.stanford.edu [Bejerano Fall11/12] 9 Lights, Action, Rolling 2001 HGCCelera Getting the “blueprint of life”

http://cs273a.stanford.edu [Bejerano Fall11/12] 10 TTATATTGAATTTTCAAAAATTCTTACTTTTTTTTTGGATGGACGCAAAGAAGTTTAATAATCATATTACATGGCATTACCACCATATA CATATCCATATCTAATCTTACTTATATGTTGTGGAAATGTAAAGAGCCCCATTATCTTAGCCTAAAAAAACCTTCTCTTTGGAACTTTC AGTAATACGCTTAACTGCTCATTGCTATATTGAAGTACGGATTAGAAGCCGCCGAGCGGGCGACAGCCCTCCGACGGAAGACTCTCCTC CGTGCGTCCTCGTCTTCACCGGTCGCGTTCCTGAAACGCAGATGTGCCTCGCGCCGCACTGCTCCGAACAATAAAGATTCTACAATACT AGCTTTTATGGTTATGAAGAGGAAAAATTGGCAGTAACCTGGCCCCACAAACCTTCAAATTAACGAATCAAATTAACAACCATAGGATG ATAATGCGATTAGTTTTTTAGCCTTATTTCTGGGGTAATTAATCAGCGAAGCGATGATTTTTGATCTATTAACAGATATATAAATGGAA AAGCTGCATAACCACTTTAACTAATACTTTCAACATTTTCAGTTTGTATTACTTCTTATTCAAATGTCATAAAAGTATCAACAAAAAAT TGTTAATATACCTCTATACTTTAACGTCAAGGAGAAAAAACTATAATGACTAAATCTCATTCAGAAGAAGTGATTGTACCTGAGTTCAA TTCTAGCGCAAAGGAATTACCAAGACCATTGGCCGAAAAGTGCCCGAGCATAATTAAGAAATTTATAAGCGCTTATGATGCTAAACCGG ATTTTGTTGCTAGATCGCCTGGTAGAGTCAATCTAATTGGTGAACATATTGATTATTGTGACTTCTCGGTTTTACCTTTAGCTATTGAT TTTGATATGCTTTGCGCCGTCAAAGTTTTGAACGATGAGATTTCAAGTCTTAAAGCTATATCAGAGGGCTAAGCATGTGTATTCTGAAT CTTTAAGAGTCTTGAAGGCTGTGAAATTAATGACTACAGCGAGCTTTACTGCCGACGAAGACTTTTTCAAGCAATTTGGTGCCTTGATG AACGAGTCTCAAGCTTCTTGCGATAAACTTTACGAATGTTCTTGTCCAGAGATTGACAAAATTTGTTCCATTGCTTTGTCAAATGGATC ATATGGTTCCCGTTTGACCGGAGCTGGCTGGGGTGGTTGTACTGTTCACTTGGTTCCAGGGGGCCCAAATGGCAACATAGAAAAGGTAA AAGAAGCCCTTGCCAATGAGTTCTACAAGGTCAAGTACCCTAAGATCACTGATGCTGAGCTAGAAAATGCTATCATCGTCTCTAAACCA GCATTGGGCAGCTGTCTATATGAATTAGTCAAGTATACTTCTTTTTTTTACTTTGTTCAGAACAACTTCTCATTTTTTTCTACTCATAA CTTTAGCATCACAAAATACGCAATAATAACGAGTAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGA TAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTT GGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAG...TTGCGAA GTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAA TGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGA TACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGT TCTTGGCAAGTTGCCAACTGACGAGATGCAGTTTCCTACGCATAATAAGAATAGGAGGGAATATCAAGCCAGACAATCTATCATTACAT TTAAGCGGCTCTTCAAAAAGATTGAACTCTCGCCAACTTATGGAATCTTCCAATGAGACCTTTGCGCCAAATAATGTGGATTTGGAAAA AGAGTATAAGTCATCTCAGAGTAATATAACTACCGAAGTTTATGAGGCATCGAGCTTTGAAGAAAAAGTAAGCTCAGAAAAACCTCAAT ACAGCTCATTCTGGAAGAAAATCTATTATGAATATGTGGTCGTTGACAAATCAATCTTGGGTGTTTCTATTCTGGATTCATTTATGTAC AACCAGGACTTGAAGCCCGTCGAAAAAGAAAGGCGGGTTTGGTCCTGGTACAATTATTGTTACTTCTGGCTTGCTGAATGTTTCAATAT CAACACTTGGCAAATTGCAGCTACAGGTCTACAACTGGGTCTAAATTGGTGGCAGTGTTGGATAACAATTTGGATTGGGTACGGTTTCG TTGGTGCTTTTGTTGTTTTGGCCTCTAGAGTTGGATCTGCTTATCATTTGTCATTCCCTATATCATCTAGAGCATCATTCGGTATTTTC TTCTCTTTATGGCCCGTTATTAACAGAGTCGTCATGGCCATCGTTTGGTATAGTGTCCAAGCTTATATTGCGGCAACTCCCGTATCATT AATGCTGAAATCTATCTTTGGAAAAGATTTACAATGATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGT TCTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATG TTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATA CCTATTCTTGACATGATATGACTACCATTTTGTTATTGTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATG TTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTA AGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGA TTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATA GTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATG CTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACT TAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGAT TGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAAT

http://cs273a.stanford.edu [Bejerano Fall11/12] 11 DNA sequencing How we obtain the sequence of nucleotides of a species …ACGTGACTGAGGACCGTG CGACTGAGACTGACTGGGT CTAGCTAGACTACGTTTTA TATATATATACGTCGTCGT ACTGATGACTAGATTACAG ACTGATTTAGATACCTGAC TGATTTTAAAAAAATATT…

http://cs273a.stanford.edu [Bejerano Fall11/12] 12 DNA Sequencing – Overview Gel electrophoresis Predominant, old technology by F. Sanger Whole genome strategies Physical mapping Walking Shotgun sequencing Computational fragment assembly The future—new sequencing technologies Pyrosequencing, single molecule methods, … “Next” Generation sequencing, Third Gen, Novel assembly techniques Future variants of sequencing Resequencing of humans Microbial and environmental sequencing Cancer genome sequencing 1975 2015

http://cs273a.stanford.edu [Bejerano Fall09/10] 13 Steps to Assemble a Genome 1. Find overlapping reads 4. Derive consensus sequence..ACGATTACAATAGGTT.. 2. Merge some “good” pairs of reads into longer contigs 3. Link contigs to form supercontigs Some Terminology read a 500-900 long word that comes out of sequencer mate pair a pair of reads from two ends of the same insert fragment contig a contiguous sequence formed by several overlapping reads with no gaps supercontig an ordered and oriented set (scaffold) of contigs, usually by mate pairs consensus sequence derived from the sequene multiple alignment of reads in a contig

http://cs273a.stanford.edu [Bejerano Fall11/12] 14 NGS: Next Generation (re)Sequencing Output = massive amounts of short, lower quality reads. New Technologies + New Algorithms = New Opportunities

Third Generation Sequencing http://www.mcb.harvard.edu/branton/index.htm Just one example: Output: very long reads of 10,000-100,000 basepairs each. We’ll be able to sequence “anything” we like. In a lab. 15 http://cs273a.stanford.edu [Bejerano Fall11/12]

100 million species 7 billion individuals 10 13 cells in a human Genomes, sequences everywhere or sequence just an active portion 16 http://cs273a.stanford.edu [Bejerano Fall11/12]

17 “Unfinished Business in a Finished Genome” 341 remaining gaps: 33 Heterochromatic, 35 Euchromatic Boundaries, 273 Euchromatic Interior regions. Centromeric, Telomeric gaps Arcocentric, rDNA clusters: chr. 13,14,15,21,22

http://cs273a.stanford.edu [Bejerano Fall11/12] 18 Copy Number Variation (CNVs) so... how representative is the reference genome? [Redon et al, 2006]

http://cs273a.stanford.edu [Bejerano Fall11/12] 20 TTATATTGAATTTTCAAAAATTCTTACTTTTTTTTTGGATGGACGCAAAGAAGTTTAATAATCATATTACATGGCATTACCACCATATA CATATCCATATCTAATCTTACTTATATGTTGTGGAAATGTAAAGAGCCCCATTATCTTAGCCTAAAAAAACCTTCTCTTTGGAACTTTC AGTAATACGCTTAACTGCTCATTGCTATATTGAAGTACGGATTAGAAGCCGCCGAGCGGGCGACAGCCCTCCGACGGAAGACTCTCCTC CGTGCGTCCTCGTCTTCACCGGTCGCGTTCCTGAAACGCAGATGTGCCTCGCGCCGCACTGCTCCGAACAATAAAGATTCTACAATACT AGCTTTTATGGTTATGAAGAGGAAAAATTGGCAGTAACCTGGCCCCACAAACCTTCAAATTAACGAATCAAATTAACAACCATAGGATG ATAATGCGATTAGTTTTTTAGCCTTATTTCTGGGGTAATTAATCAGCGAAGCGATGATTTTTGATCTATTAACAGATATATAAATGGAA AAGCTGCATAACCACTTTAACTAATACTTTCAACATTTTCAGTTTGTATTACTTCTTATTCAAATGTCATAAAAGTATCAACAAAAAAT TGTTAATATACCTCTATACTTTAACGTCAAGGAGAAAAAACTATAATGACTAAATCTCATTCAGAAGAAGTGATTGTACCTGAGTTCAA TTCTAGCGCAAAGGAATTACCAAGACCATTGGCCGAAAAGTGCCCGAGCATAATTAAGAAATTTATAAGCGCTTATGATGCTAAACCGG ATTTTGTTGCTAGATCGCCTGGTAGAGTCAATCTAATTGGTGAACATATTGATTATTGTGACTTCTCGGTTTTACCTTTAGCTATTGAT TTTGATATGCTTTGCGCCGTCAAAGTTTTGAACGATGAGATTTCAAGTCTTAAAGCTATATCAGAGGGCTAAGCATGTGTATTCTGAAT CTTTAAGAGTCTTGAAGGCTGTGAAATTAATGACTACAGCGAGCTTTACTGCCGACGAAGACTTTTTCAAGCAATTTGGTGCCTTGATG AACGAGTCTCAAGCTTCTTGCGATAAACTTTACGAATGTTCTTGTCCAGAGATTGACAAAATTTGTTCCATTGCTTTGTCAAATGGATC ATATGGTTCCCGTTTGACCGGAGCTGGCTGGGGTGGTTGTACTGTTCACTTGGTTCCAGGGGGCCCAAATGGCAACATAGAAAAGGTAA AAGAAGCCCTTGCCAATGAGTTCTACAAGGTCAAGTACCCTAAGATCACTGATGCTGAGCTAGAAAATGCTATCATCGTCTCTAAACCA GCATTGGGCAGCTGTCTATATGAATTAGTCAAGTATACTTCTTTTTTTTACTTTGTTCAGAACAACTTCTCATTTTTTTCTACTCATAA CTTTAGCATCACAAAATACGCAATAATAACGAGTAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGA TAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTT GGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAG...TTGCGAA GTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAA TGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGA TACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGT TCTTGGCAAGTTGCCAACTGACGAGATGCAGTTTCCTACGCATAATAAGAATAGGAGGGAATATCAAGCCAGACAATCTATCATTACAT TTAAGCGGCTCTTCAAAAAGATTGAACTCTCGCCAACTTATGGAATCTTCCAATGAGACCTTTGCGCCAAATAATGTGGATTTGGAAAA AGAGTATAAGTCATCTCAGAGTAATATAACTACCGAAGTTTATGAGGCATCGAGCTTTGAAGAAAAAGTAAGCTCAGAAAAACCTCAAT ACAGCTCATTCTGGAAGAAAATCTATTATGAATATGTGGTCGTTGACAAATCAATCTTGGGTGTTTCTATTCTGGATTCATTTATGTAC AACCAGGACTTGAAGCCCGTCGAAAAAGAAAGGCGGGTTTGGTCCTGGTACAATTATTGTTACTTCTGGCTTGCTGAATGTTTCAATAT CAACACTTGGCAAATTGCAGCTACAGGTCTACAACTGGGTCTAAATTGGTGGCAGTGTTGGATAACAATTTGGATTGGGTACGGTTTCG TTGGTGCTTTTGTTGTTTTGGCCTCTAGAGTTGGATCTGCTTATCATTTGTCATTCCCTATATCATCTAGAGCATCATTCGGTATTTTC TTCTCTTTATGGCCCGTTATTAACAGAGTCGTCATGGCCATCGTTTGGTATAGTGTCCAAGCTTATATTGCGGCAACTCCCGTATCATT AATGCTGAAATCTATCTTTGGAAAAGATTTACAATGATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGT TCTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATG TTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATA CCTATTCTTGACATGATATGACTACCATTTTGTTATTGTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATG TTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTA AGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGA TTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATA GTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATG CTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACT TAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGAT TGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATTT Promoter motifs 3’ UTR motifsExons Introns

http://cs273a.stanford.edu [Bejerano Fall11/12] 21 Genomes, Genes & Proteins The most visible instructions in our genome are Genes. Genes explain exactly HOW to synthesize any protein. Proteins are the work horses of every living cell....ACGTACGACTGACTAGCATCGACTACGACTAGCAC... gene Genome: cell protein

http://cs273a.stanford.edu [Bejerano Fall11/12] 22 Portals to the Human Genome GGTGCCAGGGAAAGGGCAGGAGGTGAGTGCTGGGAGGCAGCTGAGGTCAACTTCTTTTGAACTTCCACGTGGTATTTACTCAGAGCAATTGGTGCCAGAG GCTCAGGGCCCTGGAGTATAAAGCAGAATGTCTGCTCTCTGTGCCCAGACGTGAGCAGGTGAGCAGCTGGGGCGAAAGACCTGTTGGAGGCTATGAATGC AATCAAGGTGACAGACAACTGGTGCAATGATGGTAGTGGAAATGGAGGAGAGGGGATTGATTCAAGATGCATTTAGGACCAAGAATCGGGAGCTTGTGAA CGTGTGTATGAGTACTGTAGACGGAGTGGGTGTGTCATCAGAGAAGATCTGAGCATTTGGGCTTGCTCTCCTCAGAGGCCCTGCGAGTGGAGTTCAGCTT TTCCTCATGGGGCAAATCTCACTTTCGCTCCAGTTCCTGGGGCTCAGAGTCCCTGGCCCAGATGCCTCTTGCCATCTCATCTTCACCCTGCCTGGCTTCC CTTGCTTGTTCCAGGATTGTTTCATAAAGAGGGATGTGGTTGGTCTTTAACCCTATGAATGCTGGCTGAGGATGCCTGCGGAACCTGTAGTGAAGCTTTC AGGGGCTGCTCGGGTTCTGGCTGGTAGGTGAACACTGTCCATCTTGCCGGCTGGGACACAGTGACTCTGGGTAGTTGTGTAAGAGAGGGGCCCTTGGCAG ACAAACAGGTTCTTCTCTGTTGGTGGGCCAGCCAGCAGGTCAGTGGGAAGGTTAAAGGTCATGGGGTTTGGGAGAACTGGGTGAGGAGTTCAGCCCCATC CCCCGTAAAGCTCCTGGGAAGCACTTCTCTACTGGGGCAGCCCCTGATACCAGGGCACTCATTAACCCTCTGGGTGCCAGGGAAAGGGCAGGAGGTGAGT GCTGGGAGGCAGCTGAGGTCAACTTCTTTTGAACTTCCACGTGGTATTTACTCAGAGCAATTGGTGCCAGAGGCTCAGGGCCCTGGAGTATAAAGCAGAA TGTCTGCTCTCTGTGCCCAGACGTGAGCAGGTGAGCAGCTGGGGCTGTCTGCTCTCTGTGCCCAGACGTGAGCAGGTGAGCAGCTGGGGCTGTCTGCTCT CTGTGCCCAGACGTGAGCAGGTGAGCAGCTGGGGCTGTCTGCTCTCTGTGCCCAGACGTGAGCAGGTGAGCAGCTGGGGCTGTCTGCTCTCTGTGCCCAG GAAAGACCTGTTGGAGGCTATGAATGCAATCAAGGTGACAGACAACTGGTGCAATGATGGTAGTGGAAATGGAGGAGAGGGGATTGATTCAAGATGCATT TAGGACCAAGAATCGGGAGCTTGTGAACGTGTGTATGAGTACTGTAGACGGAGTGGGTGTGTCATCAGAGAAGATCTGAGCATTTGGGCTTGCTCTCCTC AGAGGCCCTGCGAGTGGAGTTCAGCTTTTCCTCATGGGGCAAATCTCACTTTCGCTCCAGTTCCTGGGGCTCAGAGTCCCTGGCCCAGATGCCTCTTGCC ATCTCATCTTCACCCTGCCTGGCTTCCCTTGCTTGTTCCAGGATTGTTTCATAAAGAGGGATGTGGTTGGTCTTTAACCCTATGAATGCTGGCTGAGGAT GCCTGCGGAACCTGTAGTGAAGCTTTCAGGGGCTGCTCGGGTTCTGGCTGGTAGGTGAACACTGTCCATCTTGCCGGCTGGGACACAGTGACTCTGGGTA GTTGTGTAAGAGAGGGGCCCTTGGCAGACAAACAGGTTCTTCTCTGTTGGTGGGCCAGCCAGCAGGTCAGTGGGAAGGTTAAAGGTCATGGGGTTTGGGA GAAACTGGGTGAGGAGTTCAGCCCCATCCCCCGTAAAGCTCCTGGGAAGCACTTCTCTACTGGGGCAGCCCCTGATACCAGGGCACTCATTAACCCTCTG GGTGCCAGGGAAAGGGCAGGAGGTGAGTGCTGGGAGGCAGCTGAGGTCAACTTCTTTTGAACTTCCACGTGGTATTTACTCAGAGCAATTGGTGCCAGAG GCTCAGGGCCCTGGAGTATAAAGCAGAATGTCTGCTCTCTGTGCCCAGACGTGAGCAGGTGAGCAGCTGGGGCGAAAGACCTGTTGGAGGCTATGAATGC AATCAAGGTGACAGACAACTGGTGCAATGATGGTAGTGGAAATGGAGGAGAGGGGATTGATTCAAGATGCATTTAGGACCAAGAATCGGGAGCTTGTGAA CGTGTGTATGAGTACTGTAGACGGAGTGGGTGTGTCATCAGAGAAGATCTGAGCATTTGGGCTTGCTCTCCTCAGAGGCCCTGCGAGTGGAGTTCAGCTT TTCCTCATGGGGCAAATCTCACTTTCGCTCCAGTTCCTGGGGCTCAGAGTCCCTGGCCCAGATGCCTCTTGCCATCTCATCTTCACCCTGCCTGGCTTCC CTTGCTTGTTCCAGGATTGTTTCATAAAGAGGGATGTGGTTGGTCTTTAACCCTATGAATGCTGGCTGAGGATGCCTGCGGAACCTGTAGTGAAGCTTTC AGGGGCTGCTCGGGTTCTGGCTGGTAGGTGAACACTGTCCATCTTGCCGGCTGGGACACAGTGACTCTGGGTAGTTGTGTAAGAGAGGGGCCCTTGGCAG ACAAACAGGTTCTTCTCTGTTGGTGGGCCAGCCAGCAGGTCAGTGGGAAGGTTAAAGGTCATGGGGTTTGGGAGAACTGGGTGAGGAGTTCAGCCCCATC CCCCGTAAAGCTCCTGGGAAGCACTTCTCTACTGGGGCAGCCCCTGATACCAGGGCACTCATTAACCCTCTGGGTGCCAGGGAAAGGGCAGGAGGTGAGT GCTGGGAGGCAGCTGAGGTCAACTTCTTTTGAACTTCCACGTGGTATTTACTCAGAGCAATTGGTGCCAGAGGCTCAGGGCCCTGGAGTATAAAGCAGAA TGTCTGCTCTCTGTGCCCAGACGTGAGCAGGTGAGCAGCTGGGGCTGTCTGCTCTCTGTGCCCAGACGTGAGCAGGTGAGCAGCTGGGGCTGTCTGCTCT CTGTGCCCAGACGTGAGCAGGTGAGCAGCTGGGGCTGTCTGCTCTCTGTGCCCAGACGTGAGCAGGTGAGCAGCTGGGGCTGTCTGCTCTCTGTGCCCAG Human Genome = three billion (3*10 9 ) basepairs:

http://cs273a.stanford.edu [Bejerano Fall11/12] 23 Genome Browser Database Primary table: positions, names, etc. Underlying Database (MySQL) Auxiliary table: related data visualize search & download

http://cs273a.stanford.edu [Bejerano Fall11/12] 24 The Human Gene Set [HGC, 2001]

http://cs273a.stanford.edu [Bejerano Fall11/12] 25 Gene Finding – The Practice Challenge: “The genes, the whole genes, and nothing but the genes”

http://cs273a.stanford.edu [Bejerano Fall11/12] 26 Meet Your Genome [Human Molecular Genetics, 3rd Edition]

http://cs273a.stanford.edu [Bejerano Fall11/12] 27 Repeats / obile Elements ("selfish DNA") Human Genome: 3*10 9 letters 1.5% known function >50% junk

http://cs273a.stanford.edu [Bejerano Fall11/12] 28

http://cs273a.stanford.edu [Bejerano Fall11/12] 29 Structural Non-Coding RNAs

http://cs273a.stanford.edu [Bejerano Fall11/12] 30 Transcripts, transcripts everywhere Human Genome Transcribed (Tx) Tx from both strands Leaky tx? Functional?

http://cs273a.stanford.edu [Bejerano Fall11/12] 31 Human Gene Regulation All these cells have the same Genome. Gene 20,000 Genes encode how to make proteins. 1,000,000 Genomic “switches” determine which and how much proteins to make. 10 13 different cells in an adult human. Hundreds of different cell types.

http://cs273a.stanford.edu [Bejerano Fall11/12] 32 Combinatorial Regulatory Code Gene 2,000 different proteins can bind specific DNA sequences. A regulatory region encodes 3-10 such protein binding sites. When all are bound by proteins the regulatory region turns “on”, and the nearby gene is activated to produce protein. Proteins DNA Protein binding site

http://cs273a.stanford.edu [Bejerano Fall11/12] 33 Cis-reg & Ultra elements from obile Elements [Yass is a small town in New South Wales, Australia.] Co-option event, probably due to favorable genomic context All other copies are destined to decay over time at a neutral rate [Bejerano et al., Nature 2006]

http://cs273a.stanford.edu [Bejerano Fall11/12] 34 unicellular multicellular Unicellular vs. Multicellular

http://cs273a.stanford.edu [Bejerano Fall11/12] 35 Signal Transduction

http://cs273a.stanford.edu [Bejerano Fall11/12] 36 Histone Code

http://cs273a.stanford.edu [Bejerano Fall11/12] 37 Every Genome is Different DNA Replication is imperfect – between individuals of the same species, even between the cells of an individual....ACGTACGACTGACTAGCATCGACTACGA... chicken egg...ACGTACGACTGACTAGCATCGACTACGA... functional junk TT CAT “anything goes” many changes are not tolerated chicken This has bad implications – disease, and good implications – evolution.

http://cs273a.stanford.edu [Bejerano Fall11/12] 39 Single Base Changes

http://cs273a.stanford.edu [Bejerano Fall11/12] 40 Larger Size Mutations [de Kok et al, 1996]

http://cs273a.stanford.edu [Bejerano Fall11/12] 41 Genome Wide Association Studies

http://cs273a.stanford.edu [Bejerano Fall11/12] 43 human mouse rat chimp chicken fugu zfish dog tetra Intelligent Designer human mouse rat chimp chicken fugu zfish dog tetra opossum cow macaque platypus opossum cow macaque platypus Comparative Genomics “Nothing in Biology Makes Sense Except in the Light of Evolution” Theodosius Dobzhansky t [Adam Siepel, Cornell]

http://cs273a.stanford.edu [Bejerano Fall11/12] 44 Fixation, Positive & Negative Selection Neutral Drift Positive Selection Negative Selection How can we detect negative selection? How can we detect positive selection?

http://cs273a.stanford.edu [Bejerano Fall11/12] 45 PhenotypeGenotype

http://cs273a.stanford.edu [Bejerano Fall11/12] 46 To Be Continued…

[Bejerano Fall11/12] 1 MW 11:00-12:15 in Beckman B302 Profs: Serafim Batzoglou, Gill Bejerano TAs: Aaron Wenger & Jim Notwell.

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[Bejerano Fall11/12] 1 MW 11:00-12:15 in Beckman B302 Profs: Serafim Batzoglou, Gill Bejerano TAs: Aaron Wenger & Jim Notwell.

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Presentation on theme: "[Bejerano Fall11/12] 1 MW 11:00-12:15 in Beckman B302 Profs: Serafim Batzoglou, Gill Bejerano TAs: Aaron Wenger & Jim Notwell."— Presentation transcript:

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