Spaghetti Code, Soupy Logic adventures in gene expression & genome annotation

1. Spaghetti Code, Soupy Logicadventures in gene expression & genome annotation Jim Kent University of California Santa Cruz

2. A Challenge Every Speaker Faces: • Who is the audience? • Bioinformaticians: • Biologists with bigger, better databases? • Geeks trading bits for bases? • Leading edge interdisciplinary super scientists?

3. Top 5 Reasons Biologists Go Into Bioinformatics • 5 - Microscopes and biochemistry are so 20th century. • 4 - Got started purifying proteins, but it turns out the cold room is really COLD. • 3 - After 23 years of school wanted to make MORE than 23,000/year in a postdoc. • 2 - Like to swear, @ttracted to $_ Perl #!! • 1 - Getting carpel tunnel from pipetting

4. Top 5 Reasons Computer People go into Bioinformatics • 5 - Bio courses have some females. • 4 - Human genome stabler than Windows XP • 3 - Having mastered binary trees, quad trees, and parse trees ready for phylogenic trees. • 2 - Missing heady froth of the internet bubble. • 1 - Must augment humanity to defeat evil artificial intelligent robots.

5. The Paradox of Genomics How does a long, static, one dimensional string of DNA turn into the remarkably complex, dynamic, and three dimensional human body? GTTTGCCATCTTTTGCTGCTCTAGGGAATCCAGCAGCTGTCACCATGTAAACAAGCCCAGGCTAGACCAGTTACCCTCATCATCTTAGCTGATAGCCAGCCAGCCACCACAGGCATGAGT

6. Models and Metaphors • When trying to understand something we like to build up metaphors and models. • Computer programs are complex systems that ultimately are built up of 0’s and 1’s, perhaps they are a model for a genome built of A,C,G and T? • Human genome lacks documentation, has accumulated 3 billion years of cruft, and does not believe in local variables. • Therefore we must look to less than straightforward software programs as guides.

7. Bioperl CORBA module sub new { my ( $class, @args) = @_; my $self = $class->SUPER::new(@args); my ( $idl, $ior, $orbname ) = $self->_rearrange( [ qw(IDL IOR ORBNAME)], @args); $self->{'_ior'} = $ior || 'biocorba.ior'; $self->{'_idl'} = $idl || $ENV{BIOCORBAIDL} || 'biocorba.idl'; $self->{'_orbname'} = $orbname || 'orbit-local-orb'; $CORBA::ORBit::IDL_PATH = $self->{'_idl'}; my $orb = CORBA::ORB_init($orbname); my $root_poa = $orb->resolve_initial_references("RootPOA"); $self->{'_orb'} = $orb; $self->{'_rootpoa'} = $root_poa; return $self; }

8. Obfuscated C #define c(n,s)case n:s;continue char x[]="((((((((((((((((((((((",w[]= "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b";char r[]={92,124,47},l[]={2,3,1 ,0};char*T[]={" |"," |","%\\|/%"," %%%",""};char d=1,p=40,o=40,k=0,*a,y,z,g= -1,G,X,**P=&T[4],f=0;unsigned int s=0;void u(int i){int n;printf( "\233;%uH\233L%c\233;%uH%c\233;%uH%s\23322;%uH@\23323;%uH \n",*x-*w,r[d],*x+*w ,r[d],X,*P,p+=k,o);if(abs(p-x[21])>=w[21])exit(0);if(g!=G){struct itimerval t= {0,0,0,0};g+=((g<G)<<1)-1;t.it_interval.tv_usec=t.it_value.tv_usec=72000/((g>> 3)+1);setitimer(0,&t,0);f&&printf("\e[10;%u]",g+24);}f&&putchar(7);s+=(9-w[21] )*((g>>3)+1);o=p;m(x);m(w);(n=rand())&255||--*w||++*w;if(!(**P&&P++||n&7936)){ while(abs((X=rand()%76)-*x+2)-*w<6);++X;P=T;}(n=rand()&31)<3&&(d=n);!d&&--*x<= *w&&(++*x,++d)||d==2&&++*x+*w>79&&(--*x,--d);signal(i,u);}void e(){signal(14, SIG_IGN);printf("\e[0q\ecScore: %u\n",s);system("stty echo -cbreak");}int main (int C,char**V){atexit(e);(C<2||*V[1]!=113)&&(f=(C=*(int*)getenv("TERM"))==( int)0x756E696C||C==(int)0x6C696E75);srand(getpid());system("stty -echo cbreak" );h(0);u(14);for(;;)switch(getchar()){case 113:return 0;case 91:case 98:c(44,k =-1);case 32:case 110:c(46,k=0);case 93:case 109:c(47,k=1);c(49,h(0));c(50,h(1 ));c(51,h(2));c(52,h(3));}}

9. Microsoft Windows mouse blue screen of death Windows XP keyboard network elaborate proprietary process

10. Looks like metaphor not enough, must study actual cells & DNA

11. How DNA is Used by the Cell

12. Promoter Tells Where to Begin Different promoters activate different genes in different parts of the body.

13. A Computer in Soup Idealized promoter for a gene involved in making hair. Proteins that bind to specific DNA sequences in the promoter region together turn a gene on or off. These proteins are themselves regulated by their own promoters leading to a gene regulatory network with many of the same properties as a neural network.

14. Genes can be transcription factors that activate or repress other genes, leading to regulatory networks such as this one from the development of the central nervous system. (Image from D’Haeseleer Somogyi 1999)

15. The Decisions of a Cell • When to reproduce? • When to migrate and where? • What to differentiate into? • When to secrete something? • When to make an electrical signal? The more rapid decisions usually are via the cell membrane and 2nd messengers. The longer acting decisions are usually made in the nucleus.

16. Nucleus Used to Appear Simple • Cheek cells stained with basic dyes. Nuclei are readily visible.

17. Mammalian Nuclei Stained in Various Ways Image from Tom Misteli lab

18. Artist’s rendition of nucleus Image from nuclear protein database

19. Chromatin

20. Turning on a gene: • Getting DNA into the right compartment of the nucleus (may involve very diffuse signals in DNA over very long distances) • Loosening up chromatin structure (this involves activator and repressors which can act over relatively long distances) • Attracting RNA Polymerase II to the transcription start site (these involve relatively close factors both upstream and downstream of transcription start).

21. Methods for Studying Transcription • Genetics in model organisms • Promoters hooked to reporter genes • Gel shifts and DNAse footprinting. • Phylogenic footprinting • Motif searches in clusters of coregulated genes.

22. Drosophila Genetics antennapediamutant normal

23. Reporter Gene Constructs promoter to study easily seen gene Drosophila embryo transfected with ftz promoter hookedup to lacz reporter gene, creating stripes where ftz promoteris active.

24. Biochemical Footprinting Assays Gel showing selective protection of DNA from nuclease digestion where transcription factor is bound. Txn factorfootprint

25. Pseudogenes

26. Creative Chaos & Genome

27. Finding Transcription Start

28. Phylogenic Footprinting

29. Mouse Paints Some Promoters RefSeq Spliced EST Mouse Fish Repeat Crystallin - a gene expressed in the eye. Coding regions are very similar to crystallins in the liver, but the promoter is different.

30. Normalized eScores

31. Mouse/Human Chrom 7 Synteny

32. Motifs in Coregulated Genes

33. Conservation Levels of Regulatory Regions

34. Transition from Private Research Interests to Role in Genome Project

35. Assembly War Story

36. Building a Better Browser

37. Pretty Adventurous Programming

38. Genome BrowserBLATGene SorterTable BrowserService Organization

39. Parasol and Kilo Cluster • UCSC cluster has 1000 CPUs running Linux • 1,000,000 BLASTZ jobs in 25 hours for mouse/human alignment • We wrote Parasol job scheduler to keep up. • Very fast and free. • Jobs are organized into batches. • Error checking at job and at batch level.

40. Individuals Institutions Acknowledgements David Haussler, Chuck Sugnet Francis Collins, Bob Waterston, Eric Lander, John Sulston, Richard Gibbs Lincoln Stein, Sean Eddy, Olivier Jaillon, David Kulp, Victor Solovyev, Ewan Birney, Greg Schuler, Deanna Church, Asif Chinwalla, Kim Worley, the Gene Cats. Everyone else! NHGRI, The Wellcome Trust, HHMI, Taxpayers in the US and worldwide. Whitehead, Sanger, Wash U, Baylor, Stanford, DOE, and the international sequencing centers. NCBI, Ensembl, Genoscope, The SNP Consortium, UCSC, Softberry, Affymetrix.

41. THE END

42. Coloring CRYGD Start gctcgttcaggggtaaaggtgtattctagatCCACAACAAGCCCCGTGGTCTAGCACAGC AAAGAGAAAAAAAGAGAACACGAAAATGCCCTTGCTCCCCTCCGGGGGCCCCTTTTGTGC GGTTCTTGCCAACGCAGCAGCCCTCCTGCTATATAGCCCGCCGCGCCgCAGCCCCACCCG CTCAGCGCCGCCGCCCCACCAGCTCAGCACCGCCGTGCGCCCAGCCAGCCATGGGGAAGG TGAGCCCAGCCTGCGCCCCGGGACCCCGGAGCTTCCTCCATCGCGGGGGCCAGAGACTGG GGCAGGAGCAGGCCTGTGAGACCTCGCCTTGTCCCGCCTTGCCTTGCAGATCACCCTCTA CGAGGACCGGGGCTTCCAGGGCCGCCACTATGAATGCAGCAGCGACCACCCCAACCTGCA GCCCTACTTGAGCCGCTGCAACTCGGCGCGCGTGGACAGCGGCTGCTGGATGCTCTATGA GCAGCCCAACTACTCGGGCCTCCAGTACTTCCTGCGCCGCGGCGACTATGCCGACCACCA GCAGTGGATGGGCCTCAGCGACTCGGTCCGCTCCTGCCGCCTCATCCCCCACGTGAGTAC ATCCTCAAGTCAGGACCCAGGCCCTCAGGACACTCACTGGAtgGTTTCAAGCAAAAGTTA AACATTAGAAGTAGTGATCAGTcacaataaCTGAGAGTGGACAAAAGATGAACTATAGTG GATTAAGTCAATAGagttTGCTCCCCACATAAGCAAAGTATTACCCAGACAcCAGTTAAT caCAATTAATCCACAAATATGTATTGAGTAGGAATGTGTCTCCTGCCctAGGGGTTGTAT

43. Trends in Society & Biology

45. (The NEED for Bioinformatics) • ~200 million bases of DNA are sequenced every day. • Not much use without assembly. • Protein and non-sequence data also being generated at a prodigious rate. • How to store it and find the parts you want? • Making models that are simple enough to understand, but rich enough to reflect the biology.

46. (My Road to a Bio PhD) • Liked bio, but too many prerequisites! • Had fun doing graphics/animation programming in 80’s & early 90’s. • Bored of endlessly shifting Microsoft APIs • Community college, UC extension to get bio BA equivalent in 97 & 98. • UC Santa Cruz bio grad school 1999 • Interested in developmental biology and how a cell makes decisions.

47. Perhaps Must Study Actual Cells

48. Spaghetti Code or Soupy Logic Steaming fresh modules in sourceforge.net Combinatorical assembly of transcription factors in cell.