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Spaghetti Code, Soupy Logic Quic kTime™ and a TIFF (Unc ompres sed) dec ompres sor are needed to see this pic ture. Steaming fresh modules in sourceforge.net QuickTime™ and a TIFF (Uncompressed) decompressor are needed to see this picture. Combinatorical assembly of transcription factors in cell. Jim Kent - University of California Santa Cruz 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? Top 5 Reasons Biologists Go Into Bioinformatics 5 - Microscopes and biochemistry are so 20th century. 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. 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 as a postdoc. 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 as a postdoc. 2 - Like to swear, @ttracted to $_ Perl #!! 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 as a postdoc. 2 - Like to swear, @ttracted to $_ Perl #!! 1 - Getting carpel tunnel from pipetting Top 5 Reasons Computer People go into Bioinformatics 5 - Bio courses actually have some females. Top 5 Reasons Computer People go into Bioinformatics 5 - Bio courses actually have some females. 4 - Human genome more stable than Windows XP Top 5 Reasons Computer People go into Bioinformatics 5 - Bio courses actually have some females. 4 - Human genome more stable than Windows XP 3 - Having mastered binary trees, quad trees, and parse trees ready for phylogenic trees. Top 5 Reasons Computer People go into Bioinformatics 5 - Bio courses actually have some females. 4 - Human genome more stable 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. Top 5 Reasons Computer People go into Bioinformatics 5 - Bio courses actually have some females. 4 - Human genome more stable 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. 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? GTTTGCCATCTTTTG CTGCTCTAGGGAATC CAGCAGCTGTCACCA TGTAAACAAGCCCAG GCTAGACCAGTTACC CTCATCATCTTAGCT GATAGCCAGCCAGCC ACCACAGGCATGAGT QuickTime™ and a TIFF (Uncompressed) decompressor are needed to see this picture. The Analogy of the Code of Life • DNA is popularly considered the code of life. • 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? BUT…. • 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. Bioperl CORBA module sub new { my ( $class, @args) = @_; my $self = $class->SUPER::new(@args); my ( $idl, $ior, $orbname ) = $self->_rearrange( [ qw(IDL IOR ORB @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; } 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+ ,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));}} Reverse Engineering Microsoft mouse blue screen of death keyboard network elaborate proprietary process Looks like ‘code’ not enough, must study actual cells & DNA QuickTime™ and a TIFF (Uncompressed) decompressor are needed to see this picture. How DNA is Used by the Cell Promoter Tells Where to Begin Different promoters activate different genes in different parts of the body. 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. 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) 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. Nucleus Used to Appear Simple QuickTime™ and a TIFF (Uncompressed) decompressor are needed to see this picture. • Cheek cells stained with basic dyes. Nuclei are readily visible. Mammalian Nuclei Stained in Various Ways QuickTime™ and a TIFF (Uncompressed) decompressor are needed to see this picture. Image from Tom Misteli lab Artist’s rendition of nucleus QuickTime™ and a TIFF (Uncompressed) decompressor are needed to see this picture. Image from nuclear protein database Chromatin QuickTime™ and a TIFF (Uncompressed) decompressor are needed to see this picture. QuickTime™ and a TIFF (Uncompressed) decompressor are needed to see this picture. 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). 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. Drosophila Genetics QuickTime™ and a TIFF (Uncompressed) decompressor are needed to see this picture. normal antennapedia mutant QuickTime™ and a TIFF (Uncompressed) decompressor are needed to see this picture. Reporter Gene Constructs promoter to study easily seen gene Qui ckTime™ and a TIFF (U ncompr essed) decompressor are needed to see thi s pi cture. Drosophila embryo transfected with ftz promoter hooked up to lacz reporter gene, creating stripes where ftz promoter is active. Biochemical Footprinting Assays Gel showing selective QuickTime™ and a protection of DNA fromTIFF (Uncompressed) decompressor are needed to see this picture. nuclease digestion where transcription factor is bound. Txn factor footprint Comparative Genomics Webb Miller Comparative Genomics at BMP10 Conservation of Gene Features 100% 95% 90% 85% 80% 75% 70% 65% 60% 55% 50% aligning identity Conservation pattern across 3165 mappings of human RefSeq mRNAs to the genome. A program sampled 200 evenly spaced bases across 500 bases upstream of transcription, the 5’ UTR, the first coding exon, introns, middle coding exons, introns, the 3’ UTR and 500 bases after polyadenylatoin. There are peaks of conservation at the transition from one region to another. Detail Near Translation Start 100% 95% 90% 85% 80% 75% 70% 65% 60% -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Note the relatively conserved base 3 before translation Start (constrained to be a G or an A by the Kozak Consensus sequence, and the first three translated bases (ATG). Normalized eScores Conservation Levels of Regulatory Regions in Human/Mouse Alignments Conservation in Multiple Alignments • As you add more species the phylogenic footprint gets sharper. • Currently genome.ucsc.edu shows multiple alignments between 8 species using Webb Miller’s multiz program on chained pairwise alignments. • The phylogenic tree has to be considered when calculating conservation levels. Simple human/rodent tree human mouse rodent rat • Mutations that occur in rodent ancestor must be counted only once • Ideally should take into consideration varying mutation rates across species. • Conservation track at genome.ucsc.edu is based on Adam Siepel’s PhyloHMM PhyloHMM on Drosophila QuickTime™ and a TIFF (Uncompressed) decompressor are needed to see this picture. • Drosophila proteasome alpha 7-1. In many genes like this one phylogenic footprint suggests promoter actually is downstream of transcription start site. Genome Evolution • Duplication, deletion, and rearrangement is as important to genome evolution as base-level mutations. • Much of this is driven by transposons – Transposon relics are ~50% of genome – Reverse transcriptase activity from transposons encourages processed pseudogene formation as well. – Transposons seed out of place recombination leading to tandem and segmental duplications, non-processed pseudogenes. • Only ~5% of human genome seems functional. • This messiness provides opportunities for the development of new genes, but makes understanding the genome a challenge. Pseudogene Data from Robert Baertsch, UCSC Grad Student QuickTime™ and a TIFF (LZW) decompressor are needed to see this picture. Mouse/Human Rearrangement Statistics Number of rearrangements of given type per megabase excluding known transposons. Chaining Alignments • Chaining bridges the gulf between syntenic blocks and base-by-base alignments. • Local alignments tend to break at transposon insertions, inversions, duplications, etc. • Global alignments tend to force non-homologous bases to align. • Chaining is a rigorous way of joining together local alignments into larger structures. Chains join together related local alignments Protease Regulatory Subunit 3 Affine penalties are too harsh for long gaps Log count of gaps vs. size of gaps in mouse/human alignment correlated with sizes of transposon relics. Affine gap scores model red/blue plots as straight lines. Before and After Chaining Chaining Algorithm • Input - blocks of gapless alignments from blastz • Dynamic program based on the recurrence relationship: score(Bi) = max(score(Bj) + match(Bi) - gap(Bi, Bj)) j<i • Uses Miller’s KD-tree algorithm to minimize which parts of dynamic programming graph to traverse. Timing is O(N logN), where N is number of blocks (which is in hundreds of thousands) Netting Alignments • Commonly multiple mouse alignments can be found for a particular human region, particularly for coding regions. • Net finds best match mouse match for each human region. • Highest scoring chains are used first. • Lower scoring chains fill in gaps within chains inducing a natural hierarchy. Net Focuses on Ortholog Net highlights rearrangements A large gap in the top level of the net is filled by an inversion containing two genes. Numerous smaller gaps are filled in by local duplications and processed pseudo-genes. Useful in finding pseudogenes Ensembl and Fgenesh++ automatic gene predictions confounded by numerous processed pseudogenes. Domain structure of resulting predicted protein must be interesting! Other tools to cybernetically enhance your mind at genome.ucsc.edu Quick Time™a nd a TIFF ( Uncomp res sed) deco mpre ssor are n eede d to s ee this picture . QuickTi me™ and a T IFF (Uncom pressed) decom pressor are needed to see t his pict ure. UCSC Gene Sorter Quick Time™a nd a TIFF ( Uncomp res sed) deco mpre ssor are n eede d to s ee this picture . • Swiss army knife for dealing with gene sets. • Presents functional data on genes including microarray expression information. • Hilights relationships and connections between genes. • Powerful data mining tool. UCSC Gene Sorter QuickTime™ and a TIFF (LZW) decompressor are needed to see this picture. Expression and other information on genes in a big sorted, linked table A Big Bioinformatics Web Site • genome.ucsc.edu gets > 100,000 hits by > 5000 scientists each day. • Involves 570,000 lines of C code, bits of awk, perl, bash, tcsh, java, r and tcl. • 1200 CPUs and 12 Terabytes of disk • 12 full time staff, 18 part time, grad student and post-doc. Site Architecture • 8 web servers running Apache and MySQL • CGI’s written in C access genome data and user interface settings in MySQL. • Genome database is bottleneck, and is replicated on each server. • Cluster of 1000 CPUs, and smaller clusters of faster CPUs create annotation files which are loaded into database. Site Sociology • 1/3 of group telecommutes. • Thursdays are devoted to reading and testing each other’s code and if necessary a one or two hour meeting. • We develop very incrementally, and do a new release once a week. • 1/4 of group is dedicated to quality assurance, I’m wanting to increase this to 1/3. • User support is shared by everyone. 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. Conclusions • Spaghetti code is not so helpful in understanding the genome. • Human genome suggests that trial and error development is likely to yield a robust version of windows within 3 billion years. • Understanding the flow of control in the genome is a problem that fascinates biologists and computer scientists alike. Further Acknowledgements Individuals Chuck Sugnet, Angie Hinrichs, Fan Hsu, Terry Furey, Heather Trumbower, Kate Rosenbloom, Hiram Clawson, Brian Raney, Rachel Harte, Bob Kuhn, Mathieu Blanchette, Donna Karolchik, David Haussler John Sulston, Richard Gibbs, Eric Lander, Francis Collins, Roderic Guigo, Michael Brent, Olivier Jaillon, David Kulp, Victor Solovyev, Ewan Birney, Greg Schuler, Deanna Church, Scott Schwartz, Ross Hardison, and everyone else! Institutions NHGRI, The Wellcome Trust, HHMI, NCI, Taxpayers in the US and worldwide. Baylor, Sanger, Wash U, Whitehead, Stanford, JGI/ DOE, Vancouver GSC, UW and the international sequencing centers. UCSC, NCBI, EBI, Ensembl, Genoscope, MGC, Intel, TIGR, Jackson Labs, Affymetrix, SwissProt. THE END