Transcript Assembling and Annotating the Draft Human Genome
The Genes, the Whole Genes, and Nothing But the Genes
Jim Kent University of California Santa Cruz
Ben Franklin - Childhood Hero
Hi Voltage Experiments
A Man of High Values
Early to bed Early to rise
Rock Collection
Shell Collection
Bottlecap Collection
Bug Collection
Jim Kent Genome Scientist not to be confused with Richard Stallman
Modern Bug Collection
if (a = b) if (string == “something”) for (x=0; x • Is an ant an individual? • Do dolphins talk with each other? • How come newts and worms can regenerate so much better than we can? • How does a plant grow from a seed, an animal from an egg? • Among vertebrates only amphibians can regenerate limbs. • The process involves dedifferentiation, repatterning, and growth. • Not likely we’ll be able to engineer this soon. • Simpler regenerations though may be tractable and medically quite important. 9 • A single cell, the fertilized egg, eventually differentiates into the ~300 different types of cells that make up an adult body. • With a few exceptions all of these cells contain the full human genome, but express only a subset of the genes. • Gene expression patterns are determined largely by the cell type, and vice versa. • Human cells become more and more specialized during development • An egg can become anything. (Initially most of it will become placenta). • Liver cells only become liver cells. • A neuron can’t even reproduce. Primary Flows of Information and Substance in Cell DNA creation regulation mRNA transcription factors splicing factors Receptors Enzymes structural proteins signaling molecules structural sugars structural lipids Environment & other cells • The cloning of Dolly the sheep showed that a differentiated genome could be reset. • An egg is huge compared to a normal cell. Putting a normal cell into an egg as Wilmut et al did, swamps out the normal cell transcription factor and receptors with egg transcription factors and receptors. • Cloning success rate sometimes improved by passing a nucleus through multiple eggs. • Parkinson’s - from the death of dopamine producing neurons in the substantia negra. • Macular degeneration - a leading cause of blindness in the elderly. • Type I Diabetes - from the death of insulin producing cells in the pancreas. From Huang Tsai, J Biomed Sci 2000:7:27-34 and Jensen et al, Diabetes 2000:49 163-176 • Cell type of parent cell. • Interactions with other cells. • Interactions with the extracellular environment. • In many cases stem cells are flexible enough that putting them into a particular tissue will cause them to differentiate into the type of cells that make up that tissue. • At low levels bone transplanted bone marrow (blood stem cells) develops into neuron in stroke victims! • Making this happen at high enough levels to be useful will likely require some engineering. Primary Flows of Information and Substance in Cell DNA creation regulation mRNA transcription factors splicing factors Receptors Enzymes structural proteins signaling molecules structural sugars structural lipids environment other cells • • • • • • • The genome A comprehensive list of genes Gene expression data Protein localization in cell Protein/protein and protein/DNA interaction information. Ways to store, display and query masses of data so human investigators can focus on relevant bits. Many talented and hardworking human investigators. • • • • • The genome >95% complete. 98% complete in April. A comprehensive list of genes - ~75% of coding regions. <50% of transcription start sites. Gene expression data - publically available on ~1/3 of genes. Protein localization in cell - very spotty. Computer predictions are about 75% accurate. Protein/protein and protein/DNA interaction information - just getting started. • Identifying genes is a prerequisite for a great deal of other research. – Expression microarrays – In situ mRNA hybridization – Producing proteins for cellular localization experiments – Etc. • The full gene including the 5’ and 3’ UTRs are critical for – Avoiding misleading fragmentation/fusion artifacts. – Understanding mRNA targeting and stability – Finding transcription factor binding sites – Understanding the regulatory networks that drive and maintain cell differentiation. • Experimental analysis is expensive. • Unreal genes can mislead: – Analysis of multiple alignments to look for active sites etc. – Protein classification systems and phylogenies • One bogus gene can lead to another as much annotation is done via homology. • mRNA/cDNA sequencing • Microarrays covering entire genome • Genetics in model organisms • Cross species protein homology • Cross species genomic homology • HMM and other purely computational genefinding. • Extract RNA from cells. • Use reverse transcriptase and a poly-U primer to convert to RNA starting at poly-A tail. • Insert cDNA into vectors that grow in E. coli • Sequence a read from one or both sides of insert using primers on vector • If EST looks to be new sequence full cDNA. • Artifacts and limitations are possible at each stage! Common cDNA Problems & Solutions • For rarely expressed genes little RNA is available. – Normalize libraries. Use embryonic and exotic tissues as mRNA source. • Splicing is not instantanious, can get retained introns. – Spin out nuclei and just use cytoplasmic mRNA – Align to genome and look for splicing • Reverse transcriptase falls off before it’s finished – Preferentially taking larger cDNAs. – G-cap selected libraries (Sugano) – Normalizing only on 5’ ends (Soares) More cDNA Problems & Solutions • Reverse transcriptase has a high error rate and is prone to small deletions. – Compare cDNA to genomic DNA – Sequence multiple cDNA clones • At a low level cell seems to tolerate a certain degree of nonsense transcription and splicing. Normalizing increases concentration of these as well as of rare genes. – Ignore everything that’s not coding (ouch) – ??? • ~10,000 cDNA sequence have been accumulated over years by various labs working on gene families and pathways. • Riken project has ~33,000 unique cDNAs in mouse. ~11,000 of these seem to have retained introns. ~3,000 are noncoding antisense. ~70% include initial ATG • Mammalian Gene Collection (MGC) has ~15,000 human cDNAs with initial ATGs. Having to resort to exotic libraries and RT-PCR to get more. • Human refSeq has ~18,000 human cDNAs. • Perlegen and Affymetrix are making microarrays that cover entire non-RepeatMasker masked genome. Results on chromosome 21 and 22 published. • Based on 25-mers. • Rarely expressed genes may not stand out above background. • Have to cope with cross-hybridization issues, GC content, etc. • Advantages - no homology required, can sense lower concentrations of mRNA than random EST sequencing. Zoomed in on right side: • Zap hapless yeast, worms, flies, and mice • Inbreed offspring and look for twisted ones. • Advantages: – Works at DNA level, so expression level doesn’t matter – You get hints of function right away. – Can look for gene interactions simply by breeding mutants. • Disadvantages: – Finding which DNA is mutated can take a long time. – Essential genes can be hard to find - all you see is reduced fertility in the inbreeding stage. – Genes only needed in certain environments and duplicated genes may be missed in screens. • Mutations occur more or less randomly across genome but • Mutations in functional areas tend to be weeded out by selection • In comparing DNA across species, the functional areas are more conserved than the nonfunctional areas in general Of Fish and Mice and Men 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. 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). • Bacteria - look for open reading frames - long stretches between start and stop codons. • Eukaryotes - introns are challenging – Look for coding exons (bounded by AG / GT) – HMMs can model coding regions and splice sites simultaniously – Generalized HMMs (genscan) can string together probable exons – Homology based ones (GeneWise) can map proteins to genome allowing for considerable evolutionary divergence. • Introns are vast, GT/AG splice signals are small. • Coding signal is stronger than start/stop signal. As a result gene fragmentation and fusion is a big problem. • Pseudo genes, processed and otherwise, mimic coding regions. • Pure HMM approaches tend to overpredict • Pure homology approaches only can tell us about what we already know. • Use EST info to constrain HMMs (Genie) • Use protein homology info on top of HMMs (fgenesh++, GenomeScan) • Use cross species genomic alignments on top of HMMs (twinscan, fgenesh2, SLAM, SGP) Individuals Institutions David Haussler, Angie Hinrichs, Chuck Sugnet, Matt Schwartz, Robert Baertsch Donna Karolchik, NHGRI, The Wellcome Trust, HHMI, NCI, Taxpayers in the US and worldwide. 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, the Gene Cats. Everyone else! Whitehead, Sanger, Wash U, Baylor, Stanford, DOE, and the international sequencing centers. NCBI, Ensembl, Genoscope, The SNP Consortium, UCSC, MGC, Softberry, Affymetrix. • 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.Naive Biological Questions
Regeneration by Nature
From Egg to Adult in 3x10
Bases
From Totempotency to Senility
An Extreme Case of Dedifferentiation
Human Diseases Involving a Small Population of Cells
Pancreas Differentiation Pathway
Cell Type Determinants
Flexibility of Stem Cells
To Understand the Body Need
Where are we now?
The Genes
The Whole Genes
Nothing But the Genes
Methods of Identifying Genes
cDNA Sequencing
cDNA Status & Summary
Whole Genome Microarrays
Cross-hybridization at Work
Genetics in Model Organisms
Cross Species Genome Comparisons
Comparative Genomics at BMP10
Conservation of Gene Features
Detail Near Translation Start
Normalized eScores
Computational Gene Finding
Basic Techniques
Limitations of Basic Approach
Composite Approaches
Computational Gene Finding
Acknowledgements
THE END
Parasol and Kilo Cluster