1 / 59

Tools for understanding the sequence, evolution, and function of the human genome.

Tools for understanding the sequence, evolution, and function of the human genome. Jim Kent and the Genome Bioinformatics Group University of California Santa Cruz. The Goal. Make the human genome understandable by humans. Step 1. Sequence the human genome.

len
Download Presentation

Tools for understanding the sequence, evolution, and function of the human genome.

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Tools for understanding the sequence, evolution, and function of the human genome. Jim Kent and the Genome Bioinformatics Group University of California Santa Cruz

  2. The Goal Make the human genome understandable by humans.

  3. Step 1 Sequence the human genome

  4. Idealized Hierarchical Shotgun Sequencing

  5. Mapping 300,000 BAC Clones Were Digested and Run on Agarose Gels Cari Soderlund’s FPC and Wash U Pathfinders Made Fingerprint Map Contigs Bob Waterston escaping management Genetic and radiation hybrid maps placed contigs on chromsomes

  6. Sequence and Assembly • BAC Clones shotgun sequenced at high throughput to 4x ‘draft’. • Assembled with Phil Green’s Phrap

  7. GigAssembler Jim Kent David Haussler (meanwhile Celera working on whole genome shotgun version)

  8. The Truth + - ? + + ? + - ? - - ? + - ? + light - darkness Keeping strands straight is the hard part

  9. “Finishing” Sequence • Using primers to end of contigs close gaps. • Checking automatic assembly especially near tandem repeats. • Checking in-silico restriction digest of BAC matches actual digest. • Time consuming - 1 year to ‘draft’ genome, 2 years to ‘finish’. • Human finished. Mouse will be finished (currently half finished). Other genomes may stay at draft stage, though draft stage can be very good these days.

  10. Now What? TGGCTTTTGAAGGGAGTTCTGTTTATATATACGTCAACATCCAGTTGGAGGTGAAAAGGTTAGCACTTGACCCAGGAAGTATCCATGTTTGTTTCAAAAATAAATCTGCTTCATAAATTTCTTCATCAGTCTTTTTTTCCATTATGAGCTTTGATTATAATAAAGGAGCTGTTATTAACTTTTATTCAAGAAAAGGCCCATCTCTTTGAAAATATTTACCACCCTTCTCCCTTTCCCCTCATGAAATGTGCCAACTTCATAGGAATTAACAAATTGTAGCCCAGCCAAATACACGGATGCTTAAGCATACCTGAAACTTGAGTATATTTATTTATTACAGACATCCTAAGACCCGTAAACTCTGCTCTGGATCATATCACTCCAGGATCTCAGAGCTGTTCATGATTGTACAGGAAATGGGGAATATCATAGGCTCACAAAGGATAACTGATAGAACTCAGTGTGGTACTTTGGGGACATCAAACATTGTGCGACATGCAAAAGACTATTCACGAATAACACAAAATATACATTCATTGTGCCATCCATCACATTAACAATTGAGCTGAAAATACATTATATCCAGCTAAGATAACTGTGGAAGGAAGAAATTGGTTTGAATAATACTTTTAGGTTCTGAATAACCCAGCACAAATTTTAAACAGAGGGTGGCCCGAGAAGAAAGGGGTAGAGATTGGGAAAGACTTAGCACAGGAAGCCGGGTTTCTGAAGTTTGTGCTCTGCAGGGCTTCTTAACTGTAAGAACAAATCAAGGCTACCCTCTGAGGCATCTGATTGGGTTTAAATGAGGGAATTTTTTCTTTCACCTATAAAATTGTACCAGTTTAGAGAGTTTGCCCACCCTGTTTTAGTAACCTAAACATTTCTAGAAAATCTGTATAAAGATAAATCTCTTAGGACAAAGTATTTACAACCAGCAAACTCACACACATGAAAATGACTTAAATTAAGGGATGAATTAATTGTGTAAACATATAGTGCATCTCTTCTTCCTGAGCTCCTGGACTCGCCTTTCGCTATATCCTACTTTCAAGGACAAGGGAGGGGAGAGCTGTACATATAGTTAGATAAAAGATGAGAAGATTCCTTCTGGCATGTTTCTGTTGGCAAAGGGAACTATTTTCCAAAAGGTCATCTGAAAGGAACAGTAGGTTCTGTGAATTCTCCTAAAAGCAGGAGGGATGTTAAGGCCCACCAGAAAATGTATGCTGGCACCCAATCTGGATGAAGGTGTTAACCCCGCACCAAGTCTCTGGTCCAGAATTATCTGCAAATATATTATCCTGGCCAGGAGCTCCCCAGATAGGATTAGAAAGGAAGAAAGAGACTGTAAATGGAAAGAAAGATAAGCTAAGCATGTGCTTTGGGTAAGAAGTCCCAGCCCAAGGAGATGCCTGGGCTGTTGTCTGGGGCTGGAGCCGCCTCAGTGGGAGGTAGTCAGAGTGTCTGAGGTAGAAGACCCCGGGGAAGGAACGCAGGGCGAAGAGCTGGACTTCTCTGAGGATTCCTCGGCCTTCTCGTCGTTTCCTGGCGGGGTGGCCGGAGAGATGGGCAAGAGACCCTCCTTCTCACGTTTCTTTTGCTTCATTCGGCGGTTCTGGAACCAGATCTTCACTTGGGTCTCGTTGAGCTGCAGGGATGCAGCGATCTCCACCCTGCGGGCGCGCGTCAGGTACTTGTTGAAGTGGAACTCCTTCTCCAGTTCCGTGAGCTGCTTGGTAGTGAAGTTGGTGCGCACCGCGTTGGGTTGACCCAGGTAGCCGTACTCTCCAACTTTCCCTGGGGCAAAGTGGGAAGCCATGAGACGGAAATGTAAAAATTTTTAAATCGACTTGAGATTCCCCACACGCTTCATGGCAACACTCAGGTAAAGAAAAGATCAAGAACTCAGCACAAATCGGGCTGTGGAGGGTGAGTGATGAGGTGTAAAGTGTTAACCTGATGTAAACCATTAGCATGGTCAGACCGGTGATTAATGGAGCCTCAAGATATTAACAGAACACTACCGTCACAATAACCACCCCCACATACTTCCTATTTCCCAAATGTATAAAATCCTTGAAAACACACCAATCCCTGAGACTTCTTTGCCCCAACACCTCTGGGCACCCTCTCCATGCACTACAACACTAGTCTGATACAAAAGCCTTTTAAAAAAAAGATCATTATTAATTTCCTTGGAAATTAAGCATACCAGCTCCTTCCAGAATAATCAAGGAGCATCCACCAACCAGCAGGACTGACCTGTTTTGGGAGGGTTTCTTTTGACTTTCATCCAGTCAAAAGTCTGCGCTGGAGAAGATGTCTCCGATGCGGGGGAGCGACAGGCTTCTTGGTGGCTGGCGTGGAGAGGGGACAAGGAGTTATTATACGTAGCCAGGGCCAGGCTCTGGTGCTCCTGTCCATATGAGTGGTGAATGTATTGAGGCGAGCCCACCGCGCCCCCAGCATAACCCTGGTGGTGGTGGTGATGCTGGACCATGGGAGATGAGAGATTTCCAGAGTAAACAGCGGGAGCGCACTGGGGGTACCCACCACTTACGTCTGCTTCCTGATTTAACGCGTAGGGGCTGTAAGGCGCACTGAAGTTCTGTGAGCCATAGCTTGGACCACAACTTGAGTGGGAGTAGGACACCCCCAGGTTCCCGGAAGTCTGGTAGGTAGCCGGCTGGGGGTGGCGATGGTGGTGGTGGTGGTGGTGGTGGGGCGAACCGATCTGCACCCCCCTGCCCACTAGGAAGCGGTCGTCGCCGCCGCAACTGTTGGCGCTGACCGCGCACGACTGGAAAGTTGTAATCCTATGGTCCGAGGGGTAGGCTCGGGCTGAGCAGGTCCCCGAGTCGCCACTGCTAAGTATGGGGTATTCCAGGAAGGAGTTCATTCTTGCATTGTCCATCTGTCACTGAGTGACCTGGTCCTGCGAAGCCCGGCGTGACTGTGCCAACTTTCTCACTTCCTC

  11. Finding the Genes Dr. Blat helping a gene find itself.

  12. SIGLEC7 - a gene with some transcriptional complexity. Sialic Acid Binding/Ig-like Lectin 7 displayed in UCSC Genome Browser

  13. Genes: Lines of Evidence • Full length human mRNA (the best!) • Protein homology with other species. • EST evidence - 1st step for much mRNA. • Evidence from genome/genome alignments • HMM based gene finders

  14. Transferrin Receptor in UCSC Genome Browser

  15. Transferrin Clicking on a “known gene” brings up a large page of information on the gene.

  16. Current state of human genome • ~99% of human genome sequenced. Last 1% will still be a challenge. • ~85% of human genes located. Substantial resources are being devoted to last 15%. • ~20% of human genes with any depth of functional annotation. Curation and integrated database are key to progress. • <1% of human regulatory regions located.

  17. Transferrin Receptor Note peaks of conservation in 3’ UTR. These include iron response elements which regulate translation of this gene.

  18. Comparative Genomics Webb Miller

  19. Comparative Genomics at BMP10

  20. Conservation of Gene Features 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.

  21. 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.

  22. Chains join together related local alignments Protease Regulatory Subunit 3

  23. 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.

  24. Before and After Chaining

  25. 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)) • 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) j<i

  26. 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.

  27. Net Focuses on Ortholog

  28. 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.

  29. Useful in finding pseudogenes Ensembl and Fgenesh++ automatic gene predictions confounded by numerous processed pseudogenes. Domain structure of resulting predicted protein must be interesting!

  30. Mouse/HumanRearrangement Statistics Number of rearrangements of given type per megabase excluding known transposons.

  31. A Rearrangement Hot Spot Rearrangements are not evenly distributed. Roughly 5% of the genome is in hot spots of rearrangements such as this one. This 350,000 base region is between two very long chains on chromosome 7.

  32. Reconstructed ancestral (boreutherian) genome for one chromosome

  33. Finding Function • We’ve located 85% of the genes, on track for 95% in a year or two. • We have SOME idea of what 30% of the genes do. • We have virtually NO idea of what the rest do.

  34. How to Find Function • Homology - guilt by association. Orthologs very valuable. • Genetics/knockouts - what happens when a gene gets broken? • RNAi is speeding this up amazingly in worms and other model organisms. • Expression - when and where is gene used? • Microarrays, in situs, GFP fusions. • Interactions - what molecules are touching? • Yeast 2 hybrid, Immunoprecipitations • Literature - finding out what we already know.

  35. Data Mining

  36. Gene Sorter - info on sets of genes

  37. Sorted by homology

  38. Sorted by genome distance

  39. Coping with Bioinformatics Tower of Babel

  40. Up in Testes, Down in Brain

  41. VisiGene • Image browser for in-situ and other gene- oriented pictures • Hopefully in the long run will have a million images covering almost all vertebrate genes. • Currently has 6000 images covering 1000 mouse transcription factors courtesy of Paul Gray et al.

  42. Gene Browser Staff • Programming: Hiram Clawson, Mark Diekhans, Rachel Harte, Angie Hinrichs, Fan Hsu, Andy Pohl, Kate Rosenbloom, Chuck Sugnet, • Docs, quality, support: Gill Barber, Ron Chao, Jennifer Jackson, Donna Karolchik, Bob Kuhn, Crystal Lynch, Ali Sultan-Qurraie, Heather Trumbower • Computer systems: Jorge Garcia, Patrick Gavin, Paul Tatarsky

More Related