Li Yingrui Talk at the Beyond the Genome Meeting 2011. "Heading for a Full Solution to Now-Generation Bioinformatics" Covers BGI's missions using "tree" view of genome analysis for discovery.

1. Heading for full solution to Now Generation Informatics BGI-Shenzhen Sep 19, 2011

2. Nothing in biology makes sense except in light of evolutionTheodosius Dobzhansky “Tree” type of thinking of Genomics They are different, they are also related

3. What is the scope of bioinformatics? Bioinformatics is to understand the tree of life. Bioinformatics will: Draw trees (basic information) Map information on trees (association/cause-effect) Show the trees (visualizations, databases, clouds)

4. Mission 1: Tree of Species A set of different genes (sequence) made different forms of life

5. Mission 1: Tree of Species Draw De novo genome assembly Multiple sequence mapping and alignment Phylogenic tree construction Map In-depth Annotation Comparative genomicss Show Genome browsers

6. Dinner “taste good, sequence it!” Peking Duck cucumber Cabbage kung pao chicken Mapotoufu oyster

7. Factory Silk and silkworm Oil and castor bean “Useful, sequence it!” Cloth and cotton

8. Zoo “look cute, sequence it!” Panda Polar bear and Penguin Antelope

9. Misson 2: Tree of Individuals A set of different variations (sequence) made different individuals/cells of Human

11. These oldest alleles are common in all populations worldwide.

12. Approximately 90% of the variability in allele frequencies is of this sort.From Mary-Claire King

13. International project to construct a next generation baseline data set for human genetics Sequence level HapMap, an order of magnitude deeper Consortium with multiple centres, platforms, funders Aims Find >95% accessible SNPs at allele frequencies above 1%, down towards 0.1% in coding regions Genotype them and place on haplotype backgrounds Also discover and characterize indels, structural variants

15. Somatic/LCL substitution rate = 7-12x higher than germline rate

16. Male mutation rate ~7x higher than female mutation rateFrom 1000G Project From Mary-Claire King Development of agriculture in the past 10,000 years and of urbanization and industrialization in the past 700 years has led to rapid populations growth and therefore to the appearance of vast numbers of new alleles, each individually rare and specific to one population or even to one family.

17. What’s the whole picture of genetic variants ? Billion Genomes Project Personal genomics with phenotype information Allele Frequency 50% 5% 0.5% 0.05% Rarer Alleles Stronger Effects Common Alleles Less Effects Very Rare Alleles Strongest Effects Eg: CFTR delta 508 PCSK9 C679X Eg: MC4R, ABCA1 1q21.1 in SCZ Common/rare Disease Mendelian Disease

19. domestication event lead to a 90% reduction in effective population size during the initial bottleneckPublished in Science 16 Oct.

20. from Andersson and Georges, Nature Reviews of Genetic5: 202-212 (2004) selective sweep: inheritance of regions around adaptive alleles extent of selective sweep for domestication in MAIZE: tb1 locus (60 to 90-kb) (Clark et al. 2004), Y1 locus (about 600-kb) (Palaisa et al. 2004)

21. Domestication Genome variation during silkworm domestication 354 candidate domesticated genes 159 tissue-specific expressed (silk gland, midgut, testis) Published in Science 16 Oct.

23. Expression level difference in placentaEPAS1: endothelial Per-Arnt-Sim (PAS) domain protein 1 The signal of selection The gene (EPAS1) showing strongest selection signal (up to 80% frequency change in allele distribution), Han: 9%; Tibetan: 87%

24. Your Micro-Environment, Your other genome?

25. PCA analysis for 85 Danish samples (based on gene profiling) BMI data Gene level

26. Misson 2. Tree of Individuals Draw (Complete spectrum of) variation identification Population frequencies and spectrums Map Selection and evolution Phenotypic traits Intermediate phenotypes

27. Misson 3: Tree of Cells Cell lineages are characterized by single biological levels and their inter-correlations.

28. On DNA Differentiate the cancer and normal cells by PCA analysis ET AML + : cancer *: normal *:cells possibly mixed （from tumor, but clustered to normal cells） these cancers are really heterogeneous. BTCC

29. Phylogenetic trees clearly show subpopulations in ET and AML cancers ET AML Essential Thrombocythemia Acute Myeloid Leukemia

30. Inferring key genes in AML (a typical heterozygous cancer) Key Gene? Key Gene for sub-pop? Consensus Tree

31. Key genes for AML MLL ALK G1~G6: different subpopulations from AML cancer MLL: myeloid/lymphoid or mixed-lineage leukemia, recurrent translocations in acute leukemias that may be characterized as acute myeloid leukemia (AML; MIM 601626), acute lymphoblastic leukemia (ALL), or mixed lineage (biphenotypic) leukemia (MLL).

32. LILRA1 G1~G6: different subpopulations from AML cancer LILRA1: leukocyte immunoglobulin-like receptor Inferring key genes in AML (a typical heterozygous cancer)

33. CTNNA1 G1~G6: different subpopulations from AML cancer CTNNA1:Leukocyte transendothelial migration; Pathways in cancer Inferring key genes in AML (a typical heterozygous cancer)

34. CTSS G1~G6: different subpopulations from AML cancer CTSS: cathepsin Inferring key genes in AML (a typical heterozygous cancer)

35. PPP2R1A G1~G6: different subpopulations from AML cancer PPP2R1A: TGF-beta signaling pathway Inferring key genes in AML (a typical heterozygous cancer)

36. DIAPH1 G1~G6: different subpopulations from AML cancer DIAPH1: Focal adhesion; Regulation of actin cytoskeleton Inferring key genes in AML (a typical heterozygous cancer)

37. LILRA1 G1~G6: different subpopulations from AML cancer LILRA1: leukocyte immunoglobulin-like receptor Inferring key genes in AML (a typical heterozygous cancer)

39. 3. Tree of cells Draw Single-cell information acquisition technologies Map Single-cell metrics measurement technologies

40. Integrating DNA variation, molecular traits, and phenotypes to construct causal gene networks Gene works in a network!

42. Finally: Where are the papers? On what paper you draw and map and show? It is harder and harder to find a platform efficient enough Sample house High-throughput biology Capable computing system with high I/O performance Interlinked database and standardized formats Bioinformatics workflows to perform in silico analysis on data

43. Making data PUBLIC! Does not mean making data downloadable in theory Does mean the public could make use of data New types of databases with operations to the data are required New academic credit system to motivate high-quality easy-to-access datasets. http://www.gigasciencejournal.com http://climb.genomics.cn

44. Acknowledgements Great International Efforts The Genome 10K Consortium The 1000 Genomes Project Consortium The 1000 Plant Genomes Project Consortium The 5000 insects Project Consortium (pending) BGI Initiatives and collaboration framework The 1000 Plant and Animal Genomes Project The 10K Microbial Genomes Project http://ldl.genomics.org.cn

45. Acknowledgements Prof. Rasmus Nielson’s lab in UC Berkeley and in University of Copenhagen Prof. Richard Durbin’s lab in Wellcome Trust Sanger Insititute Prof. Tak-Wah Lam and Siu-Ming Yiu’s lab in Department of Computer Sciences, Hong Kong University Dr. Heng Li in Broad Insititute …