Yingrui Li: Complete Solutions for Now-Generation Bioinformatics

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

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

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)

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

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

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

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

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

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

An Evolutionary perspective
The oldest human alleles originated in Africa well before the diasporas of modern humans 50,000 – 60,000 years ago.
These oldest alleles are common in all populations worldwide.
Approximately 90% of the variability in allele frequencies is of this sort.
From Mary-Claire King

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

An Evolutionary perspective
Germlinede novo substitution rate =~ 1 x10-8 per generation
Somatic/LCL substitution rate = 7-12x higher than germline rate
Male mutation rate ~7x higher than female mutation rate
From 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.

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

If selection goes another direction…lesson from the domesticated animal/plant
The history of silkworm domestication
D Domesticated
W wild
Silkworm domestication history
Silkworm phylogenetic tree
relationship is not simply follow the geographic distribution which reflect gene-flow and other population level processes related to human activities such as ancient commercial trade
domestication event lead to a 90% reduction in effective population size during the initial bottleneck
Published in Science 16 Oct.

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)

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

50 Tibetan’s and 40 Han’s exomes has been sequenced
Function further validated in
Association with blood hemoglobin level
Expression level difference in placenta
EPAS1: 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%

Your Micro-Environment, Your other genome?

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

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

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

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

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

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

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).

LILRA1
LILRA1: leukocyte immunoglobulin-like receptor

CTNNA1
CTNNA1:Leukocyte transendothelial migration; Pathways in cancer

CTSS
CTSS: cathepsin

PPP2R1A
PPP2R1A: TGF-beta signaling pathway

DIAPH1
DIAPH1: Focal adhesion; Regulation of actin cytoskeleton

LILRA1
LILRA1: leukocyte immunoglobulin-like receptor

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

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

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

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

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

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
…

Yingrui Li: Complete Solutions for Now-Generation Bioinformatics

by GigaScience, BGI Shenzhen on Sep 19, 2011

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