生物信息学中的几个计算问题简介生物信息学中的几个计算问题简介

林魁Laboratory of Computational Molecular Biology

[http://cmb.bnu.edu.cn]

College of Life SciencesBeijing Normal University

Outline

Introduction to bioinformatics

Genome annotation

Genome evolution

Metagenomics

Acknowledgements

From U.S. Department of Energy Human Genome Program. http://www.ornl.gov/hgmis

Is Biology an Informational Science?

The HGP changed how we view & practice biology.

Biology is an informational science.

Digital genome

Environmental signals

Biology has become a cross-disciplinary science.

Bioinformatics as an intersecting discipline

Mathematical sciences Computer science

Life sciences

Developing the high throughput technologies and

computational/mathematical tools required for this new

biology.

Why? Where? What? How?

• Why: Ideas for what to produce these huge datasets?

Biological background needed.

• Where: Raw data need to store, IT platforms required.

• What: Patterns in datasets that can be analyzed using

computers. Various data models and their respective

algorithms are needed.

• How: Different resources need to be integrated.

What is Bioinformatics?

• The field of biology specializing in developing hardware and software to store and analyze the huge amounts of data being generated by life scientists. (NIH)

• More than 20 different definitions can be found from Google!

• Computational Biology?• Computational Molecular Biology?

Data integrationVarious molecular biology databases

Bioinformatics applications

Data analysis

Key Challenge of Bioinformatics

The world of biology is very different from what it

was even ten years ago.

To bridge the considerable gap between technical

data production and its use by scientists for

biological discovery.

Clients

Intranetand/or

Internet

Servers

Browsers

Light applications

WWW servers

Database servers

Intensive computing servers

HTML/XML

PERL/C/C++/Java (BioPerl)

MySQL

Bioinformatics tools

Statistical analysis (R)

HPC with MPI

Schematic platform for bioinformatics applications

新一代基因组测序 (NGS) 技术

3730xl

焦磷酸测序 1 亿 bp

边合成边测序 15 亿 bp

边连接边测序 20 亿 bp

Sanger 10 万 bp双脱氧核苷酸

Technology:

Read length:Throughput:

Sequencing cost:

Sanger

LongLowHigh

Sanger

LongLowHigh

454

MiddleMiddleMiddle

454

MiddleMiddleMiddle

ShortHighLow

ShortHighLow

Trade-off between read length and sequencing cost

With the new technology

• New scientific questions emerge

• Existing questions can be answered in a way that

was not considered before.

Sequence assembly by the shotgun approach

• Master sequence short sequences,

simply by examining the sequences for

overlaps.

• No need any prior knowledge of the genome.

CGGTTGAAAGCGGTAGCGTCCATGCGTATTACTCTTGAGCGGTCGAACCTTCTGAAATCGCTGAACCACGTCCACCGGGTCGTCGAGCGTCGCAACACGATCCCGATCCTGTCCAACGTTCTGCTGCGCGCCTCCGGCGCCAATCTGGACATGAAGGCGACCGACCTCGATCTGGAAATCACCGAAGCGACCCCGGCCATGGTGGAGCAGGCTGGCGCCACCACCGTACCGGCACACCTGCTTTACGAAATCGTGCGCAAGCTGCCGGATGGTTCCGAAGTGCTTCTGGCGACCAACCCGGACGGCTCCTCCATGACCGTTGCGTCCGGCCGCTCGAAATTCTCGCTGCAATGCCTGCCGGAAGCGGATTTCCCTGACCTCACCGCCGGCACCTTCAGCCACACCTTCAAACTGAAGGCGGCCGATCTGAAGATGCTGATCGACCGGACGCAGTTTGCGATTTCGACCGAAGAGACGCGTTATTACCTGAACGGCATTTTCTTCCACACCATCGAAAGCAATGGCGAGCTGAAACTGCGCGCCGTCGCCACCGACGGTCACCGCCTTGCGCGTGCTGACGTCGATGCGCCCTCCGGCTCCGAAGGCATGCCGGGCATCATCATTCCGCGCAAGACCGTCGGTGAACTGCAGAAGCTGATGGACAATCCGGAACTGGAAGTCACAGTCGAAGTCTCGGATGCGAAGATCCGCCTGGCCATCGGTTCCGTCGTTCTGACCTCGAAGCTGATCGACGGCACCTTTCCCGATTATCAGCGCGTCATCCCAACCGGCAACGACAAGGAAATGCGCGTCGATTGCCAGACCTTCGCCCGGGCAGTGGACCGTGTTTCGACGATTTCTTCCGAGCGCGGCCGCGCCGTGAAGCTGGCGCTAACTGACGGCCAGTTGACGCTGACCGTCAACAATCCCGACTCGGGAAGTGCTACCGAAGAAGTGGCCGTTGGCTACGACAATGATTCGATGGAAATCGGCTTCAATGCCAAATATCTCCTCGACATCACGTCGCAGCTCTCCGGCGAAGATGCGATTTTTCTGCTGGCGGATGCGGGTTCGCCAACACTGGTTCGCGATACCGCCGGCGACGACGCACTCTATGTTCTGATGCCGATGCGCGTTTAAAACCGACCGTTTTCTTCAATTTTTCCAGAAACGCCGGTGGATCGCTTCATCGGCGTTTTTTGATTCGGCGAACAGGTGGCTCTACCCGTAACTGAATTTTCTCAGTTACGACATTTTGCCTTGTTTTTGCGCCAAATGGGATCAACAGTACGTAACAATTTTTTGACAATGACCAATACATCCGAGGGGAATCATGGCACTCAACCTGAAGCAACGGCTTGAACAAAAATTTGAGGAAGAAATCCGCTTTTTCAAAGGTATGGTCAGCCAGCCGAAAAAAGTCGGCGCCATTGTCCCGACGGTTCCGTCGTTCTGACCTCGAAGCTGATCGACGGCACCTTTCCCGATTATCAGCGCGTCATCCCAACCGGCAACGACAAGGAAATGCGCGTCGATTGCCAGACCTTCGCCCGGGCAGTGGACCGTGTTTCGACGATTTCTTCCGAGCGCGGCCGCGCCGTGAAGCTGGCGCTAACTGACGGCCAGTTGACGCTGACCGTCAACAATCCCGACTCGGGAAGTGCTACCGAAGAAGTGGCCGTTGGCTACGACAATGATTCGATGGAAATCGGCTTCAATGCCAAATATCTCCTCGACATCACGTCGCAGCTCTCCGGCGAAGATGCGATTTTTCTGCTGGCGGATGCGGGTTCGCCAACACTGGTTCGCGATACCGCCGGCGACGACGCACTCTATGTTCTGATGCCGATGCGCGTTTAAAACCGACCGTTTTCTTCAATTTTTCCAGAAACGCCGGTGGATCGCTTCATCGGCGTTTTTTGATTCGGCGAACAGGTGGCTCTACCCGTAACTGAATTTTCTCAGTTACGACATTTTGCCTTGTTTTTGCGCCAAATGGGATCAACAGTACGTAACAATTTTTTGACAATGACCAATACATCCGAGGGGAATCATGGCACTCAACCTGAAGCAACGGCTT

Where is a gene?

Th

ree Layers of G

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1. Transcriptional control sites

2. Non-coding RNAs

3.3. mRNAsmRNAs Evidence-based preEvidence-based pre

dictiondiction Cis-alignmentCis-alignment Trans-alignmenTrans-alignmen

tt Ab initio / de novoAb initio / de novo p p

redictionrediction

Some models

• Dynamic programming• Hidden Markov Models (HMMs)• Conditional random field (CRF)• Support vector machines (SVMs)

Cucumber Genome Annotation Project

The Institute of Vegetables and Flowers,

Chinese Academy of Agricultural Sciences

Laboratory of Computational Molecular Biology,

Beijing Normal University

CMB genome CMB genome annotation annotation

pipelinepipelineRepeatMasker

MySQL MySQL DBMSDBMS

Functional annotation:Functional annotation: Protein homology Protein homology Domain annotation (InterProScan)Domain annotation (InterProScan) Mapping to Gene OntologyMapping to Gene Ontology Mapping to KEGGMapping to KEGG

WWW service WWW service + +

VisualizationVisualization

GBrowseGBrowse

EVM

Genomic sequenceGenomic sequence ESTs/cDNAsESTs/cDNAs UniProt pro

teins UniProt pro

teins

Rfam PseudoPipe

RepeatsProtein-coding Genes

Pseudogenes

ncRNAgenes

CMB genome CMB genome annotation annotation

pipelinepipeline

gbrowser

Depending on the state of sequencing project, genomic coordinates along the chromosome may change dramatically from assembly to assembly.

Phylogenetics

• Evolutionary theory states that groups of

similar organisms are descended from a

common ancestor.

• Phylogenetic systematics (cladistics) is a

method of taxonomic classification based on

their evolutionary history.

• It was developed by Willi Hennig, a German

entomologist, in 1950.

Major reasons to use phylogenetics

Understand the lineage of different species

Organizing principle to sort species into a taxonomy

Understand how various functions evolved

Understand forces and constraints on evolution

Perform multiple sequence alignment

Predict gene function (phylogenetic footprint)

Species/Gene Trees

Species tree (how are my species related?) contains only one representative from each

species

when did speciation take place?

all nodes indicate speciation events

Gene tree (how are my genes related?)often contains a number of genes from a single

species.

nodes relate either to speciation or gene

duplication events.

Species tree

Phylogenomics: Genome trees

Explore genome evolution based on large data sets of DNA or protein sequences.

Using entire genomes to infer a species tree (Eisen and Fraser 2003).

Based on maximum genetic information and average out the anomalies.

Has become the standard for reconstructing reliable phylogenies (Ciccarelli et al, 2006; Daubin et al. 2002).

Phylogenomics and the tree of life

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Taxonomic resolution of some of the novel approaches– Creating ever-more robust phylogeniesever-more robust phylogenies on the basis

of diverse data sets.

Try

Evolutionary theory is evolving

100 trillion microbial cells

The dominant form of life on Earth

~1,000 Gbp of microbial genome sequences per gram of soil !

Metagenomics offers a way forward• Who is out there?

• What are they doing?

What is being done by the communityWhat is being done by the community?

Why genomics is not enough

• Most microbes cannot be cultured

• Microbial diversity and variation have no limits

Definition:

Both a set of research techniques & a research field.

Difference between metagenomics & microbial

genomics

基于 16S rRNA 的分析方法 : 快速而高效 , 应用广泛

“Who is out there ？” 经过多年的发展和完善 (Olsen et al. 1986)

Renaissance currently (Tringe & Hugenholtz 2008)

16S rRNA 的高保守性 variable regions

包含大量 rRNA 基因序列（ >200,000 ）的数据库 ( Cole et al. 2005; Medini et al. 2008) encountering the limitations of existing tools

hsp60 gene: more sensitive than SSU rRNA

Cartoon of

the general

structure of

the bacterial

16S rRNA

gene

Who is there?

Analysis of diversity in the human gut microbial community based on surveys of a limited number of humans.

“strain-level”

‘‘species’’

‘‘genus’’

‘‘family’’

Broad-range PCR amplification and sequencing of microbial 16S rRNA genes

Microbial diversity in environmental

samples

We require the evolutionary and ecological mechanisms

Why? Clusters of very closely related sequences at the tips of phylogenetic trees separated by relatively long branches.

Functional analysis of complex microbial communities (EGTs)

From Turnbaugh et al. 2009. A core gut microbiome in obese and lean twins. Nature

Relative abundance of major phyla and relative abundance of categories of function.

Pathways and subnetworks reflect the adaptation of microbial communities across environments and habitats.

From Gianoulis et al. 2009. PNAS

Resolving strain-level

heterogeneity

Sequence divergence

Gene content difference

Multiple strain sequence types

Gene insertion

Gene rearrangement

Allen & Banfield (2005) Community genomics in microbial ecology and evolution. Nat Rev Microbiol, 3, 489-498

拟展开的工作• 不同生境（或样本）的群落基因组间比较分析，

具体阐明关键环境因子的改变如何导致群落组成的变异 ;

• 结合基因表达和代谢等数据探讨不同群落的基因组与主要生态系统过程（如氮的固定，碳的降解，反硝化作用以及厌氧微生物的除铵作用等）之间的相关关系。

Acknowledgements

Beijing Normal University Beijing Normal University

All members of LCMB, BNUAll members of LCMB, BNU

http://cmb.bnu.edu.cn

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