Bioinformatics Data Analysis and Tools2 [wk 19] 03/04/08 Microarray data analysis JaapHeringa 3 [wk...

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Master’s course

Bioinformatics Data Analysis and Tools

Lecture 1: Introduction

Centre for Integrative BioinformaticsFEW/FALW

[email protected]

CENTR

FORINTEGRATIVE

BIOINFORMATICSVU

E

Course objectives

• There are two extremes in bioinformatics work– Tool users (biologists): know how to press the

buttons and know the biology but have no clue what happens inside the program

– Tool shapers (informaticians): know the algorithms and how the tool works but have no clue about the biology

Both extremes can be dangerous at times, need a breed that can do both

Course objectives• How do you become a good bioinformatics

problem solver?– You need to know basic analysis and data mining

modes– You need to know some important backgrounds of

analysis and prediction techniques (e.g. statistical thermodynamics)

– You need to have knowledge of what has been done and what can be done (and what not)

• Is this enough to become a creative tool developer?– Need to like doing it– Experience helps

Course objectivesThe most important thing in tools creating (and

science in general)

• Be able to ask the proper question!– Should address a real problem– Should be targeted– Should be solvable

• Bioinformatics challenge: from Genomics to Systems Biology– Bottom up: start at the components, assemble and

learn the system– Top down: observe the system behaviour, model

and learn the details– What about bottom down or top up questions?

Contents (tentative dates)Date Lecture Title Lecturer

1 [wk 19] 01/04/08 Introduction Jaap Heringa

2 [wk 19] 03/04/08 Microarray data analysis Jaap Heringa

3 [wk 20] 08/04/08 Machine learning Jaap heringa

4 [wk 21] 10/04/08 Clustering algorithms Bart van Houte

5 [wk 21] 15/04/08 Feature Selection Bart van Houte

6[wk 23] 17/04/08 Molecular Simulation & Sampling Techniques Anton Feenstra

7[wk 23] 22/04/08 Introduction to Statistical Thermodynamics I Anton Feenstra

8[wk 24] 24/04/08 Introduction to Statistical Thermodynamics II Anton Feenstra

9[wk 24] 06/05/08 Databases and parsing Sandra Smit

10[wk 24] 08/05/08 Semantic Web and Ontologies Frank van Harmelen

11[wk 25] 13/05/08 Parallelisation& Grid Computing Thilo Kielmann

12[wk 25] 15/05/08 Application area I: Protein Domain Prediction Jaap Heringa

13[wk 25] 20/05/08 Application Area II: Repeats Detection Jaap Heringa

At the end of this course…

• You will have seen a couple of algorithmic examples

• You will have got an idea about methods used in the field

• You will have a firm basis of the physics and thermodynamics behind a lot of processes and methods

• You will have learned about state-of-the-art computational issues, such as Semantic Web and HTP Computing

• You will have an idea of and some experience as to what it takes to shape a bioinformatics tool

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Bioinformatics

“Studying informatic processes in biological systems” (Hogeweg)

Applying algorithms and mathematical formalisms tobiology (genomics)

“ Information technology applied to the management and analysis of biological data” (Attwood and Parry-Smith)

This course

• General theory of crucial algorithms (GA, NN, HMM, SVM, etc..)

• Method examples• Research projects within own group

– Repeats– Domain boundary prediction

• Physical basis of biological processes and of (stochastic) tools

BioinformaticsLarge - external(integrative) Science Human

Planetary Science Cultural AnthropologyPopulation Biology SociologySociobiology PsychologySystems BiologyBiology Medicine

Molecular BiologyChemistryPhysics

Small – internal (individual)

Bioinformatics Genomic Data Sources

• DNA/protein sequence

• Expression (microarray)

• Proteome (xray, NMR,

mass spectrometry)

• PPI

• Metabolome

• Physiome (spatial,

temporal)

Integrativebioinformatics

Protein structural data explosion

Protein Data Bank (PDB): 14500 Structures (6 March 2001)10900 x-ray crystallography, 1810 NMR, 278 theoretical models, others...

MathematicsStatistics

ComputerScience

Informatics

BiologyMolecular

biology

Medicine

Chemistry

Physics

Bioinformatics

Bioinformatics inspiration and cross-fertilisation

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Joint international programming initiatives

• Bioperlhttp://www.bioperl.org/wiki/Main_Pagehttp://bioperl.org/wiki/How_Perl_saved_human_genome

• Biopythonhttp://www.biopython.org/

• BioTclhttp://wiki.tcl.tk/12367

• BioJavawww.biojava.org/wiki/Main_Page

Algorithms in bioinformatics• string algorithms• dynamic programming• machine learning (NN, k-NN, SVM, GA, ..)• Markov chain models• hidden Markov models• Markov Chain Monte Carlo (MCMC) algorithms• stochastic context free grammars• EM algorithms• Gibbs sampling• clustering• tree algorithms (suffix trees)• graph algorithms• text analysis• hybrid/combinatorial techniques and more…

Some techniques can be reapplied to many different problems, e.g. clustering, NN, etc.

Algorithms in bioinformatics

• Approaches in methods can be reapplied, e.g. kowledge-based (inverse) prediction

• Different approaches can be combined, e.g. consensus prediction, metaservers

DBT

hits

PSSM

Q

Discarded sequences

Run query sequence against

database

Run PSSM against database

PSIPSI--BLAST iterationBLAST iteration

Fold recognition by threading:THREADER and GenTHREADER

Query sequence

Compatibility scores

Fold 1

Fold 2

Fold 3

Fold N

Polutant recognition by microarray mapping:

Compatibility scores

Cond. 1

Cond. 2

Cond. 3

Cond. N

Contaminant 1

Contaminant 2

Contaminant 3

Contaminant N

Query array

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Protein-protein interaction prediction• Two extreme approaches, phlogenetic

prediction and Molecular Dynamics (MD) simulation

• Mesoscopic modelling

• Soft-core Molecular Dynamics (MD)– Fuzzy residues

– Fuzzy (surface)

locations

ENFIN WP5 - BioRange (Anton Feenstra)

• Protein-protein interaction prediction

• Mesoscopic modelling

• Soft-core Molecular Dynamics (MD)– Fuzzy residues

– Fuzzy (surface) locations

Where are important new questions?

New neighbouring disciplines• Translational Medicine

A branch of medical research that attempts to more directly connect basic research to patient care. Translational medicine is growing in importance in the healthcare industry, and is a term whose precise definition is in flux. In particular, in drug discovery and development, translational medicine typically refers to the "translation" of basic research into real therapies for real patients. The emphasis is on the linkage between the laboratory and the patient's bedside, without a real disconnect. This is often called the "bench to bedside" definition.

• Computational Systems BiologyComputational systems biology aims to develop and use efficient algorithms, data structures and communication tools to orchestrate the integration of large quantities of biological data with the goal of modeling dynamic characteristics of a biological system. Modeled quantities may include steady-state metabolic flux or the time-dependent response of signaling networks. Algorithmic methods used include related topics such as optimization, network analysis, graph theory, linear programming, grid computing, flux balance analysis, sensitivity analysis, dynamic modeling, and others.

• Neuro-informatics Neuroinformatics combines neuroscience and informatics research to develop and apply the advanced tools and approaches that are essential for major advances in understanding the

structure and function of the brain

Translational Medicine

• “From bench to bed side”

• Genomics data to patient data

• Integration

Natural progression of a gene

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TERTIARY STRUCTURE (fold)TERTIARY STRUCTURE (fold)

Genome

Expressome

Proteome

Metabolome

Functional GenomicsFunctional GenomicsFrom gene to functionFrom gene to function

Systems Biologyis the study of the interactions between the components of a biological system, and how these interactions give rise to the function and behaviour of that system (for example, the enzymes and metabolites in a metabolic pathway). The aim is to quantitatively understand the system and to be able to predict the system’s time processes

• the interactions are nonlinear• the interactions give rise to emergent properties,

i.e. properties that cannot be explained by the components in the system

Systems Biologyunderstanding is often achieved through modeling and simulation of the system’s components and interactions.

Many times, the ‘ four Ms’ cycle is adopted:

Measuring

Mining

Modeling

Manipulating

Neuroinformatics

• Understanding the human nervous system is one of the greatest challenges of 21st century science.

• Its abilities dwarf any man-made system -perception, decision-making, cognition and reasoning.

• Neuroinformatics spans many scientific disciplines - from molecular biology to anthropology.

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Neuroinformatics• Main research question: How does the brain and

nervous system work?• Main research activity: gathering neuroscience

data, knowledge and developing computational models and analytical tools for the integration and analysis of experimental data, leading to improvements in existing theories about the nervous system and brain.

• Results for the clinic: Neuroinformatics provides tools, databases, models, networks technologies and models for clinical and research purposes in the neuroscience community and related fields.

Bioinformatics algorithms

For problems such as alignenment, secondary/tertiary structure prediction, phylogenetic tree determination, etc.

Algorithmic main components:

• Search function

• Scoring function

Bioinformatics algorithms

• Search function– Search space can be large

– High time complexity of algorithms, or even NP-complete/NP-hard problems

• Scoring function– Also called the Objective Function

– Often the most important

Pair-wise alignmentComplexity of the problem

Combinatorial explosion- 1 gap in 1 sequence: n+1 possibilities- 2 gaps in 1 sequence: (n+1)n - 3 gaps in 1 sequence: (n+1)n(n-1), etc.

2n (2n)! 22n

= ~n (n!)2

√√√√πn

2 sequences of 300 a.a.: ~1088 alignments2 sequences of 1000 a.a.: ~10600 alignments!

T D W V T A L KT D W L - - I K

Levinthal’s paradox (1969)

• Denatured protein refolds in ~ 0.1 – 1000 seconds

• Protein with e.g. 100 amino acids each with 2 torsions (φen ψ)Each can assume 3 conformations (1 trans, 2 gauche)3100x2 ≅ 1095 possible conformations!

• Or: 100 amino acids with 3 possibilities in Ramachandran plot (α, β, coil): 3100 » 1047 conformations

• If the protein can visit one conformation in one ps (10-12s) exhaustive search costs 1047 x 10-12 s = 1035 s ≅ 1027

years!(the lifetime of the universe ≅ 1010 years…)

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Phylogenetic tree combinatorial explosion

Number of unrooted trees = ( )

( )!32

!523 −

−− n

nn

Number of rooted trees =( )

( )!22

!322 −

−− n

nn

Combinatoric explosion

# sequences # unrooted # rootedtrees trees

2 1 13 1 34 3 155 15 1056 105 9457 945 10,3958 10,395 135,1359 135,135 2,027,02510 2,027,025 34,459,425

A recap on Scor ing and BenchmarkingQUERY

DATABASE

True Positive

True Negative

True Positive

False Positive

True Negative False Negative

T

POSITIVES

NEGATIVES

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Evaluating multiple alignments (MSAs)Evaluating multiple alignments (MSAs)• Conflicting standards of truth

– evolution

– structure

– function

• With orphan sequences no additional information

• Benchmarks depending on reference alignments

• Quality issue of available reference alignment databases

• Different ways to quantify agreement with reference alignment (sum-of-pairs, column score)

• “Charlie Chaplin” problemCharlie Chaplin once joined a Charlie-Chaplin competition in disguise and became third. What does this tell you about the jury’s ‘objective function’ ?

Evaluation measures��

��

��

What fraction of the matched amino acid pairs (or alignment columns) in the reference MSA are recreated in the query MSA?

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Scoring a single MSA with the Sum-of-pairs (SP) score

Sum-of-Pairs score

• Calculate the sum of all pairwise alignment scores

• This is equivalent to taking the sum of all matched a.a. pairs

• This can be done using gap penalties or not

Good alignments should have a high SP score, but it is not always the case that the true biological alignment has the highest score.

BAliBASE benchmark alignmentsBAliBASE benchmark alignmentsThompson et al. (Thompson et al. (19991999) NAR ) NAR 2727, , 26822682..

88 categories:categories:•• cat. cat. 1 1 -- equidistantequidistant

•• cat. cat. 2 2 -- orphan sequenceorphan sequence

•• cat. cat. 3 3 -- 2 2 distant groupsdistant groups

•• cat. cat. 4 4 –– long overhangslong overhangs

•• cat. cat. 5 5 -- long insertions/deletionslong insertions/deletions

•• cat. cat. 6 6 –– repeatsrepeats

•• cat. cat. 7 7 –– transmembrane proteinstransmembrane proteins

•• cat. cat. 8 8 –– circular permutationscircular permutations

Evaluating multiple alignmentsEvaluating multiple alignments

You can score a single MSA using the sum of all matched amino acid pairs score. This is also referred to as the Sum-of-Pairs (SP) score.. (a bit confusing with the SP score for comparing a query alignment with a reference alignment )


∆��

��

Many test alignments have a higher SP score than the reference alignment (“Charlie Chaplin problem’)


Many test alignments have a higher SP score than the reference alignment (“Charlie Chaplin problem’)

Comparing T-coffeewith other methods

Column scores are used here

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BAliBASE benchmark alignments

If you are a better program on average, this does not mean you win in all cases…

How do you know what is the situation in your case? Even with a better program you can be unlucky..

Example from course DPSFAP: inverse folding

Top score structure 20 a.a. fragments in the high specificity regions -- Sequence: 3icb (residues 31–50) Protein Star ting position Score Cα α α α r.m.s.d. Secondary structure (DSSP)

to native (A° )

3icb 31 –7.36 0.00 HHHHH TTTSSSSS HHHHH

1bbk B 32 –6.18 5.65 GGT SSS TT EE S E

1ezm 254 –5.93 4.61 HHHHT TT HHHHHHHHH

8cat A 73 –5.84 8.68 SEEEEEEEEEE S TTT

3enl 196 –5.84 3.82 HHHHHH GGGG B TTS B

1tie 59 –5.75 6.17 EESS SS TT EEEEES

3gap A 97 –5.73 3.11 EEHHHHHHHTTT TTTHHHH

1tfd 71 –5.59 6.50 EEEEEEE S SSS S E

1gsr A 159 –5.54 2.93 HHHHH TTTTTT HHHHHHH

1apb 149 –5.53 4.14 HHHHHHHHHHHHTT GGGE

Random 5.88 A°

Ł The native structure is on top

Top-scoring structural 20 a.a. fragments in regions where the native state does not have lowest scores but the Cα RMSDs are low -- Sequence: 3icb (residues 36–55) Protein Star ting position Score Cα α α α r.m.s.d. Secondary structure (DSSP)

to native (A° )

1mba 75 –9.54 3.16 HHHHTT HHHHHHHHHHHHH

1mbc 72 –8.59 3.84 HHHHTTT TTTHHHHHHHHH

3gap A 102 –8.43 3.54 HHHHTTT TTTHHHHHHHHH

1ezm 186 –7.83 5.44 ETTTTBSSS SEESSSGGG

1hmd A 67 –7.47 4.76 TTHHHHHHHHHHHHHHHHT

1sdh A 37 –7.42 4.65 HHHHHHH GGGGGGGGGG

2ccy A 36 –7.34 4.38 TTHHHHHHHHHHHHHHGGG

1ama 298 –7.11 2.67 HHHHHHSHHHHHHHHHHHHH

3icb 36 –7.08 0.00 TTTSSSSS HHHHHHHH S

1pbx A 30 –7.06 4.79 HHHHHHH GGGGGGSTTSS Random RMSD: 5.79 A°

Ł The native structure is not on top

Inaccurate scoring functions• Since most scoring functions do not rank

solutions properly, often ensembles of predictions are taken– For example, take the top 10 (or top 5%)

predicted structures, is the true structure among those?

– In alignment: next to optimal (highest scoring) alignment, the ensemble of supoptimal alignments is taken

• Biology is also capricious, e.g. the native protein structure does not always have the lowest internal energy

• Integrate data sources

• Integrate methods

• Integrate data through method integration (biological model)

Integrative bioinformatics

Data

Algorithm

BiologicalInterpretation

(model)

tool

Integrative bioinformaticsData integration

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Data 1 Data 2 Data 3


Data 1

Algorithm 1


(model) 1

tool

Algorithm 2


(model) 2

Algorithm 3


(model) 3

Data 2 Data 3

“Nothing in Biology makes sense except in the light of evolution” (Theodosius Dobzhansky (1900-1975))

“Nothing in Bioinformatics makes sense except in the light of Biology”

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