Intro Protein structure Motifs Motif databases End. Last time. Probability based methods How find a good root? Reliability Reconciliation analysis

Size: px
Start display at page:

Download "Intro Protein structure Motifs Motif databases End. Last time. Probability based methods How find a good root? Reliability Reconciliation analysis"

Transcription

1 Last time Probability based methods How find a good root? Reliability Reconciliation analysis

2 Today Intro to proteinstructure Motifs and domains

3 First dogma of Bioinformatics Sequence structure function

4 First dogma of Bioinformatics Sequence structure function Want to avoid determining structure Expensive Difficult Sometimes impossible?

5 First dogma of Bioinformatics Sequence structure function Want to avoid determining structure Expensive Difficult Sometimes impossible? Bioinfo dream: Structure from sequence! How does the protein fold?

6 Ab initio folding? Folding from sequence seems out of reach

7 Ab initio folding? Folding from sequence seems out of reach But...:

8 What to do in silico? 1. Compromise and use what you ve got. Recycle structures

9 What to do in silico? 1. Compromise and use what you ve got. Recycle structures 2. Find and understand protein building blocks: motifs and domains.

10 What to do in silico? 1. Compromise and use what you ve got. Recycle structures 2. Find and understand protein building blocks: motifs and domains. 3. Identify certain protein types: transmembrane proteins

11 What to do in silico? 1. Compromise and use what you ve got. Recycle structures 2. Find and understand protein building blocks: motifs and domains. 3. Identify certain protein types: transmembrane proteins 4. Why bother? Sequences are informative!

12 Example 1: Motifs and domains (Bjarnadottir et al, 2004) Some typical G-protein coupled receptors Small circles: glycolization sites Other symbols: domains

13 Example 2: Domains and structure

14 Our goals Motifs: Representation and use

15 Our goals Motifs: Representation and use Domains: Definitions, hidden Markov models (HMM), applications, databases

16 Our goals Motifs: Representation and use Domains: Definitions, hidden Markov models (HMM), applications, databases PSI-Blast: Sensitive search tool

17 Our goals Motifs: Representation and use Domains: Definitions, hidden Markov models (HMM), applications, databases PSI-Blast: Sensitive search tool Secondary structure: In general and the TM special case

18 Motifs Short subsequences, DNA or AA, 5 20 positions long.

19 Motifs Short subsequences, DNA or AA, 5 20 positions long. Foremost application: binding sites

20 Motifs Short subsequences, DNA or AA, 5 20 positions long. Foremost application: binding sites Motifs grouped in families. Confused terminology.

21 Motifs Short subsequences, DNA or AA, 5 20 positions long. Foremost application: binding sites Motifs grouped in families. Confused terminology. Fingerprints: Combinations of motifs

22 Motif representation MTWDNRLAAFAQNYANQRA MTWDNRLAAYAQNYANQRI MTWDNRLAAYAQNYANQRI MTWDDGLAAYAQNYANQRA VSWSTKLQAYAQSYANQRI LTWDDQVAAYAQNYASQLA LTWDDQVAAYAQNYASQLA LTWDDQVAAYAQNYASQLA VSWSTKLQGFAQSYANQRI MSWDANLASRAQNYANSRA VSWSTKLQAFAQNYANQRI LRWDEKVAAYARNYANQRK LRWDEKVAAYARNYANQRK VSWSTKLQAFAQNYANQRI LVWNDELAQIAQVWANQCN LVWNDELAQIAQVWANQCN LTWDDEVAAYAQNYVSQLA LTWDDQVAAYAQNYASQLA VSWSTKLQAFAQNYANQRI LVWSDELAYIAQVWANQCQ LVWNDELAYVAQVWANQCQ... Motif V5TPXLIKE 95 seqs, width 19. Multialignment (shortened)

23 Motif representation MTWDNRLAAFAQNYANQRA MTWDNRLAAYAQNYANQRI MTWDNRLAAYAQNYANQRI MTWDDGLAAYAQNYANQRA VSWSTKLQAYAQSYANQRI LTWDDQVAAYAQNYASQLA LTWDDQVAAYAQNYASQLA LTWDDQVAAYAQNYASQLA VSWSTKLQGFAQSYANQRI MSWDANLASRAQNYANSRA VSWSTKLQAFAQNYANQRI LRWDEKVAAYARNYANQRK LRWDEKVAAYARNYANQRK VSWSTKLQAFAQNYANQRI LVWNDELAQIAQVWANQCN LVWNDELAQIAQVWANQCN LTWDDEVAAYAQNYVSQLA LTWDDQVAAYAQNYASQLA VSWSTKLQAFAQNYANQRI LVWSDELAYIAQVWANQCQ LVWNDELAYVAQVWANQCQ... Motif V5TPXLIKE 95 seqs, width 19. Multialignment Pattern notation, eg: [LMV]-[RSTV]-W-[DSN]-... (shortened)

24 Motif representation MTWDNRLAAFAQNYANQRA MTWDNRLAAYAQNYANQRI MTWDNRLAAYAQNYANQRI MTWDDGLAAYAQNYANQRA VSWSTKLQAYAQSYANQRI LTWDDQVAAYAQNYASQLA LTWDDQVAAYAQNYASQLA LTWDDQVAAYAQNYASQLA VSWSTKLQGFAQSYANQRI MSWDANLASRAQNYANSRA VSWSTKLQAFAQNYANQRI LRWDEKVAAYARNYANQRK LRWDEKVAAYARNYANQRK VSWSTKLQAFAQNYANQRI LVWNDELAQIAQVWANQCN LVWNDELAQIAQVWANQCN LTWDDEVAAYAQNYVSQLA LTWDDQVAAYAQNYASQLA VSWSTKLQAFAQNYANQRI LVWSDELAYIAQVWANQCQ LVWNDELAYVAQVWANQCQ... Motif V5TPXLIKE 95 seqs, width 19. Multialignment Pattern notation, eg: [LMV]-[RSTV]-W-[DSN]-... Profiles and PSSM, PWM (shortened)

25 Motif representation MTWDNRLAAFAQNYANQRA MTWDNRLAAYAQNYANQRI MTWDNRLAAYAQNYANQRI MTWDDGLAAYAQNYANQRA VSWSTKLQAYAQSYANQRI LTWDDQVAAYAQNYASQLA LTWDDQVAAYAQNYASQLA LTWDDQVAAYAQNYASQLA VSWSTKLQGFAQSYANQRI MSWDANLASRAQNYANSRA VSWSTKLQAFAQNYANQRI LRWDEKVAAYARNYANQRK LRWDEKVAAYARNYANQRK VSWSTKLQAFAQNYANQRI LVWNDELAQIAQVWANQCN LVWNDELAQIAQVWANQCN LTWDDEVAAYAQNYVSQLA LTWDDQVAAYAQNYASQLA VSWSTKLQAFAQNYANQRI LVWSDELAYIAQVWANQCQ LVWNDELAYVAQVWANQCQ... (shortened) Motif V5TPXLIKE 95 seqs, width 19. Multialignment Pattern notation, eg: [LMV]-[RSTV]-W-[DSN]-... Profiles and PSSM, PWM Visualize with sequences logo

26 M GIVKFRSAQHYE 4 YWLYHSN D IR V 9 SYT Q EAGDKYSE 12 ST MAAVTK VM 15 RNFHYWV SLNR Intro Protein structure Motifs Motif databases End Sequence logo bits 4 3 LM 2 1 T 0 IV SKV E C IRQ AYN P TNDC D KT E N M ASGQ M V L A TI QTA RSN Y H N LAVRT KI M F E I R HWM Q D KR N Q A I S W TEV LRC DN KQ P R AQKS T VAI N EH H D SYG E K Y TQ SA G THY R PSSM of PR00837A (V5TPXLIKE;) 95 sequences. Height indicate conservation (Too many details: Height is the Kullback-Leibler distance to the uniform distribution) Symbol height proportional to frequency

27 Start of translation

28 Phosporelation site, PKA (Blom et al, 1998)

29 Profiles Multialignments convenient Patterns sparse with information Logos are pretty pictures!

30 C G TGAA Intro Protein structure Motifs Motif databases End Profiles Multialignments convenient Patterns sparse with information Logos are pretty pictures! Profile: Matrix F with frequency information F r,c is fraction r in position c Pos: A C GC C G TCA G T T 5 T WebLogo 3.0b14 bits 2.0

31 Profiles Pos: A C G T F r,c = n r,c /n, where n r,c number of r in position c, and n is sequence count.

32 Profiles Pos: A C G T F r,c = n r,c /n, where n r,c number of r in position c, and n is sequence count. For A in position 1: n A,1 = 12 and n = 20

33 Profiles Pos: A C G T Probability of AACATT being produced by profile: = Is that good? Interpretation?

34 PSSM: Better than profile Want a log-odds score!

35 PSSM: Better than profile Want a log-odds score! PSSM=Position Specific Scoring Matrix

36 PSSM: Better than profile Want a log-odds score! PSSM=Position Specific Scoring Matrix M r,c = 10 log 2 ( Fr,c /π r ), where πr is frequency of r in our data.

37 PSSM: Better than profile Want a log-odds score! PSSM=Position Specific Scoring Matrix M r,c = 10 log 2 ( Fr,c /π r ), where πr is frequency of r in our data. Let π A = π C = π G = π T = 0.25.

38 PSSM: Better than profile Want a log-odds score! PSSM=Position Specific Scoring Matrix M r,c = 10 log 2 ( Fr,c /π r ), where πr is frequency of r in our data. Let π A = π C = π G = π T = M A,1 = 10 log 2 (F A,1 /0.25) = 10 log 2 (0.6/0.25) = 12.6

39 PSSM: Better than profile Want a log-odds score! PSSM=Position Specific Scoring Matrix M r,c = 10 log 2 ( Fr,c /π r ), where πr is frequency of r in our data. Let π A = π C = π G = π T = M A,1 = 10 log 2 (F A,1 /0.25) = 10 log 2 (0.6/0.25) = 12.6 M C,2 = 10 log 2 (F C,2 /0.25) = 10 log 2 (0.25/0.25) = 0

40 Profile: PSSM: PSSM M from our profile F Pos: A C G T Pos: A C G T

41 Profile: PSSM: PSSM M from our profile F Pos: A C G T Pos: A C G T Score for AACATT: = 40.5

42 Generalizing with PSSM? How handle a new variant of a motif? Pos: A C G T

43 Generalizing with PSSM? How handle a new variant of a motif? Pos: A C G T Score for ATCTTT? = 56

44 Pseudo counts for profiles Idea: Pretend you have seen all possible motifs

45 Pseudo counts for profiles Idea: Pretend you have seen all possible motifs Pseudo counts: α r is number of pseudo observations of r.

46 Pseudo counts for profiles Idea: Pretend you have seen all possible motifs Pseudo counts: α r is number of pseudo observations of r. Include in profile calculations: F r,c = n r,c + α r n + r α r

47 Pseudo counts for profiles Idea: Pretend you have seen all possible motifs Pseudo counts: α r is number of pseudo observations of r. Include in profile calculations: F r,c = n r,c + α r n + r α r Example 1: Let α A = α C = α G = α T = 1. F A,1 = = 0.54.

48 Pseudo counts for profiles Idea: Pretend you have seen all possible motifs Pseudo counts: α r is number of pseudo observations of r. Include in profile calculations: F r,c = n r,c + α r n + r α r Example 1: Let α A = α C = α G = α T = 1. F A,1 = = Example 2: We had n C,1 = 0. F C,1 = = 0.042

49 Pseudo counts for profiles Idea: Pretend you have seen all possible motifs Pseudo counts: α r is number of pseudo observations of r. Include in profile calculations: F r,c = n r,c + α r n + r α r Example 1: Let α A = α C = α G = α T = 1. F A,1 = = Example 2: We had n C,1 = 0. F C,1 = = Result: Can use PSSM to find novel motifs

50 Fast motif searches Motifs are small, therefore easy to search with. Fast.

51 Fast motif searches Motifs are small, therefore easy to search with. Fast. Blast variants exists for motifs.

52 Fast motif searches Motifs are small, therefore easy to search with. Fast. Blast variants exists for motifs. E-value theory same thanks to log-odds score!

53 Motif databases PROSITE: Important binding sites What motifs does my protein have? Profiles Pattern notation Careful documentation

54 Motif databases PROSITE: Important binding sites What motifs does my protein have? Profiles Pattern notation Careful documentation BLOCKS: Origin to BLOSUM. Presents multialignments! Assembled by most conserved parts of domains.

55 Motif databases PROSITE: Important binding sites What motifs does my protein have? Profiles Pattern notation Careful documentation BLOCKS: Origin to BLOSUM. Presents multialignments! Assembled by most conserved parts of domains. PRINTS: What motif combinations does my protein have?

56 Next time PSI-Blast Protein domains Domain databases Hidden Markov models?

Week 10: Homology Modelling (II) - HHpred

Week 10: Homology Modelling (II) - HHpred Week 10: Homology Modelling (II) - HHpred Course: Tools for Structural Biology Fabian Glaser BKU - Technion 1 2 Identify and align related structures by sequence methods is not an easy task All comparative

More information

Statistical Machine Learning Methods for Bioinformatics II. Hidden Markov Model for Biological Sequences

Statistical Machine Learning Methods for Bioinformatics II. Hidden Markov Model for Biological Sequences Statistical Machine Learning Methods for Bioinformatics II. Hidden Markov Model for Biological Sequences Jianlin Cheng, PhD Department of Computer Science University of Missouri 2008 Free for Academic

More information

Protein Bioinformatics. Rickard Sandberg Dept. of Cell and Molecular Biology Karolinska Institutet sandberg.cmb.ki.

Protein Bioinformatics. Rickard Sandberg Dept. of Cell and Molecular Biology Karolinska Institutet sandberg.cmb.ki. Protein Bioinformatics Rickard Sandberg Dept. of Cell and Molecular Biology Karolinska Institutet rickard.sandberg@ki.se sandberg.cmb.ki.se Outline Protein features motifs patterns profiles signals 2 Protein

More information

Hidden Markov Models (HMMs) and Profiles

Hidden Markov Models (HMMs) and Profiles Hidden Markov Models (HMMs) and Profiles Swiss Institute of Bioinformatics (SIB) 26-30 November 2001 Markov Chain Models A Markov Chain Model is a succession of states S i (i = 0, 1,...) connected by transitions.

More information

Large-Scale Genomic Surveys

Large-Scale Genomic Surveys Bioinformatics Subtopics Fold Recognition Secondary Structure Prediction Docking & Drug Design Protein Geometry Protein Flexibility Homology Modeling Sequence Alignment Structure Classification Gene Prediction

More information

Quantitative Bioinformatics

Quantitative Bioinformatics Chapter 9 Class Notes Signals in DNA 9.1. The Biological Problem: since proteins cannot read, how do they recognize nucleotides such as A, C, G, T? Although only approximate, proteins actually recognize

More information

CAP 5510: Introduction to Bioinformatics CGS 5166: Bioinformatics Tools. Giri Narasimhan

CAP 5510: Introduction to Bioinformatics CGS 5166: Bioinformatics Tools. Giri Narasimhan CAP 5510: Introduction to Bioinformatics CGS 5166: Bioinformatics Tools Giri Narasimhan ECS 254; Phone: x3748 giri@cis.fiu.edu www.cis.fiu.edu/~giri/teach/bioinfs15.html Describing & Modeling Patterns

More information

Genome Annotation. Qi Sun Bioinformatics Facility Cornell University

Genome Annotation. Qi Sun Bioinformatics Facility Cornell University Genome Annotation Qi Sun Bioinformatics Facility Cornell University Some basic bioinformatics tools BLAST PSI-BLAST - Position-Specific Scoring Matrix HMM - Hidden Markov Model NCBI BLAST How does BLAST

More information

Statistical Machine Learning Methods for Biomedical Informatics II. Hidden Markov Model for Biological Sequences

Statistical Machine Learning Methods for Biomedical Informatics II. Hidden Markov Model for Biological Sequences Statistical Machine Learning Methods for Biomedical Informatics II. Hidden Markov Model for Biological Sequences Jianlin Cheng, PhD William and Nancy Thompson Missouri Distinguished Professor Department

More information

Syllabus of BIOINF 528 (2017 Fall, Bioinformatics Program)

Syllabus of BIOINF 528 (2017 Fall, Bioinformatics Program) Syllabus of BIOINF 528 (2017 Fall, Bioinformatics Program) Course Name: Structural Bioinformatics Course Description: Instructor: This course introduces fundamental concepts and methods for structural

More information

Sequence Analysis and Databases 2: Sequences and Multiple Alignments

Sequence Analysis and Databases 2: Sequences and Multiple Alignments 1 Sequence Analysis and Databases 2: Sequences and Multiple Alignments Jose María González-Izarzugaza Martínez CNIO Spanish National Cancer Research Centre (jmgonzalez@cnio.es) 2 Sequence Comparisons:

More information

Christian Sigrist. November 14 Protein Bioinformatics: Sequence-Structure-Function 2018 Basel

Christian Sigrist. November 14 Protein Bioinformatics: Sequence-Structure-Function 2018 Basel Christian Sigrist General Definition on Conserved Regions Conserved regions in proteins can be classified into 5 different groups: Domains: specific combination of secondary structures organized into a

More information

Position-specific scoring matrices (PSSM)

Position-specific scoring matrices (PSSM) Regulatory Sequence nalysis Position-specific scoring matrices (PSSM) Jacques van Helden Jacques.van-Helden@univ-amu.fr Université d ix-marseille, France Technological dvances for Genomics and Clinics

More information

Computational Genomics and Molecular Biology, Fall

Computational Genomics and Molecular Biology, Fall Computational Genomics and Molecular Biology, Fall 2014 1 HMM Lecture Notes Dannie Durand and Rose Hoberman November 6th Introduction In the last few lectures, we have focused on three problems related

More information

Hidden Markov Models in computational biology. Ron Elber Computer Science Cornell

Hidden Markov Models in computational biology. Ron Elber Computer Science Cornell Hidden Markov Models in computational biology Ron Elber Computer Science Cornell 1 Or: how to fish homolog sequences from a database Many sequences in database RPOBESEQ Partitioned data base 2 An accessible

More information

Genome Annotation Project Presentation

Genome Annotation Project Presentation Halogeometricum borinquense Genome Annotation Project Presentation Loci Hbor_05620 & Hbor_05470 Presented by: Mohammad Reza Najaf Tomaraei Hbor_05620 Basic Information DNA Coordinates: 527,512 528,261

More information

-max_target_seqs: maximum number of targets to report

-max_target_seqs: maximum number of targets to report Review of exercise 1 tblastn -num_threads 2 -db contig -query DH10B.fasta -out blastout.xls -evalue 1e-10 -outfmt "6 qseqid sseqid qstart qend sstart send length nident pident evalue" Other options: -max_target_seqs:

More information

Grundlagen der Bioinformatik, SS 08, D. Huson, May 2,

Grundlagen der Bioinformatik, SS 08, D. Huson, May 2, Grundlagen der Bioinformatik, SS 08, D. Huson, May 2, 2008 39 5 Blast This lecture is based on the following, which are all recommended reading: R. Merkl, S. Waack: Bioinformatik Interaktiv. Chapter 11.4-11.7

More information

Sequence and Structure Alignment Z. Luthey-Schulten, UIUC Pittsburgh, 2006 VMD 1.8.5

Sequence and Structure Alignment Z. Luthey-Schulten, UIUC Pittsburgh, 2006 VMD 1.8.5 Sequence and Structure Alignment Z. Luthey-Schulten, UIUC Pittsburgh, 2006 VMD 1.8.5 Why Look at More Than One Sequence? 1. Multiple Sequence Alignment shows patterns of conservation 2. What and how many

More information

Bioinformatics. Proteins II. - Pattern, Profile, & Structure Database Searching. Robert Latek, Ph.D. Bioinformatics, Biocomputing

Bioinformatics. Proteins II. - Pattern, Profile, & Structure Database Searching. Robert Latek, Ph.D. Bioinformatics, Biocomputing Bioinformatics Proteins II. - Pattern, Profile, & Structure Database Searching Robert Latek, Ph.D. Bioinformatics, Biocomputing WIBR Bioinformatics Course, Whitehead Institute, 2002 1 Proteins I.-III.

More information

Sequence Analysis, '18 -- lecture 9. Families and superfamilies. Sequence weights. Profiles. Logos. Building a representative model for a gene.

Sequence Analysis, '18 -- lecture 9. Families and superfamilies. Sequence weights. Profiles. Logos. Building a representative model for a gene. Sequence Analysis, '18 -- lecture 9 Families and superfamilies. Sequence weights. Profiles. Logos. Building a representative model for a gene. How can I represent thousands of homolog sequences in a compact

More information

HMMs and biological sequence analysis

HMMs and biological sequence analysis HMMs and biological sequence analysis Hidden Markov Model A Markov chain is a sequence of random variables X 1, X 2, X 3,... That has the property that the value of the current state depends only on the

More information

Neural Networks for Protein Structure Prediction Brown, JMB CS 466 Saurabh Sinha

Neural Networks for Protein Structure Prediction Brown, JMB CS 466 Saurabh Sinha Neural Networks for Protein Structure Prediction Brown, JMB 1999 CS 466 Saurabh Sinha Outline Goal is to predict secondary structure of a protein from its sequence Artificial Neural Network used for this

More information

MEME - Motif discovery tool REFERENCE TRAINING SET COMMAND LINE SUMMARY

MEME - Motif discovery tool REFERENCE TRAINING SET COMMAND LINE SUMMARY Command line Training Set First Motif Summary of Motifs Termination Explanation MEME - Motif discovery tool MEME version 3.0 (Release date: 2002/04/02 00:11:59) For further information on how to interpret

More information

Moreover, the circular logic

Moreover, the circular logic Moreover, the circular logic How do we know what is the right distance without a good alignment? And how do we construct a good alignment without knowing what substitutions were made previously? ATGCGT--GCAAGT

More information

Exercise 5. Sequence Profiles & BLAST

Exercise 5. Sequence Profiles & BLAST Exercise 5 Sequence Profiles & BLAST 1 Substitution Matrix (BLOSUM62) Likelihood to substitute one amino acid with another Figure taken from https://en.wikipedia.org/wiki/blosum 2 Substitution Matrix (BLOSUM62)

More information

1-D Predictions. Prediction of local features: Secondary structure & surface exposure

1-D Predictions. Prediction of local features: Secondary structure & surface exposure 1-D Predictions Prediction of local features: Secondary structure & surface exposure 1 Learning Objectives After today s session you should be able to: Explain the meaning and usage of the following local

More information

GS 559. Lecture 12a, 2/12/09 Larry Ruzzo. A little more about motifs

GS 559. Lecture 12a, 2/12/09 Larry Ruzzo. A little more about motifs GS 559 Lecture 12a, 2/12/09 Larry Ruzzo A little more about motifs 1 Reflections from 2/10 Bioinformatics: Motif scanning stuff was very cool Good explanation of max likelihood; good use of examples (2)

More information

Sequence Alignments. Dynamic programming approaches, scoring, and significance. Lucy Skrabanek ICB, WMC January 31, 2013

Sequence Alignments. Dynamic programming approaches, scoring, and significance. Lucy Skrabanek ICB, WMC January 31, 2013 Sequence Alignments Dynamic programming approaches, scoring, and significance Lucy Skrabanek ICB, WMC January 31, 213 Sequence alignment Compare two (or more) sequences to: Find regions of conservation

More information

Algorithms in Bioinformatics

Algorithms in Bioinformatics Algorithms in Bioinformatics Sami Khuri Department of omputer Science San José State University San José, alifornia, USA khuri@cs.sjsu.edu www.cs.sjsu.edu/faculty/khuri Pairwise Sequence Alignment Homology

More information

15-381: Artificial Intelligence. Hidden Markov Models (HMMs)

15-381: Artificial Intelligence. Hidden Markov Models (HMMs) 15-381: Artificial Intelligence Hidden Markov Models (HMMs) What s wrong with Bayesian networks Bayesian networks are very useful for modeling joint distributions But they have their limitations: - Cannot

More information

Lecture 8 Learning Sequence Motif Models Using Expectation Maximization (EM) Colin Dewey February 14, 2008

Lecture 8 Learning Sequence Motif Models Using Expectation Maximization (EM) Colin Dewey February 14, 2008 Lecture 8 Learning Sequence Motif Models Using Expectation Maximization (EM) Colin Dewey February 14, 2008 1 Sequence Motifs what is a sequence motif? a sequence pattern of biological significance typically

More information

Plan for today. ! Part 1: (Hidden) Markov models. ! Part 2: String matching and read mapping

Plan for today. ! Part 1: (Hidden) Markov models. ! Part 2: String matching and read mapping Plan for today! Part 1: (Hidden) Markov models! Part 2: String matching and read mapping! 2.1 Exact algorithms! 2.2 Heuristic methods for approximate search (Hidden) Markov models Why consider probabilistics

More information

Biology 644: Bioinformatics

Biology 644: Bioinformatics A stochastic (probabilistic) model that assumes the Markov property Markov property is satisfied when the conditional probability distribution of future states of the process (conditional on both past

More information

CSCE 478/878 Lecture 9: Hidden. Markov. Models. Stephen Scott. Introduction. Outline. Markov. Chains. Hidden Markov Models. CSCE 478/878 Lecture 9:

CSCE 478/878 Lecture 9: Hidden. Markov. Models. Stephen Scott. Introduction. Outline. Markov. Chains. Hidden Markov Models. CSCE 478/878 Lecture 9: Useful for modeling/making predictions on sequential data E.g., biological sequences, text, series of sounds/spoken words Will return to graphical models that are generative sscott@cse.unl.edu 1 / 27 2

More information

Stephen Scott.

Stephen Scott. 1 / 27 sscott@cse.unl.edu 2 / 27 Useful for modeling/making predictions on sequential data E.g., biological sequences, text, series of sounds/spoken words Will return to graphical models that are generative

More information

CAP 5510 Lecture 3 Protein Structures

CAP 5510 Lecture 3 Protein Structures CAP 5510 Lecture 3 Protein Structures Su-Shing Chen Bioinformatics CISE 8/19/2005 Su-Shing Chen, CISE 1 Protein Conformation 8/19/2005 Su-Shing Chen, CISE 2 Protein Conformational Structures Hydrophobicity

More information

Gibbs Sampling Methods for Multiple Sequence Alignment

Gibbs Sampling Methods for Multiple Sequence Alignment Gibbs Sampling Methods for Multiple Sequence Alignment Scott C. Schmidler 1 Jun S. Liu 2 1 Section on Medical Informatics and 2 Department of Statistics Stanford University 11/17/99 1 Outline Statistical

More information

Bioinformatics (GLOBEX, Summer 2015) Pairwise sequence alignment

Bioinformatics (GLOBEX, Summer 2015) Pairwise sequence alignment Bioinformatics (GLOBEX, Summer 2015) Pairwise sequence alignment Substitution score matrices, PAM, BLOSUM Needleman-Wunsch algorithm (Global) Smith-Waterman algorithm (Local) BLAST (local, heuristic) E-value

More information

Learning Sequence Motif Models Using Expectation Maximization (EM) and Gibbs Sampling

Learning Sequence Motif Models Using Expectation Maximization (EM) and Gibbs Sampling Learning Sequence Motif Models Using Expectation Maximization (EM) and Gibbs Sampling BMI/CS 776 www.biostat.wisc.edu/bmi776/ Spring 009 Mark Craven craven@biostat.wisc.edu Sequence Motifs what is a sequence

More information

Algorithms in Bioinformatics FOUR Pairwise Sequence Alignment. Pairwise Sequence Alignment. Convention: DNA Sequences 5. Sequence Alignment

Algorithms in Bioinformatics FOUR Pairwise Sequence Alignment. Pairwise Sequence Alignment. Convention: DNA Sequences 5. Sequence Alignment Algorithms in Bioinformatics FOUR Sami Khuri Department of Computer Science San José State University Pairwise Sequence Alignment Homology Similarity Global string alignment Local string alignment Dot

More information

Motifs, Profiles and Domains. Michael Tress Protein Design Group Centro Nacional de Biotecnología, CSIC

Motifs, Profiles and Domains. Michael Tress Protein Design Group Centro Nacional de Biotecnología, CSIC Motifs, Profiles and Domains Michael Tress Protein Design Group Centro Nacional de Biotecnología, CSIC Comparing Two Proteins Sequence Alignment Determining the pattern of evolution and identifying conserved

More information

Lecture 4: Evolutionary Models and Substitution Matrices (PAM and BLOSUM)

Lecture 4: Evolutionary Models and Substitution Matrices (PAM and BLOSUM) Bioinformatics II Probability and Statistics Universität Zürich and ETH Zürich Spring Semester 2009 Lecture 4: Evolutionary Models and Substitution Matrices (PAM and BLOSUM) Dr Fraser Daly adapted from

More information

6.047 / Computational Biology: Genomes, Networks, Evolution Fall 2008

6.047 / Computational Biology: Genomes, Networks, Evolution Fall 2008 MIT OpenCourseWare http://ocw.mit.edu 6.047 / 6.878 Computational Biology: Genomes, Networks, Evolution Fall 2008 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.

More information

Data Mining in Bioinformatics HMM

Data Mining in Bioinformatics HMM Data Mining in Bioinformatics HMM Microarray Problem: Major Objective n Major Objective: Discover a comprehensive theory of life s organization at the molecular level 2 1 Data Mining in Bioinformatics

More information

Sequence Alignment Techniques and Their Uses

Sequence Alignment Techniques and Their Uses Sequence Alignment Techniques and Their Uses Sarah Fiorentino Since rapid sequencing technology and whole genomes sequencing, the amount of sequence information has grown exponentially. With all of this

More information

EECS730: Introduction to Bioinformatics

EECS730: Introduction to Bioinformatics EECS730: Introduction to Bioinformatics Lecture 07: profile Hidden Markov Model http://bibiserv.techfak.uni-bielefeld.de/sadr2/databasesearch/hmmer/profilehmm.gif Slides adapted from Dr. Shaojie Zhang

More information

Protein Structure Prediction Using Neural Networks

Protein Structure Prediction Using Neural Networks Protein Structure Prediction Using Neural Networks Martha Mercaldi Kasia Wilamowska Literature Review December 16, 2003 The Protein Folding Problem Evolution of Neural Networks Neural networks originally

More information

Similarity searching summary (2)

Similarity searching summary (2) Similarity searching / sequence alignment summary Biol4230 Thurs, February 22, 2016 Bill Pearson wrp@virginia.edu 4-2818 Pinn 6-057 What have we covered? Homology excess similiarity but no excess similarity

More information

Hidden Markov Models. Terminology, Representation and Basic Problems

Hidden Markov Models. Terminology, Representation and Basic Problems Hidden Markov Models Terminology, Representation and Basic Problems Data analysis? Machine learning? In bioinformatics, we analyze a lot of (sequential) data (biological sequences) to learn unknown parameters

More information

Lecture 3: Markov chains.

Lecture 3: Markov chains. 1 BIOINFORMATIK II PROBABILITY & STATISTICS Summer semester 2008 The University of Zürich and ETH Zürich Lecture 3: Markov chains. Prof. Andrew Barbour Dr. Nicolas Pétrélis Adapted from a course by Dr.

More information

COMP 598 Advanced Computational Biology Methods & Research. Introduction. Jérôme Waldispühl School of Computer Science McGill University

COMP 598 Advanced Computational Biology Methods & Research. Introduction. Jérôme Waldispühl School of Computer Science McGill University COMP 598 Advanced Computational Biology Methods & Research Introduction Jérôme Waldispühl School of Computer Science McGill University General informations (1) Office hours: by appointment Office: TR3018

More information

Lecture 4: Evolutionary models and substitution matrices (PAM and BLOSUM).

Lecture 4: Evolutionary models and substitution matrices (PAM and BLOSUM). 1 Bioinformatics: In-depth PROBABILITY & STATISTICS Spring Semester 2011 University of Zürich and ETH Zürich Lecture 4: Evolutionary models and substitution matrices (PAM and BLOSUM). Dr. Stefanie Muff

More information

Jessica Wehner. Summer Fellow Bioengineering and Bioinformatics Summer Institute University of Pittsburgh 29 May 2008

Jessica Wehner. Summer Fellow Bioengineering and Bioinformatics Summer Institute University of Pittsburgh 29 May 2008 Journal Club Jessica Wehner Summer Fellow Bioengineering and Bioinformatics Summer Institute University of Pittsburgh 29 May 2008 Comparison of Probabilistic Combination Methods for Protein Secondary Structure

More information

CISC 636 Computational Biology & Bioinformatics (Fall 2016)

CISC 636 Computational Biology & Bioinformatics (Fall 2016) CISC 636 Computational Biology & Bioinformatics (Fall 2016) Predicting Protein-Protein Interactions CISC636, F16, Lec22, Liao 1 Background Proteins do not function as isolated entities. Protein-Protein

More information

QB LECTURE #4: Motif Finding

QB LECTURE #4: Motif Finding QB LECTURE #4: Motif Finding Adam Siepel Nov. 20, 2015 2 Plan for Today Probability models for binding sites Scoring and detecting binding sites De novo motif finding 3 Transcription Initiation Chromatin

More information

Hidden Markov Models and Their Applications in Biological Sequence Analysis

Hidden Markov Models and Their Applications in Biological Sequence Analysis Hidden Markov Models and Their Applications in Biological Sequence Analysis Byung-Jun Yoon Dept. of Electrical & Computer Engineering Texas A&M University, College Station, TX 77843-3128, USA Abstract

More information

- conserved in Eukaryotes. - proteins in the cluster have identifiable conserved domains. - human gene should be included in the cluster.

- conserved in Eukaryotes. - proteins in the cluster have identifiable conserved domains. - human gene should be included in the cluster. NCBI BLAST Services DELTA-BLAST BLAST (http://blast.ncbi.nlm.nih.gov/), Basic Local Alignment Search tool, is a suite of programs for finding similarities between biological sequences. DELTA-BLAST is a

More information

Tools and Algorithms in Bioinformatics

Tools and Algorithms in Bioinformatics Tools and Algorithms in Bioinformatics GCBA815, Fall 2013 Week3: Blast Algorithm, theory and practice Babu Guda, Ph.D. Department of Genetics, Cell Biology & Anatomy Bioinformatics and Systems Biology

More information

CISC 889 Bioinformatics (Spring 2004) Sequence pairwise alignment (I)

CISC 889 Bioinformatics (Spring 2004) Sequence pairwise alignment (I) CISC 889 Bioinformatics (Spring 2004) Sequence pairwise alignment (I) Contents Alignment algorithms Needleman-Wunsch (global alignment) Smith-Waterman (local alignment) Heuristic algorithms FASTA BLAST

More information

A multi-source domain annotation pipeline for quantitative metagenomic and metatranscriptomic functional profiling

A multi-source domain annotation pipeline for quantitative metagenomic and metatranscriptomic functional profiling A multi-source domain annotation pipeline for quantitative metagenomic and metatranscriptomic functional profiling Ari Ugarte, Riccardo Vicedomini, Juliana Silva Bernardes, Alessandra Carbone 9 September,

More information

Today. Last time. Secondary structure Transmembrane proteins. Domains Hidden Markov Models. Structure prediction. Secondary structure

Today. Last time. Secondary structure Transmembrane proteins. Domains Hidden Markov Models. Structure prediction. Secondary structure Last time Today Domains Hidden Markov Models Structure prediction NAD-specific glutamate dehydrogenase Hard Easy >P24295 DHE2_CLOSY MSKYVDRVIAEVEKKYADEPEFVQTVEEVL SSLGPVVDAHPEYEEVALLERMVIPERVIE FRVPWEDDNGKVHVNTGYRVQFNGAIGPYK

More information

Intro Secondary structure Transmembrane proteins Function End. Last time. Domains Hidden Markov Models

Intro Secondary structure Transmembrane proteins Function End. Last time. Domains Hidden Markov Models Last time Domains Hidden Markov Models Today Secondary structure Transmembrane proteins Structure prediction NAD-specific glutamate dehydrogenase Hard Easy >P24295 DHE2_CLOSY MSKYVDRVIAEVEKKYADEPEFVQTVEEVL

More information

Tutorial 4 Substitution matrices and PSI-BLAST

Tutorial 4 Substitution matrices and PSI-BLAST Tutorial 4 Substitution matrices and PSI-BLAST 1 Agenda Substitution Matrices PAM - Point Accepted Mutations BLOSUM - Blocks Substitution Matrix PSI-BLAST Cool story of the day: Why should we care about

More information

11.3 Decoding Algorithm

11.3 Decoding Algorithm 11.3 Decoding Algorithm 393 For convenience, we have introduced π 0 and π n+1 as the fictitious initial and terminal states begin and end. This model defines the probability P(x π) for a given sequence

More information

Chapter 7: Rapid alignment methods: FASTA and BLAST

Chapter 7: Rapid alignment methods: FASTA and BLAST Chapter 7: Rapid alignment methods: FASTA and BLAST The biological problem Search strategies FASTA BLAST Introduction to bioinformatics, Autumn 2007 117 BLAST: Basic Local Alignment Search Tool BLAST (Altschul

More information

Multiple Sequence Alignment, Gunnar Klau, December 9, 2005, 17:

Multiple Sequence Alignment, Gunnar Klau, December 9, 2005, 17: Multiple Sequence Alignment, Gunnar Klau, December 9, 2005, 17:50 5001 5 Multiple Sequence Alignment The first part of this exposition is based on the following sources, which are recommended reading:

More information

Motifs and Logos. Six Introduction to Bioinformatics. Importance and Abundance of Motifs. Getting the CDS. From DNA to Protein 6.1.

Motifs and Logos. Six Introduction to Bioinformatics. Importance and Abundance of Motifs. Getting the CDS. From DNA to Protein 6.1. Motifs and Logos Six Discovering Genomics, Proteomics, and Bioinformatics by A. Malcolm Campbell and Laurie J. Heyer Chapter 2 Genome Sequence Acquisition and Analysis Sami Khuri Department of Computer

More information

Homology Modeling (Comparative Structure Modeling) GBCB 5874: Problem Solving in GBCB

Homology Modeling (Comparative Structure Modeling) GBCB 5874: Problem Solving in GBCB Homology Modeling (Comparative Structure Modeling) Aims of Structural Genomics High-throughput 3D structure determination and analysis To determine or predict the 3D structures of all the proteins encoded

More information

Modeling Motifs Collecting Data (Measuring and Modeling Specificity of Protein-DNA Interactions)

Modeling Motifs Collecting Data (Measuring and Modeling Specificity of Protein-DNA Interactions) Modeling Motifs Collecting Data (Measuring and Modeling Specificity of Protein-DNA Interactions) Computational Genomics Course Cold Spring Harbor Labs Oct 31, 2016 Gary D. Stormo Department of Genetics

More information

Multiple sequence alignment

Multiple sequence alignment Multiple sequence alignment Multiple sequence alignment: today s goals to define what a multiple sequence alignment is and how it is generated; to describe profile HMMs to introduce databases of multiple

More information

Lab 3: Practical Hidden Markov Models (HMM)

Lab 3: Practical Hidden Markov Models (HMM) Advanced Topics in Bioinformatics Lab 3: Practical Hidden Markov Models () Maoying, Wu Department of Bioinformatics & Biostatistics Shanghai Jiao Tong University November 27, 2014 Hidden Markov Models

More information

CONCEPT OF SEQUENCE COMPARISON. Natapol Pornputtapong 18 January 2018

CONCEPT OF SEQUENCE COMPARISON. Natapol Pornputtapong 18 January 2018 CONCEPT OF SEQUENCE COMPARISON Natapol Pornputtapong 18 January 2018 SEQUENCE ANALYSIS - A ROSETTA STONE OF LIFE Sequence analysis is the process of subjecting a DNA, RNA or peptide sequence to any of

More information

Chapter 5. Proteomics and the analysis of protein sequence Ⅱ

Chapter 5. Proteomics and the analysis of protein sequence Ⅱ Proteomics Chapter 5. Proteomics and the analysis of protein sequence Ⅱ 1 Pairwise similarity searching (1) Figure 5.5: manual alignment One of the amino acids in the top sequence has no equivalent and

More information

Markov Chains and Hidden Markov Models. = stochastic, generative models

Markov Chains and Hidden Markov Models. = stochastic, generative models Markov Chains and Hidden Markov Models = stochastic, generative models (Drawing heavily from Durbin et al., Biological Sequence Analysis) BCH339N Systems Biology / Bioinformatics Spring 2016 Edward Marcotte,

More information

Introduction to Bioinformatics Online Course: IBT

Introduction to Bioinformatics Online Course: IBT Introduction to Bioinformatics Online Course: IBT Multiple Sequence Alignment Building Multiple Sequence Alignment Lec1 Building a Multiple Sequence Alignment Learning Outcomes 1- Understanding Why multiple

More information

First generation sequencing and pairwise alignment (High-tech, not high throughput) Analysis of Biological Sequences

First generation sequencing and pairwise alignment (High-tech, not high throughput) Analysis of Biological Sequences First generation sequencing and pairwise alignment (High-tech, not high throughput) Analysis of Biological Sequences 140.638 where do sequences come from? DNA is not hard to extract (getting DNA from a

More information

09/06/25. Computergestützte Strukturbiologie (Strukturelle Bioinformatik) Non-uniform distribution of folds. Scheme of protein structure predicition

09/06/25. Computergestützte Strukturbiologie (Strukturelle Bioinformatik) Non-uniform distribution of folds. Scheme of protein structure predicition Sequence identity Structural similarity Computergestützte Strukturbiologie (Strukturelle Bioinformatik) Fold recognition Sommersemester 2009 Peter Güntert Structural similarity X Sequence identity Non-uniform

More information

An Introduction to Sequence Similarity ( Homology ) Searching

An Introduction to Sequence Similarity ( Homology ) Searching An Introduction to Sequence Similarity ( Homology ) Searching Gary D. Stormo 1 UNIT 3.1 1 Washington University, School of Medicine, St. Louis, Missouri ABSTRACT Homologous sequences usually have the same,

More information

Stephen Scott.

Stephen Scott. 1 / 21 sscott@cse.unl.edu 2 / 21 Introduction Designed to model (profile) a multiple alignment of a protein family (e.g., Fig. 5.1) Gives a probabilistic model of the proteins in the family Useful for

More information

Mixture Mode for Peptide Mass Fingerprinting ASMS 2003

Mixture Mode for Peptide Mass Fingerprinting ASMS 2003 Mixture Mode for Peptide Mass Fingerprinting ASMS 2003 1 Mixture Mode: New in Mascot 1.9 All peptide mass fingerprint searches now test for the possibility that the sample is a mixture of proteins. Mascot

More information

Multiple Sequence Alignment

Multiple Sequence Alignment Multiple Sequence Alignment BMI/CS 576 www.biostat.wisc.edu/bmi576.html Colin Dewey cdewey@biostat.wisc.edu Multiple Sequence Alignment: Tas Definition Given a set of more than 2 sequences a method for

More information

Hidden Markov Models. based on chapters from the book Durbin, Eddy, Krogh and Mitchison Biological Sequence Analysis via Shamir s lecture notes

Hidden Markov Models. based on chapters from the book Durbin, Eddy, Krogh and Mitchison Biological Sequence Analysis via Shamir s lecture notes Hidden Markov Models based on chapters from the book Durbin, Eddy, Krogh and Mitchison Biological Sequence Analysis via Shamir s lecture notes music recognition deal with variations in - actual sound -

More information

Grundlagen der Bioinformatik Summer semester Lecturer: Prof. Daniel Huson

Grundlagen der Bioinformatik Summer semester Lecturer: Prof. Daniel Huson Grundlagen der Bioinformatik, SS 10, D. Huson, April 12, 2010 1 1 Introduction Grundlagen der Bioinformatik Summer semester 2010 Lecturer: Prof. Daniel Huson Office hours: Thursdays 17-18h (Sand 14, C310a)

More information

Gibbs sampling. Massimo Andreatta Center for Biological Sequence Analysis Technical University of Denmark.

Gibbs sampling. Massimo Andreatta Center for Biological Sequence Analysis Technical University of Denmark. Gibbs sampling Massimo Andreatta Center for Biological Sequence Analysis Technical University of Denmark massimo@cbs.dtu.dk Technical University of Denmark 1 Monte Carlo simulations MC methods use repeated

More information

Quantifying sequence similarity

Quantifying sequence similarity Quantifying sequence similarity Bas E. Dutilh Systems Biology: Bioinformatic Data Analysis Utrecht University, February 16 th 2016 After this lecture, you can define homology, similarity, and identity

More information

Sequence Analysis 17: lecture 5. Substitution matrices Multiple sequence alignment

Sequence Analysis 17: lecture 5. Substitution matrices Multiple sequence alignment Sequence Analysis 17: lecture 5 Substitution matrices Multiple sequence alignment Substitution matrices Used to score aligned positions, usually of amino acids. Expressed as the log-likelihood ratio of

More information

Computational Molecular Biology (

Computational Molecular Biology ( Computational Molecular Biology (http://cmgm cmgm.stanford.edu/biochem218/) Biochemistry 218/Medical Information Sciences 231 Douglas L. Brutlag, Lee Kozar Jimmy Huang, Josh Silverman Lecture Syllabus

More information

Sequence Analysis '17 -- lecture 7

Sequence Analysis '17 -- lecture 7 Sequence Analysis '17 -- lecture 7 Significance E-values How significant is that? Please give me a number for......how likely the data would not have been the result of chance,......as opposed to......a

More information

Page 1. References. Hidden Markov models and multiple sequence alignment. Markov chains. Probability review. Example. Markovian sequence

Page 1. References. Hidden Markov models and multiple sequence alignment. Markov chains. Probability review. Example. Markovian sequence Page Hidden Markov models and multiple sequence alignment Russ B Altman BMI 4 CS 74 Some slides borrowed from Scott C Schmidler (BMI graduate student) References Bioinformatics Classic: Krogh et al (994)

More information

Introduction to Bioinformatics

Introduction to Bioinformatics Introduction to Bioinformatics Jianlin Cheng, PhD Department of Computer Science Informatics Institute 2011 Topics Introduction Biological Sequence Alignment and Database Search Analysis of gene expression

More information

Biological Systems: Open Access

Biological Systems: Open Access Biological Systems: Open Access Biological Systems: Open Access Liu and Zheng, 2016, 5:1 http://dx.doi.org/10.4172/2329-6577.1000153 ISSN: 2329-6577 Research Article ariant Maps to Identify Coding and

More information

Hidden Markov Models Hamid R. Rabiee

Hidden Markov Models Hamid R. Rabiee Hidden Markov Models Hamid R. Rabiee 1 Hidden Markov Models (HMMs) In the previous slides, we have seen that in many cases the underlying behavior of nature could be modeled as a Markov process. However

More information

20 Grundlagen der Bioinformatik, SS 08, D. Huson, May 27, Global and local alignment of two sequences using dynamic programming

20 Grundlagen der Bioinformatik, SS 08, D. Huson, May 27, Global and local alignment of two sequences using dynamic programming 20 Grundlagen der Bioinformatik, SS 08, D. Huson, May 27, 2008 4 Pairwise alignment We will discuss: 1. Strings 2. Dot matrix method for comparing sequences 3. Edit distance 4. Global and local alignment

More information

Introductory course on Multiple Sequence Alignment Part I: Theoretical foundations

Introductory course on Multiple Sequence Alignment Part I: Theoretical foundations Sequence Analysis and Structure Prediction Service Centro Nacional de Biotecnología CSIC 8-10 May, 2013 Introductory course on Multiple Sequence Alignment Part I: Theoretical foundations Course Notes Instructor:

More information

RNA Search and! Motif Discovery" Genome 541! Intro to Computational! Molecular Biology"

RNA Search and! Motif Discovery Genome 541! Intro to Computational! Molecular Biology RNA Search and! Motif Discovery" Genome 541! Intro to Computational! Molecular Biology" Day 1" Many biologically interesting roles for RNA" RNA secondary structure prediction" 3 4 Approaches to Structure

More information

Lecture 2, 5/12/2001: Local alignment the Smith-Waterman algorithm. Alignment scoring schemes and theory: substitution matrices and gap models

Lecture 2, 5/12/2001: Local alignment the Smith-Waterman algorithm. Alignment scoring schemes and theory: substitution matrices and gap models Lecture 2, 5/12/2001: Local alignment the Smith-Waterman algorithm Alignment scoring schemes and theory: substitution matrices and gap models 1 Local sequence alignments Local sequence alignments are necessary

More information

Bioinformatics: Secondary Structure Prediction

Bioinformatics: Secondary Structure Prediction Bioinformatics: Secondary Structure Prediction Prof. David Jones d.jones@cs.ucl.ac.uk LMLSTQNPALLKRNIIYWNNVALLWEAGSD The greatest unsolved problem in molecular biology:the Protein Folding Problem? Entries

More information

Genome 559 Wi RNA Function, Search, Discovery

Genome 559 Wi RNA Function, Search, Discovery Genome 559 Wi 2009 RN Function, Search, Discovery The Message Cells make lots of RN noncoding RN Functionally important, functionally diverse Structurally complex New tools required alignment, discovery,

More information

Alignment principles and homology searching using (PSI-)BLAST. Jaap Heringa Centre for Integrative Bioinformatics VU (IBIVU)

Alignment principles and homology searching using (PSI-)BLAST. Jaap Heringa Centre for Integrative Bioinformatics VU (IBIVU) Alignment principles and homology searching using (PSI-)BLAST Jaap Heringa Centre for Integrative Bioinformatics VU (IBIVU) http://ibivu.cs.vu.nl Bioinformatics Nothing in Biology makes sense except in

More information