1. Introduction to computational biology
Craig A. Stewart
Indiana University
1 July 2005
Höchstleistungsrechenzentrum Stuttgart

2. License terms
Please cite as: Stewart, C.A Introduction to computational biology. Tutorial presented at High Performance Computing Center, Stuttgart. 1 July 2005, Stuttgart, Germany.
Some figures are shown here taken from web, under an interpretation of fair use that seemed reasonable at the time and within reasonable readings of copyright interpretations. Such diagrams are indicated here with a source url. In several cases these web sites are no longer available, so the diagrams are included here for historical value. Except where otherwise noted, by inclusion of a source url or some other note, the contents of this presentation are © by the Trustees of Indiana University. This content is released under the Creative Commons Attribution 3.0 Unported license ( This license includes the following terms: You are free to share – to copy, distribute and transmit the work and to remix – to adapt the work under the following conditions: attribution – you must attribute the work in the manner specified by the author or licensor (but not in any way that suggests that they endorse you or your use of the work). For any reuse or distribution, you must make clear to others the license terms of this work.

3. Planned schedule for the day
9:00-9:15 Introduction and objectives
9:15-10:00 An introduction to the biological basis [11]
10:00-10:15 Biological data sources [30]
10:15-10:30 Similarity searching and Alignment [39]
10:30-10:45 Coffee Break
10:45-11:30 Similarity searching and alignment, con;t
11:30-12:00 Multiple Alignment [58]
12:00-13:00 Lunch
13:00-13:30 Phylogenetics [75]
13:30-14:00 RNA and Protein Structure [91]
14:00-14:30 Systems Biology [119]
14:30:14:45 Miscellaneous semi-random topics [138]

4. Plan & Objectives
Materials focus on open source software (generally not the presenters own work)
Objectives. At the end of the class, participants should:
understand enough biology to understand key computational biology problems, and be familiar with some strategies for collaborating with biologists and biomedical scientists
be conversant with key open source applications in computational biology and bioinformatics, and current problems in these areas
Be ready to download some code and start making it better!

5. Motivation The “-omics” trend
Finding press pieces about huge computing problems is easy
How many bio codes really scale to hundreds of processors?
What are the coming high performance needs of biologists?
Importance of computational biology and bioinformatics to the HPC community
The challenges and promise are real
Successes and failures so far
Successes: Protein structure, Genome assembly, Surgical assistance, Phylogenetics
Mismatched priorities: Ab initio protein folding
Not yet successful: Drug discovery

6. What has changed recently?
Bioinformatics not new
Protein structure
Phylogenetics
What is new is high-throughput sequencing:
Lots more data
The possibility of going from a knowledge of the DNA sequence to an understanding of diseases and health

7. Genome Projects Timeline
First virus (SV40) sequenced (5224 base pairs)
DOE announces Human Genome Initiative
First complete map of all human chromosomes
First living organism sequenced (H. influenzae) 2 Mb
Yeast (S. cerevisiae) - 12 Mb
Intestinal bacterium (E. coli) - 5 Mb
Nematode worm (C. elegans) Mb
Celera announcement; Public effort regroups
Human Chromosome 22 – 34 Mb
Joint announcement by NHGRI – Celera
“As good as it gets” human genome
This slide based on slide by Manfred D. Zorn

8. Definitions
Computational Biology: any use of advanced information technology in the study of biological problems.
“Bioinformatics applies the principles of information sciences and technologies to make the vast, diverse and complex life sciences data mnore understandable and useful” (NIH BISTIC Committee grants1.nih.gov/grants/bistic/CompuBioDef.pdf)
Genomics – study of genomes and gene function
Proteomics – study of proteins and protein function
___omics –

9. Challenges Different types of biological data at different scales
Data of varying quality
Much of the underlying biology is not well understood
Prior to the availability of high-throughput sequencing, scientists could only study small pieces of the genetic information of any organism.
Now the entire genome of several organisms has been completed, but knowing the genome is different than knowing how it works!

10. Comparison of Complexity
Physics & Chemistry
2 elementary particles
4 forces
112 elements
When random events occur it is often possible to study average behavior
Typically ahistoric (astrophysics an exception)
Biology
3B base pairs in humans
Min. 30,000 genes in humans
~1.5M species
Individual random events important; no law of large numbers
Intensely historic, heavily contingent

11. Complexity, Con't Chip design Cells All components known
Device physics for individual components known
Itanium has 3 x 10^8 connections and 2 x 10^8 devices
Unified basic currency (electrons)
Computer program required to understand (e.g. SPICE)
Cells
Components not known
Function of individual components not known
# components ~10^13
No unified basic currency
Ecell, Karyote, etc. attempting to model cells
Computer programs do not yet permit full understanding

12. A rapid introduction to key elements of biology

13. Why is it important to know some biology?
Would you study numerical methods without knowing some mathematics?
Much current biological knowledge is very specific to particular organisms, genes, or diseases
If you just wade into the available data online you can do some very silly things.
Anopheles gambiae
From mosquito/mtm/index.html
Source Library:Centers for Disease Control
Photo Credit:Jim Gathany

14. Central dogma of biology
The central dogma of biology is that genes act to create phenotypes through a flow of information form DNA to RNA to proteins, to interactions among proteins (regulatory circuits and metabolic pathways), and ultimately to phenotypes. Collections of individual phenotypes constitute a population (first put forward by Crick in 1958)

15. Cell Structure Eukaryotes Chromosomes linear
Eukaryotes
Chromosomes linear
Introns, exons, postprocessing
Nucleus & nuclear wall
Mictochondria and (in plants) Chloroplasts
Prokaryotes
Chromosome circular
Location is everything
No nucleus
No plastids

16. Four (or Five) Bases
DNA consists of four nucleotides: Cytosine, Thymine, Adenine, and Guanine.
In the double helix, A&T are always bound, and C&G are always bound to each other
RNA consists of four nucleotides as well: Cytosine, Uracil, Adenine, and Guanine
RNA may loop back on itself but it does not form a double helix

20. Translating DNA to RNA and Transcribing RNA to Proteins
AAAAAGGAGCAAATT
DNA 1 4 2 5 3 6
UUUUUCCUCGUUUAA
RNA
One possible amino acid string
Phe Asn Asp Ala

21. Genetic Code Ala Alanine Leu Leucine Arg Arginine Lys Lysine
Asn Asparagine
Asp Aspartic acid
Cys Cysteine
Glu Glutamic acid
Gln Glutamine
Gly Glycine
His Histidine
Ile Isoleucine
Leu Leucine
Lys Lysine
Met Methionine
Phe Phenylalanine
Pro Proline
Ser Serine
Thr Threonine
Trp Tryptophan
Tyr Tyrosine
Val Valine
Original8Hour/Genetics/geneticcode.html

22. Sickle Cell Normal RBC GAG codes for Glutamine disc-Shaped, soft
easily flow through small blood vessels
lives for 120 days
Sickle RBC
GTG codes for Valine
sickle-Shaped, hard
often get stuck in small blood vessels
lives for 20 days or less
Malaria vs. Anaemia!
ency/imagepages/1223.htm

23. What is a Gene?
An inheritable trait associated with a region of DNA that codes for a polypeptide chain or specifies an RNA molecule which in turn has an influence on some characteristic phenotype of the organism.
Early views: genes lined up on the chromosome like beads on a string; one gene => one protein
Examples of genes: color blindness, sickle-cell anaemia
Mendelian genes, Sex-linked genes, Quantitative traits
Annotation: Extraction, definition, and interpretation of features on the genome sequence
Annotations vs. genes:
Many annotations describe features that constitute a gene.
Others may not always directly correspond in this way
An annotation is what we think… nature may disagree!
Inheritance problem with annotations

25. Human Chromosomes
Prokaryotes and Eukaryotes that reproduce asexually just get a copy of all of their genes from their ‘parent’
Eukaryotes (that includes us) that reproduce sexually receive one chromosome from each of our parents. This means we have two copies of the same information – one copy from each parent
Genes can be dominant (brown eyes) or recessive (blue eyes)
Human_Genome/graphics/slides/
elsikaryotype.html

26. Mendelian Genetics
Round peas are “dominant” to that a pea with the genotype RR or Rr is going to look round
Only peas with the rr genotype will look wrinkled
For very simple Mendelian genes, you can use something called a Punnet square
Many genes are inherited in ways OTHER than simple Mendelian genetics, which is why sequencing the geneome has been very important!

27. Gene Components Prokaryotes Location is everything
Essentially all of the DNA is transcribed (few mitochondrial diseases)
Eukaryotes
Non-contiguous pieces of DNA may comprise one gene
Start sequence (complicated and long)
Stop Codons – end transcription
Exons – portions of sequence that are transcribed and used
Introns – portions of sequence that are not used

28. Alternate splicing

29. Population genetics & evolution
Mutations create the raw material for evolution
Natural selection and chance affect the frequency with which particular genes or DNA sequences are present in populations
Given enough time and enough change, evolution, speciation, and so forth happen
Genes can be ‘fixed’ or ‘maintained in an equilibrium’ in a population by chance or through natural selection

30. Key points (so far)
Biological processes are complicated; the historicity and complexity of biological processes and our lack of understanding of many matters makes biology an interesting topic!
The fundamental dogma of molecular biology is that genes act to create phenotypes through a flow of information form DNA to RNA to proteins, to interactions among proteins (regulatory circuits and metabolic pathways), and ultimately to phenotypes. Collections of individual phenotypes constitute a population.
DNA consists of four base pairs (ATCG). A is always paired with T; C always paired with G.
DNA is translated into RNA. RNA consists of four base pairs as well (AUCG).
The linear structure of DNA is transcribed into RNA and then into proteins. Proteins have their 3D configuration as the basis for their structure.

31. Bioinformatics data sources

32. MedLine & PubMed Medline:
U.S. National Library of Medicine – NLM Medline
~12 million references on life sciences/biomedicine (since 1996)
Pubmed - Standard search tool for Medline
Structured Language - Medical Subject Heading

33. Genomic, Proteomic, etc. data sources
A tremendous amount of data is available through public data sources via the Web, ftp, or by other means.
To analyze biological data, we first have to get it….
Several ways to organize presentation of material – by site, by type, etc. We will organize by data type.
Types of Databases:
Chromosomal (
DNA/Genes
Protein
Biochemistry and metabolic pathways
Microarray
Web collections

34. DNA databases
GenBank. Operated by NCBI (National Center for Biotechnology Information).
European Molecular Biology Laboratory – Nucleotide Sequence Database.
DNA Database of Japan (DDBJ).
All share data daily. Update conflicts avoided by policy.
Differences are in “value added” and interfaces

35. Data Structures Current
Primary DNA repository data based on ASN.1. Makes possible linkages among many types of biomedical info.
The software libraries now often handle XML as well
Software toolkits and docs available at
Genbank Flat File format
FASTA
>gi|532319|pir|TVFV2E|TVFV2E envelope protein
ELRLRYCAPAGFALLKCNDADYDGFKTNCSNVSVVHCTNLMNTTVTTGLLLNGSYSENRT
QIWQKHRTSNDSALILLNKHYNLTVTCKRPGNKTVLPVTIMAGLVFHSQKYNLRLRQAWC

37. Protein Structure NCBI Swiss-Prot/TrEMBL at http://www.expasy.org/
Note: 125,744 chemically determined vs 861,482 inferred from automated translation of DNA sequences!!!!!
Protein Data Base – PDB - one of the first online bioinformatics databases!!!

38. Biochemistry and pathways
ENZYME (part of the ExPASy system)
BIND (part of the NCBI system)
Pathways
PathDB
Kegg WIT

39. Web Resources - General NCBI http://www.ncbi.nlm.nih.gov/
EBI Biocatalog
IUBio Archive

40. Similarity matching & Sequence Alignment

41. Why pattern matching (and what are the problems)
US!
and…
Bonobo

42. Problems!
For proteins, 95% similarity is ~ identical, 80% similarity is a lot. Even less similarity than that needed for DNA
Database techniques inadequate – they are too precise!
Datasets very large to search
Homology
Common ancestry
Sequence (and usually structure) conservation
Homology is inferred rather than measured
Identity
Objective and well defined
Can be quantified easily, but not very useful!
Similarity
Most common method used, but not as easily defined

43. How do sequences differ?
CGTACCGTTAATAT
CGTACCGATAATAT
Differences in individual bases
Bases may be added to a sequence
Bases may be deleted from a sequence
CGTACCCCGTAATAT
CGTACC . .GTAATAT
CGTACCGTTAATAT
CGTACCG . . .ATAT

44. Alignment
An alignment is an arrangement of two sequences opposite one another
It shows where they are different and where they are similar
We want to find the optimal alignment - the most similarity and the least differences
Alignments have two aspects:
Quantity: To what degree are the sequences similar (percentage, other scoring method)
Quality: Regions of similarity in a given sequence

45. Scoring Alignments CGTACCGTTAATAT CGTACCG . . .ATAT CGTACCGTTAATAT
GCTAAATTC
++ x x
GC AAGTT
Matches are good: they get a positive value
Mismatches are bad: they get a negative value
Gaps are bad: they get a negative value
Gap opening penalty
Gap extension penalty
Score = Matches –Mismatches
-∑{gap opening penalty +(length)*gap length penalty}
CGTACCGTTAATAT
CGTACCG . . .ATAT
CGTACCGTTAATAT
CGT. C . GTT .ATAT

46. Dotter
Simple way to get a feel for how sequences compare to each other.
Used both with DNA and Protein sequences
"A dot-matrix program with dynamic threshold control suited for genomic DNA and protein sequence analysis" Erik L.L. Sonnhammer and Richard Durbin Gene 167(2):GC1-10 (1995)
Modular nature of proteins

47. Local Alignments with BLAST
Basic Linear Alignment Search Tool
First a quick demo (hopefully)
So, what did we do?
BLAST – Basic Linear Alignment Search Tool
In particular, BLASTn (for nucleotides)
Altschul, S.F., Gish, W., Miller, W., Myers, E.W., and Lipman, D.J Basic Local alignment search tool. Journal of Molecular Biology 215:

48. (Original) BLAST Algorithm
Original algorithm does not permit gaps
The original BLAST algorithm is a local (heuristic) alignment tool
Given a search sequence, e.g. ACGTAGGCATGAA
BLAST first makes a list of all “words” of a given length that would possibly have a score of at least T against the search string.
In the case of this example there would be (at least) the following:
ACGTAGGCATG
CGTAGGCATGA
GTAGGCATGAA

49. (Original) BLAST Algorithm, 2
BLAST takes the list of all words with a score of at least T against the string one is trying to match…. and then searches a database for any matches to these words. So if one were using the example and the NR database, BLAST would search NR for all occurrences of the words:
ACGTAGGCATG
CGTAGGCATGA
GTAGGCATGAA
Suppose BLAST finds in the NR database an exact match to
BLAST then attempts to extend the match in both directions
ACGTAGGCATGA
So now we have an exact match of 12 letters

50. (Original) BLAST algorithm,3
So BLAST keeps going, and in this case would stop at an exact match of 13 letters (if one existed), since 13 letters was the entire initial search string:
ACGTAGGCATGAA
BLAST has a stopping algorithm for dropping particular search directions, or stopping altogether

51. BLAST algorithm in more detail
The BLAST algorithm searches for MSPs – Maximal Scoring Pairs – such that the score of sequences cannot be improved either by lengthening it or shortening it. “Pairs” here refers to a string – or a substring – of the initial string used as the search string – and one or more strings or substrings found in a database.
The search starts with the creation of all possible subwords of a given length (default typically 11 for DNA sequences, 3 amino acids for protein sequences) that would score at least T when matched against the original search string. (T is short for Threshold)
BLAST searches for any occurrence of each of these words that have a score of at least T. This is a “hit” – or a “High Scoring Pair (HSP)”
The search then continues by trying to extend these HSPs.
Suppose “S” is the best score found for a word of length k. BLAST stops trying to extend words when the score drops a certain amount below the best value S in the previous round.
BLAST continues on and on until it is no longer possible to improve the score of HSPs by making them longer.
Then it generates a list of the best HSPs. Default is a cutoff E-value of 10
BLAST (original) has an infinite gap penalty

52. PSI BLAST Position Specific Iterative BLAST
Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 1997 Sep 1;25(17):
Required two non-overlapping similarities with search term to occur within a certain distance (A) on the genome
Permits gaps in the alignments
Can be iterated to allow for user-specified scoring matrices By default, uses the BLOSUM-62 Matrix

53. PSI BLAST
Two hits, T=11 A=40 vs One hit, T=13
In the original BLAST, the step of extending the length of the ‘hits’ took ~90% of execution time.
The initial threshold value T must be lower than with the original BLAST, but far fewer hits are pursued, meaning that the extension time is lower
vol25/issue17/images/gka56202.gif

54. Example Problem (hoffentlich)

55. mpiBLAST http://mpiblast.lanl.gov/

56. mpiBLAST Algorithm
Darling, A.E., L. Carey, W.-C. Feng The design, implementation, and evaluation of mpiBLAST. Presented at ClusterWorld
Algorithm
Database is segmented. Portions of database are placed on data storage devices on multiple nodes in a HPC system. mpiformatdb is a wrapper for the BLAST formatdb program. Number of subdivisions specified by user
Foreman/worker algorithm. Portions of the database are assigned to workers, using a greedy algorithm

57. mpiBLAST performance
Scaling can be super linear when pieces are small enough that they fit into memory
Scalability limitations due to communication, implicit barrier before assembly of results
If pieces of data distributed out to workers are larger than available RAM, then scaling is still good but not super linear
Blast is the most heavily used bioinformatics tool in existence. Parallelization of BLAST has huge payoff for practicing biologists

58. BLAST superlinear scaling as memory phenomenon
Standard BLAST running on a system with 128 MB of memory.
Conclusion: Performance degrades due to extra disk I/O when the database is larger than core memory.
Slide courtesy of Wu-chun Feng
Los Alamos National Laboratory

59. Multiple Alignment

60. Multiple Alignment
Sequences lined up so that homologous residues are next to one another
Color reflects residue type (e.g., green = hydrophobic)
This slide based on a slide created by Dr. Richard Repasky, Indiana University

61. Uses
Alignments reveal the degree to which sequences have been conserved
Most functional sequences are conserved
Multiple alignment is used to locate them
Functional groups of enzymes
Predict protein structure
Gene promoters
Unknown functional units of non-coding regions of DNA
Alignments necessary to estimate evolutionary trees
This slide based on a slide created by Dr. Richard Repasky, Indiana University

62. Progressive alignments
Pairwise dynamic programming alignment algorithms can be extended to multiple sequences but scale poorly to large numbers of sequences (or to long sequences)
Heuristic algorithms are employed. Commonly used heuristic methods are progressive - build multiple alignment by aggregating from paired alignments
Order in which sequences are added determined by a guide-tree that reflects similarity/distance
Guide tree constructed from sequences
Closely related sequences aggregated/added first
Errors in early additions tend to propagate
Algorithms differ in strategy for minimizing error propagation
Algorithms also differ in guide tree construction & scoring
This slide based on a slide created by Dr. Richard Repasky, Indiana University

63. Progressive alignment steps
1 - align B & C
2 - align D & E
3 - align (D & E) & A
4 -align (D & E & A) & (B & C)
This slide based on a slide created by Dr. Richard Repasky, Indiana University

64. Three algorithms CLUSTAL W T-COFFEE ProbCons
Oldest of three & most widely used
Initial alignment error not addressed
Good performance by adding realistic details
T-COFFEE
Initial alignment error addressed by using consistency methods
Uses CLUSTAL W, improves performance
ProbCons
New this year
Initial alignment error also addressed by consistency methods
Uses hidden Markov models
This slide based on a slide created by Dr. Richard Repasky, Indiana University

65. CLUSTAL W
Thompson et al Nucleic Acids Res. 22:
Uses dynamic programming with distance matrices and gap penalties for alignments
Selective use of scoring matrices
Strict matrices for closely related sequences
Permissive matrices for distantly related sequences
Relatedness determined by branch lengths in guide tree
Uses residue-specific gap penalties from reference alignments
This slide based on a slide created by Dr. Richard Repasky, Indiana University

66. CLUSTAL W
Gap penalties reduced in short stretches of hydrophilic residues (usually associated with bends and are gap-prone)
Gap penalties increased in areas within 8 residues of existing gaps because such gaps are rare in reference alignments
Sequences weighted by relatedness
Attempt to correct for unbalanced sampling across guide tree
Closely related sequences discounted in importance
Progression
Leaves of tree joined by dynamic programming
Leaves joined with internal nodes by sequence-profile alignment
Internal nodes joined by profile-profile alignment
This slide based on a slide created by Dr. Richard Repasky, Indiana University

67. Example output FOS_RAT MMFSGFNADYEASSSRCSSASPAGDSLSYYHSPADSFSSMGSPVNT
FOS_MOUSE MMFSGFNADYEASSSRCSSASPAGDSLSYYHSPADSFSSMGSPVNT
FOS_CHICK MMYQGFAGEYEAPSSRCSSASPAGDSLTYYPSPADSFSSMGSPVNS
FOSB_MOUSE -MFQAFPGDYDS-GSRCSS-SPSAESQ--YLSSVDSFGSPPTAAAS
FOSB_HUMAN -MFQAFPGDYDS-GSRCSS-SPSAESQ--YLSSVDSFGSPPTAAAS
*:..* .:*:: .***** **:.:* * *..***.* :.. :*:
FOS_RAT IPTVTAISTSPDLQWLVQPTLVSSVAPSQ TRAPHPYGLP
FOS_MOUSE IPTVTAISTSPDLQWLVQPTLVSSVAPSQ TRAPHPYGLP
FOS_CHICK VPTVTAISTSPDLQWLVQPTLISSVAPSQ NRG-HPYGVP
FOSB_MOUSE VPTVTAITTSQDLQWLVQPTLISSMAQSQGQPLASQPPAVDPYDMP
FOSB_HUMAN VPTVTAITTSQDLQWLVQPTLISSMAQSQGQPLASQPPVVDPYDMP
:******:** **********:**:* **... ::. .**.:* :

68. ClustalW-MPI
Li, K.-B ClustalW-MPI: ClustalW analysis using distributed and parallel computing. Bioinformatics 19:
Initial pairwise alignment process is parallelized and scales very well
Multiple alignment process is parallelized and scales modestly
Scaling tests published thus far up to 16 processors, reduces time from hours to minutes

69. Consistency Methods
Make estimates based on more information - “averaging”
Lazy teacher analogy
In progressive multiple alignment, use as much information as possible when adding sequences to the alignment
T-COFFEE: each position in one alignment is weighted by consistency in all alignments of all pairs of sequences that include the sequences being aligned
ProbCons: posterior probabilities in pairwise HMM alignments weighted by posterior probabilities of same positions in other alignments
This slide based on a slide created by Dr. Richard Repasky, Indiana University

70. T-COFFEE
Notredame, et al. (2000, J. Mol. Biol. 302: )
Gives better alignments than CLUSTAL W on benchmark datasets
Avoids problem of early bad gaps using consistency methods
Progressive alignment based on weights pooled from all pairwise alignments rather than currently accumulated sequences
This slide based on a slide created by Dr. Richard Repasky, Indiana University

71. T-COFFEE steps Calculation of weights Progression
All pairwise alignments using CLUSTAL W, local alignments using FASTA Lalign
For each aligned base pair in each pair of sequences calculate weight
Aggregate weights for aligned base pairs using triplets of sequences
Progression
Align all sequences pairwise using weights
Build guide tree from pairwise alignments
Progressively build multiple alignment using tree and weights
This slide based on a slide created by Dr. Richard Repasky, Indiana University

72. ProbCons Http://probcons.stanford.edu
ISMB 2004, Bioinformatics 20:Supplement 1
Constistency methods & HMM to align
Gives better alignments than CLUSTAL W & T-COFFEE on alignment benchmarks
Use HMM to align all pairs of sequences
Keep posterior probability matrices & update each value by averaging over all alignments in which the sequence position occurs. Do twice.
Create a guide tree from expected accuracies (sums of posterior probabilities of highest summing path in matrix)
Progressive alignment objective function is sum of posterior probabilities for all aligned residues
This slide based on a slide created by Dr. Richard Repasky, Indiana University

73. Multiple alignment viewers
CLUSTAL X - X windows
ftp://ftp-igbmc.u-strasbg.fr/pub/ClustalX/
Jalview - Java
Variable color schemes
Editing
Front end to aligners
This slide based on a slide created by Dr. Richard Repasky, Indiana University

74. Abstracting Multiple Alignments
Hidden Markov models can be used to describe alignments
Called profile HMMS
Think of them as definitions of proteins or averages
Useful for aligning newly discovered sequences
Search sequence databases for sequences that match the alignment profile (Consider the alternative!)
Build databases of profiles and search for profiles that match query sequences
This slide based on a slide created by Dr. Richard Repasky, Indiana University

75. HMMER http://hmmer.wustl.edu/
Profile HMMs for protein sequence analysis
Builds profiles from existing alignments
Searches sequence databases for molecules that match profiles
Can be used to construct db’s of profiles and to search for profiles that match sequences
Generates random sequences from profiles
Also available as a parallel code using PVM
Has been ported to vector supercomputers
Scales reasonably well as regards number of processors. Does not scale as well as regards size of the biological problem

76. Phylogenetic Inference

77. Building Phylogenetic Trees
Goal: an objective means by which phylogenetic trees can be estimated in tolerable amounts of wall-clock time, producing phylogenetic trees with measures of their uncertainty
All evolutionary changes are described as bifurcating trees
-genes or gene products
-organisms

78. Why is tree-building a HPC problem?
The number of bifurcating unrooted trees for n taxa is
(2n-5)!/ (n-3)! 2n-3
for 50 taxa the number of possible trees is ~1074; most scientists are interested in much larger problems
NP-hard problem
The number of rooted trees is (2n-5)!

79. Stochastic change of DNA
Markov process, independent for each site: 4 x 4 matrix for DNA, 20 x 20 for amino acids
A C G T
A p(A->A) p(A->C) p(A->G) …
C p(C->A) p(C->C) p(C->G) …
G .
T .
Transitions more probable than transversions.
Must account for heterogeneity in substitution rates among sites (DNArates – Olsen)

80. fastDNAml Developed by Gary Olsen
Derived from Felsensteins’s PHYLIP programs
One of the more commonly used ML methods
The first phylogenetic software implemented in a parallel program (at Argonne National Laboratory, using P4 libraries)
Olsen, G.J.,et al fastDNAml: a tool for construction of phylogenetic trees of DNA sequences using maximum likelihood. Computer Applications in Biosciences 10: 41-48
MPI version produced by Indiana University in collaboration with Gary Olsen available from

81. fastDNAml algorithm – adding taxa
Optimize tree for 3 (randomly chosen) taxa - only one topology possible
Randomly pick another taxon –
(2i-5) trees possible
Keep the best (maximum likelihood tree)

82. Basic fastDNAml algorithm - Branch rearrangement
Move any subtree crossing n vertices (if n=1 there are 2i-6 possibilities)
Keep best resulting tree
Repeat this step until local swapping no longer improves likelihood value

83. fastDNAml algorithm con’t: Iterate
Get sequence data for next taxon
Add new taxa (2i-5)
Keep best
Local rearrangements (2i-6)
Keep going….
When all taxa have been added, perform a full tree check

84. Overview of parallel program flow
Program modules
Master (generates trees, receives back from Foreman best tree at each step)
Foreman (dispatches trees to workers, determines best tree, tracks activity of workers)
Worker
Monitor (instrumentation)
Parallel versions include fault tolerance features (useful in large clusters and grid computing)

85. Performance of fastDNAml

86. SC2003 HPC Challenge (“It seemed like a good idea at the time”)
Are Hexapods a single evolutionary group? Are ecdysozoans a single evolutionary group?

87. A partial bestiary
All organism illustrations copyright Jennifer Fairman,
Used by agreement

88. Software and data analysis
Non-grid preparatory work
Download sequences from NCBI (67 Taxa, 12,162 bp, mitochondrial genes for 12 proteins)
Align sequences with Multi-Clustal
Determine rate parameters with TreePuzzle
Grid preparatory work
Analyze performance of fastDNAml with Vampir
Meetings via Access Grid & CoVise
The grid software
PACXMPI – Grid/MPI middleware
Covise – Collaboration and visualization
Application Framework – Matthias Hess
fastDNAml – Maximum Likelihood phylogenetics

89. Application framework, COVISE, FastDNAml
ML analysis of phylogenetic trees based on DNA sequences
Foreman/worker MPI program
Heuristic search for best trees
For 67 taxa: ~10109 trees
Goal: 300 bootstraps, 10 jumbles per – 3000 executions (more than 3x typical!)

90. Why this project on a grid?
Important & time-sensitive biological question requiring massive computer resources
A biologically-oriented code that scales well
Grid middleware environment & collaboration tool well suited to the task at hand
Opportunity to create a grid spanning every continent on earth (except Antarctica)
It seemed like a good idea at the time

91. The results
Hundreds of trees were analyzed during the course of the week
The biological results are still being analyzed
Our HPC challenge project was awarded the prize for “Most geographically distributed application”
We have not yet become rich or famous

92. RNA & Protein Structure

93. RNA & Protein Structure
Want to know functions
Function dictated by structure
Need structure to understand function
Empirical determination of structure difficult/expensive
Shortcut: predict structure from sequence
Algorithms & software for predicting structure

94. RNA Nucleotide sequence Composition differs from DNA
Thymine replaced by uracil
Alphabet: C, G, A, U
Types of RNA
Messenger RNA - template from DNA - decoded to produce protein
Transfer RNA - interface attached to amino acids - identifies amino acid to protein producing machinery
Ribosomal RNA - protein producing machinery
Regulatory RNA - small polynucleotides that bind to other molecules and alter behavior
Catalytic RNA - most catalyze reactions of DNA

95. RNA secondary structure
Single stranded
RNA folds on itself
Complementary bases join
A – U, G - C
Forms loops & hairpins
In nature, structure nearly minimizes energy
Energy - more or less bending/stress on bond angles
Zuker algorithm minimizes calculated energy
tRNA

96. RNA Structure – Vienna RNA
Package consists of several parts (from the web site):
RNAfold - predict minimum energy secondary structures and pair probabilities
RNAeval - evaluate energy of RNA secondary structures
RNAheat - calculate the specific heat (melting curve) of an RNA sequence
RNAinverse - inverse fold (design) sequences with predefined structure
RNAdistance - compare secondary structures
RNApdist - compare base pair probabilities
RNAsubopt - complete suboptimal folding

97. Protein Enzymes - catalysts
Regulatory - bind with molecules to alter behavior
Transport - move here to there as oxygen in hemoglobin
Storage - e.g., caches of nitrogen or metal ions
Mobility - contractile & motile proteins (muscle, flagella)
Structural proteins - fill space, provide support
Scaffold - supports for construction of macro molecules
Defense/Attack - immune system proteins, venom

98. Structure and function of proteins
Enzymes receive most attention
Enzymes catalyze reactions = lower energy required
Place reactants in favorable positions for reaction
Location is everything
Example enolase

101. Primary structure - amino acids
Share nitrogen group (amino)
Share acid group (carboxyl)
Differ in side chains
20 common amino acids
Polymerize amino-to-carboxyl
Side chains determine secondary & tertiary structure

102. Secondary structure & Bond rotation
Reflects angles in amino acid chain
Shape of the peptide chain over short sequences
Determined by amino acid composition

103. Angles of rotation & secondary structure
course/3_geometry/rama.html

104. Visualization: ways to view molecules
Wireframe
Often used by crystallographers while interpreting data
Frames fit nicely in electron density mesh
Space filled (Van der Waals radii)
Often used in docking applications
Van derWaals radii useful for thinking about hydrogen bonds

105. Visualization: ways to view molecules
Richardson-type (also ribbon or noodle)
Depict secondary and tertiary structure
Omit details of atoms and polymerization
Ball and stick
Usually more useful for ligands than for whole proteins
Atoms & covalent bonds

106. Molecular viewing software
Most programs do several types of visualizations
VRML – Cosmo Player
RASMOL -
RasTop -
CHIME -
Swiss Pdb Viewer -
MICE -
Many tend to be touchy about browsers and plugins

107. Empirical structure X-ray crystallography
Yields 3-D plot of electron density in space
Create model of molecule that matches electron density
~127,863 entries in SwissProt
~857,950 entries in TrEMBL

108. Secondary structure prediction
Induction: assign structure based on sequence similarity to proteins of known structure
Consensus methods do best
Search database for similar sequences
Align sequences
Apply several algorithms (e.g., neural network, nearest-neighbor) to predict structure type
Take consensus of predictions
JPRED:

109. Tertiary structure prediction
Long segments fold
Folds are held in place by molecular forces (e.g., electrostatic, hydrogen bonds, some covalent bonds)
Proteins fold to minimize energy
Folding algorithms seek conformation with minimum energy
Two main methods
Ab initio
Fold recognition

110. Criteria Goal: prediction of molecule position within 1 angstrom
Remember, location is everything in enzymes
Measuring quality of fit
Root mean square of atom distances from correct position
RMSD = √ (∑di2)/N
Q3 = (true positives + true negatives)/total residues
Better than 70% right is really good!

111. Fold Recognition (Threading)
Impose known folds on molecule; evaluate fit
Dissimilar sequences may fold similarly
Number of possible folds is finite
Many methods of fitting (e.g., dynamic programming, Gibbs sampling, hidden Markov models)
Calculate energy or distances
Web services - many methods
General recommendation: use many methods and build a consensus

112. Ab Initio methods L. From the beginning (O. E. D.)
A real ab initio chemist would complain about use of the term
A scoring function is used to judge conformations
Search function used to explore conformational space
Criterion: usually minimize free energy
Scoring function types
Molecular mechanics calculations
Use empirically derived scoring functions based on probability distributions of data in Protein Data Bank
Search function may be coarse-grained or fine-grained, usually matches granularity of scoring function

113. Amber Assisted Model Building with Energy Refinement
Not open source – modest license cost
D.A. Pearlman, D.A. Case, J.W. Caldwell, W.R. Ross, T.E. Cheatham, III, S. DeBolt, D. Ferguson, G. Seibel and P. Kollman. AMBER, a computer program for applying molecular mechanics, normal mode analysis, molecular dynamics and free energy calculations to elucidate the structures and energies of molecules. Comp. Phys. Commun. 91, 1-41 (1995)
Simulated annealing approach with energy refinements

114. GAMESS General Atomic and Molecular Electronic Structure System
M.W.Schmidt, M.W., K.K.Baldridge, J.A.Boatz, S.T.Elbert, M.S.Gordon, J.H.Jensen, S.Koseki, N.Matsunaga, K.A.Nguyen, S.Su, T.L.Windus, M.Dupuis, J.A.Montgomery General Atomic and Molecular Electronic Structure System J. Comput. Chem.14:
NPACI/SDSC Web portal for GAMESS:
No-cost academic license
It’s parallel

115. CHARMM CHARMM (Chemistry at HARvard Molecular Mechanics)
For prediction of macromolecular structure
Not open source - modest license cost
CHARMM: A Program for Macromolecular Energy, Minimization, and Dynamics Calculations, J. Comp. Chem. 4, (1983), by B. R. Brooks, R. E. Bruccoleri, B. D. Olafson, D. J. States, S. Swaminathan, and M. Karplus.
CHARMM: The Energy Function and Its Parameterization with an Overview of the Program, in The Encyclopedia of Computational Chemistry, 1, , P. v. R. Schleyer et al., editors (John Wiley & Sons: Chichester, 1998), by A. D. MacKerell, Jr., B. Brooks, C. L. Brooks, III, L. Nilsson, B. Roux, Y. Won, and M. Karplus.

116. Rosetta Work with fragments 3-9 amino acids in length
Restrict conformations of individual fragments to the distribution of conformations exhibited in real proteins
Fragment conformations modeled stochastically using distributions of observed conformations
Seek array of conformations that minimize energy
Local minima likely
Run many searches
Cluster results & pick large clusters as likely conformations
Typically licensed at no cost for academic purposes

117. Molecular Docking Will two molecules bind?
Usually interested in docking of small molecules (e.g., drug candidates) to proteins
Small molecule called ligand (from Latin ligare - to bind)
Specific question: will ligand bind to a receptor in a protein?
Receptor usually the largest pocket in the surface of a protein
Steps
Characterize receptor site
Orient ligand(s) & evaluate

118. Molecular Docking
Process: usually create negative image of receptor site and ask whether ligands take that conformation
Rigid models use a grid search
Models with flexible surfaces
Usually assume receptor fixed and ligand flexible.
Explore conformation of ligand (simulated annealing)
Models with flexible surfaces do better than those with fixed surfaces
Autodock is a commonly used package
Flexible model
Can do simulated annealing and genetic algorithms

119. Systems Biology

120. Systems Biology Special issue of Science: 295, Mar. 2002
Special issue of Nature: 420, Nov. 2002
“Systems biology is a new field in biology that aims at a systems-level understanding of biological systems.”
Nobody’s quite sure what it is, but it sure is hot!
graphics/slides/images/ _web.gif

121. Historical approach to biological experiments
From Lazebnik, Y Cancer cell 2:179: Traditional biological experimentation much like the process of trying to fix a broken radio (or if you are or were a 12-year old boy…)
Some typical steps:
Cataloguing components and their attributes
Perturbing the system
Knock-out experiments
Drawing diagrams
Eventually may find a component that, when replaced, repairs the radio
In a very complex system, knowing what all of the parts are, and knowing the function of individual pathways, may still not tell you how the systems work. It may simply be impossible to deduce this from 1-st order interactions
Interactions, multiple changes
Power supply and other components (well-known PC repair example!)
Change everything all at once so that we’ll never know what worked!

122. Systems Biology
Systems biology emphasizes close integration of experiment, theory and computational modeling
Goal: understanding the structure and dynamics of biological systems, placing the parts in the context of the dynamic whole
Studies the complex interactions of many levels of biological information
Quantitative, predictive models are central
Computational modeling in particular is a key tool
Why model
You are forced to really state what you are hypothesizing
Allows you to understand an *approximation* of reality in great detail
Computational Cell Biology Springer Verlag (Fall et al, eds).
Foundations of systems biology. MIT Press, Kitano (ed)

123. A small sampling BALSA BASIS BIOCHAM BioCharon biocyc2SBML BioGrid
BioNetGen
BioPathways Explorer
Bio Sketch Pad
BioSPICE Dashboard
BioSpreadsheet
BioUML
Cellware
Cytoscape
DBsolve
Dizzy
E-CELL
FluxAnalyzer
Gepasi
INSILICO discovery
Jarnac
JDesigner
JSIM
JWS
Karyote

124. Example - MCell
MCell is: A General Monte Carlo Simulator of Cellular Microphysiology.
MCell focuses on simulations using a Brownian dynamics random walk algorithm.
MCell's use to date has been focused on the microphysiology of synaptic transmission.
Images and MCell-related material courtesy of Joel R. Stiles, Pittsburgh SupercomputingCenter and Carnegie Mellon University, and Thomas M. Bartol, Computational Neurobiology Laboratory, The Salk Institute.

125. MCell Scalability
Images and MCell-related material courtesy of Joel R. Stiles,
Pittsburgh Supercomputing Center and Carnegie Mellon University,
and Thomas M. Bartol, Computational Neurobiology Laboratory,
The Salk Institute.

126. CompuCell
CompuCell currently uses a combination of "extended Potts model" for cell sorting and clustering, and "Schnakenberg Reaction Diffusion" equations to establish the underlying chemical field to which cells respond and form typical patterns found in such biological systems as a growing chicken limb.
Image courtesy of James Glazier

127. SBML Systems Biology Markup Language
There is currently a proliferation of software, but no single package answers all needs
Systems Biology Markup Language
Purpose: develop software and standards to
Enable sharing of simulation & analysis software
Enable sharing of models
Goal: make it easier to share than to reimplement
An XML-based markup language
Active and functional leadership and reasonable funding stream
SBML is focused on biochemical networks, but of all of the biology-oriented markup languages, it seems to be the one with the most traction
Permits storage, transmission, and reuse of models
Consists of “levels”

128. What does an SBML model look like?
<?xml version="1.0" encoding="UTF-8" ?>
- <sbml xmlns=" version="2" level="1" xmlns:celldesigner="
- <model name="ban00010">
- <annotation>
  <celldesigner:modelVersion>2.2</celldesigner:modelVersion>
  <celldesigner:modelDisplay sizeX="876" sizeY="1177" />
- <celldesigner:listOfCompartmentAliases>
- <celldesigner:compartmentAlias id="ca1" compartment="uVol">
  <celldesigner:class>SQUARE</celldesigner:class>
  <celldesigner:bounds x="10.0" y="10.0" w="856" h="1157" />
  </celldesigner:compartmentAlias>
  </celldesigner:listOfCompartmentAliases>
- <celldesigner:listOfSpeciesAliases> -

129. SBML Levels Level 1 – Biochemical networks. Frozen.
Level 2 – enhancements and extensions to level 1. Frozen June 2003.
Uses MathML for equation specifications
Uses same metadatascheme as CellML (exp named function defs), catalysts, time delays
Fixes minor issues in Level 1 specification
Any Level 1 model can be run within software that supports Level 2
Level 3 – current development effor

130. Components of a Level 1 or 2 model
Compartment: a well-stirred container
Species: chemical compounds
Reaction: transformation, transport, or binding process involving a species. May have a rate parameter
Parameter: a quantity that has a symbolic name (global and local)
Unit definition
Rule: added to set constraints, initial conditions, bounds, etc on the reactions
Everything in SBML is one of the above!

131. So you actually want to run one…
MANY programs will handle a model written in SBML
libSBML provides a C/C++ API if you want to write your own
Math SBML – an open source toolbox for running SBML models within Mathematica
SBML Toolbox – the equivalent for MatLab
While an open source toolkit for a proprietary software package seems odd at first blush…
There is a KEGG to SBML converter!

132. JWS Online
From

134. CellML
Originally designed to describe and exchange models of cellular and subcellular processes.
XML-based specification of interchange of cell model information
Includes:
Information about model structure
Math, based on MathML
Metadata about the model
Project of Bioengineering Institute of University of Auckland with support from Physiome Sciences Inc.

135. BioSpice Lead by Adam Arkin – a DARPA-backed effort
Described in some detail in two recent issues of “-Omics”
More licensing term details than many open source efforts
The BioSpice Dashboard may be one of the better “integrative” tools under development at present
Uses SBML for model specification

136. Systems biology URLs SBW & SBML www.sbw-sbml.org
NetBuilder strc.herts.ac.uk/bio/Maria/NetBuilder
CellML
Jarnac + JDesigner
Gepasi
Virtual Cell (NIH-supported)
E-CELL (based in Japan
JigCell gnida.cs.vt.edu/~cellcyclepse/
DARPA BioSPICE
Karyote

137. Some Good Books
Winter, P.C., G.I. Hickey, H.L. Fletcher Instant notes in genetics. Springer-Verlag, NY. ISBM
Durbin, R., S. Eddy, A. Krogh, G. Mitchison Biological sequence analysis. Cambridge University Press.
Gibas, C., and P. Jambeck Developing bioinformatics computer skills. O’Reilly.
Tisdall, J Beginning perl for bioinformatics. O’Reilly.
Gusfield, D Algorithms on strings, trees, and sequences. Cambridge University Press.
Berman, F., G.C. Fox, A.J.G. Hey. (eds) Grid computing: making the grid infrastructure a reality. Wiley, Sussex

138. And a few semi-random other matters

139. GeneIndex
Location of initiators, promoters, etc. a key question in genomics
First step in this is creating a dictionary of words of various lengths (many possible next steps)
To be useful, analysis must be performed on entire genomes at once
GeneIndex finds frequencies and positions of all words of a given length in a DNA sequence. Visualization with Tcl/Tk. Genome is broken up into n sections, where n = number of processors
After each segment is analyzed, linked lists are joined

141. GeneIndex Scalability: Speedup

142. BioPerl Duct tape for DNA/protein sequence bioinformatics Perl API for
Reading many data formats/data sources
Writing many data formats/data sources
Manipulating data objects in well known ways
Object oriented Perl module(s)
As with all frameworks, it can be extremely painful for quick and dirty applications
Siblings: BIOPYTHON ( BIOJAVA (

143. Apple bioclusters Apple Xserve clusters: head node plus compute nodes
Batch & queuing with Platform LSF, Sun Grid Engine, Open PBS, or PBS Pro
Parallel API’s: MPICH, MPI Pro, LAM/MPI
Globus tookit available
iNquiry Bioinformatics tools available
Many open source bioinformatics packages pre-compiled
All available through Pise web interface
“Easy” system/cluster administration tools

144. BIRN Biomedical Informatics Research Network http://www.nbirn.net/
NIH-sponsored attempt to create health-oriented cyberinfrastructure
Function BIRN – brain function and disorders, e.g. schizophrenia
Morphometry BIRN – brain structural disorders, e.g. Alzheimers
Mouse BIRN – studying mouse brain and mouse models of human brain disorders
Grid technology, using federated data system approach, based on Globus, SRB, etc.

145. Grid.org Volunteer cycles to handle various problems
Uses United Devices software
Human proteome project – uses Rosetta
Cancer research – does screening against potential projects

146. Computational biology, biomedical research, and HPC
Two challenges:
Scalability of applications
Wall-clock time sensitivity
Bioinformatics, Genomics, Proteomics, ____ics will radically change understanding of biological function and the way biomedical research is done.
Traditional biomedical researchers must take advantage of new possibilities
Computer-oriented researchers must take advantage of the knowledge held by traditional biomedical researchers

147. Future directions & needs
Drug Discovery
Target generation – so what
Target verification – that’s important!
Toxicity prediction – VERY important!! (Cholesterol example)
Counterintuitive problem: the more personalized a therapy is, the smaller its target audience!
Many biologists are unfamiliar with the real possibilities
Useful applications may require straightforward application of well known principles, but writing a parallel application that can be used to treat people is a very difficult challenge
Attacks on all fronts simultaneously are needed
Interactive Tera-scale applications might for many biologists be more valuable right now than Peta-scale applications (even if we had them!)
Portals and the TeraGrid –> solutions to problems that biologists care about
Lots of open source codes are out there waiting for you

148. Acknowledgments
Some of the research described herein was supported by the following:\
The Indiana Genomics Initiative of Indiana University, supported in part by Lilly Endowment Inc.
The Indiana METACyt Initiative of Indiana University, supported in part by Lilly Endowment Inc.
Shared University Research grants from IBM, Inc. to Indiana University.
National Science Foundation under Grant No and Grant No. CDA Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Some of the ideas presented here were developed while the senior author was a visiting scientist at Höchstleistungsrechenzentrum Universität Stuttgart. Thanks to HLRS and everyone I have worked with there, especially Michael Resch, Matthias Müller, Peggy Lindner, Matthias Hess, Rainer Keller, and Edgar Gabriel.
John Herrin, Malinda Lingwall, & W. Leslie Teach assisted with graphics