Iosif Vaisman

Email: ivaisman@gmu.edu

Introduction to Bioinformatics

NIH working definition of bioinformatics and computational biology (July 2000)

The NIH Biomedical Information Science and Technology Initiative Consortium agreed on the following definitions of bioinformatics and computational biology recognizing that no definition could completely eliminate overlap with other activities or preclude variations in interpretation by different individuals and organizations.

Bioinformatics: Research, development, or application of computational tools and approaches for expanding the use of biological, medical, behavioral or health data, including those to acquire, store, organize, archive, analyze, or visualize such data.

Computational Biology: The development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, behavioral, and social systems.

Bioinformatics bibliography(papers with the word “bioinformatics” in title or abstract)

0100200300400500600700800900

1000

1988 1991 1994 1997 2000

Medline

ISI

PNAS

Liebman MN, Molecular modeling of protein structure and function: a bioinformatic approach.

J Comput Aided Mol Des 1988, 1(4):323-41

Dynamics of Database Growth

EMBL Sequence Database

100

10000

1000000

100000000

1983 1987 1991 1995 1999 2003

Comparative Sequence Sizes

• Yeast chromosome 3 350,000

• Escherichia coli (bacterium) genome 4,600,000

• Largest yeast chromosome now mapped 5,800,000

• Entire yeast genome 15,000,000

• Smallest human chromosome (Y) 50,000,000

• Largest human chromosome (1) 250,000,000

• Entire human genome 3,000,000,000

The String Alignment Problemstring - a sequence of characters from some alphabet

given: two strings acbcdb and cadbd

one of possible alignments:

a c - - b c d b- c a d b - d -

scoring function:exact match +2mismatch -1insertion -1

score:3 . (2) + 5 . (-1) = 1

The String Alignment Problem

given: two strings CTCATG and TACTTG

C T C A - T - G | | | |. T - A C T T G

score:4 . (2) + 4 . (-1) = 4

C T C A T G | | |T A C T T G

score:3 . (2) + 3 . (-1) = 3

Entropy and Redundancy of Language

CUR F W D DIS AND P

A SED IEND ROUGHT EATH EASE AIN BLES FR B BR AND AG

Entropy and Redundancy of Language

The sequences are 65% identical

A CURSED FIEND WROUGHT DEATH DISEASE AND PAIN|| |||| ||||| ||||||| ||||| ||||| |||A BLESSED FRIEND BROUGHT BREATH AND EASE AGAIN

** CUR**** F*****W******* D***** DIS*****AND P***|| |||| ||||| ||||||| ||||| ||||| |||**BLES****FR*****B*******BR*****AND ***** AG***

Substitution Matrices

• Dayhoff (or MDM, or PAM) - Derived from global alignments of closely related sequences

PAM100 - number referes to evolutionary distance (Percentage of Acceptable point Mutations per 108 years)

PAM100PAM100 PAM100 PAM100

PAM150PAM200

100 million years

200 million years

300 million years

Substitution Matrices

• BLOSUM (BLOcks SUbstitution Matrix) -Derived from local, ungapped alignments of distantly related sequences BLOSUM62 - number refers to the minimum percent identity

Reference: Henikoff & Henikoff Proteins 17:49, 1993

Selecting a Matrix

• Compared sequences are related: 200 PAM or 250 PAM

• Database scanning: 120 PAM

• Local alignment search: 40 PAM, 120 PAM, 250 PAM

• Detection of related sequences using BLAST: BLOSUM 62

THERE IS NO “ONE SIZE FITS ALL” MATRIX !

Low PAM:short segments,high similarity

High PAM:long segments,low similarity

Matrix Example

A B C D E F G H I K .. 1.5 0.2 0.3 0.3 0.3 -0.5 0.7 -0.1 0.0 0.0 .. A 1.1 -0.4 1.1 0.7 -0.7 0.6 0.4 -0.2 0.4 .. B 1.5 -0.5 -0.6 -0.1 0.2 -0.1 0.2 -0.6 .. C 1.5 1.0 -1.0 0.7 0.4 -0.2 0.3 .. D 1.5 -0.7 0.5 0.4 -0.2 0.3 .. E

1.5 -0.6 -0.1 0.7 -0.7 .. F 1.5 -0.2 -0.3 -0.1 .. G 1.5 -0.3 0.1 .. H 1.5 -0.2 .. I 1.5 .. K

Dayhoff’s Acceptable Point Mutations

Ala AArg R 30Asn N 109 17Asp D 154 0 532Cys C 33 10 0 0Gln Q 93 120 50 76 0Glu E 266 0 94 831 0 422Gly G 579 10 156 162 10 30 112His H 21 103 226 43 10 243 23 10Ile I 66 30 36 13 17 8 35 0 3Leu L 95 17 37 0 0 75 15 17 40 253Lys K 57 477 322 85 0 147 104 60 23 43 39Met M 29 17 0 0 0 20 7 7 0 57 207 90Phe F 20 7 7 0 0 0 0 17 20 90 167 0 17Pro P 345 67 27 10 10 93 40 49 50 7 43 43 4 7Ser S 772 137 432 98 117 47 86 450 26 20 32 168 20 40 269Thr T 590 20 169 57 10 37 31 50 14 129 52 200 28 10 73 696Trp W 0 27 3 0 0 0 0 0 3 0 13 0 0 10 0 17 0Tyr Y 20 3 36 0 30 0 10 0 40 13 23 10 0 260 0 22 23 6Val V 365 20 13 17 33 27 37 97 30 661 303 17 77 10 50 43 186 0 17 A R N D C Q E G H I L K M F P S T W Y Ala Arg Asn Asp Cys Gln Glu Gly His Ile Leu Lys Met Phe Pro Ser Thr Trp Tyr

Search and alignment entropy

• Information content per position: pam10 - 3.43 bits pam120 - 0.98 bits pam160 - 0.70 bits pam250 - 0.38 bits

blosum62 - 0.70 bits

• Information requirements: for search - 30 bits for alignment - 16 bit

Search and alignment entropy

Query length Substitution matrix Gap costs <35 PAM-30 ( 9,1) 35-50 PAM-70 (10,1) 50-85 BLOSUM-80 (10,1) >85 BLOSUM-62 (11,1)

Recommended matrices for different query length

FASTA AlgorithmS

eque

nce

ASequence B

First run(identities)

1

FASTA AlgorithmS

eque

nce

A

Sequence B

Rescoring usingPAM matrix

high score low score

2

The score of the highest scoring initial region is saved as the init1 score.

FASTA AlgorithmS

eque

nce

A

Sequence B

Joining threshold - eliminates disjointed segments

3

Non-overlapping regions are joined. The score equals sum of the scores of the regions minus a gap penalty. The score of the highest scoring region, at the end of this step, is saved as the initn score.

FASTA Algorithm

Alignmentoptimizationusing dynamicprogramming

Seq

uenc

e A

Sequence B 4

The score for this alignment is the opt score.

FASTA Algorithm

FastA uses a simple linear regression against the natural log of the search set sequence length to calculate a normalized z-score for the sequence pair.

Using the distribution of the z-score, the program can estimate the number of sequences that would be expected to produce, purely by chance, a z-score greater than or equal to the z-score obtained in the search. This is reported as the E() score.

• When init1=init0=opt: 100 % homology over the matched stretch.

• When initn > init1: more than 1 matching region in the database with poorly matching separating regions.

• When opt > initn: the matching regions are greatly improved by adding gaps in one or both of the sequences.

FASTA Results

BLAST - Basic Local Alignment Search Tool

• Blast programs use a heuristic search algorithm. The programs use the statistical methods of Karlin and Altschul (1990,1993).

• Blast programs were designed for fast database searching, with minimal sacrifice of sensitivity to distant related sequences.

BLAST Algorithm

Query sequence of length L

Maximium of L-w+1 words(typically w = 3 for proteins)

For each word from the query sequence find the list of words with high score using a substitutionmatrix (PAM or BLOSUM)

Word list

1

BLAST Algorithm

Database sequences

Exact matches of words from the word list to the database sequences

Word list

2

BLAST Algorithm

3

Maximal Segment Pairs (MSPs)

For each exact word match, alignment is extended in both directions to find high score segments

Gapped BLAST

• The Gapped Blast algorithm allows gaps to be introduces into the alignments. That means that similar regions are not broken into several segments.

• This method reflects biological relationships much better.

BLAST family of programs• blastp - amino acid query sequence against a protein

sequence database • blastn - nucleotide query sequence against a

nucleotide sequence database • blastx - nucleotide query sequence translated in all

reading frames against a protein database • tblastn - protein query sequence against a nucleotide

sequence database dynamically translated in all reading frames

• tblastx - six-frame translations of a nucleotide query sequence against the six-frame translations of a nucleotide sequence database.

Database Searches• Run Blast first, then depending on your results run a

finer tool (Fasta, Smith-Waterman, etc.) • Where possible use translated sequence.• E() < 0.05 is statistically significant, usually

biologically interesting. Check also 0.05 < E() <10 because you might find interesting hits.

• Pay attention to abnormal composition of the query sequence, it usually causes biased scoring.

• Split large query sequence ( if >1000 for DNA, >200 for protein).

• If the query has repeated segments, remove them and repeat the search.

Documenting the Search

• Algorithm(s)

• Substitution matrix

• Gap penalty (FASTA)

• Name of database

• Version of database

• Computer used

MULTIPLE SEQUENCE ALIGNMENT

Computational complexity

Alignment of protein sequences with 200 amino acid residues:

# of sequences CPU time

2 1 sec

3 200 sec

10 2008

sec

Multiple alignment

VTISCTGSSSNIGAG-NHVKWYQQLPGVTISCTGTSSNIGS--ITVNWYQQLPGLRLSCSSSGFIFSS--YAMYWVRQAPGLSLTCTVSGTSFDD--YYSTWVRQPPGPEVTCVVVDVSHEDPQVKFNWYVDG--ATLVCLISDFYPGA--VTVAWKADS--AALGCLVKDYFPEP--VTVSWNSG---VSLTCLVKGFYPSD--IAVEWESNG--

Column cost: the sum of costs for all possible pairs

Multiple alignment

A correct multiple alignment corresponds to an evolutionary history:

no correct way to determine practical way - to find an alignment with the maximum score

Multiple sequence alignment

Given k (k > 2) sequences, s1,…, sk, each sequence

consisting of characters from an alphabet A multiple alignment is a a rectangular array, consisting of characters from the alphabet A’ (A + "-"), that satisfies the following 3 conditions:

1. There are exactly k rows. 2. Ignoring the gap character, row number i is

exactly the sequence si. 3. Each column contains at least one character

different from "-".

Consensus

Plurality - minimum number of votes for a consensusThreshold - scoring matrix value below which a symbol may not vote for a coalition.Sensitivity - minimum score to select consensusProfiles - blocks of prealigned sequences

Multiple alignment algorithm

1. Pairwise alignments (progressive pairwise alignments) 2. Distance matrix calculation3. Guide tree creation (hierarchical clustering)4. New sequence addition

Scoring system (distances)

Sreal(ij) - Srand(ij)

Siden(ij) - Srand(ij)x 100D(ij) = -ln

Sreal(ij) - observed similarity score for two aligned sequences i and j

Siden(ij) - average of the two scores for each sequence aligned with itself

Srand(ij) - average score determined from 100 global randomizations of the two sequences

The distances D(ij) are used to generate the distance matrix from which the approximate guide tree is generated.

Multiple alignment

Multiple alignment

Segment - line joining two vertices

Each unit m-dimensional cube in the lattice contains 2m -1 segments

A

B

(0,0)

(1,1)

(0,1)

(1,0)

A

B

C

(0,0,0)

(1,1,1)

Multiple alignment

Alignment Path for 3 Sequences(0,0,0), (1,0,0), (2,1,0), (3,2,0), (3,3,1), (4,3,2)

Multiple alignment

Pairwise Projections of the Alignment

V S N - S- S N A -- - - A S

Alignment statistics

Rablpb Humcetp Rabcetp Bovbpi Humlbpa Ratlbp Maccetp Humbpi 1 2 3 4 5 6 7 8

478 67% 65% 19% 19% 18% 42% 43%1 0 82% 80% 39% 39% 36% 64% 65% 0 1% 0% 5% 5% 12% 2% 2%

327 483 58% 16% 16% 16% 39% 41%2 400 0 75% 38% 38% 35% 62% 63% 5 0 0% 5% 5% 12% 1% 1%

318 284 482 18% 18% 17% 40% 43%3 390 367 0 38% 38% 35% 64% 64% 4 1 0 5% 5% 12% 1% 1%

96 84 95 494 95% 74% 20% 21%4 198 192 194 0 98% 84% 40% 41% 30 29 28 0 0% 7% 6% 5%

Alignment score

Rablpb Humcetp Rabcetp Bovbpi Humlbpa Ratlbp Maccetp Humbpi 1 2 3 4 5 6 7 8

1 4077

2 5358 4129

3 5323 5650 4096

4 8103 8229 8112 4210

5 8109 8243 8118 4332 4219

6 8535 8672 8575 5511 5519 4261

7 6474 6531 6500 8103 8119 8572 4103

8 6392 6434 6378 8033 8035 8520 5508 4083

1 2 3 4 5 6 7 8

Alignment visualization Humlbpa : Rablpb : Ratlbp : Humcetp : Maccetp : Rabcetp : Humbpi : Bovbpi :

* * * * 50 M---MGALARALPS-ILLALLLTSTPEALGA-NPGLVARITDKGLQYAAQEGLLALQM---MGTWARALLGSTLLSLLLAAAPGALGT-NPGLITRITDKGLEYAAREGLLALQM---MKSATGPLLP-TLLGLLLLSIPRTQGV-NPAMVVRITDKGLEYAAKEGLLSLQM---MLAATVLT---LALLGNAHACSKGTSH-EAGIVCRITKPALLVLNHETAKVIQM---MLAATVLT---LALLGNVHACSKGTSH-KAGIVCRITKPALLVLNQETAKVIQ-----------------------ACPKGASY-EAGIVCRITKPALLVLNQETAKVVQMRENMARGPCNAPRWVSLMVLVAIGTAVTAAVNPGVVVRISQKGLDYASQQGTAALQM---MARGPDTARRWATLVVLAALGTAVTTT-NPGIVARITQKGLDYACQQGVLTLQm m l g66 RI3 L 2 6Q

: 52 : 53 : 52 : 50 : 50 : 33 : 57 : 53

Humlbpa : Rablpb : Ratlbp : Humcetp : Maccetp : Rabcetp : Humbpi : Bovbpi :

: 130 : 131 : 130 : 131 : 131 : 114 : 135 : 131

Identity

Summary view

Alignment visualization Humlbpa : Rablpb : Ratlbp : Humcetp : Maccetp : Rabcetp : Humbpi : Bovbpi :

* * * * 50 M---MGALARALPS-ILLALLLTSTPEALGA-NPGLVARITDKGLQYAAQEGLLALQM---MGTWARALLGSTLLSLLLAAAPGALGT-NPGLITRITDKGLEYAAREGLLALQM---MKSATGPLLP-TLLGLLLLSIPRTQGV-NPAMVVRITDKGLEYAAKEGLLSLQM---MLAATVLT---LALLGNAHACSKGTSH-EAGIVCRITKPALLVLNHETAKVIQM---MLAATVLT---LALLGNVHACSKGTSH-KAGIVCRITKPALLVLNQETAKVIQ-----------------------ACPKGASY-EAGIVCRITKPALLVLNQETAKVVQMRENMARGPCNAPRWVSLMVLVAIGTAVTAAVNPGVVVRISQKGLDYASQQGTAALQM---MARGPDTARRWATLVVLAALGTAVTTT-NPGIVARITQKGLDYACQQGVLTLQm m l g66 RI3 L 2 6Q

: 52 : 53 : 52 : 50 : 50 : 33 : 57 : 53

Physico-chemical properties Humlbpa : Rablpb : Ratlbp : Humcetp : Maccetp : Rabcetp : Humbpi : Bovbpi :

* * * * 50 .---.G.LA...PS-...A...TST.EALG.-.....A...D................---.GTWA....GST..S.....A.GALGT-.....T...D.......R........---.KS..GP..P-T..G...LSI..TQGV-..A..V...D.......K....S...---.L...VLT---.A..GNAH..S....H-EA........PA.LVLNH.TAKV...---.L...VLT---.A..GN.H..S....H-KA........PA.LVLN..TAKV..-----------------------.....A.Y-EA........PA.LVLN..TAKV........RGPCNAP...S......IGTAV.A.......V...Q...D..S...TA....---..RGPDTAR..AT....A.LGTAV..T-.....A...Q...D..C.....T..m m l g66 RI3 L 2 6Q

: 52 : 53 : 52 : 50 : 50 : 33 : 57 : 53

Differences mode

Alignment visualization (tree)

Sequence Logos: a quantitative graphical display for binding sites and proteins

Reference: Schneider, T.D. Meth. Enzym 274:445, 1996

Sequence Logos

Sequence Logos

Multiple Alignment Programs

• Pileup (GCG): Needleman and Wunsch algorithm for pairwise alignment and UPGMA method for tree

construction

• CLUSTAL: Wilbur and Lipman algorithm for pairwise alignment (CABIOS 8:189, 1992)

• PIMA: pattern-matching based algorithm (PNAS 87:118, 1990)

• TreeAlign: phylogenetic algorithm (Meth. Enzymol. 18:626, 1990)

Patterns in protein sequencesPatterns in protein sequences

Regular ExpressionsPatterns described in a standard way are known as regular expressions

x-x or x-x-x

not D or E

I or L or V

x(2,3)

{DE}

[ILV]

END.

C-terminal>

N-terminal<

separator-

repetitions( )

NOT{ }

OR[ ]

ANYx

Regular Expressions

[AC]-x-V-x(4)-{ED}.

[Ala or Cys]-any-Val-any-any-any-any-{any but Glu or Asp}

...LKHVAYVFQALIYWIK...

...AVEMAGVKYLQVQHGS...

...LYTGAIVTNNDGPYMA...

...KEYKCKVEKELTDICN...

PROSITE Database Current version contains 1079 documentation entries that describe 1459 different patterns, rules and profiles/matrices

[ST]-x(2)-[DE] Casein kinase II phosphorylation site

[AG]-x(4)-G-K-[ST] ATP/GTP-binding site motif A (P-loop)

Y-x-[NQH]-K-[DE]-[IVA]-F-[LM]-R-[ED]Heat shock hsp90 proteins family signature

http://www.expasy.ch/prosite

Blocks DatabaseBlocks are multiply aligned ungapped segments corresponding to the most highly conserved regions of proteins

DMA_VIBCH|Q08318 (85) SCTQWWPPF 77 HEMK_MYCLE|P45832 (181) DLFVAQPTL 100 MT57_ECOLI|P25240 (111) DGALGNPPF 13 MTC1_CHVN1|Q01511 (172) NFVFLDPPY 8 MTC1_COREQ|P42828 (71) QLSFSCPPF 49 MTH2_HAEHA|P00473 (32) KIAFFDPQY 52 MTH3_HAEIN|P43871 (23) HAIISDIPY 73 MTM1_MICAM|P50190 (306) AAVLTNPPF 14 MTM2_MORBO|P23192 (25) QLAVIDPPY 10 MTMU_MYCSP|P43641 (37) QVIYADPPW 13 MTR1_RHOSH|P14751 (60) QLIICDPPY 8....................................

N-6 Adenine-specific DNA methylases proteinswidth=9 seqs=78

http://www.blocks.fhcrc.org/

Pfam DatabasePfam is a large collection of multiple sequence alignments and hidden Markov models covering many common protein domains

TYY1_HUMAN/383-407 YVCPF.DGCN...KKFAQSTNLKSHILT...H ZG52_XENLA/61-83 YTCT...QCN...KQFSHSAQLRAHIST...H KRUP_DROME/306-328 YTCE...ICD...GKFSDSNQLKSHMLV...H YKQ8_CAEEL/78-102 YKCT...VCR...KDISSSESLRTHMFKQ.HH DEFI_CHICK/268-292 YECP...NCK...KRFSHSGSYSSHISSK.KC ZFH1_DROME/389-413 FGCD...NCG...KRFSHSGSFSSHMTSK.KC YL57_CAEEL/42-65 YLCY...YCG...KTLSDRLEYQQHMLK..VH ZFA_MOUSE/542-564 FKCD...ICL...LTFSDTKEVQQHALV...H BASO_HUMAN/719-742 FQCD...ICK...KTFKNACSVKIHHKN..MH HUNB_DROME/297-319 FQCD...KCS...YTCVNKSMLNSHRKS...H SFP1_YEAST/598-623 FKCPV.IGCE...KTYKNQNGLKYHRLH..GH ZG29_XENLA/62-84 FVCT...VCG...KTYKYKHGLNTHLHS...H

Zinc finger, C2H2 type

http://pfam.wustl.edu/

Other Motif Databases

PRINTS : a compendium of protein fingerprints. A fingerprint is a group of conserved motifs used to characterise a protein familyhttp://bioinf.man.ac.uk/dbbrowser/PRINTS/

DOMO : a protein domain databasehttp://www.infobiogen.fr/~gracy/domo/home.htm

ProDom : a protein domain database http://protein.toulouse.inra.fr/prodom.html

InterPro Database

InterPro : integrated resource for the commonly used signature databases - Pfam, PRINTS, PROSITE, ProDom and SWISS-PROT + TrEMBL.

Current release of InterPro (3.2) contains 3939 entries, representing 1009 domains, 2850 families, 65 repeats and 15 post-translational modification sites.

http://www.ebi.ac.uk/interpro

InterPro Database

DNA

RNA

mRNA

TRANSCRIPTION

SPLICING

PROMOTERELEMENTS

PROTEIN

TRANSLATION

STARTCODON

STOPCODON

SPLICESITES

From genes to proteins

From genes to proteins

Chr

omos

ome

19 g

ene

map

Computational Gene Prediction

•Where the genes are unlikely to be located?

•How do transcription factors know where to bind a region of DNA?

•Where are the transcription, splicing, and translation start and stop

signals?

•What does coding region do (and non-coding regions do not) ?

•Can we learn from examples?

•Does this sequence look familiar?

Measures of Prediction Accuracy

TN FPFN TN TNTPFNTP FN

REALITY

PREDICTION

PR

ED

ICT

ION

REALITY

TP

FN TN

FP

c

cnc

ncSn = TP / (TP + FN)

Sp = TP / (TP + FP)

Sensitivity

Specificity

Nucleotide Level

Measures of Prediction Accuracy

REALITY

PREDICTION

Exon Level

WRONGEXON

CORRECTEXON

MISSINGEXON

Sn =Sensitivitynumber of correct exonsnumber of actual exons

Sp =Specificitynumber of correct exons

number of predicted exons

Spliced Alignment (Procrustes)

•New genomic sequence

•Selection of candidate exonsAUG --- GU initial exonsAG --- GU internal exonsAG --- UAA or UAG or UGA terminal exons

•Filtration (based on the codon usge statistics)

•Construction of all possible chains of candidate exons

•Finding a chain with the maximum global similarity to the target protein

Spliced Alignment (Procrustes)

Predicted Exon Assembly(Procrustes)

PCR Primers Prediction (GenePrimer)

Exon 1085..1182 (98) hit using first 2 primers Exon 1628..1676 (49) missed Exon 1900..2001 (102) hit using first 8 primers Exon 2110..2184 (75) missed Exon 2516..2722 (207) hit using first 4 primers Exon 3385..3472 (88) missed Exon 3546..3746 (201) hit using first primer ...

GRAIL gene identification program

POSSIBLE EXONSREFINED EXON

POSITIONSFINAL EXON CANDIDATES

Suboptimal Solutions for the Human Growth Hormone Gene (GeneParser)

GeneMark Accuracy Evaluation

Gene Discovery Exercisehttp://metalab.unc.edu/pharmacy/Bioinfo/Gene

Bibliographyhttp://linkage.rockefeller.edu/wli/gene/list.html

andhttp://www-hto.usc.edu/software/procrustes/fans_ref/