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Introduction to Bioinformatics. Iosif Vaisman. Email: ivaisman@gmu.edu. NIH working definition of bioinformatics and computational biology (July 2000).

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iosif vaisman

Introduction to Bioinformatics

Iosif Vaisman

Email: ivaisman@gmu.edu

nih working definition of bioinformatics and computational biology july 2000
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

Liebman MN, Molecular modeling of protein

structure and function: a bioinformatic approach.

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

Bioinformatics bibliography(papers with the word “bioinformatics” in title or abstract)
comparative sequence sizes
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 problem
The String Alignment Problem

string - 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 -

score:

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

scoring function:

exact match +2

mismatch -1

insertion -1

the string alignment problem1
The String Alignment Problem

given: two strings CTCATG and TACTTG

C T C A T G

| | |

T A C T T G

score:

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

C T C A - T - G

| | | |

. T - A C T T G

score:

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

entropy and redundancy of language
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 language1
Entropy and Redundancy of Language

** CUR**** F*****W******* D***** DIS*****AND P***

|| |||| ||||| ||||||| ||||| ||||| |||

**BLES****FR*****B*******BR*****AND ***** AG***

The sequences are 65% identical

A CURSED FIEND WROUGHT DEATH DISEASE AND PAIN

|| |||| ||||| ||||||| ||||| ||||| |||

A BLESSED FRIEND BROUGHT BREATH AND EASE AGAIN

substitution matrices

PAM100

PAM100

PAM100

PAM100

PAM200

PAM150

Substitution Matrices
  • Dayhoff (or MDM, or PAM) - Derived from global alignments of closely related sequencesPAM100 - number referes to evolutionary distance (Percentage of Acceptable point Mutations per 108 years)

300 million years

200 million years

100 million years

substitution matrices1
Substitution Matrices
  • BLOSUM (BLOcks SUbstitution Matrix) -Derived from local, ungapped alignments of distantly related sequencesBLOSUM62 - number refers to the minimum percent identity

Reference: Henikoff & Henikoff Proteins17:49, 1993

selecting a matrix
Selecting a Matrix

Low PAM:

short segments,

high similarity

High PAM:

long segments,

low similarity

  • 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 !

matrix example
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
Dayhoff’s Acceptable Point Mutations

Ala A

Arg R 30

Asn N 109 17

Asp D 154 0 532

Cys C 33 10 0 0

Gln Q 93 120 50 76 0

Glu E 266 0 94 831 0 422

Gly G 579 10 156 162 10 30 112

His H 21 103 226 43 10 243 23 10

Ile I 66 30 36 13 17 8 35 0 3

Leu L 95 17 37 0 0 75 15 17 40 253

Lys K 57 477 322 85 0 147 104 60 23 43 39

Met M 29 17 0 0 0 20 7 7 0 57 207 90

Phe F 20 7 7 0 0 0 0 17 20 90 167 0 17

Pro P 345 67 27 10 10 93 40 49 50 7 43 43 4 7

Ser S 772 137 432 98 117 47 86 450 26 20 32 168 20 40 269

Thr T 590 20 169 57 10 37 31 50 14 129 52 200 28 10 73 696

Trp W 0 27 3 0 0 0 0 0 3 0 13 0 0 10 0 17 0

Tyr Y 20 3 36 0 30 0 10 0 40 13 23 10 0 260 0 22 23 6

Val 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
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 entropy1
Search and alignment entropy

Recommended matrices for different query length

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)

fasta algorithm

Sequence B

Sequence A

FASTA Algorithm

1

First run

(identities)

fasta algorithm1

2

Sequence B

Rescoring using

PAM matrix

high score

low score

Sequence A

FASTA Algorithm

The score of the highest

scoring initial region is

saved as the init1 score.

fasta algorithm2

Sequence B

Sequence A

FASTA Algorithm

3

Joining threshold - eliminates disjointed segments

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 algorithm3

Sequence B

Sequence A

FASTA Algorithm

4

Alignment

optimization

using dynamic

programming

The score for this alignment

is the opt score.

fasta algorithm4
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.

slide22

FASTA Results

  • 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.
blast basic local alignment search tool
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
BLAST Algorithm

1

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 substitution

matrix (PAM or BLOSUM)

Word list

blast algorithm1
BLAST Algorithm

2

Database sequences

Word list

Exact matches of words from the word list

to the database sequences

blast algorithm2
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
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
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
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
Documenting the Search
  • Algorithm(s)
  • Substitution matrix
  • Gap penalty (FASTA)
  • Name of database
  • Version of database
  • Computer used
computational complexity
Computational complexity

Alignment of protein sequences with 200 amino acid residues:

multiple alignment
Multiple alignment

VTISCTGSSSNIGAG-NHVKWYQQLPG

VTISCTGTSSNIGS--ITVNWYQQLPG

LRLSCSSSGFIFSS--YAMYWVRQAPG

LSLTCTVSGTSFDD--YYSTWVRQPPG

PEVTCVVVDVSHEDPQVKFNWYVDG--

ATLVCLISDFYPGA--VTVAWKADS--

AALGCLVKDYFPEP--VTVSWNSG---

VSLTCLVKGFYPSD--IAVEWESNG--

Column cost: the sum of costs for all possible pairs

multiple alignment1
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 alignment1
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
Consensus

Plurality - minimum number of votes for a consensus

Threshold - scoring matrix value below which a symbol

may not vote for a coalition.

Sensitivity - minimum score to select consensus

Profiles - blocks of prealigned sequences

multiple alignment algorithm
Multiple alignment algorithm

1. Pairwise alignments (progressive pairwise alignments)

2. Distance matrix calculation

3. Guide tree creation (hierarchical clustering)

4. New sequence addition

scoring system distances

Sreal(ij) - Srand(ij)

D(ij)= -ln

x 100

Siden(ij) - Srand(ij)

Scoring system (distances)

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 alignment3

(1,1,1)

C

(1,0)

(1,1)

B

B

(0,0)

(0,1)

A

(0,0,0)

A

Multiple alignment

Segment - line joining two vertices

Each unit m-dimensional cube in the lattice

contains 2m -1 segments

multiple alignment4
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 alignment5
Multiple alignment

V S N - S

- S N A -

- - - A S

Pairwise Projections of the Alignment

alignment statistics
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
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
Alignment visualization

Identity

Summary view

alignment visualization1
Alignment visualization

Physico-chemical properties

Differences mode

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

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

multiple alignment programs
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 (CABIOS8:189, 1992)
  • PIMA: pattern-matching based algorithm (PNAS87:118, 1990)
  • TreeAlign: phylogenetic algorithm (Meth. Enzymol. 18:626, 1990)
regular expressions

x

ANY

[ ]

OR

[ILV]

I or L or V

{ }

NOT

{DE}

not D or E

( )

repetitions

x(2,3)

x-x or x-x-x

-

separator

<

N-terminal

>

C-terminal

.

END

Regular Expressions

Patterns described in a standard way are known as regular expressions

regular expressions1
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
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 database
Blocks Database

Blocks are multiply aligned ungapped segments corresponding

to the most highly conserved regions of proteins

N-6 Adenine-specific DNA methylases proteins

width=9 seqs=78

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

....................................

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

pfam database
Pfam Database

Pfam is a large collection of multiple sequence alignments and

hidden Markov models covering many common protein domains

Zinc finger, C2H2 type

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

http://pfam.wustl.edu/

other motif databases
Other Motif Databases

PRINTS : a compendium of protein fingerprints.

A fingerprint is a group of conserved motifs used

to characterise a protein family

http://bioinf.man.ac.uk/dbbrowser/PRINTS/

DOMO : a protein domain database

http://www.infobiogen.fr/~gracy/domo/home.htm

ProDom : a protein domain database

http://protein.toulouse.inra.fr/prodom.html

interpro database
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

slide61

From genes to proteins

DNA

PROMOTER

ELEMENTS

TRANSCRIPTION

RNA

SPLICE

SITES

SPLICING

mRNA

START

CODON

STOP

CODON

TRANSLATION

PROTEIN

computational gene prediction
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

FN

TN

FN

TP

FN

TN

TN

TP

FP

REALITY

PREDICTION

REALITY

Sensitivity

c

nc

Sn = TP / (TP + FN)

FP

TP

c

PREDICTION

Specificity

FN

nc

TN

Sp = TP / (TP + FP)

Measures of Prediction Accuracy

Nucleotide Level

measures of prediction accuracy1

number of correct exons

Sensitivity

Sn =

number of actual exons

number of correct exons

Sp =

Specificity

number of predicted exons

Measures of Prediction Accuracy

Exon Level

MISSING

EXON

WRONGEXON

CORRECTEXON

REALITY

PREDICTION

spliced alignment procrustes
Spliced Alignment (Procrustes)
  • New genomic sequence
  • Selection of candidate exons AUG --- GU initial exons AG --- GU internal exons AG --- 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
pcr primers prediction geneprimer
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

REFINED EXON

POSITIONS

FINAL EXON CANDIDATES

POSSIBLE EXONS

GRAIL gene identification program
slide75

Bibliography

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

and

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

Gene Discovery Exercise

http://metalab.unc.edu/pharmacy/Bioinfo/Gene

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