Similarity searches in biological sequence databases€¦ · september 2004 Page 4 Genbank entry example LOCUS AF455746_1 80 aa PRI 08-JAN-2002 DEFINITION ubiquitin-conjugating enzyme

september 2004 Page 1

Similarity searches in biological sequence databases

Volker Flegel


Outline

Keyword search in databases• General concept

Examples• SRS http://srs.ebi.ac.uk• Entrez http://www.ncbi.nih.gov/Entrez/index.html• Expasy http://www.expasy.uniprot.org/search/textSearch.shtml

Similarity searches in databases• Goal• Definitions• Alignment visualisation• Alignment algorithms

Examples• FASTA• BLAST and its gory details


Keyword search

Accessing database entries• Each database uses its own specific access methods

Several kinds of search possibilities according to the data stored– Identification number (unique)– Authors– Keywords, ...

Biological sequence databases• Use a unique identification number to retrieve a specific sequence• This identification number must remain constant accross the database

releases

• Genbank / EMBL / DDBJaccession.version

• Swiss-Protaccession and id (Note: id may change)


Genbank entry example

LOCUS AF455746_1 80 aa PRI 08-JAN-2002DEFINITION ubiquitin-conjugating enzyme [Homo sapiens].ACCESSION AAL58874PID g18087414VERSION AAL58874.1 GI:18087414DBSOURCE locus AF455746 accession AF455746.1KEYWORDS .SOURCE human.ORGANISM Homo sapiens

Eukaryota; Metazoa; Chordata; Craniata; Vertebrata; Euteleostomi;Mammalia; Eutheria; Primates; Catarrhini; Hominidae; Homo.

REFERENCE 1 (residues 1 to 80)AUTHORS Poloumienko,A.TITLE Exon-intron structure of the mammalian ubiquitin-conjugating enzyme

(HR6A) genesJOURNAL Unpublished

COMMENT Method: conceptual translation supplied by author.FEATURES Location/Qualifiers

source 1..80/organism="Homo sapiens"/db_xref="taxon:9606"/chromosome="X"/cell_line="MCF-7"

Protein 1..80/product="ubiquitin-conjugating enzyme"

CDS 1..80/gene="HR6A"/coded_by="join(AF455746.1:<1..64,AF455746.1:1057..1145,AF455746.1:1594..>1680)"

ORIGIN 1 teeypnkppt vrfvskmfhp nvyadgsicl dilqnrwspt ydvssiltsi qslldepnpn61 spansqaaql yqenkreyek

//


SwissProt entry exampleID UBCA_HUMAN STANDARD; PRT; 152 AA.AC P49459;DT 01-FEB-1996 (Rel. 33, Created)DT 01-FEB-1996 (Rel. 33, Last sequence update)DT 16-OCT-2001 (Rel. 40, Last annotation update)DE Ubiquitin-conjugating enzyme E2-17 kDa (EC 6.3.2.19)DE (Ubiquitin-protein ligase) (Ubiquitin carrier protein) (HR6A).GN UBE2A.OS Homo sapiens (Human).OC Eukaryota; Metazoa; Chordata; Craniata; Vertebrata; Euteleostomi;OC Mammalia; Eutheria; Primates; Catarrhini; Hominidae; Homo.OX NCBI_TaxID=9606;RN [1]RP SEQUENCE FROM N.A.RX MEDLINE=92020951; PubMed=1717990;RA Koken M.H.M., Reynolds P., Jaspers-Dekker I., Prakash L., Prakash S.,RA Bootsma D., Hoeijmakers J.H.J.;RT "Structural and functional conservation of two human homologs of theRT yeast DNA repair gene RAD6.";RL Proc. Natl. Acad. Sci. U.S.A. 88:8865-8869(1991).(...)DR EMBL; M74524; AAA35981.1; -.DR HSSP; P25865; 2AAK.DR MIM; 312180; -.DR InterPro; IPR000608; UBQ_conjugat.DR Pfam; PF00179; UQ_con; 1.DR SMART; SM00212; UBCc; 1.DR PROSITE; PS00183; UBIQUITIN_CONJUGAT_1; 1.DR PROSITE; PS50127; UBIQUITIN_CONJUGAT_2; 1.KW Ubiquitin conjugation; Ligase; Multigene family.FT BINDING 88 88 UBIQUITIN (BY SIMILARITY).SQ SEQUENCE 152 AA; 17243 MW; 7A86173D5FAE6DE1 CRC64;

MSTPARRRLM RDFKRLQEDP PAGVSGAPSE NNIMVWNAVI FGPEGTPFGD GTFKLTIEFTEEYPNKPPTV RFVSKMFHPN VYADGSICLD ILQNRWSPTY DVSSILTSIQ SLLDEPNPNSPANSQAAQLY QENKREYEKR VSAIVEQSWR DC

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Similarity searches

Concept• Generalisation (asymmetric) of a pairwise comparison

sequence sequence

sequence

database

Query Subject

Pairwise alignment

Similarity searches

database database

Database vs. database


Theoretical considerations

Similar to those of pairwise comparison • Sequence divergence is due to evolutionary mechanisms• Sequence similarity allows information extrapolation:

Sequence history and originBiological function3D structure

Alignement types• Global Alignment between the complete sequence A and the

complete sequence B• Local Alignment between a sub-sequence of A and a sub-

sequence of B

Computer implementation (Algorithms)• Dynamic programing• Global Needleman-Wunsch• Local Smith-Waterman


Problems to solve

Similarity search mechanism• A pairwise comparison is done successively between the query and every

sequence of the database

Obstacles• The complexity of the task is proportional to the size of the database

Extremely long running time of the searchDifficult biological interpretation of the results

Solutions• Reduce search time by using more powerful computers• Reduce search time by using newer and faster algorithms (heuristics)• Sort and analyse the resulting alignments using statistical methods


Definitions

QuerySequence that is being compared against the database.

Subject Sequence of the database that matches the query.

Exact algorithm An exact algorithm is guaranteed to find the best alignment, or at least one of the best in case of a tie.

Heuristic algorithm A heuristic algorithm is not guaranteed to find the best alignment.But good ones often do, and much quicker than exact ones.


Some more definitions

Identity Proportion of pairs of identical residues between two aligned sequences.Generally expressed as a percentage.This value strongly depends on how the two sequences are aligned.

SimilarityProportion of pairs of similar residues between two aligned sequences.If two residues are similar is determined by a substitution matrix.This value also depends strongly on how the two sequences are aligned, as well as on the substitution matrix used.

Homology Two sequences are homologous if and only if they have a common ancestor.There is no such thing as a level of homology ! (It's either yes or no)

• Homologous sequences do not necessarily serve the same function...

• ... Nor are they always highly similar: structure may be conserved while sequence is not.


Alignment score

Amino acid substitution matrices • Example: PAM250• Most used: Blosum62

Raw score of an alignment

TPEA¦| |APGA

TPEA¦| |APGA

Score = 1 = 9+ 6 + 0 + 2


Insertions and deletions

Gap penalties

• Opening a gap penalizes an alignment score• Each extension of a gap penalizes the alignment's score• The gap opening penalty is in general higher than the gap extension

penalties (simulating evolutionary behavior)

• The raw score of a gapped alignment is the sum of all amino acid substitutions from which we subtract the gap opening and extensionpenalties.

Seq A GARFIELDTHE----CAT||||||||||| |||

Seq B GARFIELDTHELASTCAT

Seq A GARFIELDTHE----CAT||||||||||| |||

Seq B GARFIELDTHELASTCAT

gap

gap openinggap extension


Alignment visualisation

Matrix - Text - DotplotAn alignment is a path through a graphDotPlot: Graphical view in 2 dimensions

Visual aid to identify regions of similarity

Address: www.isrec.isb-sib.ch/java/dotlet/Dotlet.html

Tissue-Type plasminogen Activator

Urokinase-Type plasm

inogen Activator

Seq B A-CA-CA| || |

Seq A ACCAAC-

Seq B A-CA-CA| || |

Seq A ACCAAC-

Seq B ACA--CA|

Seq A A-CCAAC

Seq B ACA--CA|

Seq A A-CCAAC


Optimal alignment extension

How to extend optimaly an optimal alignment• An optimal alignment up to positions i and j can be extended in 3 ways.• Keeping the best of the 3 guarantees an extended optimal alignment.

Seq A a1 a2 a3 ... ai-1 ai

Seq B b1 b2 b3 ... bj-1 bj



• We have the optimal alignment extended from i and j by one residue.





ai+1

bj+1

ai+1

bj+1

Score = Scoreij + Substi+1 j+1





ai+1

-

ai+1

-Score = Scoreij - gap





-

bj+1

-

bj+1

Score = Scoreij - gap


Exact algorithms (Needleman-Wunsch / Smith - Waterman)

Simple example (Needleman-Wunsch)

• Scoring system:Match score: 2Mismatch score: -1Gap penalty: -2

Note• We have to keep track of the origin of the score for each element in the

matrix.This allows to build the alignment by traceback when the matrix has been completely filled out.

• Computation time is proportional to the size of sequences (n x m).

G A T T A

0 -2 -4 -6 -8 -10

G -2

A -4

A -6

T -8

T -10

C -12

G A T T A

0 -2 -4 -6 -8 -10

G -2 2 0 -2 -4 -6

A -4 0 4

A -6

T -8

T -10

C -12

0 - 2

0 - 2

2 + 2

G A T T A

0 -2 -4 -6 -8 -10

G -2 2 0 -2 -4 -6

A -4 0 4 2 0 -2

A -6 -2 2 3 1 2

T -8 -4 0 4 5 3

T -10 -6 -2 2 6 4

C -12 -8 -4 0 4 5

F(i-1,j) F(i,j)

s(xi,yj)

F(i-1,j-1)

-d

F(i,j-1)

-d

F(i,j): score at position i, js(xi,yj): match or mismatch score (or substitution matrix

value) for residues xi and yjd: gap penalty (positive value)

GA-TTA|| ||GAATTC

GA-TTA|| ||GAATTC


Heuristic algorithms

Faster but less sensitive• They use the dynamic programming approach like exact algorithms• They try to limit its use to sequences which seem interesting

The heuristic part of the algorithm tries to make a clever guess at which sequences would produce an interesting alignment.

FASTA • Developped by Lipman and Pearson in 1985• Tries to find sequences having identical words (or k-tuples = k

consecutive residues) in common on a same diagonal.• Compares the query sequentially to all those sequences in the database.

Blast • Developped by Altschul et al. in 1990• The most used and cited bioinformatics tool in biology• Online tutorial: www.ncbi.nlm.nih.gov/Education/BLASTinfo/tut1.html


A Blast for each query

Different programs are available according to the type of query

Program Query Database

blastp protein protein

blastn nucleotide nucleotide

blastx

protein

nucleotide

protein

tblastn

protein protein

nucleotide

tblastx

protein

nucleotide

protein

nucleotide

VS

VS

VS

VS

VS


Access to Blast

Web access • Numerous web sites offer access to Blast servers

NCBI (USA) where the Blast program was created– Provide access to all Blast options and numerous databases– User interface not very intuitive

• URL: www.ncbi.nlm.nih.gov/BLAST

EMBnet (i.e. Swiss node located in Lausanne at the SIB)– Several servers across the world– Provide access to all Blast options– Provide a simplified and an advanced user interface– Wide choice of databases

• URL: www.ch.embnet.org/software/bBLAST.html (Simple user interface)

www.ch.embnet.org/software/aBLAST.html (Advanced user interface)


Blast: the gory details

Blast algorithm: creating a list of similar words

RELQuery

RSLRSL

AAAAACAAD

YYY

AAAAACAAD

YYY

List of all possible words with3 amino acid residues

...

ACT

RSL

TVF

ACT

RSL

TVF

List of words matching thequery with a score > T

score > T

......

LKPLKP

LKPLKP

score < T

A substitution matrix is used to compute the word scoresA substitution matrix is used to compute the word scores


Blast: the gory details

ACT

RSL

TVF

ACT

RSL

TVF

List of words matching thequery with a score > T

......

Blast algorithm: eliminating sequences without word hits

ACTACTACT

RSL

RSL TVF

RSLRSL

RSLRSL TVFTVF

Database sequences

List of sequences containing words similar to the query (hits)

List of sequences containing words similar to the query (hits)

Search forexact matches


Blast: the gory details (The End)

Blast algorithm: extension of hits

Database sequence

Query A

Ungapped extension if:• 2 "Hits" are on the same diagonal

but at a distance less than A

Database sequence

Query A

Extension using dynamic programming• limited to a restricted region


Statistical evaluation of results

Alignments are evaluated according to their score• Raw score

It's the sum of the amino acid substitution scores and gap penalties (gap opening and gap extension)Depends on the scoring system (substitution matrix, etc.)Different alignments should not be compared based only on the raw score

• Normalised scoreIs independent of the scoring systemAllows the comparison of different alignmentsUnits: expressed in bits


Statistical evaluation of results

Statistics derived from the scores• p-value

Probability that an alignment with this score occurs by chance in a database of this sizeThe closer the p-value is towards 0, the better the alignment

• e-valueNumber of matches with this score one can expect to find by chance in a database of this sizeThe closer the e-value is towards 0, the better the alignment

• Relationship between e-value and p-value:In a database containing N sequencese = p x N

100%

0%

N

0


Low complexity regions

• Regions with a high frequency of only a few type of residues (= low complexity regions) may produce high scoring but biological uninteresting alignments, e.g. polyserine

• Such regions are, by default, filtered out by Blast. They appear masked with 'X' in the alignment.They are not taken into account for score computation


Basic Blast on EMBnet

www.ch.embnet.org/software/bBLAST.html

Select the type of query

Select the substitution matrix to use

Select your input type:Either a raw sequence or an accession or id number, as well as the database from which blast should retrieve your query

Select the protein database to search with either blastp, blastx

Select the nucleotide database to search with either blastn, tblastn, tblastx


Advanced Blast on EMBnet

www.ch.embnet.org/software/aBLAST.html

• Greater choice of databases to search• Advanced Blast parameter modification


Search results

Graphical visualisation and description of alignment scores


Search results

Alignment example

• Normalised score, raw score and e-value• Percentage of identical aligned residues, percentage of aligned residues

having a positive score in the substitution matrix• Alignment (local) between the query and the database sequence. The

middle line shows if a residue is conserved or not• Low complexity region is masked with a series of 'X'


Search results

Search details (at the bottom of the results)

• Size of the database searched• Scoring system parameters• Details about the number of hits

found


Conclusions

Blast: the most used database search tool • Fast and very reliable even for a heuristic algorithm• Does not necessarily find the best alignment, but most of the

time it finds the best matching sequences in the database• Easy to use with default parameters• Solid statistical framework for the evaluation of scores

but... • The biologist's expertise is still essential to the analysis of the

results !Tips and tricks

• For coding sequences always search at the protein level• Mask low complexity regions• Use a substitution matrix adapted to the expected divergence of

the searched sequences (nevertheless most of the time BLOSUM62 works well)

• If there are only matches to a limited region of your query, cutout that region and rerun the search with the remaining part of your query

Documents

Similarity searches in biological sequence databases€¦ · september 2004 Page 4 Genbank entry example LOCUS AF455746_1 80 aa PRI 08-JAN-2002 DEFINITION ubiquitin-conjugating enzyme