sequence alignment and searching

8/6/2019 Sequence Alignment and Searching

1/54

Sequence Alignment and Searching

By: Sarika goyal


2/54

What is the purpose of sequence alignment?

Identification of homology and homologous sites inrelated sequences

Inference of evolutionary history that lead to thedifferences in observed sequences


3/54

The Problem

Biological problem

Finding a way to compare and represent

similarity or dissimilarity betweenbiomolecular sequences (DNA, RNA or aminoacid)


4/54

The Problem

Computational problem

Finding a way to perform inexact or approximatematching of subsequences within strings ofcharacters

Sequence comparison and alignment is a centralproblem in computational biology: High sequence similarity usually => structural

or functional similarity


5/54

Substring and subsequence

Example:

xyz is a subsequence within axayaz, butNOT a substring

Characters in a substring must be contiguous


6/54

Types of comparisons and alignment methods

LOLOCALCAL GLOBALGLOBAL

TWO SEQUENCESTWO SEQUENCES

(Pairwise alignment)(Pairwise alignment)

Database search againstquery sequences

BLASTalgorithm

Comparison of twosequences;

First step in multiplesequence alignment

THREE OR MORETHREE OR MORESEQUENCESSEQUENCES

(Multiple alignment)(Multiple alignment)

Defining consensussequences, protein

structural motifs anddomains, regulatoryelements in DNA etc.

Determination of conservedresidues and domains;

Introductory step inmolecular phylogenetic

analysis

According tosequenceCoverage:According to

number ofsequences:


7/54

Introduction to sequence alignment

Given two text strings:

First string = a b c d e

Second string = a c d ef

a reasonable alignment would

bea b c d e -

a - c d e f

We must choose criteria sothat algorithm can choosethe best alignment.

For the sequences gctgaacgand ctataatc:

An uninformative alignment:

-------gctgaacg

ctataatc-------

An alignment without gaps

gctgaacg

ctataatc

An alignment with gaps

gctga-a--cg--ct-ataatc

And another

gctg-aa-cg

-ctataatc-


8/54

The dotplot (1)

A simple picture that gives an overview of the similaritiesbetween two sequences

Dotplot showing identities between sequences (DOROTHYHODGKIN) and(DOROTHYCROWFOOTHODGKIN):

Letters corresponding toisolatedmatches are shown innon-bold type. The longestmatching regions, shown inboldface, are DOROTHY andHODGKIN. Shorter matchingregions, such as the OTH ofdorOTHy and RO ofdoROthyand cROwfoot, are noise.


9/54

The dotplot (2)

Dotplot showing identitiesbetween a repetitivesequence(ABRACADABRACADABRA)and itself. The repeatsappear on severalsubsidiary diagonalsparallel to the maindiagonal.


10/54

The dotplot (3)

Dotplot showingidentities between thepalindromic sequence

MAX I STAY AWAY ATSIX AM and itself. Thepalindrome revealsitself as a stretch ofmatchesperpendicularto the main diagonal.


11/54

Dotplots and sequence alignments

Any path through thedotplot from upper left tolower right passes througha succession of cells, eachof which picks out a pair ofpositions, one from therow and one from thecolumn, that correspond inthe alignment; or thatindicates a gap in one ofthe sequences. The pathneed not pass throughfilled-in points only.However, the more filled-inpoints on the diagonalsegments of the path, themore matching residues inthe alignment.

Corrseponding alignment:

DOROTHY--------HODGKIN

DOROTHYCROWFOOTHODGKIN


12/54

Measures of sequence similarity

Two measures of distance between two character strings: The Hamming distance, defined between two strings of equal

length, is the number of positions with mismatching characters. The Levenshtein, or edit distance, between two strings of not

necessarily equal length, is the minimal number of 'editoperations' required to change one string into the other, wherean edit operation is a deletion, insertion or alteration of a single

character in either sequence.

agtc

cgta Hamming distance = 2

ag-tcc

cgctca Levenshtein distance = 3


13/54

The Edit Distance between two strings

Definition: The edit distance between two strings is

defined as the minimum number of editoperations insertions, deletions, &substitutions needed to transform the firststring into the second. For emphasis, notethat matches are not counted.

Example: AATT and AATG

Distance = 1 (edit operation of substitution)


14/54

String alignment

An edit transcript is a way to represent a particulartransformation of one string into another Emphasizes point mutations in the model

An alignment displays a relationship between twostrings Global alignment means for each string, entire string

is involved in the alignment Examples:

A A G C A

A A _ C _


15/54

Sequence diversion

Sequences may have diverged from commonancestor through mutations: Substitution (AAGC AAGT) Insertion (AAG AAGT) Deletion (AAGC AAG)


16/54

Scoring schemes

In molecular biology, certain changes are more likelyto occur than others Amino acid substitutions tend to be conservative In nucleotide sequences, transitions are more frequent

than transversions

-> We want to give different weights todifferent edit operations

Example: a DNA substitution matrix:

a g c t

a 20 10 5 5

g 10 20 5 5

c 5 5 20 10

t 5 5 10 20


17/54

BLAST the workhorse of bioinformaticshttp://www.ncbi.nlm.nih.gov/BLAST

BLAST = Basic localalignment searchtool

When you have anucleotide orprotein sequence

that you want tosearch againstsequence databases to determine what

the sequence is to find related

sequences

(homologs)


18/54

Different BLAST programs


19/54

DNA can be translated into six potential proteins

5 CAT CAA

5 ATC AAC

5 TCA ACT

5 GTG GGT

5 TGG GTA

5 GGG TAG

DNA potentially encodes six proteins

5 CATCAACTACAACTCCAAAGACACCCTTACACATCAACAAACCTACCCAC 3

3 GTAGTTGATGTTGAGGTTTCTGTGGGAATGTGTAGTTGTTTGGATGGGTG 5


20/54

(1) Choose the sequence (query)

(2) Select the BLAST program

(3) Choose the database to search

(4) Choose optional parameters

Then click BLAST

Four components to a BLAST search


21/54

Step 1: Choose your sequence

Sequence can be input inFASTA format or as accessionnumber


22/54

Example of the FASTA format for a BLAST query


23/54

Step 2: Choose the BLAST program

Program Input Database 1

blastn DNA DNA

1blastp protein protein

6blastx DNA protein

6tblastn protein DNA 36

tblastx DNA DNA


24/54

Step 2: Choose the BLAST program


25/54

Step 3: choose the database

nr = non-redundant (most general database)

dbest = database of expressed sequence tags

dbsts = database of sequence tag sites

pdb = sequences derived from 3d structure of proteins

Patents = Nucleotide sequences derived from patentdivision of GenBank.


26/54

Step 4a: Select optional search parameters

You can...

choose the organism to search

turn filtering on/off change the substitution matrix

change the expect (e) value

change the word size

change the output format


27/54


CD search


28/54


Entrez!

Filter

Scoring matrix

Word sizeExpect

organism


29/54

filtering


30/54


31/54

Step 4b: optional formatting parameters

Alignment view

Descriptions

Alignments

page 97


32/54

BLAST format options


33/54

Alignments Views

Pairwise

Standard BLAST alignment in pairs of query sequence and data

match.

Query: 251 tgaccggtaacgaccgcaccctggacgtcatggcgctggatgtggtgtggacggcgga 3

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

Sbjct: 248575 tgaccggtaaagaccgcagcttggacgtgatggcgatggatgtggtgtggacagcgga248634


34/54


35/54

database

query

program


36/54


37/54

High scores

low e values


38/54

Algorithm for Blast


39/54

How a BLAST search works

The central idea of the BLAST

algorithm is to confine attention

to segment pairs that contain aword pair of length W with a score

of at least T.

Altschul et al. (1990)


40/54

BLAST algorithm principles

(Basic Local Alignment Search Tool)

Main idea:

1. Construct a dictionary of all thewords in the query

2. Initiate a local alignment for eachword match between query and DB

query

DB


41/54

BLAST Original Version

Dictionary:

All words of length k

Alignment initiated between words of alignment score T

Alignment:

Ungapped extensions until score below statistical threshold

Output:

All local alignments with score > statistical threshold

Background


42/54

The BLAST Algorithm

3 Stages

Preprocessing of the query

Generation of hits

Extension of the hits

Background


43/54

Step 1: Preprocessing of the query

Speed gained by minimizing search space Alignments require word hits ( word size = W)

Sequence 1

word hits

Sequen

ce

2

Background


44/54

Step 1: Preprocessing of the query (Contd.)

Threshold score = T

Neighborhood words of RGD

Wand Tmodulate speed andsensitivity

RGD 17

KGD 14

QGD 13RGE 13

EGD 12

HGD 12

NGD 12

RGN 12

AGD 11

MGD 11

RAD 11

RGQ 11

RGS 11

RND 11

RSD 11

SGD 11

TGD 11

T=12

Background


45/54

Step 2: Generation of hits

A hit is made with one or several successive pairs ofsimilar words.

All the possible hits between the query sequence and

sequences from databases are calculated in this way.

query

Background


46/54

Step 3: Extension

Each hit is extended in both directions. Extension is terminated when the maximum score drops

belowX.

DB

query

scan

Background


47/54

The BLAST Algorithm: Summary


48/54

BLAST Original Version

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

C

C

C

T

T

C

C

T

G

G

A

T

T

G

C

G

AExample:

k = 4,

T = 4

The matching word GGTCinitiates an alignment

Extension to the left andright with no gaps

Output:GTAAGGTCC

GTTAGGTCC


49/54

Evalue - number of unrelated databank sequences expectedto yield same or higher score by pure chance

BLAST results: List of hits


50/54

Fundamental unit of the BLASTalgorithm output

HSP (high scoring pair) Aligned fragments of query and detectedsequence with similarity score exceeding a

set cutoff value

Score = 61 (27.8 bits), Expect = 1.8e-65, Sum P(4) = 1.8e-65 Identities = 10/17

(58%), Positives = 16/17 (94%)

Query: 81 SGDLSMLVLLPDEVSDL 97

+GD+SM +LLPDE++D+

Sbjct: 259 AGDVSMFLLLPDEIADV 275

E-value (Expectation)

HSP

BLAST results: High scoring pairs (HSPs)


51/54

BLAST Confidence measures

Score and bit-score :depend on scoring method

E-value (Expect value) : number of unrelated databasesequences expected to yield same or higher score by purechance

The Expect value is used as a convenient way to create a significancethreshold for reporting results. When the Expect value is increasedfrom the default value of 10, a larger list with more low-scoring hits canbe reported. (E-value approching zero => significant alignment)

An E value of 1 assigned to a hit can be interpreted as in a database ofthe current size one might expect to see 1 match with a similar scoresimply by chance.


52/54

Statistical Terminology

True positive: A hit returned from a database search which is homologouswith the query sequence. GOOD

False positive: A hit returned from a database search which is nothomologous with the query sequence. BAD

True negative: A sequence which is not homologous with the querysequence is not returned from database search. GOOD

False negative: A sequence which is homologous with the querysequence is not returned from database search. BAD

Sensitivity: A program which is sensitive picks up on most true positive.

Selectivity: A program which is selective does not include false positives.


53/54

Conclusion

Treat BLAST searches as scientific experiments

Dont use the default parameters Default changes from time to time

BLAST is quite complicated. But its very useful.


54/54

Thanks

sequence alignment and searching

Documents