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SEQUENCE ANALYSIS. By Jyotika Bhati. Bioinformatics. The design , construction and use of software tools to generate , store , annotate , access and analyse data and information relating to Molecular Biology. OR. Biologists doing “stuff” with computers?. What is Sequence ?.

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Sequence analysis

SEQUENCE ANALYSIS

By

JyotikaBhati


Bioinformatics

The design, construction and use of software tools to generate, store, annotate, access and analyse data and information relating to Molecular Biology

OR

Biologists doing “stuff” with computers?


What is sequence
What is Sequence ?

  • A sequence is an ordered list of objects (or events).

  • Biological sequence is a single, continuous molecule of nucleic acid or protein.

  • Sequence analysis in bioinformatics is an automated, computer-based examination of characteristic fragments, e.g. of a DNA strand.

  • The term "sequence analysis" in biology implies subjecting a DNA or peptide sequence to sequence alignment, sequence databases, repeated sequence searches, or other bioinformatics methods on a computer.


Nucleotide sequence databases
Nucleotide Sequence Databases

  • NCBI (National Center for Biotechnology Information)

  • EMBL (European Molecular Biology Laboratory)

  • DDBJ (DNA DataBank of Japan)


Protein sequence database
Protein Sequence Database

  • SWISS-PROT

  • TrEMBL


Sequence alignment
Sequence Alignment

  • The identification of residue-residue correspondences

  • The basic tool in bioinformatics

WHY Sequence Alignment ?

  • For discovering functional, structural and evolutionary information in biological sequences

  • Eases further tasks like:

  • Annotation of new sequences

  • Modeling of protein structures

  • Design and analysis of gene expression experiments


Basic steps in sequence alignment
Basic Steps in Sequence Alignment

  • Comparison of sequences to find similarity and dissimilarity in compared sequences

  • Identification of gene-structures, reading frames, distributions of introns and exons and regulatory elements

  • Finding and comparing point mutations to get the genetic marker

  • Revealing the evolutionary and genetic diversity

  • Function annotation of genes.


The concept
The Concept

  • An alignment is a mutual arrangement of two sequences

  • Exhibits where two sequences are similar, and where they differ

  • An ‘optimal’ alignment – most correspondences and the least differences

  • Sequences that are similar probably have the same function

    Sequence alignment involves the identification of the correct location of deletions and insertions that have occurred in either of the two lineages since the divergence from a common ancestor.


Terms of sequence comparison
Terms of sequence comparison

Sequence identity

  • Exactly same Nucleotide/AminoAcid in same position

    Sequence similarity

  • Substitutions with similar chemical properties

    Sequence homology

  • General term that indicates evolutionary relatedness among sequences

  • Sequences are homologous if they are derived from a common ancestral sequence.


Homology
Homology

  • Homology designates a qualitative relationship of common descent between entities

  • Two genes are either homologs or not !

  • It doesn’t make sense to say “two genes are 43% homologous”

  • It doesn’t make sense to say “John is 43% diabetic”

    Two genes are orthologs if they originated from single ancestral gene in the most recent common ancestor of their respective genomes

    Two genes are paralogs if they are related by duplication


Things to consider
Things to consider

  • To find the best alignment one needs to examine all possible alignment

  • To reflect the quality of the possible alignments one needs to score them

  • There can be different alignments with the same highest score

  • Variations in the scoring scheme may change the ranking of alignments


Manual alignment
Manual alignment

  • When there are few gaps and the two sequences are not too different from each other, a reasonable alignment can be obtained by visual inspection.

  • Advantages: (1) use of a powerful and trainable tool (the brain, well… some brains).(2) ability to integrate additional data

    Disadvantage : The method is subjective and unscalable.


Types of alignment
Types of Alignment

  • Pairwise Alignment

    - Multiple Alignment

  • Dot Matrix Method

  • Dynamic Programming

  • Word Method

  • Dynamic Programming

  • Progressive Methods

  • Iterative Methods

  • Motif Finding


Pairwise sequence alignment
Pairwise Sequence Alignment

  • One pair of elements at a time

  • Challenge – Find optimum alignment of 2 seqs with some degree of similarity

  • Optimality is based on SCORE

  • Score reflects the no. of paired characters in the 2 seqs and the no. and length of gaps introduced to adjust the seqs so that max no. of characters are in alignment


A pairwise alignment consists of a series of paired bases, one base from each sequence. There are three types of pairs:(1) matches = the same nucleotide appears in both sequences. (2) mismatches = different nucleotides are found in the two sequences. (3) gaps = a base in one sequence and a null base in the other.

Gap

Match

Mismatch

GCGGCCCATCAGGTACTTGGTG -G

GCGT TCCATC - - CTGGTTGGTGTG


Dot matrix method
Dot Matrix Method

  • Established in 1970 by A.J. Gibbs and G.A.McIntyre

  • Method for comparing two nucleotide/aa sequences

  • each sequence builds one axis of the grid

  • one puts a dot, at the intersection of same letters appearing in both sequences

  • scan the graph for a series of dots reveals similarity or a string of same characters

  • longer sequences can also be compared on a single page, by using smaller dots


Dot matrix method1
Dot Matrix Method

  • the dot matrix method reveals the presence of insertions or deletions

  • comparing a single sequence to itself can reveal the presence of a repeat of a subsequence

  • self comparison can reveal several features:

    – similarity between chromosomes

    – tandem genes

    – repeated domains in a protein sequence

    – regions of low sequence complexity (same

    characters are often repeated)


Tools generating dot matrices
Tools generating Dot Matrices

  • Dotlet (Java based web-application)

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

  • Compare & dotplot programmes in GCG Wisconsin Package (Genetics Computer Group [commercial])

  • GeneAssist package of ABI/Perkin Elmer

  • DOTTER (available on dapsas, UNIX X-Windows)

  • DNA Strider (Macintosh only)


Dot matrix methods
Dot Matrix Methods

  • When to use :

    – unless the sequences are known to be very

    much alike

  • Demerits

    – doesn’t readily resolve similarity that is

    interrupted by insertion or deletions

    – Difficult to find the best possible alignment

    (optimal alignment)

    – most computer programs don’t show an actual

    alignment


Pairwise alignment the problem
Pairwise alignment: the problem

The number of possible pairwise alignments increases explosively with the length of the sequences:

Two protein sequences of length 100 amino acids can be aligned in approximately 1060 different ways

Time needed to test all possibilities is same order of magnitude as the entire lifetime of the universe.


Global versus local alignments
Global versus local alignments

Global alignment: align full length of both sequences. (The “Needleman-Wunsch” algorithm).

Local alignment: find best partial alignment of two sequences (the “Smith-Waterman” algorithm).

Global alignment

Seq 1

Local alignment

Seq 2


Global sequence alignment
Global Sequence Alignment

  • The Needleman–Wunsch algorithm performs a global alignment

  • An example of dynamic programming

  • First application of dynamic programming to biological sequence comparison

  • Suitable when the two sequences are of similar length, with a significant degree of similarity throughout

  • Aim: The best alignment over the entire length of two sequences


Steps in nw algorithm
Steps in NW Algorithm

  • Initialization

  • Scoring

  • Trace back (Alignment)

Consider the two DNA sequences to be globally aligned are:

ATCG (x=4, length of sequence 1)

TCG (y=3, length of sequence 2)


Why Gap Penalties?

  • The optimal alignment of two similar sequences is usually that which

    • maximizes the number of matches and

    • minimizes the number of gaps.

    • There is a tradeoff between these two

      • - adding gaps reduces mismatches

  • Permitting the insertion of arbitrarily many gaps can lead to high scoring alignments of non-homologous sequences.

  • Penalizing gaps forces alignments to have relatively few gaps.


  • Initialization step
    Initialization Step

    • Create a matrix with X +1 Rows and Y +1 Columns

    • The 1st row and the 1st column of the score matrix are filled as multiple of gap penalty


    Scoring
    Scoring

    • The score of any cell C(i, j) is the maximum of:

      scorediag = C(i-1, j-1) + S(i, j)

      scoreup = C(i-1, j) + g

      scoreleft = C(i, j-1) + g

      where S(i, j) is the substitution score for letters i and j, and g is the gap penalty


    Scoring1
    Scoring ….

    • Example:

      The calculation for the cell C(2, 2):

      scorediag = C(i-1, j-1) + S(i, j) = 0 + -1 = -1

      scoreup = C(i-1, j) + g = -1 + -1 = -2

      scoreleft = C(i, j-1) + g = -1 + -1 = -2


    Scoring2
    Scoring ….

    • Final Scoring Matrix

      Note: Always the last cell has the maximum alignment score: 2


    Trace back

    • The trace back step determines the actual alignment(s) that result in the maximum score

    • There are likely to be multiple maximal alignments

    • Trace back starts from the last cell, i.e. position X, Y in the matrix

    • Gives alignment in reverse order


    Trace back
    Trace back ….

    • There are three possible moves: diagonally (toward the top-left corner of the matrix), up, or left

    • Trace back takes the current cell and looks to the neighbor cells that could be direct predecessors. This means it looks to the neighbor to the left (gap in sequence #2), the diagonal neighbor (match/mismatch), and the neighbor above it (gap in sequence #1). The algorithm for trace back chooses as the next cell in the sequence one of the possible predecessors


    Trace back1
    Trace back ….

    • The only possible predecessor is the diagonal match/mismatch neighbor. If more than one possible predecessor exists, any can be chosen. This gives us a current alignment of

      Seq 1: G

      |

      Seq 2: G


    Trace back2
    Trace back ….

    • Final Trace back

      Best Alignment:

      A T C G

      | | | |

      _ T C G


    Local sequence alignment
    Local Sequence Alignment

    • The Smith-Waterman algorithm performs a local alignment on two sequences

    • It is an example of dynamic programming

    • Useful for dissimilar sequences that are suspected to contain regions of similarity or similar sequence motifs within their larger sequence context

    • Aim: The best alignment over the conserved domain of two sequences


    Differences in needleman wunsch and smith waterman algorithms
    Differences in Needleman-Wunsch and Smith-Waterman Algorithms

    • In the initialization stage, the first row and first column are all filled in with 0s

    • While filling the matrix, if a score becomes negative, put in 0 instead

    • In the traceback, start with the cell that has the highest score and work back until a cell with a score of 0 is reached.


    Three steps in smith waterman algorithm
    Three steps in Smith-Waterman Algorithm Algorithms

    • Initialization

    • Scoring

    • Trace back (Alignment)

      Consider the two DNA sequences to be globally aligned are:

      ATCG (x=4, length of sequence 1)

      TCG (y=3, length of sequence 2)


    Initialization step1
    Initialization Step Algorithms

    • Create a matrix with X +1 Rows and Y +1 Columns

    • The 1st row and the 1st column of the score matrix are filled with 0s


    Scoring3
    Scoring Algorithms

    • The score of any cell C(i, j) is the maximum of:

      scorediag = C(i-1, j-1) + S(I, j)

      scoreup = C(i-1, j) + g

      scoreleft = C(i, j-1) + g

      And

      0

      (here S(i, j) is the substitution score for letters i and j, and g is the gap penalty)


    Scoring4
    Scoring …. Algorithms

    • Example:

      The calculation for the cell C(2, 2):

      scorediag = C(i-1, j-1) + S(I, j) = 0 + -1 = -1

      scoreup = C(i-1, j) + g = 0 + -1 = -1

      scoreleft = C(i, j-1) + g = 0 + -1 = -1


    Scoring5
    Scoring …. Algorithms

    • Final Scoring Matrix

      Note: It is not mandatory that the last cell has the maximum alignment score!


    Trace back3
    Trace back Algorithms

    • The trace back step determines the actual alignment(s) that result in the maximum score

    • There are likely to be multiple maximal alignments

    • Trace back starts from the cell with maximum value in the matrix

    • Gives alignment in reverse order


    Trace back4
    Trace back …. Algorithms

    • There are three possible moves: diagonally (toward the top-left corner of the matrix), up, or left

    • Trace back takes the current cell and looks to the neighbor cells that could be direct predecessors. This means it looks to the neighbor to the left (gap in sequence #2), the diagonal neighbor (match/mismatch), and the neighbor above it (gap in sequence #1). The algorithm for trace back chooses as the next cell in the sequence one of the possible predecessors. This continues till cell with value 0 is reached.


    Trace back5
    Trace back …. Algorithms

    • The only possible predecessor is the diagonal match/mismatch neighbor. If more than one possible predecessor exists, any can be chosen. This gives us a current alignment of

      Seq 1: G

      |

      Seq 2: G


    Trace back6
    Trace back …. Algorithms

    • Final Trace back

      Best Alignment:

      T C G

      | | |

      T C G


    • The Algorithmstrue alignment between two sequences is the one that reflects accurately the evolutionary relationships between the sequences.

    • Since the true alignment is unknown, in practice we look for the optimal alignment, which is the one in which the numbers of mismatches and gaps are minimized according to certain criteria.

    • Unfortunately, reducing the number of mismatches results in an increase in the number of gaps, and viceversa.


    FASTA Algorithms

    1) Derived from logic of the dot plot

    • compute best diagonals from all frames of alignment

      2) Word method looks for exact matches between words in query and test sequence

    • hash tables (fast computer technique)

    • DNA words are usually 6 bases

    • protein words are 1 or 2 amino acids

    • only searches for diagonals in region of word matches = faster searching

      FastA searches can be done on the WWW FastA server at EBI: http://www2.ebi.ac.uk/fasta3/


    FASTA Algorithm Algorithms


    Makes Longest Diagonal Algorithms

    3) after all diagonals found, tries to join diagonals by adding gaps

    4) computes alignments in regions of best diagonals


    FASTA Alignments Algorithms


    FASTA Format Algorithms

    • simple format used by almost all programs

    • >header line with a [return] at end

    • Sequence (no specific requirements for line length, characters, etc)

    >URO1 uro1.seq Length: 2018 November 9, 2000 11:50 Type: N Check: 3854 ..

    CGCAGAAAGAGGAGGCGCTTGCCTTCAGCTTGTGGGAAATCCCGAAGATGGCCAAAGACA

    ACTCAACTGTTCGTTGCTTCCAGGGCCTGCTGATTTTTGGAAATGTGATTATTGGTTGTT

    GCGGCATTGCCCTGACTGCGGAGTGCATCTTCTTTGTATCTGACCAACACAGCCTCTACC

    CACTGCTTGAAGCCACCGACAACGATGACATCTATGGGGCTGCCTGGATCGGCATATTTG

    TGGGCATCTGCCTCTTCTGCCTGTCTGTTCTAGGCATTGTAGGCATCATGAAGTCCAGCA

    GGAAAATTCTTCTGGCGTATTTCATTCTGATGTTTATAGTATATGCCTTTGAAGTGGCAT

    CTTGTATCACAGCAGCAACACAACAAGACTTTTTCACACCCAACCTCTTCCTGAAGCAGA

    TGCTAGAGAGGTACCAAAACAACAGCCCTCCAAACAATGATGACCAGTGGAAAAACAATG

    GAGTCACCAAAACCTGGGACAGGCTCATGCTCCAGGACAATTGCTGTGGCGTAAATGGTC

    CATCAGACTGGCAAAAATACACATCTGCCTTCCGGACTGAGAATAATGATGCTGACTATC

    CCTGGCCTCGTCAATGCTGTGTTATGAACAATCTTAAAGAACCTCTCAACCTGGAGGCTT


    BLAST Searches AlgorithmsGenBank

    [BLAST= Basic Local Alignment Search Tool]

    The NCBI BLASTweb server lets you compare your query sequence to various sections of GenBank:

    • nr = non-redundant (main sections)

    • month = new sequences from the past few weeks

    • ESTs

    • human, drososphila, yeast, or E.coli genomes

    • proteins (by automatic translation)

  • This is a VERY fast and powerful computer.


  • BLAST Algorithms

    • Uses word matching like FASTA

    • Similarity matching of words (3 aa’s, 11 bases)

      • does not require identical words.

    • If no words are similar, then no alignment

      • won’t find matches for very short sequences

    • Does not handle gaps well


    BLAST Algorithm Algorithms


    BLAST Word Matching Algorithms

    MEAAVKEEISVEDEAVDKNI

    MEA

    EAA

    AAV

    AVK

    VKE

    KEE

    EEI

    EIS

    ISV

    ...

    Break query into words:

    Break database

    sequences into words:


    Compare Word Lists Algorithms

    Database Sequence Word Lists

    RTT AAQ

    SDG KSS

    SRW LLN

    QEL RWY

    VKI GKG

    DKI NIS

    LFC WDV

    AAV KVR

    PFR DEI

    ……

    Query Word List:

    MEA

    EAA

    AAV

    AVK

    VKL

    KEE

    EEI

    EIS

    ISV

    ?

    Compare word lists by Hashing

    (allow near matches)


    Find locations of matching words Algorithmsin database sequences

    ELEPRRPRYRVPDVLVADPPIARLSVSGRDENSVELTMEAT

    • MEA

    • EAA

    • AAV

    • AVK

    • KLV

    • KEE

    • EEI

    • EIS

    • ISV

    TDVRWMSETGIIDVFLLLGPSISDVFRQYASLTGTQALPPLFSLGYHQSRWNY

    IWLDIEEIHADGKRYFTWDPSRFPQPRTMLERLASKRRVKLVAIVDPH



    HSPs are Aligned Regions Algorithms

    • The results of the word matching and attempts to extend the alignment are segments

      - called HSPs (High-scoring Segment Pairs)

    • BLAST often produces several short HSPs rather than a single aligned region


    Searching on the web blast at ncbi
    Searching on the web: BLAST at NCBI Algorithms

    Very fast computer dedicated to running BLAST searches

    Many databases that are always up to date

    Nice simple web interface

    But you still need knowledge about BLAST to use it properly

    http://blast.ncbi.nlm.nih.gov/Blast.cgi


    low complexity sequence filtered Algorithms

    BLAST Output: Alignments

    >gi|730028|sp|P40692|MLH1_HUMAN DNA mismatch repair protein Mlh1 1)

    Length = 756

    Score = 233 bits (593), Expect = 8e-62

    Identities = 117/131 (89%), Positives = 117/131 (89%)

    Query: 1 IETVYAAYLPKNTHPFLYLSLEISPQNVDVNVHPTKHEVHFLHEESILERVQQHIESKLL 60

    IETVYAAYLPKNTHPFLYLSLEISPQNVDVNVHPTKHEVHFLHEESILERVQQHIESKLL

    Sbjct: 276 IETVYAAYLPKNTHPFLYLSLEISPQNVDVNVHPTKHEVHFLHEESILERVQQHIESKLL 335

    Query: 61 GSNSSRMYFTQTLLPGLAGPSGEMVKXXXXXXXXXXXXXXDKVYAHQMVRTDSREQKLDA 120

    GSNSSRMYFTQTLLPGLAGPSGEMVK DKVYAHQMVRTDSREQKLDA

    Sbjct: 336 GSNSSRMYFTQTLLPGLAGPSGEMVKSTTSLTSSSTSGSSDKVYAHQMVRTDSREQKLDA 395

    Query: 121 FLQPLSKPLSS 131

    FLQPLSKPLSS

    Sbjct: 396 FLQPLSKPLSS 406


    DNA vs. Protein searches Algorithms

    • DNA is composed of 4 characters: A,G,C,T It is anticipated that on the average, at least 25% of the residues of any 2 unrelated aligned sequences, would be identical.

    • Protein sequence is composed of 20 characters (aa). The sensitivity of the comparison is improved. It is accepted that convergence of Proteins is rare, meaning that high similarity between 2 proteins always means homology.


    DNA vs. Protein searches Algorithms

    • What should we use to search for similarity, the nucleotide or the protein sequences?

    • If we have a nucleotide sequence, should we search the DNA databases only? Or should we translate it to protein and search protein databases? Note, that by translating into aa sequence, we’ll presumably lose information, since the genetic code is degenerate, meaning that two or more codons can be translated to the same amino acid.


    Gly Ala Ile Leu asp Arg Algorithms

    -GGAGCCATATTAGATAGA-

    -GGAGCAATTTTTGATAGA-

    Gly Ala Ile Phe asp Arg

    Nucleotide, amino-acid sequences

    • 3 different DNA positions but only one different amino acid position:

    2 of the nucleotide substitutions are therefore synonymous and one is non-synonymous.

    DNA yields more phylogenetic information than proteins. The nucleotide sequences of a pair of homologous genes have a higher information content than the amino acid sequences of the corresponding proteins, because mutations that result in synonymous changes alter the DNA sequence but do not affect the amino acid sequence. (Amino-acid sequences are more efficiently aligned).


    DNA vs. Protein searches Algorithms

    • What about very different DNA sequences that code for similar protein sequences? We certainly do not want to miss those.

    • Conclusion: We should use proteins for database similarity searches when possible.


    DNA vs. Protein searches Algorithms

    • The reasons for this conclusion are:

    • When comparing DNA sequences, we get significantly more random matches than we get with proteins.

    • The DNA databases are much larger, and grow faster than Protein databases. Bigger database means more random hits!

    • For DNA we usually use identity matrices, for protein more sensitive matrices like PAM and BLOSUM, which allow for better search results.

    • The conservation in evolution, protein are rarely mutated.


    Input Query Algorithms

    Amino Acid Sequence

    DNA Sequence

    Blastp

    tblastn

    blastn

    blastx

    tblastx

    Compares

    Against

    Protein

    Sequence

    Database

    Compares

    Against

    translated

    Nucleotide

    Sequence

    Database

    Compares

    Against

    Nucleotide

    Sequence

    Database

    Compares

    Against

    Protein

    Sequence

    Database

    Compares

    Against

    translated

    nucleotide

    Sequence

    Database

    An Overview of BLAST


    Why use BLAST? Algorithms

    • To discover functional, structural and evolutionary similarities

    • Because “similarity” may be an indicator of “homology” and thus provide some insight into function or gene identification.

    • Applications include

      • identifying orthologs and paralogs

      • discovering new genes or proteins

      • exploring protein structure and function


    Meaningfulness
    Meaningfulness Algorithms

    • Is the alignment correct ?

    • Can I make it better ?

    • Which programs are best ?

    • How do you know if its correct ?


    Is the alignment correct
    Is the Alignment AlgorithmsCorrect ?

    • What do mean by correct ?

      • Mathematically rigorous

      • Biologically meaningful

      • Operationally useful


    Can you make it better
    Can you make it Algorithmsbetter ?

    • Only if you know what you doing !

    • Define better ?

    • What’s the goal ?

    • What’s the biology ?


    Which programs are best
    Which programs are Algorithmsbest ?

    • No simple answer

    • Depends on the particular problem

    • Recent objective studies help answer this problem

    • Some tools to help compare alignments


    How do you know it is correct
    How do you Algorithmsknow it is correct ?

    • Methods to evaluate the alignment

    • Methods to evaluate the program/algorithm

    • Structural information

    • Biology



    THANK YOU Algorithms


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