Tools and Softwares

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Tools and Softwares

• Group 13
• Shashi Ranjan
• Murali Sivaramakrishnan
• Ognjen Perisic

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Introduction

• Importance of aligning two sequences
• Identify the structure and function
• Evolutionary similarities
• Why use computers to do this alignment?
• Sequences are very, very, very long
• Need to evaluate many alignments

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Tools and Softwares

• BLAST
• PIP MAKER
• FASTA
• Motif Explorer
• MACAW
• Five Methods for finding
  Conserved Segments.

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BLAST Overview Basic Local Alignment Search Tool

• Dynamic programming O(mn) !
• BLAST heuristically finds high scoring segment
  pairs (HSP)
• Identical length segments from 2 sequences with
  high scores
• I.e. ungapped local alignments

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BLAST Nuts Bolts

• Given query sequence q, word length w, word
  score threshold T, segment score threshold S
• Generate a word set (neighborhood words) that
  score at least T when compared to words form q
• Search the dB for matches to words in list, a
  match indicates a possible alignment
• Extend all matches to seek high-scoring segment
  pairs and return those pairs

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BLAST Extensions

• Gapped BLAST
• 3 Changes to the original BLAST
• Word extension criteria modified
• Allow gapped alignments, need to find just one
  ungapped alignment
• Use dynamic programming of Smith-Waterman to
  produce final alignment

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BLAST Extensions

• Position Specific Iterated BLAST
• Basic Idea
• Use results from BLAST query to build a profile
• Search dB with profile instead of initial query
• Iterate

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BLAST Programs

• NCBI BLAST ---- http//ncbi.nlm.nih.gov/BLAST/

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PipMaker

• WWW site for comparing DNA sequences
• Identifies conserved sequences
• Provides graphical output in PDF or PS format
• Uses a variant of Gapped BLAST - BLASTz
• http//bio.cse.psu.edu

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Tools And Software

• FASTA
• Motif Explorer
• Accessibility

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FASTA Overview

1. Given two sequences x,y and ksize of exactly
   matching strings.
2. Tabulate the offset between x,y for all matching
   strings of size k (k-tuples)
3. For offset with high frequency, try to combine
   their k-tuples into regions
4. Extend the best regions without gaps as long as
   score improves. (using the substitution matrix
   such as PAM250) (init1)
5. Check if un-gapped regions can be combined with a
   small gapped region. (initn used to rank the
   library sequences.)
6. Construct an optimal alignment of the highest
   ranking seq. (opt)

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FASTA- offset tabulation

• X HARFYAAQIVL
• Y VDMAAQIA
• K-tuple offsets of x (k1)
• A(2,6,7), I(9), , Q(8), V(10),
• Offset difference for each k-tuple of Y

V D M A A Q I A
9 -2 -3 2 2 -6
2 1 -2
3 2 -1
Offsets -6 -5 -4 -3 -2 -1 0 1 2 3 4 8 9
Frequency 1 1 2 1 1 4 1 1
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FASTA
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FASTA Package
FASTA scan a protein or DNA sequence library for similar sequences.
TFASTA compare a protein sequence to a DNA sequence library.
FASTX, FASTY compare a DNA sequence to a protein sequence database.
FASTF, TFASTF compare an ordered peptide mixture against a protein or translated DNA database.
FASTS, TFASTS compare a set of short peptide fragments against a protein or translated DNA database.
LFASTA, PLFASTA concentrates more on the local regions and reports more than on one sequence.
RDF2 for evaluating the statistical significance of a similarity score.
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Motif Explorer
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Motif Explorer
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Motif Explorer - Architecture
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Accessibility

• FASTA
• www.ebi.ac.uk/fasta33/
• http//bioweb.pasteur.fr/seqanal/interfaces/fasta.
  html
• http//fasta.bioch.virginia.edu/
• Motif Explorer
• www.cbc.med.umn.edu/gst/MotifExplorer.html
• www.arabidopsis.org/links/motif_search.html

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MACAWA Workbench for Multiple Alignment
Construction and Analysis

• Allows user to construct multiple protein
  alignments by locating, analyzing, editing and
  combining blocks of aligned sequence segments.
• MACAW incorporate several features
• Regions of local similarity are located by a
  search algorithm that avoids many of the
  limitations of individual algorithms
• The statistical significance of blocks of
  similarity is evaluated using a mathematical
  theory developed during paste 15 years
• User can edit each block by moving its boundaries
  or by eliminating particular segments, and blocks
  may be linked to form a composite multiple
  alignment.

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Theoretical boundaries

• Problem For a set of n protein sequences each of
  length l, a region of similarity common to all
  may begin anywhere in each sequence and it should
  be compared to all other sequences, that is ln
  alignments should be checked. This search space
  is too large.
• MACAW imposes a single condition on the alignment
  it seeks that all segments show a minimal
  amount of mutual similarity. This can examine all
  of the search space in O(n2l2) time.

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Terminology

• For a set of n sequences, one subset of segments
  of some specific length from each of m (mltn)
  sequences forms a m-block, or simply a block.
• Any set of m sequences locked into a specific
  alignment, with no gaps allowed, is a m-diagonal
  or simply a diagonal.
• An aligned set of n amino acids is n-column or
  a column.

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Algorithm

• Set of scores is used for comparison of two
  proteins, with or without gaps. MACAW uses scores
  for aligning amino acids called PAM-250.
• The score of the block is the sum of the scores
  assigned to each of its columns.
• Score of the column is the sum of all pairwise
  similarity scores of the amino acids it
  comprises. Those SP scores are called Sum of
  the Pairs. MACAW can use some different, more
  biologically realistic set of scores.
• Search routine seeks only blocks in which all
  pairs of segments are contained in pairwise
  subalignments with score greater than or equal to
  some threshold T. T should be chosen so that
  about 10 of the diagonals are marked. For n
  sequences of total (aggregate) length L, each
  sequence must be compared with others, and this
  takes O(L2) time. This is the most time-consuming
  step.
• For any multiple diagonal, all implied
  2-diagonals must have been marked during first
  phase.

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Algorithm

• The basic problem is that homologies that exist
  in a n-diagonal may be represented by n-blocks,
  or by 4, 3 or 2 blocks or some other combinations
  of disconnected blocks. To solve this problem
  MACAW uses heuristic approach in two steps
• Program searches for the highest 2-block in every
  2-diagonal using threshold T and marks the amino
  acid pairs it contains.
• Columns are represented as graphs. Every vertex
  is amino acid and edge between them exist if they
  are marked in step 1. MACAW searches for the most
  connected (with as many as possible edges) graphs
  and connects them into one block.

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Algorithm

• This procedure doesnt mark all the relations
  between sequences, so MACAW allows user to edit
  blocks.
• Every amino acid is colored according to the
  number of edges it is connected.

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Example
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Five experimental methods for finding conserved
sequences in multiple alignments of gene
regulatory regions

• A conserved character in DNA is one that was
  probably present in the common ancestral species
  and has been preserved in the contemporary
  species being examined.
• Two of the methods are already in common use
  they are based on good column agreement and high
  information content.
• Three additional methods find blocks with minimal
  evolutionary, blocks that differ in at most k
  positions pre row from a center sequence that is
  unknown a priori. The center sequence in the
  latter two methods is a way to model potential
  binding sites for known or unknown proteins in
  DNA sequences or it is a common ancestor of the
  species represented in the alignment.
• Parameters common to all of the
  tools/programs/algorithm
• The minimum length of the regions to be reported
• The minimum number of sequences, which must be
  active, are selectable by the user

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Five tools

• agree This utility locates regions in a given
  alignment that have good column agreement.
•  
• The length of the region is often a reliable
  indicator that some functionality was preserved
  across the species, but conservation doesnt need
  to be perfect and such regions might be
  fragmented into conserved pieces too small to be
  detected, so a systematic way to link the smaller
  regions is needed.
• The two utilities infocon and phylogen are trying
  to solve this problem. The idea is to assign a
  numerical score to each column and then look for
  runs of columns meeting the following two
  conditions
• their cumulative score (obtained by adding
  together the individual column scores) is no
  smaller than the score of any of their sub-runs
• they are maximal with this property, i.e. they
  are not contained in any longer run having the
  property 1.
•  
• The infocon tool finds full runs of columns with
  high information content in the given alignment.
  To do this, each column is assigned an
  intermediate score that measures its information
  content, based on the frequencies of the letter
  both within the column and within the alignment
  as whole.
•  

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Five tools

• phylogen This program scores the columns by
  their evolutionary relationships among the
  sequences of the given alignment implied by a
  supplied phylogenetic tree. The phylogenetic tree
  has a leaf node for each species and each
  internal node represents a putative common
  ancestor for the species in its sub-tree.

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Five tools

• kkno This program scans the alignment to
  determine, starting at each position, the longest
  region in which no row differs from a specified,
  known center sequence in more than k positions.
• center A C C G T G C A G
• 1 2 3 4 5 6 7 8 9
• human A G C G T G C A C
• rabbit A C C G T A C A T
• mouse T C C G T A C A C
• kunk This program is similar to kkno except
  that the center sequence is not known a priori
  instead, the program computes the best center
  sequence for each conserved region it finds. For
  each column in the alignment, the algorithm
  recursively examines all possible center
  sequences starting at that position to see how
  far the region can be extended and back-tracks
  when the extension becomes impossible.