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Sequence analysis – an overviewPowerPoint Presentation

Sequence analysis – an overview

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Presentation Transcript

- Systematic development and application of Computing and Computational solution techniques to biological data to investigate biological process and make novel observations

General approach

Bioinformatics era

Organism

Functions

Cell

Chromosome

DNA

Sequences

Information Explosion

- GENOME
- PROTEOME
- TRANSCRIPTOME
- METABOLOME

Databases

- Literature
- Sequences
- Structure
- Pathways
- Expression ratios

Databases

- Textual
- Symbolic (manipulation possible)
- Numeric (computation possible)
- Graphs (visualization )

January Issue

Integrated Database Search Engines

http://www.ncbi.nlm.nih.gov/Entrez/

http://srs.ebi.ac.uk

http://www.genome.ad.jp/dbget/

Analysis

- Individual sequences
- Between sequences
- Within a genome
- Between genomes

Sequence Analysis

- Sequence segments which has a functional role will show a bias in composition , correlation
- Computational methods tries to capture bias, regularities, correlations
- Scale invarient properties

Sequence Analysis

- Sequence comparison
- Pattern Finding –repeats, motifs,restriction sites
- Gene Prediction
- Phylogenetic analysis

intergenic

TSS

RBS

CDS

TF

-10

-35

TF -> Transcription Factor Sites

TSS->Transcription Start Sites

RBS -> Ribosome Binding sites

CDS - > Coding Sequence (or) Gene

- Biological functions
- Regulation or Modulation
- Specific binding (Specified DNA pattern)

- Promoter
- Splice site
- Ribosome binding site
- Transcription Factor sites
- Restriction Enzymes sites

I

M

E

R

The dimer is constructed such that it has bifoldsymmetry allowing the recognition helix of the second protein sub-unit to make the same groove binding interactions as the first. The distance between the recognition helices is 34 angstroms which corresponds to one turn of the B-DNA double helix. This means that when the recognition helix of one sub-unit binds in the groove of a specific region of DNA, the second sub-units' helix can also bind in the DNA groove, one turn along from the first helix

Experimental methods

- Foot print expts. (Dnase )
- Methylation Interference
- Immuno precipitation assay
- Compilation and Model building

TF1

TF2

TF1

TF3

-145

-120

-40

Design Oligos covering these regions for studying promoter activity

Carry out EMSA

Carry out Reporter assay

Carry out in-vivo experiments

Make Observations

BS1

Reporter Gene

BS2

-56

-30

-15

-105

BS2

Reporter Gene

BS1

-150

-100

-50

BS1

Measure Expression

Statement of the problem

- Given a collection of known binding sites, develop a representation of those sites that can be used to search new sequences and reliably predict where additional binding sites occur.

Ribosome Binding sites : Alignment

Reference

- Variability becomes inherent in biological sequences
- manifesting at various length scales
- Statistical and probabilistic framework is ideal for studying these characteristics

Sequence Analysis AND Prediction Methods

- Consensus
- Position Weight Matrix (or) Profiles
- Computational Methods
- Neural Networks
- Markov Models
- Support Vector Machines
- Decision Tree
- Optimization Methods

Describing features using frequency matrices

- Goal: Describe a sequence feature (or motif) more quantitatively than possible using consensus sequences
- Need to describe how often particular bases are found in particular positions in a sequence feature

Describing features using frequency matrices

- Definition: For a feature of length m using an alphabet of ncharacters, a frequency matrixis an n by m matrix in which each element contains the frequency at which a given member of the alphabet is observed at a given position in an aligned set of sequences containing the feature

Frequency matrices (continued)

- Three uses of frequency matrices
- Describe a sequence feature
- Calculate probability of occurrence of feature in a random sequence
- Calculate degree of match between a new sequence and a feature

Frequency Matrices, PSSMs, and Profiles

- A frequency matrix can be converted to a Position-Specific Scoring Matrix (PSSM) by converting frequencies to scores
- PSSMs also called Position Weight Matrixes (PWMs) or Profiles

Methods for converting frequency matrices to PSSMs

- Using log ratio of observed to expected
where m(j,i) is the frequency of character j observed at position i and f(j) is the overall frequency of character j (usually in some large set of sequences)

Finding occurrences of a sequence feature using a Profile

- As with finding occurrences of a consensus sequence, we consider all positions in the target sequence as candidate matches
- For each position, we calculate a score by “looking up” the value corresponding to the base at that position

Positions (Columns in alignment)

V1

x12 + x21 + x33 + x44 + x52

TAGCT AGTGC

if is above a threshold it is a site

V1

Building a PSSM

Set of Aligned Sequence Features

PSSM builder

PSSM

Expected frequencies of each sequence element

Searching for sequences related to a family with a PSSM

Set of Aligned Sequence Features

PSSM builder

Expected frequencies of each sequence element

PSSM

Sequences that match above threshold

PSSM search

Threshold

Positions and scores of matches

Set of Sequences to search

Consensus sequences vs. frequency matrices

- consensus sequence or a frequency matrix which one to use?
- If all allowed characters at a given position are equally "good", use IUB codes to create consensus sequence
- Example: Restriction enzyme recognition sites

- If some allowed characters are "better" than others, use frequency matrix
- Example: Promoter sequences

- If all allowed characters at a given position are equally "good", use IUB codes to create consensus sequence

Consensus sequences vs.frequency matrices

- Advantages of consensus sequences: smaller description, quicker comparison
- Disadvantage: lose quantitative information on preferences at certain locations

- Expected variation per column can be calculated
- Low entropy means higher conservation
- Entropy yields amount of information per column

Entropy Or Uncertainty

- The entropy (H) for a column is:
- a: is a residue,
- fa: frequency of residue a in a column,
- fa Pa as N becomes large

Information Content

- Maximum Uncertainty = log2 n
- For DNA, log2 4 = 2
- For Protein log2 20
Information content I(x)

I (x) = Maximum Uncertainty – Observed Uncertainty

Note : Observed Uncertainty = Observed Uncertainty – small size sample correction

Binding site regions comprises of both signal(s)(binding site) and noise (background).

Studies have shown that the information content is above zero at the exact binding site and in the vicinity the it averages to zero

The important question is how to delineate the

signal or binding site from the background.

One possible approach is to treat the binding

site (signal) as an outlier from the surrounding

(background) sequences.

Krishnamachari et al J.theor.biol 2004 site) and noise (background).

Assumption of independence site) and noise (background).

- Prediction models assumes independence
- Markov models of higher order require large data sets
- This require better data mining approaches

Regulatory sequence analysis site) and noise (background).

- Analysis of upstream sequences of co-regulated genes (micro-array expts.)
- Phylogenetic foot-printing – Motif discovery

Thanks site) and noise (background).

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