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Sequence analysis – an overview A.Krishnamachari [email protected] Definition of Bioinformatics • Systematic development and application of Computing and Computational solution techniques to biological data to investigate biological process and make novel observations Research in Biology 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/ COG Locus link Uni Gene Human – Mouse Map Primary sequences Structures Expression data Pathways DNA Gene 1000 Protein Genome 108 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 TF -35 -10 TF -> Transcription Factor Sites TSS->Transcription Start Sites RBS -> Ribosome Binding sites CDS - > Coding Sequence (or) Gene RBS CDS Protein-DNA interactions • Biological functions • Regulation or Modulation • Specific binding (Specified DNA pattern) DNA binding sites • Promoter • Splice site • Ribosome binding site • Transcription Factor sites • Restriction Enzymes sites The dimer is constructed such that it has bifold symmetry 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 Odd Even DNA binding sites - Model Experimental methods Foot print expts. (Dnase ) Methylation Interference Immuno precipitation assay Compilation and Model building TF1 -145 TF1 TF3 -120 TF2 TF1 -40 Design Oligos covering these regions for studying promoter activity Carry out EMSA Carry out Reporter assay Carry out in-vivo experiments Make Observations BS2 -105 BS2 -150 -100 BS1 -56 Reporter Gene -30 -15 Reporter Gene BS1 -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. Reference 1. Variability becomes inherent in biological sequences 2. manifesting at various length scales 3. 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 Strict consensus - TATA Loose consensus - (A/T)R(G/C)YG Weight matrix OR profile 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 n characters, a frequency matrix is 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 score (i) log m( j,i) / f ( j) 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) Nucleotide s 1 2 3 4 5 A x11 x21 x31 x41 x51 T x12 x22 x32 x42 x52 G x13 x23 x33 x43 x53 C x14 x24 x34 x44 x54 TAGCT AGTGC V1 x12 + x21 + x33 + x44 + x52 if V1 is above a threshold it is a site Building a PSSM Set of Aligned Sequence Features Expected frequencies of each sequence element PSSM builder PSSM Searching for sequences related to a family with a PSSM Set of Aligned Sequence Features Expected frequencies of each sequence element PSSM builder PSSM Threshold Set of Sequences to search PSSM search Sequences that match above threshold Positions and scores of matches 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 Consensus sequences vs. frequency matrices • Advantages of consensus sequences: smaller description, quicker comparison • Disadvantage: lose quantitative information on preferences at certain locations Shannon Entropy • 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: H fa log( fa ) residues ( a ) • a: is a residue, • fa: frequency of residue a in a column, • fa Pa as N becomes large H P log P i i A,T ,G ,C i Information • Information Gain(I)= H before – H after Genomic composition • H before = H after Hg pa log pa a A,T, G,C p log p i i A, T,G, C i 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 I 2 p log p i i i A,T ,G ,C Note : Observed Uncertainty = Observed Uncertainty – small size sample correction Shine-Dalgarno Spacer Translation start site 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 Assumption of independence • Prediction models assumes independence • Markov models of higher order require large data sets • This require better data mining approaches Regulatory sequence analysis • Analysis of upstream sequences of coregulated genes (micro-array expts.) • Phylogenetic foot-printing – Motif discovery