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Promoter Prediction in E.coli using ANN A.Krishnamachari Bioinformatics Centre, JNU [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 intergenic TSS TF -35 -10 TF -> Transcription Factor Sites TSS->Transcription Start Sites RBS -> Ribosome Binding sites CDS - > Coding Sequence (or) Gene RBS CDS Statement of the problem • Given a set of known sequences pertaining to a specific biological feature , develop a computational method to search for new members or sequences Computational Methods • Pattern Recognition • Pattern classification • Optimisation Methods Sequence Analysis AND Prediction Methods • Consensus • Position Weight Matrix (or) Profiles • Machine Learning Methods – Neural Networks – Markov Models – Support Vector Machines – Decision Tree – Optimization Methods Statistical Learning Consensus sequence TA TAA T 49%,54% and 58% 14 sites out of 291 sequences [Lisser and Margalitt] Mismatches but which one? 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) • Using amino acid substitution matrix (Dayhoff similarity matrix) [see later] 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 Linear Classification Problems Measure2 Measure1 Nonlinear Classification Problem Measure 2 Measure 1 (Artificial) Neural Network What Is A Neural Network ? A computational construct based on biological neuron . A neural network can: •Learn by adapting its synaptic weights to changes in the surrounding environments; •handle imprecise, fuzzy, noisy, and probabilistic information; and •generalize from known tasks or examples to unknown ones. Artificial neural networks (ANNs) attempt to mimic some,or all of these characteristics. Neural Network • Characterised by: - its pattern of connections between the neurons (Network Architecture) - its method of determining the weights on the connections (training or learning algorithm) Why Neural Network:Applications -Little or no incomplete understanding of the problem to be solved (very little theory) -Abundant data available Neural Networks: Applications • • • • • • Pattern classification Speech synthesis and recognition Image compression Clustering Medical Diagnosis Manufacturing Neural Network:Bioinformatics • • • • • Binding sites prediction Protein Secondary Structure prediction Protein folds Micro array data clustering Gene prediction Neural Networks • Supervised Learning • Unsupervised Learning Output inputs Layer 1 (input) 1 Layer 2 (output) W1,3 3 2 W2,3 Direction of information flow Summation Operation xi * wij=x1*w1+x2*w2+x3w3….+xnWnj Thresholding functions Output = 0 if Output = 1 if Output 1 1 x*w < T x*w >T Output 1 Threshold=0 0 0 T Output Input Logistic Transfer function 1 Output = - 1+e Weight updates W(k+1) = w(k)+ µ[T9k) – w(k)x(k)]x(k) for 0 ≤ k≤ N-1 Learning Concepts • Generally – the target output is 1 for +ve – The target output is 0 for –ve – But practically (0.9, 0.1) combination • Stopping criterion Based on certain epochs or cycles Based on certain error estimates Nucleic Acid A T G C 1 2 Position In a sequence Of K nucleotides K-1 K let the following binary values represent each base A="0001 C="0010 G="0100 T="1000 then G=4 A or C = "0011 = 3 A,G or T = "1101 = 13 etc. Nucleic Acid 1A 2G A T G C 0 0 0 1 1 0 0 0 Position In a sequence Of K nucleotides K-1 G 0 K T 1 1 0 0 0 0 0 Wi,j P=50 N=50 TP + TN =100 Note: Training and Test sequences are fixed length 1 2 …………50 1 2 …………50 n=10 N=500 TRAINING CGTAGCTATAGTGGG TTTAAACCCAAGAAT TATGGAATTTGGAAG TTTAGGATAGCACAG GATAAGGCCTAGATA TTTATGCATGAGATG TEST CCTGAACTGAG ATGATATATA A GTGAAATTCCG Prediction Method Input Output Input Layer -1 Hidden layer Output Layer Error function A (local) weight B (global) Learning Recognition <x,f(x)> x NN NN h(x) Example Disease A diagnosis: x – gene expression data (vector of numbers) f(x) – A positive / A negative (boolean 0/1) <x,f(x)> - set of known values Evaluation Mechanism • Sensitivity = TP TP + FN Specificity = TN TN + FP C = (TPxTN – FPxFN) (TP+FP)x(FP+TN)x(TN+FN)x(FN+TP) Cross - Validation • Benchmarking the Network Performance • Step:1 Divide the training set into “N” partitions • Step: 2 Train the “N-1” partitions and Test the Left out • Step: 3 Evaluate the Performance 1. Nucleic Acids Res. 1992 Aug 25;20(16):4331-8. An assessment of neural network and statistical approaches for prediction of E. coli promoter sites. Horton PB, Kanehisa M. 2. Nucleic Acids Res. 1994 Jun 11;22(11):2158-65. Analysis of E.coli promoter structures using neural networks. Mahadevan I, Ghosh I. O’Neill MC Training back-propagation neural networks to define and detect DNA-binding sites. Nucleic Acids Res. 1991 Jan 25;19(2):313-8. Escherichia coli promoters: neural networks develop distinct descriptions in learning to search for promoters of different spacing classes. Nucleic Acids Res. 1992 Jul 11;20(13):3471-7. A general procedure for locating and analyzing protein-binding sequence motifs in nucleic acids. Proc Natl Acad Sci U S A. 1998 Sep 1;95(18):10710-5. Pedersen AG, Engelbrecht Investigations of Escherichia coli promoter sequences with artificial neural networks: new signals discovered upstream of the transcriptional startpoint. Proc Int Conf Intell Syst Mol Biol. 1995;3:292-9. Basic Gene Grammars and DNA-ChartParser for language processing of Escherichia coli promoter DNA sequences Siu-wai Leung, Chris Mellish and Dave Robertson E.Coli PROMOTER DATA Earlier Studies • Data set corresponding to 17 spacing class • Very high threshold values Network Model Performance pBR322(CW) 17-1 34/35 -5, 125,805,1021,12 339,477,1584,16 26,4278 57,1970,4130 17-2 33/35 …. …. 17-3 34/35 17-4 32/35 -5, 1584, 1970,4130 125,807,1226,42 78 Poll(4/4) pBR322(CCW) New New New New NEW Disadvantages of MLP • i) Slow convergence, • ii) Training relies heavily on the choice of the number of hidden layers and • iii) Mode of prediction is generally based on high threshold values. Improvements in Prediction • Pre processing of the data based on DNA structure • Clean Model Structural Atlas of E.coli : J Mol Biol. 2000 Jun 16;299(4):907-30. A DNA structural atlas for Escherichia coli. Pedersen AG, Jensen LJ, Brunak S, Staerfeldt HH, Ussery DW. 1) The size of the positive data set may be increased by incorporating point mutations in non-sensitive positions 2) Negative data sets are generated by several ways i.e. a) Shuffling or randomising the positive data set. This does not destroy the the correlations between the bases completely. b) Using random sequences with a biased composition, c) Extracting the sequences from gene coding segments. 3) For the learning phase, the number of positive and negative input vectors are not generally proportionate and there is no standard prescription 4) Convergence factor and predictive ability depend on the size and the number of input vectors .