Mature microrna identification via the use of a Naive Bayes classifier
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1 Mature microrna identification via the use of a Naive Bayes classifier Master Thesis Gkirtzou Katerina Computer Science Department University of Crete 13/03/2009 Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
2 Outline 1 Introduction 2 Methodology Naive Bayes Classifier Datasets Feature Definition Feature Selection 3 Results Training the Naive Bayes Classifier Finding the Best Mature Candidate Comparison with Other Methods 4 Conclusions Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
3 Outline 1 Introduction 2 Methodology Naive Bayes Classifier Datasets Feature Definition Feature Selection 3 Results Training the Naive Bayes Classifier Finding the Best Mature Candidate Comparison with Other Methods 4 Conclusions Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
4 MicroRNAs Definition MicroRNAs (mirnas) are small single stranded RNAs, on average 22nt long, generated from endogenous hairpin shaped transcripts with post transcriptional activity. Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
5 MicroRNAs Definition MicroRNAs (mirnas) are small single stranded RNAs, on average 22nt long, generated from endogenous hairpin shaped transcripts with post transcriptional activity. Significance MicroRNAs have been observed to participate in: Developmental timing. Cell proliferation and cell differentiation. Apoptosis. Diseases, such as diabetes and cancer. Anti viral defense. Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
6 MicroRNA Biogenesis Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
7 Related Work Disadvantages Hypothesize that only a single mature is produced from every hairpin structure (BayesMiRNAfind 2006, ProMiR 2006) Hypothesize that pri-mirnas are always processed by the Drosha complex, whose cleavage cite determines the start position of the mature (Microprocessor SVM 2006). Mature candidate is provided only for the human precursors, which are expressed in specific cell lines (SSCprofiler 2009). Evaluation of performance is often measured in terms of true positive rate alone, ignoring the false positive rate (ProMiR 2006, BayesMiRNAfind 2006, Tao 2007, mircos 2007, SSCprofiler 2009) Distance distribution of predicted compared to true matures is not provided (BayesMiRNAfind 2006, Tao 2007, mircos 2007, SSCprofiler 2009). Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
8 Objectives Goal Build a classifier considering biological information of precursor mirna, such as sequence or secondary structure, capable of identifying the mature mirna(s) within a precursor mirna with high accuracy. Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
9 Objectives Goal Build a classifier considering biological information of precursor mirna, such as sequence or secondary structure, capable of identifying the mature mirna(s) within a precursor mirna with high accuracy. Output The model s output is the predicted start position of the mature mirna(s) for each precursor sequence. Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
10 Outline 1 Introduction 2 Methodology Naive Bayes Classifier Datasets Feature Definition Feature Selection 3 Results Training the Naive Bayes Classifier Finding the Best Mature Candidate Comparison with Other Methods 4 Conclusions Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
11 Outline 1 Introduction 2 Methodology Naive Bayes Classifier Datasets Feature Definition Feature Selection 3 Results Training the Naive Bayes Classifier Finding the Best Mature Candidate Comparison with Other Methods 4 Conclusions Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
12 Naive Bayes Classifier Advantages of Naive Bayes Achieves strong performance in many real problems, despite its simplified assumptions. Requires small amount of training data. Contribution of features is easily derived. Our decision surface P(C mature x) P(C non mature x) > λ Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
13 Outline 1 Introduction 2 Methodology Naive Bayes Classifier Datasets Feature Definition Feature Selection 3 Results Training the Naive Bayes Classifier Finding the Best Mature Candidate Comparison with Other Methods 4 Conclusions Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
14 Datasets Typical two class classification problem Positive data: experimental verified mature mirnas. Negative data: What is a non mature mirna? Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
15 Datasets Typical two class classification problem Positive data: experimental verified mature mirnas. Negative data: What is a non mature mirna? Observations Known mirna precursors do not produce multiple overlapping mature mirnas from the same arm of the foldback precursor. The search area of the classifier will be a mirna precursor sequence. Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
16 Datasets Typical two class classification problem Positive data: experimental verified mature mirnas. Negative data: What is a non mature mirna? Observations Known mirna precursors do not produce multiple overlapping mature mirnas from the same arm of the foldback precursor. The search area of the classifier will be a mirna precursor sequence. Solution! Create negative data by sliding 1 base pair in both stem arms of the precursor with a window with size the same as the produced mature mirna. Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
17 Produce Negative Data Negative Data Positive Data SP EP SP EP Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
18 Produce Negative Data Negative Data Positive Data SP EP SP EP 1 22 Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
19 Produce Negative Data Negative Data Positive Data SP EP SP EP Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
20 Produce Negative Data Negative Data Positive Data SP EP SP EP Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
21 Produce Negative Data Negative Data Positive Data SP EP SP EP Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
22 Produce Negative Data Negative Data Positive Data SP EP SP EP Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
23 Produce Negative Data Negative Data Positive Data SP EP SP EP Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
24 Produce Negative Data Negative Data Positive Data SP EP SP EP Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
25 Produce Negative Data Negative Data Positive Data SP EP SP EP Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
26 Produce Negative Data Negative Data Positive Data SP EP SP EP Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
27 Produce Negative Data Negative Data Positive Data SP EP SP EP Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
28 Produce Negative Data Negative Data Positive Data SP EP SP EP Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
29 Produce Negative Data Negative Data Positive Data SP EP SP EP Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
30 Produce Negative Data Negative Data Positive Data SP EP SP EP Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
31 Produce Negative Data Negative Data Positive Data SP EP SP EP Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
32 Produce Negative Data Negative Data Positive Data SP EP SP EP Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
33 Produce Negative Data Negative Data Positive Data SP EP SP EP Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
34 Produce Negative Data Negative Data Positive Data SP EP SP EP Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
35 Produce Negative Data Negative Data Positive Data SP EP SP EP Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
36 Produce Negative Data Negative Data Positive Data SP EP SP EP Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
37 Produce Negative Data Negative Data Positive Data SP EP SP EP Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
38 Produce Negative Data Negative Data Positive Data SP EP SP EP Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
39 Produce Negative Data Negative Data Positive Data SP EP SP EP Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
40 Produce Negative Data Negative Data Positive Data SP EP SP EP Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
41 Outline 1 Introduction 2 Methodology Naive Bayes Classifier Datasets Feature Definition Feature Selection 3 Results Training the Naive Bayes Classifier Finding the Best Mature Candidate Comparison with Other Methods 4 Conclusions Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
42 Position oriented features Example of a position oriented features Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
43 Position oriented features Example of a position oriented features Areas of position oriented features Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
44 Distance oriented features Distance of Starting Position Distance of Ending Position Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
45 Outline 1 Introduction 2 Methodology Naive Bayes Classifier Datasets Feature Definition Feature Selection 3 Results Training the Naive Bayes Classifier Finding the Best Mature Candidate Comparison with Other Methods 4 Conclusions Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
46 Feature Selection Feature Selection Ranking Method 1 For each feature estimate the probability mass functions in both positive and negative data. Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
47 Feature Selection Feature Selection Ranking Method 1 For each feature estimate the probability mass functions in both positive and negative data. 2 Using the symmetric K-L divergence estimate a score for each feature. Symmetric Kullback Leibler divergence The divergence between the positive (P) and negative (N) probability distribution: where D KL (P N) = i P(i) log 2 Sym D KL = 1 2 (D KL(P N) + D KL (N P)) P(i) N(i) Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
48 Feature Selection Feature Selection Ranking Method 1 For each feature estimate the probability mass functions in both positive and negative data. 2 Using the symmetric K-L divergence estimate a score for each feature. 3 Rank features according to the K-L provided score. Symmetric Kullback Leibler divergence The divergence between the positive (P) and negative (N) probability distribution: where D KL (P N) = i P(i) log 2 Sym D KL = 1 2 (D KL(P N) + D KL (N P)) P(i) N(i) Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
49 Feature Selection Feature Selection Ranking Method 1 For each feature estimate the probability mass functions in both positive and negative data. 2 Using the symmetric K-L divergence estimate a score for each feature. 3 Rank features according to the K-L provided score. 4 Train the classifier using the top K features. Incoporate features gradually only if it helps increasing the performance of the classifier. Symmetric Kullback Leibler divergence The divergence between the positive (P) and negative (N) probability distribution: where D KL (P N) = i P(i) log 2 Sym D KL = 1 2 (D KL(P N) + D KL (N P)) P(i) N(i) Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
50 Outline 1 Introduction 2 Methodology Naive Bayes Classifier Datasets Feature Definition Feature Selection 3 Results Training the Naive Bayes Classifier Finding the Best Mature Candidate Comparison with Other Methods 4 Conclusions Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
51 Datasets Training Dataset Version 10.1 mirbase Organism Precursor True Mature Negative Mature Human Mouse Test Dataset Version 12 mirbase Organism Precursor True Mature Human Mouse Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
52 Implementation Specifications Extra Parameters: tune over a 10 fold cross validation The size of the flanking regions, N. The size of the scanning window, W. The number of features used in the classifier, K. The type of information the position oriented features hold. Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
53 Implementation Specifications Extra Parameters: tune over a 10 fold cross validation The size of the flanking regions, N. The size of the scanning window, W. The number of features used in the classifier, K. The type of information the position oriented features hold. Evaluation Specification The validation sets consisted of true mirna precursors, whose mature mirnas were left out from training in the cross validation procedure. Candidates mature mirnas were produced by sliding 1 base pair in both stem arms of the precursor with a fixed size sliding window, W. Evaluation was estimated based on exact match of the starting position of the predicted compared to the real mature mirna. Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
54 Outline 1 Introduction 2 Methodology Naive Bayes Classifier Datasets Feature Definition Feature Selection 3 Results Training the Naive Bayes Classifier Finding the Best Mature Candidate Comparison with Other Methods 4 Conclusions Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
55 Information contained in Position Oriented Features Classifier s Description Sensitivity Specificity Sequence Based Naive Bayes Classifiers 0nt flanking region 67.10% 55.10% 5nt flanking region 76.04% 53.34% 7nt flanking region 75.96% 53.20% 10nt flanking region 79.15% 47.01% 12nt flanking region 74.30% 51.33% Structure Based Naive Bayes Classifiers 0nt flanking region 65.70% 54.30% 5nt flanking region 76.34% 52.64% 7nt flanking region 77.85% 54.29% 10nt flanking region 81.01% 56.63% 12nt flanking region 79.89% 55.51% Combined Naive Bayes Classifiers 0nt flanking region 68.50% 62.50% 5nt flanking region 71.32% 65.34% 7nt flanking region 74.26% 66.46% 10nt flanking region 76.50% 65.61% 12nt flanking region 77.81% 64.14% Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
56 Information contained in Distance Oriented Features Distance Oriented Naive Bayes Classifiers AUC Distance oriented Window Window Window Window Features 18nt 20nt 22nt 24nt HS HS-HE HS-HE-ES HS-HE-ES-EE HS : the distance of the starting position of the mature mirna from the hairpin. HE : the distance of the ending position of the mature mirna from the hairpin. ES : the distance of the starting position of the mature mirna from the ends of the precursor. EE : the distance of the ending position of the mature mirna from the ends of the precursor. Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
57 Searching the Optimun Naive Bayes Classifier HS and Position Oriented Naive Bayes Classifiers AUC Flanking Window Window Window Window Region 18nt 20nt 22nt 24nt 0nt nt nt nt nt nt Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
58 The ROC Curve of the Best Naive Bayes Classifier Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
59 Outline 1 Introduction 2 Methodology Naive Bayes Classifier Datasets Feature Definition Feature Selection 3 Results Training the Naive Bayes Classifier Finding the Best Mature Candidate Comparison with Other Methods 4 Conclusions Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
60 Top Scorer in Cross Validation Distance from truth Percent % ± % ± % ± % ± % ± % ± % ± % Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
61 Top Scorer in Test Data Distance from truth Percent % ± % ± % ± % ± % ± % ± % ± % Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
62 MicroRNA Biogenesis Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
63 Top Scorer and its Duplex in Cross Validation Distance from truth Percent % ± % ± % ± % ± % ± % ± % ± % Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
64 Top Scorer and its Duplex in Test Data Distance from truth Percent % ± % ± % ± % ± % ± % ± % ± % Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
65 Outline 1 Introduction 2 Methodology Naive Bayes Classifier Datasets Feature Definition Feature Selection 3 Results Training the Naive Bayes Classifier Finding the Best Mature Candidate Comparison with Other Methods 4 Conclusions Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
66 Comparison with ProMiR Dataset Analysis Initial Dataset: 200 experimental human and mouse precursors. ProMiR predicted as precursors: 178/200. ProMiR predicted wrong stem for 78/178. Our Model predicted wrong stem for 94/178 if we consider as computational truth the top scorer of the precursor. Our Model predicted wrong stem for 0/178 if we consider as computational truth the top scorer of the precursor and its duplex. Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
67 Distance Distributions for Correct Stem Prediction Distance from truth Percent ProMiR 0 7% ±1 12% ±2 23% ±3 28% ±4 36% ±5 49% ±6 55% ±7 65% Top Scorer of our Model Distance from truth Percent % ± % ± % ± % ± % ± % ± % ± % Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
68 Distance Distributions for Correct Stem Prediction Distance from truth Percent ProMiR 0 7% ±1 12% ±2 23% ±3 28% ±4 36% ±5 49% ±6 55% ±7 65% Top Scorer and its Duplex of our Model Distance from truth Percent % ± % ± % ± % ± % ± % ± % ± % Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
69 Comparison with BayesMiRNAfind Dataset Analysis Initial Dataset: 200 experimental human and mouse precursors. BayesMiRNAfind predicted as precursors: 101/200. BayesMiRNAfind predicted wrong stem for 45/101. Our Model predicted wrong stem for 53/101 if we consider as computational truth the top scorer of the precursor. Our Model predicted wrong stem for 0/101 if we consider as computational truth the top scorer of the precursor and its duplex. Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
70 Distance Distributions for Correct Stem Prediction Distance from truth Percent BayesMiRNAfind % ± % ± % ± % ± % ± % ± % ± % Top Scorer of our Model Distance from truth Percent % ± % ± % ± % ± % ± % ± % ± % Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
71 Distance Distributions for Correct Stem Prediction Distance from truth Percent BayesMiRNAfind % ± % ± % ± % ± % ± % ± % ± % Top Scorer and its Duplex of our Model Distance from truth Percent % ± % ± % ± % ± % ± % ± % ± % Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
72 Outline 1 Introduction 2 Methodology Naive Bayes Classifier Datasets Feature Definition Feature Selection 3 Results Training the Naive Bayes Classifier Finding the Best Mature Candidate Comparison with Other Methods 4 Conclusions Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
73 Conclusions Innovations Feature Selection is based on Kullback Leibler divergence. Performance is estimated based on AUC, in comparison with other methods that their performance are estimated based on sensitivity. Provide distance distributions for true matures. Flexibility to select between top scorer per stem or top scorer and its duplex per precursor. Simple algorithm with quite strong performance. Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
74 Conclusions Innovations Feature Selection is based on Kullback Leibler divergence. Performance is estimated based on AUC, in comparison with other methods that their performance are estimated based on sensitivity. Provide distance distributions for true matures. Flexibility to select between top scorer per stem or top scorer and its duplex per precursor. Simple algorithm with quite strong performance. Comparison Program Percent for ±4nt Program Percent for ±4nt ProMir 36.00% BayesMiRNA 41.07% Top Scorer 67.47% Top Scorer 77.08% Duplex 71.35% Duplex 78.50% Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
75 Conclusions Conclusion Our findings suggest that position specific sequence and structure information and the distance of the starting position from the hairpin combined with a simple Bayes classifier achieve a good performance on the challenging task of mature mirna identification. Future Work Examine different error costs per class. Use stronger classifier, such as support vector machines (SVM). Use as training input the mirna mirna duplex. Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
76 Publications K. Gkirtzou, P. Tsakalides and P. Poirazi. Mature microrna identification via the use of a Naive Bayes classifier. In proceedings of BIBE, A. Oulas, A. Boutla, K. Gkirtzou, M. Reczko, K. Kalantidis and P. Poirazi. Prediction of novel microrna genes in cancer associated genomic regions a combined computational and experimental approach. In press, Nucleic Acids Research. K. Gkirtzou, P. Tsakalides and P. Poirazi. MatureFind: a tool for identifying mature mirnas in mammalians precursors. Manuscript in preparation. Gkirtzou K. (CSD UOC) Mature microrna identification 13/03/ / 44
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