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A probabilistic approach to microRNA-target binding

A probabilistic approach to microRNA-target binding. Taylor Tsuboi. miRNA. short (22 nucleotide) RNA sequence present in eukaryotic cells post-transcriptional gene regulation bind to mRNA and generally result in translational repression and gene silencing.

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A probabilistic approach to microRNA-target binding

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  1. A probabilistic approach to microRNA-target binding Taylor Tsuboi

  2. miRNA • short (22 nucleotide) RNA sequence present in eukaryotic cells • post-transcriptional gene regulation • bind to mRNA and generally result in translational repression and gene silencing

  3. complementary alignment of mature miRNA sequence with mRNA binding site create an alphabet representing distinct nucleotide pair types  miRNA-mRNA duplex sequence

  4. Markov Chains  Simplistic approach: zero-order Markov chain (doesn't account for preceding and succeeding subsequences) S1N being the sequence of interest, Sj being the random variable representing the letter at position j, and sj being its realization. Think of this as a sequence of letters with length N with a probability assigned to each position. Say we're looking at 5-letter words, where at each position, each letter in the set {a,b,c,d} is an equally likely possibility.  P(aabb) = P(S1=a)*P(S2=a)*P(S3=b)*P(S4=b)

  5. Markov chains and miRNA(Variable-Length Markov Chain - VLMC) where Lj and Lj′ are the optimal lengths for the preceding and succeeding subsequences respectively, and sj-Ljj-1 and sj+Lj′ j+1 are those subsequences. This modification allows us to consider the "context" surrounding each symbol in the sequence, and better models miRNA-mRNA duplex sequences. The length of sequences may slightly vary, so we normalize the sum of log-likelihoods over the sequence length.

  6. Data set analyzed Common data set provided by Yang et al. allows comparisons between the new model and previous works. Contained 233 data pairs from drosophila, Caenorhabditiselegans, human, mouse, rat and zebrafish, where 195 of them are positive interactions and 38 are negatives. Also analyzed an extended data set containing these points as well as more recent experimentally supported positive and negative miRNA-mRNA pairs. (Positive data points indicate a targeting reaction between the miRNA sequence and the mRNA sequence)

  7. Results New algorithm evaluated against two major current approaches: thermodynamic stability and sequence complementarity. Thermodynamic stability is modeled by RNAHybrid, which calculates the minimum free energy required to form the duplex from predicted structure information. Sequence complementarity is modeled by miRTif, which counts the frequencies of predefined motifs, which then feed a machine learning method to form optimal hyperplanes to separate positive and negative examples. Compared three models based on sensitivity, specificity, accuracy, and AUC (probability that a model will rank a random positive instance over a random negative one)

  8. Results (tabulated)

  9. Duplex alphabet

  10. Duplex alphabet (continued) Gradually decrease number of letters in the alphabet (by combining similar pairings) and analyze data. The Level-2 alphabet is chosen.

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