http://bartellab.wi.mit.edu/publication_reprints/Lewis_Cell03.pdf

Prediction of Mammalian MicroRNA Targets

http://bio.gsnu.ac.kr/research/miRNA/RNAss/Mfold/miRNAss_JMB_1999_Mathews.pdf

Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure.

Background
Secondary structure forms an important intermediate level of description of nucleic acids that encapsulates the dominating part of the folding energy, is often well conserved in evolution, and is routinely used as a basis to explain experimental findings. Based on carefully measured thermodynamic parameters, exact dynamic programming algorithms can be used to compute ground states, base pairing probabilities, as well as thermodynamic properties.

/pdf/viennarna-package-2-0-4q9zpqix5v.pdf

ViennaRNA Package 2.0

Two small temporal RNAs (stRNAs), lin-4 and let-7, control developmental timing in Caenorhabditis elegans. We find that these two regulatory RNAs are members of a large class of 21- to 24-nucleotide noncoding RNAs, called microRNAs (miRNAs). We report on 55 previously unknown miRNAs in C. elegans. The miRNAs have diverse expression patterns during development: a let-7 paralog is temporally coexpressed with let-7; miRNAs encoded in a single genomic cluster are coexpressed during embryogenesis; and still other miRNAs are expressed constitutively throughout development. Potential orthologs of several of these miRNA genes were identified in Drosophila and human genomes. The abundance of these tiny RNAs, their expression patterns, and their evolutionary conservation imply that, as a class, miRNAs have broad regulatory functions in animals.

/pdf/an-abundant-class-of-tiny-rnas-with-probable-regulatory-vtv94uvmvm.pdf

An Abundant Class of Tiny RNAs with Probable Regulatory Roles in Caenorhabditis elegans

Binary classifiers are routinely evaluated with performance measures such as sensitivity and specificity, and performance is frequently illustrated with Receiver Operating Characteristics (ROC) plots. Alternative measures such as positive predictive value (PPV) and the associated Precision/Recall (PRC) plots are used less frequently. Many bioinformatics studies develop and evaluate classifiers that are to be applied to strongly imbalanced datasets in which the number of negatives outweighs the number of positives significantly. While ROC plots are visually appealing and provide an overview of a classifier's performance across a wide range of specificities, one can ask whether ROC plots could be misleading when applied in imbalanced classification scenarios. We show here that the visual interpretability of ROC plots in the context of imbalanced datasets can be deceptive with respect to conclusions about the reliability of classification performance, owing to an intuitive but wrong interpretation of specificity. PRC plots, on the other hand, can provide the viewer with an accurate prediction of future classification performance due to the fact that they evaluate the fraction of true positives among positive predictions. Our findings have potential implications for the interpretation of a large number of studies that use ROC plots on imbalanced datasets.

/pdf/the-precision-recall-plot-is-more-informative-than-the-roc-w78tv6wyk3.pdf

The Precision-Recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets

Computer codes for computation and comparison of RNA secondary structures, the Vienna RNA package, are presented, that are based on dynamic programming algorithms and aim at predictions of structures with minimum free energies as well as at computations of the equilibrium partition functions and base pairing probabilities. An efficient heuristic for the inverse folding problem of RNA is introduced. In addition we present compact and efficient programs for the comparison of RNA secondary structures based on tree editing and alignment. All computer codes are written in ANSI C. They include implementations of modified algorithms on parallel computers with distributed memory. Performance analysis carried out on an Intel Hypercube shows that parallel computing becomes gradually more and more efficient the longer the sequences are.

L. S. Bonhoeffer

Papers

Fast folding and comparison of RNA secondary structures