Andrew S. Peek

IDT SciTools: a suite for analysis and design of nucleic acid oligomers

DNA and RNA oligomers are used in a myriad of diverse biological and biochemical experiments. These oligonucleotides are designed to have unique biophysical, chemical and hybridization properties. We have created an integrated set of bioinformatics tools that predict the properties of native and chemically modified nucleic acids and assist in their design. Researchers can select PCR primers, probes and antisense oligonucleotides, find the most suitable sequences for RNA interference, calculate stable secondary structures, and evaluate the potential for two sequences to interact. The latest, most accurate thermodynamic algorithms and models are implemented. This free software is available at http://www.idtdna.com/SciTools/SciTools.aspx.

Marsupial-specific microRNAs evolved from marsupial-specific transposable elements.

Using a direct miRNA cloning strategy we previously identified fourteen marsupial- or species-specific microRNAs in the marsupial species Monodelphis domestica. In the present study we examined each of the pre-miRNAs and their flanking sequences and demonstrate that half of these miRNAs evolved from marsupial-specific transposable elements. These findings reinforce the view that transposable elements are a previously unappreciated source of new, lineage-specific microRNAs.

Comparing Artificial Neural Networks, General Linear Models and Support Vector Machines in Building Predictive Models for Small Interfering RNAs

Background Exogenous short interfering RNAs (siRNAs) induce a gene knockdown effect in cells by interacting with naturally occurring RNA processing machinery. However not all siRNAs induce this effect equally. Several heterogeneous kinds of machine learning techniques and feature sets have been applied to modeling siRNAs and their abilities to induce knockdown. There is some growing agreement to which techniques produce maximally predictive models and yet there is little consensus for methods to compare among predictive models. Also, there are few comparative studies that address what the effect of choosing learning technique, feature set or cross validation approach has on finding and discriminating among predictive models. Principal Findings Three learning techniques were used to develop predictive models for effective siRNA sequences including Artificial Neural Networks (ANNs), General Linear Models (GLMs) and Support Vector Machines (SVMs). Five feature mapping methods were also used to generate models of siRNA activities. The 2 factors of learning technique and feature mapping were evaluated by complete 3×5 factorial ANOVA. Overall, both learning techniques and feature mapping contributed significantly to the observed variance in predictive models, but to differing degrees for precision and accuracy as well as across different kinds and levels of model cross-validation. Conclusions The methods presented here provide a robust statistical framework to compare among models developed under distinct learning techniques and feature sets for siRNAs. Further comparisons among current or future modeling approaches should apply these or other suitable statistically equivalent methods to critically evaluate the performance of proposed models. ANN and GLM techniques tend to be more sensitive to the inclusion of noisy features, but the SVM technique is more robust under large numbers of features for measures of model precision and accuracy. Features found to result in maximally predictive models are not consistent across learning techniques, suggesting care should be taken in the interpretation of feature relevance. In the models developed here, there are statistically differentiable combinations of learning techniques and feature mapping methods where the SVM technique under a specific combination of features significantly outperforms all the best combinations of features within the ANN and GLM techniques.

Identification of sequence motifs significantly associated with antisense activity

BackgroundPredicting the suppression activity of antisense oligonucleotide sequences is the main goal of the rational design of nucleic acids. To create an effective predictive model, it is important to know what properties of an oligonucleotide sequence associate significantly with antisense activity. Also, for the model to be efficient we must know what properties do not associate significantly and can be omitted from the model. This paper will discuss the results of a randomization procedure to find motifs that associate significantly with either high or low antisense suppression activity, analysis of their properties, as well as the results of support vector machine modelling using these significant motifs as features.ResultsWe discovered 155 motifs that associate significantly with high antisense suppression activity and 202 motifs that associate significantly with low suppression activity. The motifs range in length from 2 to 5 bases, contain several motifs that have been previously discovered as associating highly with antisense activity, and have thermodynamic properties consistent with previous work associating thermodynamic properties of sequences with their antisense activity. Statistical analysis revealed no correlation between a motifs position within an antisense sequence and that sequences antisense activity. Also, many significant motifs existed as subwords of other significant motifs. Support vector regression experiments indicated that the feature set of significant motifs increased correlation compared to all possible motifs as well as several subsets of the significant motifs.ConclusionThe thermodynamic properties of the significantly associated motifs support existing data correlating the thermodynamic properties of the antisense oligonucleotide with antisense efficiency, reinforcing our hypothesis that antisense suppression is strongly associated with probe/target thermodynamics, as there are no enzymatic mediators to speed the process along like the RNA Induced Silencing Complex (RISC) in RNAi. The independence of motif position and antisense activity also allows us to bypass consideration of this feature in the modelling process, promoting model efficiency and reducing the chance of overfitting when predicting antisense activity. The increase in SVR correlation with significant features compared to nearest-neighbour features indicates that thermodynamics alone is likely not the only factor in determining antisense efficiency.

miRNA Profile of a Triassic Common Mammalian Ancestor and PremiRNA Evolution in the Three Mammalian Reproductive Lineages

MicroRNAs (miRNAs) are major factors in the regulation of gene expression. Recent evolutionary studies of miRNAs indicate that important biological innovations, such as the advent of bilateral symmetry and placental reproduc- tion, are accompanied by bursts of miRNA creation which are subsequently conserved via purifying selection. The emer- gence of eutherian (placental) mammals followed by as much as fifty million years the appearance of the first true mam- mals in the late Triassic, some 230 million years ago. We have utilized microRNA inventories of eutherian, metatherian (marsupial), monotreme (platypus), and chicken genomes to assemble a minimal microRNA profile of the last common ancestor of all mammals consisting of 171 miRNAs. This profile suggests that the rise of placental reproduction launched a more than three-fold expansion of microRNAs. In addition to expansion of the microRNA repertoire, the conserved mi- croRNAs from five mammalian and one avian genome show evidence for conforming to a canonical phylogenetic history as well as dramatic deviations from the assumptions of molecular clock-like rates and the equality of substitution rates among lineages. We also show that many of these basal mammalian miRNAs are highly expressed in eutherian placenta thus creating an opportunity to gain insight on how microRNAs acquire new targets and new functions.

An X chromosome MicroRNA Cluster in the Marsupial Species Monodelphis domestica

MicroRNAs (miRNAs) are an important class of posttranscriptional gene expression regulators. In the course of mapping novel marsupial-specific miRNAs in the genome of the gray short-tailed opossum, Monodelphis domestica, we encountered a cluster of 39 actual and potential miRNAs spanning 102 kb of the X chromosome. Analysis of the cluster revealed that 37 of the 39 miRNAs are predicted to form thermodynamically stable hairpins, and at least 3 members have been directly cloned from M. domestica tissues. The sequence characteristics of these miRNAs suggest that they all descended from a single common ancestor. Further, 2 distinct families appear to have diversified from the ancestral sequence through different duplication mechanisms: one through a series of simple tandem duplications and the other through a recurrent transposon-mediated duplication process.