PFRED: A computational platform for siRNA and antisense oligonucleotides design [preprint]

PFRED a software application for the design, analysis, and visualization of antisense oligonucleotides and siRNA is described. The software provides an intuitive user-interface for scientists to design a library of siRNA or antisense oligonucleotides that target a specific gene of interest. Moreover, the tool facilitates the incorporation of various design criteria that have been shown to be important for stability and potency. PFRED has been made available as an open-source project so the code can be easily modified to address the future needs of the oligonucleotide research community. A compiled version is available for downloading at https://github.com/pfred/pfred-gui/releases as a java Jar file. The source code and the links for downloading the precompiled version can be found at https://github.com/pfred

More complete gene silencing by fewer siRNAs: transparent optimized design and biophysical signature

Highly accurate knockdown functional analyses based on RNA interference (RNAi) require the possible most complete hydrolysis of the targeted mRNA while avoiding the degradation of untargeted genes (off-target effects). This in turn requires significant improvements to target selection for two reasons. First, the average silencing activity of randomly selected siRNAs is as low as 62%. Second, applying more than five different siRNAs may lead to saturation of the RNA-induced silencing complex (RISC) and to the degradation of untargeted genes. Therefore, selecting a small number of highly active siRNAs is critical for maximizing knockdown and minimizing off-target effects. To satisfy these needs, a publicly available and transparent machine learning tool is presented that ranks all possible siRNAs for each targeted gene. Support vector machines (SVMs) with polynomial kernels and constrained optimization models select and utilize the most predictive effective combinations from 572 sequence, thermodynamic, accessibility and self-hairpin features over 2200 published siRNAs. This tool reaches an accuracy of 92.3% in cross-validation experiments. We fully present the underlying biophysical signature that involves free energy, accessibility and dinucleotide characteristics. We show that while complete silencing is possible at certain structured target sites, accessibility information improves the prediction of the 90% active siRNA target sites. Fast siRNA activity predictions can be performed on our web server at

PFRED: A computational platform for siRNA and antisense oligonucleotides design

PFRED a software application for the design, analysis, and visualization of antisense oligonucleotides and siRNA is described. The software provides an intuitive user-interface for scientists to design a library of siRNA or antisense oligonucleotides that target a specific gene of interest. Moreover, the tool facilitates the incorporation of various design criteria that have been shown to be important for stability and potency. PFRED has been made available as an open-source project so the code can be easily modified to address the future needs of the oligonucleotide research community. A compiled version is available for downloading at https://github.com/pfred/pfred-gui/releases/tag/v1.0 as a java Jar file. The source code and the links for downloading the precompiled version can be found at https://github.com/pfred

Efficient siRNA selection using hybridization thermodynamics

Small interfering RNA (siRNA) are widely used to infer gene function. Here, insights in the equilibrium of siRNA-target hybridization are used for selection of efficient siRNA. The accessibilities of siRNA and target mRNA for hybridization, as measured by folding free energy change, are shown to be significantly correlated with efficacy. For this study, a partition function calculation that considers all possible secondary structures is used to predict target site accessibility; a significant improvement over calculations that consider only the predicted lowest free energy structure or a set of low free energy structures. The predicted thermodynamic features, in addition to siRNA sequence features, are used as input for a support vector machine that selects functional siRNA. The method works well for predicting efficient siRNA (efficacy >70%) in a large siRNA data set from Novartis. The positive predictive value (percentage of sites predicted to be efficient for silencing that are) is as high as 87.6%. The sensitivity and specificity are 22.7 and 96.5%, respectively. When tested on data from different sources, the positive predictive value increased 8.1% by adding equilibrium terms to 25 local sequence features. Prediction of hybridization affinity using partition functions is now available in the RNAstructure software package

OligoWalk: an online siRNA design tool utilizing hybridization thermodynamics

Given an mRNA sequence as input, the OligoWalk web server generates a list of small interfering RNA (siRNA) candidate sequences, ranked by the probability of being efficient siRNA (silencing efficacy greater than 70%). To accomplish this, the server predicts the free energy changes of the hybridization of an siRNA to a target mRNA, considering both siRNA and mRNA self-structure. The free energy changes of the structures are rigorously calculated using a partition function calculation. By changing advanced options, the free energy changes can also be calculated using less rigorous lowest free energy structure or suboptimal structure prediction methods for the purpose of comparison. Considering the predicted free energy changes and local siRNA sequence features, the server selects efficient siRNA with high accuracy using a support vector machine. On average, the fraction of efficient siRNAs selected by the server that will be efficient at silencing is 78.6%. The OligoWalk web server is freely accessible through internet at http://rna.urmc.rochester.edu/servers/oligowalk

Kernel methods in genomics and computational biology

Support vector machines and kernel methods are increasingly popular in genomics and computational biology, due to their good performance in real-world applications and strong modularity that makes them suitable to a wide range of problems, from the classification of tumors to the automatic annotation of proteins. Their ability to work in high dimension, to process non-vectorial data, and the natural framework they provide to integrate heterogeneous data are particularly relevant to various problems arising in computational biology. In this chapter we survey some of the most prominent applications published so far, highlighting the particular developments in kernel methods triggered by problems in biology, and mention a few promising research directions likely to expand in the future

Analysis of Antisense Expression by Whole Genome Tiling Microarrays and siRNAs Suggests Mis-Annotation of Arabidopsis Orphan Protein-Coding Genes

MicroRNAs (miRNAs) and trans-acting small-interfering RNAs (tasi-RNAs) are small (20-22 nt long) RNAs (smRNAs) generated from hairpin secondary structures or antisense transcripts, respectively, that regulate gene expression by Watson-Crick pairing to a target mRNA and altering expression by mechanisms related to RNA interference. The high sequence homology of plant miRNAs to their targets has been the mainstay of miRNA prediction algorithms, which are limited in their predictive power for other kingdoms because miRNA complementarity is less conserved yet transitive processes (production of antisense smRNAs) are active in eukaryotes. We hypothesize that antisense transcription and associated smRNAs are biomarkers which can be computationally modeled for gene discovery.We explored rice (Oryza sativa) sense and antisense gene expression in publicly available whole genome tiling array transcriptome data and sequenced smRNA libraries (as well as C. elegans) and found evidence of transitivity of MIRNA genes similar to that found in Arabidopsis. Statistical analysis of antisense transcript abundances, presence of antisense ESTs, and association with smRNAs suggests several hundred Arabidopsis 'orphan' hypothetical genes are non-coding RNAs. Consistent with this hypothesis, we found novel Arabidopsis homologues of some MIRNA genes on the antisense strand of previously annotated protein-coding genes. A Support Vector Machine (SVM) was applied using thermodynamic energy of binding plus novel expression features of sense/antisense transcription topology and siRNA abundances to build a prediction model of miRNA targets. The SVM when trained on targets could predict the "ancient" (deeply conserved) class of validated Arabidopsis MIRNA genes with an accuracy of 84%, and 76% for "new" rapidly-evolving MIRNA genes.Antisense and smRNA expression features and computational methods may identify novel MIRNA genes and other non-coding RNAs in plants and potentially other kingdoms, which can provide insight into antisense transcription, miRNA evolution, and post-transcriptional gene regulation

Thermodynamics and kinetics of antisense oligonucleotide hybridization to a structured mRNA target

Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, February 2002.Includes bibliographical references (p. 165-178).Antisense oligonucleotides have the potential to selectively inhibit the expression of any gene with a known sequence. Antisense-based therapies are under development for the treatment of infectious diseases as well as complex genetic disorders. Although there have been some remarkable successes, realizing this potential is proving difficult because of problems with oligonucleotide stability, specificity, affinity, and delivery. Each of these limitations has been addressed experimentally through the use of chemically-modified oligonucleotides and oligonucleotide conjugates, with much success in enhancing oligonucleotide efficacy. These early studies have shown that selection of target site, once considered a trivial problem, is critical to the success of antisense strategies. It has become clear that the efficacy of antisense oligonucleotides is a strong function of the structure of the target mRNA. Though single-stranded, RNA molecules are typically folded into complex three-dimensional structures, formed primarily by intramolecular Watson-Crick base-pairing. If an oligonucleotide is complementary to a sequence embedded in the three dimensional structure, the oligonucleotide may not be able to bind to its target site and exert its therapeutic effect. Because the majority of the structure of RNA molecules is due to Watson-Crick base-pairing, relatively accurate predictions of these folding interactions can be made from algorithms that locate the structure with the most favorable free energy of folding.(cont.) Taking advantage of the predictability of RNA structures, this thesis addresses the problem of antisense target site selection, first from a theoretical and subsequently an experimental standpoint. A thermodynamic model to predict the binding affinity of oligonucleotides for their target mRNA is described and validated using multiple in vitro and cell-culture based experimental data sets. Subsequently, direct experimental comparisons with theoretical predictions are made on the well-characterized rabbit-[beta]-globin (RBG) mRNA, using a novel, centrifugal, binding affinity assay. The importance of the hybridization kinetics is also explored, as is the role of association kinetics in defining the rate of cleavage by the enzyme ribonuclease H (RNase H). Finally, the applicability of the model in identifying biologically active oligonucleotides is demonstrated.by S. Patrick Walton.Sc.D

Gold nanoparticles for nanotheranostics in leukemia – Addressing Chronic Myeloid Leukemia

Leukemia is a type of cancer that initiates in the bone marrow and results in the unregulated production of immature white blood cells (leukemic cells). The most homogenous subgroup of the disease is chronic myeloid leukemia (CML) accounting for nearly 1.5 million patients worldwide. Virtually all cases harbor the genetic translocation t(9;22)(q34.1;q11.2) resulting in the BCR-ABL1 gene fusion, that encodes for BCR-ABL1 tyrosine kinase. CML treatment success relies on an early diagnosis and the intense research towards developing effective tyrosine kinase inhibitors (TKI). Nanotechnology offers unprecedent advantages to tackle the shortcomings of conventional procedures for the management of CML. Gold nanoparticles (AuNPs) have unique optical properties suitable for ex vivo biosensing applications, but can also function in vivo as nanocarriers in a theranostic approach that links treatment with diagnosis according to patient’s molecular profile. A gold nanoprobe (Au-nanoprobe) colorimetric assay was optimized for the detection of the most frequent BCR-ABL1 isoform (e14a2) and was validated on fully characterized clinical samples. This simple and cheap method enabled the direct detection of e14a2-expressing RNA samples, with accuracy and high specificity. The Au-nanoprobe assay was translated onto a microfluidics chip, resulting in a faster outcome with smaller sample volumes, due to the scale and design of the device. Additionally, a new therapeutic strategy was designed to overcome CML resistance to first line therapy, such as imatinib (IM). BCR-ABL1 gene silencing was effectively achieved in vitro, using AuNPs functionalized with polyethylene glycol and a hairpin-shaped antisense single stranded DNA (ssDNA) oligonucleotide. Furthermore, the nanoconstruct allowed to reduce the dose of IM, when tested in a combined approach, and potentiated a viability decrease of K562 cells resistant to IM. The results of this thesis strongly suggest that AuNPs are a suitable and flexible tool for CML nanotheranostics, improving detection and a personalized treatment strategy