Herbert H. Tsang

Meta-analysis of small RNA-sequencing errors reveals ubiquitous post-transcriptional RNA modifications

Recent advances in DNA-sequencing technology have made it possible to obtain large datasets of small RNA sequences. Here we demonstrate that not all non-perfectly matched small RNA sequences are simple technological sequencing errors, but many hold valuable biological information. Analysis of three small RNA datasets originating from Oryza sativa and Arabidopsis thaliana small RNA-sequencing projects demonstrates that many single nucleotide substitution errors overlap when aligning homologous non-identical small RNA sequences. Investigating the sites and identities of substitution errors reveal that many potentially originate as a result of post-transcriptional modifications or RNA editing. Modifications include N1-methyl modified purine nucleotides in tRNA, potential deamination or base substitutions in micro RNAs, 3′ micro RNA uridine extensions and 5′ micro RNA deletions. Additionally, further analysis of large sequencing datasets reveal that the combined effects of 5′ deletions and 3′ uridine extensions can alter the specificity by which micro RNAs associate with different Argonaute proteins. Hence, we demonstrate that not all sequencing errors in small RNA datasets are technical artifacts, but that these actually often reveal valuable biological insights to the sites of post-transcriptional RNA modifications.

SARNA-Predict: Accuracy Improvement of RNA Secondary Structure Prediction Using Permutation-Based Simulated Annealing

Ribonucleic acid (RNA), a single-stranded linear molecule, is essential to all biological systems. Different regions of the same RNA strand will fold together via base pair interactions to make intricate secondary and tertiary structures that guide crucial homeostatic processes in living organisms. Since the structure of RNA molecules is the key to their function, algorithms for the prediction of RNA structure are of great value. In this article, we demonstrate the usefulness of SARNA-Predict, an RNA secondary structure prediction algorithm based on Simulated Annealing (SA). A performance evaluation of SARNA-Predict in terms of prediction accuracy is made via comparison with eight state-of-the-art RNA prediction algorithms: mfold, Pseudoknot(pknotsRE), NUPACK, pknotsRG-mfe, Sfold, HotKnots, ILM, and STAR. These algorithms are from three different classes: heuristic, dynamic programming, and statistical sampling techniques. An evaluation for the performance of SARNA-Predict in terms of prediction accuracy was verified with native structures. Experiments on 33 individual known structures from eleven RNA classes (tRNA, viral RNA, antigenomic HDV, telomerase RNA, tmRNA, rRNA, RNaseP, 5S rRNA, Group I intron 23S rRNA, Group I intron 16S rRNA, and 16S rRNA) were performed. The results presented in this paper demonstrate that SARNA-Predict can out-perform other state-of-the-art algorithms in terms of prediction accuracy. Furthermore, there is substantial improvement of prediction accuracy by incorporating a more sophisticated thermodynamic model (efn2).

SARNA-Predict: A Study of RNA Secondary Structure Prediction Using Different Annealing Schedules

This paper presents an algorithm for RNA secondary structure prediction based on simulated annealing (SA) and also studies the effect of using different types of annealing schedules. SA is known to be effective in solving many different types of minimization problems and for being able to approximate global minima in the solution space. Based on free energy minimization techniques, this permutation-based SA algorithm heuristically searches for the structure with a free energy value close to the minimum free energy DeltaG for that strand, within given constraints. Other contributions of this paper include the use of permutation-based encoding for RNA secondary structure and the swap mutation operator. Also, a detailed study of the convergence behavior of the algorithm is conducted and various annealing schedules are investigated. An evaluation of the performance of the new algorithm in terms of prediction accuracy is made via comparison with the dynamic programming algorithm mfold for thirteen individual known structures from four RNA classes (5S rRNA, Group I intron 23 rRNA, Group I intron 16S rRNA and 16S rRNA). Although dynamic programming algorithms for RNA folding are guaranteed to give the mathematically optimal (minimum energy) structure, the fundamental problem of this approach seems to be that the thermodynamic model is only accurate within 5-10%. Therefore, it is difficult for a single sequence folding algorithm to resolve which of the plausible lowest-energy structure is correct. The new algorithm showed comparable results with mfold and demonstrated a slightly higher specificity

The significance of thermodynamic models in the accuracy improvement of RNA secondary structure prediction using permutation-based simulated annealing

Ribonucleic acid, a single stranded linear molecule, is essential to all biological systems. Different regions of the same RNA strand will fold together via base pair interactions to make intricate secondary and tertiary structures that guide crucial homeostatic processes in living organisms. Since the structure of RNA molecules is key to their function, algorithms for the prediction of RNA structure are of great value. This paper discusses significant improvements made to SARNA-Predict, an RNA secondary structure prediction algorithm based on Simulated Annealing (SA). One major improvement is the incorporation of a sophisticated thermodynamic model (efn2). This model is used by mfold to rank sub-optimal structures, but cannot be used directly by mfold during the structure prediction. Experiments on eight individual known structures from four RNA classes (5S rRNA, Group I intron 23S rRNA, Group I intron 16S rRNA and 16S rRNA) were performed. The data demonstrate the robustness and the effectiveness of our improved prediction algorithm. The new algorithm shows results which surpass the dynamic programming algorithm mfold in terms of prediction accuracy on all tested structures.

SARNA-Predict: A Simulated Annealing Algorithm for RNA Secondary Structure Prediction

Ribonucleic acid (RNA) plays fundamental roles in cellular processes and its structure is directly related to its functions. This paper describes and presents a novel algorithm for RNA secondary structure prediction based on simulated annealing (SA). SA is known to be effective in solving many different types of minimization problems and for finding the global minima in the solution space. Based on free energy minimization techniques, this permutation-based SA algorithm heuristically searches for the structure with a free energy value close to the minimum free energy DeltaG for that strand, within given constraints. A detailed study of the convergence behavior of the algorithm is conducted and various cooling schedules are investigated. An evaluation of the performance of the new algorithm in terms of prediction accuracy is made via comparison with the dynamic programming algorithm mfold for eight individual known structures from three RNA classes (5S rRNA, Group I intron 16S rRNA and 16S rRNA). The significant contribution of this algorithm is in showing comparable results with the most common dynamic programming prediction application mfold and surpassing results from an evolutionary algorithm (EA)

SARNA-Predict-pk: Predicting RNA secondary structures including pseudoknots

Pseudoknots are RNA tertiary structures which perform essential biological functions. This paper presents SARNA-Predict-pk, an algorithm for pseudoknotted RNA secondary structure prediction based on Simulated Annealing (SA). The research presented here extends previous work of SARNA-Predict and incorporates a new thermodynamic model into the algorithm, effectively enabling it to predict pseudo-knotted RNA structures. An evaluation of the performance of SARNA-Predict-pk in terms of prediction accuracy is made via comparison with several state-of-the-art prediction algorithms. We measured the sensitivity and specificity using five prediction algorithms. Three of these are dynamic programming algorithms: Pseudoknot (pknotsRE), NUPACK, and pknotsRG-mfe. The other two are heuristic algorithms: SARNA-Predict-pk and HotKnots algorithms. An evaluation for the performance of SARNA-Predict-pk in terms of prediction accuracy was verified with native structures. Experiments on ten individual known structures from six RNA classes (tRNA, viral RNA, anti-genomic HDV, telomerase RNA, tmRNA, and RNaseP) were performed. The results presented in this paper demonstrate that SARNA-Predict-pk can out-perform other state-of-the-art algorithms in terms of prediction accuracy.

Industry in the Classroom: Equipping Students with Real-World Experience A reflection on the effects of industry partnered projects on computing education

This paper reports a software engineering class focused around experiential learning through an industry-partnered project. It includes a students perspective on the class experience. The authors argue that software engineering classes that only utilize trivial homework neglect crucial software development soft skills and fail to prepare students for industry employment. By focusing the courses around and industry-partnered project, students were able to integrate the fundamental concepts of software engineering while being equipped with real-world experience. The authors believe the proposed approach allows students to be better equipped for the industry and provides them valuable experience in their future career.

Dynalink: A Framework for Dynamic Criminal Network Visualization

Understanding the temporal development and patterns of criminal networks is important for law enforcement and intelligence agencies to investigate and prevent crimes. Extracting and visualizing criminal networks from a large amount of crime data has been a challenge over the past years. In particular, the visualization of the dynamic development of such networks over time has been difficult in many ways. Recent advancement of visual analytics provides new analytical reasoning tools to explore and analyze a large amount of data with interactive visual interfaces. By employing the ideas of visual analytics, we propose here a framework to visualize dynamic criminal networks, which is called Dynalink. The interactive and visual features of Dynalink can be useful in discovering and analyzing both relational and temporal patterns of criminal networks.

Modeling HIV spread through sexual contact using a cellular automaton

Having a risky sexual behavior increases the likelihood of infection by the Human Immunodeficiency Virus (HIV), which causes the Acquired Immunodeficiency Syndrome (AIDS). This has been a long lasting problem in high-risk populations such as sex workers: individuals in this population may face drug addiction and share infected needles, or have unprotected sex, and both issues can result in an HIV infection that may then be transmitted to other parts of the population. To study the dynamics of the HIV epidemic in such a high-risk community, we propose a model in which the population is represented as a cellular automaton. At the macro-level, our model accounts for the fact that the sexual behavior of an individual is influenced by the social norms of his acquaintances (social network) as well as by his awareness of HIV status. At the micro-level, randomized neighborhoods provide an explicit representation of personal interactions standing for the large number of non-repeated encounters in populations at risk. Our simulations study the dynamics of the disease for different social norms as well as the probability that a seropositive individual get tested.

rnaDesign: Local search for RNA secondary structure design

The RNA secondary structure design (SSD) problem is a recently emerging research topic motivated by applications such as customized drug design and the self-assembly of RNA nano-objects. This paper presents a novel local search algorithm, rnaDesign for SSD solving. An evaluation of the algorithm performance in terms of sequence affinity and structure specificity is made through comparison with another algorithm, RNAinverse. Experiments were performed on RNA secondary structures including three biologically existing data sets and one random structure set. Empirical results show that rnaDesign outperforms RNAinverse in terms of structure designability; sequences designed by rnaDesign also exhibit better thermodynamic stability with relatively lower folding energy. Furthermore, we demonstrate through parameter tuning experiments that using a combination of heuristic search strategies leads to better design performance; there also exists a strong correlation between the heuristic values in use and solution quality.