Long non-coding RNA structure and function: Is there a link?

RNA has emerged as the prime target for diagnostics, therapeutics and the development of personalized medicine. In particular, the non-coding RNAs (ncRNAs) that do not encode proteins, display remarkable biochemical versatility. They can fold into complex structures and interact with proteins, DNA and other RNAs, modulating the activity, DNA targets or partners of multiprotein complexes. Thus, ncRNAs confer regulatory plasticity and represent a new layer of epigenetic control that is dysregulated in disease. Intriguingly, for long non-coding RNAs (lncRNAs, >200 nucleotides length) structural conservation rather than nucleotide sequence conservation seems to be crucial for maintaining their function. LncRNAs tend to acquire complex secondary and tertiary structures and their functions only impose very subtle sequence constraints. In the present review we will discuss the biochemical assays that can be employed to determine the lncRNA structural configurations. The implications and challenges of linking function and lncRNA structure to design novel RNA therapeutic approaches will also be analyzed

A Firefly-inspired method for protein structure prediction in lattice models

We introduce a Firefly-inspired algorithmic approach for protein structure prediction over two different lattice models in three-dimensional space. In particular, we consider three-dimensional cubic and three-dimensional face-centred-cubic (FCC) lattices. The underlying energy models are the Hydrophobic-Polar (H-P) model, the Miyazawa–Jernigan (M-J) model and a related matrix model. The implementation of our approach is tested on ten H-P benchmark problems of a length of 48 and ten M-J benchmark problems of a length ranging from 48 until 61. The key complexity parameter we investigate is the total number of objective function valuations required to achieve the optimum energy values for the H-P model or competitive results in comparison to published values for the M-J model. For H-P instances and cubic lattices, where data for comparison are available, we obtain an average speed-up over eight instances of 2.1, leaving out two extreme values (otherwise, 8.8). For six M-J instances, data for comparison are available for cubic lattices and runs with a population size of 100, where, a priori, the minimum free energy is a termination criterion. The average speed-up over four instances is 1.2 (leaving out two extreme values, otherwise 1.1), which is achieved for a population size of only eight instances. The present study is a test case with initial results for ad hoc parameter settings, with the aim of justifying future research on larger instances within lattice model settings, eventually leading to the ultimate goal of implementations for off-lattice models

Crispr/Cas9 editing reveals novel mechanisms of clustered microRNA regulation and function

MicroRNAs (miRNAs) are important regulators of diverse physiological and pathophysiological processes. MiRNA families and clusters are two key features in miRNA biology. Here we explore the use of CRISPR/Cas9 as a powerful tool to delineate the function and regulation of miRNA families and clusters. We focused on four miRNA clusters composed of miRNA members of the same family, homoclusters or different families, hetero-clusters. Our results highlight different regulatory mechanisms in miRNA cluster expression. In the case of the miR-497~195 cluster, editing of miR-195 led to a significant decrease in the expression of the other miRNA in the cluster, miR-497a. Although no gene editing was detected in the miR-497a genomic locus, computational simulation revealed alteration in the three dimensional structure of the pri miR-497~195 that may affect its processing. In cluster miR- 143~145 our results imply a feed-forward regulation, although structural changes cannot be ruled out. Furthermore, in the miR-17~92 and miR-106~25 clusters no interdependency in miRNA expression was observed. Our findings suggest that CRISPR/Cas9 is a powerful gene editing tool that can uncover novel mechanisms of clustered miRNA regulation and function

Gene selection guided by feature interdependence

Cancers could normally be marked by a number of differentially expressed genes which show enormous potential as biomarkers for a certain disease. Recent years, cancer classification based on the investigation of gene expression profiles derived by high-throughput microarrays has widely been used. The selection of discriminative genes is, therefore, an essential preprocess step in carcinogenesis studies. In this paper, we have proposed a novel gene selector using information-theoretic measures for biological discovery. This multivariate filter is a four-stage framework through the analyses of feature relevance, feature interdependence, feature redundancy-dependence and subset rankings, and having been examined on the colon cancer data set. Our experimental result show that the proposed method outperformed other information theorem based filters in all aspect of classification errors and classification performance

Two simulated annealing-based heuristics for the job shop scheduling problem

In this paper, we present two simulated annealing-based algorithms for the classical, general job shop scheduling problem where the objective is to minimize the makespan. We consider sets of jobs consisting of tasks and sets of machines, which can handle at most one task at a time. To represent the problem, we employ the model of disjunctive graphs. Simulated annealing has been applied to this problem earlier, e.g., by Van Laarhoven et al., where the neighborhood function is based on reversing a single arc of a longest path of the underlying graph. In our approach, we analyze a neighborhood function which involves a non-uniform generation probability. To obtain the neighbors of a schedule, we reverse more than a single arc of longest paths and perform a control on the increase of the makespan. The selection of the arcs depends on the number of longest paths to which a particular arc belongs. Furthermore, we have designed two cooling schedules which employ a detailed analysis of the objective function. Depending on the specified neighborhood relation, the expected run-times can be either O(n2+ε) or O(n3+ε/m) for the first cooling schedule and O(n5/2+ε·m1/2) or O(n7/2+ε/m1/2) for the second cooling schedule, where n is the number of tasks, m the number of machines and ε represents View the MathML source. Our computational experiments on small to large benchmark problems have shown that within short series of consecutive trials relatively stable results equal or close to optimal solutions are repeatedly obtained, including the well-known benchmark problems FT10 and LA38. We could improve five upper bounds for the YN1, YN4, SWV12, SWV13, and SWV15 benchmark problems, e.g., for SWV13 the gap between the lower and the former upper bound has been shortened by about 57%. In our approach we rely only on basic information about the given problem instance

Robust signature discovery for affymetrix GeneChip® cancer classification

Phenotype prediction is one of the central issues in genetics and medical sciences research. Due to the advent of high-throughput screening technologies, microarray-based cancer classification has become a standard procedure to identify cancer-related gene signatures. Since gene expression profiling in transcriptome is of high dimensionality, it is a challenging task to discover a biologically functional signature over different cell lines. In this article, we present an innovative framework for finding a small portion of discriminative genes for a specific disease phenotype classification by using information theory. The framework is a data-driven approach and considers feature relevance, redundancy, and interdependence in the context of feature pairs. Its effectiveness has been validated by using a brain cancer benchmark, where the gene expression profiling matrix is derived from Affymetrix Human Genome U95Av2 GeneChip®. Three multivariate filters based on information theory have also been used for comparison. To show the strengths of the framework, three performance measures, two sets of enrichment analysis, and a stability index have been used in our experiments. The results show that the framework is robust and able to discover a gene signature having a high level of classification performance and being more statistically significant enriched

Fast parallel heuristics for the job shop scheduling problem

The paper is dealing with parallelized versions of simulated annealing-based heuristics for the classical job shop scheduling problem. The scheduling problem is represented by the disjunctive graph model and the objective is to minimize the length of longest paths. The problem is formulated for l jobs where each job has to process exactly one task on each of the m machines. The calculation of longest paths is the critical computation step of our heuristics and we utilize a parallel algorithm for this particular problem where we take into account the specific properties of job shop scheduling. In our heuristics, we employ a neighborhood relation which was introduced by Van Laarhoven et al. (Operations Research 40(1) (1992) 113–25). To obtain a neighbor, a single arc from a longest path is reversed and these transition steps always guarantee the feasibility of schedules. We designed two cooling schedules for homogeneous Markov chains and additionally we investigated a logarithmic cooling schedule for inhomogeneous Markov chains. Given O(n3) processors and a known upper bound Λ=Λ(l,m) for the length of longest paths, the expected run-times of parallelized versions are View the MathML source for the first cooling schedule and View the MathML source for the second cooling schedule, where n=lm is the number of tasks. For the logarithmic cooling schedule, a speed-up of View the MathML source can be achieved. When Markov chains of constant length are assumed, we obtain a polylogarithmic run-time of View the MathML source for the first cooling schedule. The analysis of famous benchmark problems led us to the conjecture that Λ⩽O(l+m) could be a uniform upper bound for the completion time of job shop scheduling problems with l jobs on m machines. Although the number of processors is very large, the particular processors are extremely simple and the parallel processing system is suitable for hardware implementations

A memetic approach to protein structure prediction in triangular lattices

Protein structure prediction (PSP) remains one of the most challenging open problems in structural bioinformatics. Simplified models in terms of lattice structure and energy function have been proposed to ease the computational hardness of this combinatorial optimization problem. In this paper, we describe a clustered meme-based evolutionary approach for PSP using triangular lattice model. Under the framework of memetic algorithm, the proposed method extracts a pool of cultural information from different regions of the search space using data clustering technique. These highly observed local substructures, termed as meme, are then aggregated centrally for further refinements as second stage of evolution. The optimal utilization of 'explore-and-exploit' feature of evolutionary algorithms is ensured by the inherent parallel architecture of the algorithm and subsequent use of cultural information