The utility of NBS profiling for plant systematics: a first study in tuber-bearing Solanum species

Systematic relationships are important criteria for researchers and breeders to select materials. We evaluated a novel molecular technique, nucleotide binding site (NBS) profiling, for its potential in phylogeny reconstruction. NBS profiling produces multiple markers in resistance genes and their analogs (RGAs). Potato (Solanum tuberosum L.) is a crop with a large secondary genepool, which contains many important traits that can be exploited in breeding programs. In this study we used a set of over 100 genebank accessions, representing 49 tuber-bearing wild and cultivated Solanum species. NBS profiling was compared to amplified fragment length polymorphism (AFLP). Cladistic and phenetic analyses showed that the two techniques had similar resolving power and delivered trees with a similar topology. However, the different statistical tests used to demonstrate congruency of the trees were inconclusive. Visual inspection of the trees showed that, especially at the lower level, many accessions grouped together in the same way in both trees; at the higher level, when looking at the more basal nodes, only a few groups were well supported. Again this was similar for both techniques. The observation that higher level groups were poorly supported might be due to the nature of the material and the way the species evolved. The similarity of the NBS and AFLP results indicate that the role of disease resistance in speciation is limite

GiViP: A Visual Profiler for Distributed Graph Processing Systems

Analyzing large-scale graphs provides valuable insights in different application scenarios. While many graph processing systems working on top of distributed infrastructures have been proposed to deal with big graphs, the tasks of profiling and debugging their massive computations remain time consuming and error-prone. This paper presents GiViP, a visual profiler for distributed graph processing systems based on a Pregel-like computation model. GiViP captures the huge amount of messages exchanged throughout a computation and provides an interactive user interface for the visual analysis of the collected data. We show how to take advantage of GiViP to detect anomalies related to the computation and to the infrastructure, such as slow computing units and anomalous message patterns.Comment: Appears in the Proceedings of the 25th International Symposium on Graph Drawing and Network Visualization (GD 2017

Elephant Search with Deep Learning for Microarray Data Analysis

Even though there is a plethora of research in Microarray gene expression data analysis, still, it poses challenges for researchers to effectively and efficiently analyze the large yet complex expression of genes. The feature (gene) selection method is of paramount importance for understanding the differences in biological and non-biological variation between samples. In order to address this problem, a novel elephant search (ES) based optimization is proposed to select best gene expressions from the large volume of microarray data. Further, a promising machine learning method is envisioned to leverage such high dimensional and complex microarray dataset for extracting hidden patterns inside to make a meaningful prediction and most accurate classification. In particular, stochastic gradient descent based Deep learning (DL) with softmax activation function is then used on the reduced features (genes) for better classification of different samples according to their gene expression levels. The experiments are carried out on nine most popular Cancer microarray gene selection datasets, obtained from UCI machine learning repository. The empirical results obtained by the proposed elephant search based deep learning (ESDL) approach are compared with most recent published article for its suitability in future Bioinformatics research.Comment: 12 pages, 5 Tabl

Information visualization for DNA microarray data analysis: A critical review

Graphical representation may provide effective means of making sense of the complexity and sheer volume of data produced by DNA microarray experiments that monitor the expression patterns of thousands of genes simultaneously. The ability to use ldquoabstractrdquo graphical representation to draw attention to areas of interest, and more in-depth visualizations to answer focused questions, would enable biologists to move from a large amount of data to particular records they are interested in, and therefore, gain deeper insights in understanding the microarray experiment results. This paper starts by providing some background knowledge of microarray experiments, and then, explains how graphical representation can be applied in general to this problem domain, followed by exploring the role of visualization in gene expression data analysis. Having set the problem scene, the paper then examines various multivariate data visualization techniques that have been applied to microarray data analysis. These techniques are critically reviewed so that the strengths and weaknesses of each technique can be tabulated. Finally, several key problem areas as well as possible solutions to them are discussed as being a source for future work