Abstract Background Although RNA-seq data are traditionally used for quantifying gene expression levels, the same data could be useful in an integrated approach to compute genetic distances as well. Challenges to using mRNA sequences for computing genetic distances include the relatively high conservation of coding sequences and the presence of paralogous and, in some species, homeologous genes. Results We developed a new computational method, RNA-clique, for calculating genetic distances using assembled RNA-seq data and assessed the efficacy of the method using biological and simulated data. The method employs reciprocal BLASTn followed by graph-based filtering to ensure that only orthologous genes are compared. Each vertex in the graph constructed for filtering represents a gene in a specific sample under comparison, and an edge connects a pair of vertices if the genes they represent are best matches for each other in their respective samples. The distance computation is a function of the BLAST alignment statistics and the constructed graph and incorporates only those genes that are present in some complete connected component of this graph. As a biological testbed we used RNA-seq data of tall fescue (Lolium arundinaceum), an allohexaploid plant ( $$2n = 14\text { Gb}$$ 2 n = 14 Gb ), and bluehead wrasse (Thalassoma bifasciatum), a teleost fish. RNA-clique reliably distinguished individual tall fescue plants by genotype and distinguished bluehead wrasse RNA-seq samples by individual. In tests with simulated RNA-seq data, the ground truth phylogeny was accurately recovered from the computed distances. Moreover, tests of the algorithm parameters indicated that, even with stringent filtering for orthologs, sufficient sequence data were retained for the distance computations. Although comparisons with an alternative method revealed that RNA-clique has relatively high time and memory requirements, the comparisons also showed that RNA-clique’s results were at least as reliable as the alternative’s for tall fescue data and were much more reliable for the bluehead wrasse data. Conclusion Results of this work indicate that RNA-clique works well as a way of deriving genetic distances from RNA-seq data, thus providing a methodological integration of functional and genetic diversity studies.

Memphis, Tennessee

A solar car is an electric vehicle that runs entirely on solar energy.Designing, building and racing solar cars has been a longstanding worldwide challenge for engineering and computer science students, with the overarching goal being to design devices that use sustainable energy sources.This article describes our experience and educational outcomes (the modeling and design of computer-based systems in a way that demonstrates comprehension of the trade-offs involved in design choice) attained while designing the network architecture for a solar car project.The computer science members of the University of Kentucky Solar Car Strategy Team are tasked to reliably collect and analyze car data in real-time, both to assist in the car development process, and then to provide important sensor readings to the driver during racing.The challenges in designing the architecture and protocols for the computer system that supports the solar car are to ensure that: (1) energy consumption is minimal, (2) data collection is reliable, (3) the network system is secure, and (4) the implementation of the system is not overly complex.Our computer system supporting the telemetry tasks uses three micro-controllers to collect and send data over serial communications to a master micro-controller (the Raspberry Pi), that parses and stores data in an on-board database.We compare three protocols: a simple USB-based protocol, and two protocols used in traditional non-solar cars: CAN and Ethernet.We analyze (1) their energy consumption over a period of time, (2) their reliability (by performing stress tests such as disconnecting devices and driving over bumpy terrain), (3) their security (by attempting to compromise the system by remotely sending data over communication lines), and (4) their complexity in terms of time and effort for implementation and development.

I1 Proceedings of the Fifteenth Annual UT- KBRIN Bioinformatics Summit 2016 Eric C. Rouchka, Julia H. Chariker, Benjamin J. Harrison, Juw Won Park P1 CC-PROMISE: Projection onto the Most Interesting Statistical Evidence (PROMISE) with Canonical Correlation to integrate gene expression and methylation data with multiple pharmacologic and clinical endpoints Xueyuan Cao, Stanley Pounds, Susana Raimondi, James Downing, Raul Ribeiro, Jeffery Rubnitz, Jatinder Lamba P2 Integration of microRNA-mRNA interaction networks with gene expression data to increase experimental power Bernie J Daigle, Jr. P3 Designing and writing software for in silico subtractive hybridization of large eukaryotic genomes Deborah Burgess, Stephanie Gehrlich, John C Carmen P4 Tracking the molecular evolution of Pax gene Nicholas Johnson; Chandrakanth Emani P5 Identifying genetic differences in thermally dimorphic and state specific fungi using in silico genomic comparison Stephanie Gehrlich, Deborah Burgess, John C Carmen P6 Identification of conserved genomic regions and variation therein amongst Cetartiodactyla species using next generation sequencing Kalpani De Silva, Michael P Heaton, Theodore S Kalbfleisch P7 Mining physiological data to identify patients with similar medical events and phenotypes Teeradache Viangteeravat, Rahul Mudunuri, Oluwaseun Ajayi, Fatih Şen, Eunice Y Huang P8 Smart brief for home health monitoring Mohammad Mohebbi, Luaire Florian, Douglas J Jackson, John F Naber P9 Side-effect term matching for computational adverse drug reaction predictions AKM Sabbir, Sally R Ellingson P10 Enrichment vs robustness: A comparison of transcriptomic data clustering metrics Yuping Lu, Charles A Phillips, Michael A Langston P11 Deep neural networks for transcriptome-based cancer classification Rahul K Sevakula, Raghuveer Thirukovalluru, Nishchal K. Verma, Yan Cui P12 Motif discovery using K-means clustering Mohammed Sayed, Juw Won Park P13 Large scale discovery of active enhancers from nascent RNA sequencing Jing Wang, Qi Liu, Yu Shyr P14 Computationally characterizing genomic pipelines and benchmarking results using GATK best practices on the high performance computing cluster at the University of Kentucky Xiaofei Zhang, Sally R Ellingson P15 Development of approaches enabling the identification of abnormal gene expression from RNA-Seq in personalized oncology Naresh Prodduturi, Gavin R Oliver, Diane Grill, Jie Na, Jeanette Eckel-Passow, Eric W Klee P16 Processing RNA-Seq data of plants infected with coffee ringspot virus Michael M Goodin, Mark Farman, Harrison Inocencio, Chanyong Jang, Jerzy W Jaromczyk, Neil Moore, Kelly Sovacool P17 Comparative transcriptomics of three Acinetobacter baumanii clinical isolates with different antibiotic resistance patterns Leon Dent, Mike Izban, Sammed Mandape, Shruti Sakhare, Siddharth Pratap, Dana Marshall P18 Metagenomic assessment of possible microbial contamination in the equine reference genome assembly M Scotty DePriest, James N MacLeod, Theodore S Kalbfleisch P19 Molecular evolution of cancer driver genes Chandrakanth Emani, Hanady Adam, Ethan Blandford, Joel Campbell, Joshua Castlen, Brittany Dixon, Ginger Gilbert, Aaron Hall, Philip Kreisle, Jessica Lasher, Bethany Oakes, Allison Speer, Maximilian Valentine P20 Biorepository Laboratory Information Management System Naga Satya V Rao Nagisetty, Rony Jose, Teeradache Viangteeravat, Robert Rooney, David Hains

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Background Recent technological advances in Next Generation Sequencing (NGS) have reduced both the cost and time required to produce Large Data Sets (LDS) of nucleotide sequences. These advances have led to an exponential proliferation of nucleotide sequence data coupled with an exacerbation of a persistent conundrum: the level of difficulty in generating LDS is rapidly decreasing, but the exposure, development and training of students and investigators in the bioinformatic approaches requisite to the proper and correct analysis of such data sets is experiencing a parallel increase in difficulty.

Background As part of the fungal endophyte genomes project, we maintain genome browsers for several dozen strains of fungi from the Clavicipitaceae and related families. These genome browsers are based on the GBrowse software, with a large collection of in-house software for visualization, analysis, and searching of genome features. Although GBrowse supports serving multiple data sources, such as distinct genome assemblies, from a single GBrowse instance, there are advantages to maintaining separate instances for each genome. Besides permitting per-genome customizations of the software, page layout, and database schemas, our use of separate instances also allows us to maintain different security and password requirements for genomes in different stages of publication. Materials and methods We have developed a suite of software for deploying and maintaining a large collection of GBrowse instances. This software, a combination of Perl, shell libraries, and scripts, automates the process of deploying the software, databases, and configuration required to make a new customized genome browser available online; and furthermore automates loading each instance’s database with genome sequences, annotations, and other data. To maintain a mostly synchronized codebase while allowing distinct configuration, we record each instance’s software and configuration as a branch in a Subversion version control repository. This use of version control ensures that bug fixes and software improvements are easily applied to each relevant instance, without losing customizations. Results We describe the components of our genome browser instances, the design and implementation of our deployment software, and various challenges and practical considerations we have encountered while using this software to maintain genome browsers for nearly fifty organism strains and assembly versions.

Background We present FPD2GB2, a collection of scripts to automate data migration of a custom genome database and browser to a new implementation. The fungal endophytes genome project (http://www.endophyte.uky.edu/) hosted at the University of Kentucky uses a custom genome browser and database to support several genome sequencing, annotation, and analysis projects. This system is currently based around a locally-developed genome annotation database, visualized using the GBrowse version 1 genome browser together with a large amount of custom code that supports metadata and rendering features not supported by GBrowse 1. Since our initial development of our genome site, a newer version of GBrowse, version 2, has been released. Unlike GBrowse version 1, version 2 supports a very expressive database schema based on the standardized GFF3 format. This schema natively supports the types of data that we currently store in our custom database. In order to simplify maintenance, to ease the upgrade path to future versions of GBrowse, and to improve interoperability with software and websites that support GBrowse and the GFF3 format, we have decided to migrate our custom database to the GFF3 format supported by GBrowse 2. This migration allows us to simplify and in some cases even eliminate much of our custom code. We discuss the challenges presented by such a data migration task, and describe the FPD2GB2 script collection we developed to automate the process.

Background We present our work on using the XSEDE high-performance computing (HPC) network to support and facilitate hands-on bioinformatics tasks for participants of our Essentials of Next Generation Sequencing (NGS) workshop, as well as for students and other learners. In the summer of 2012, the University of Kentucky hosted the NGS workshop, attended by faculty and students from across the Commonwealth who were introduced to the laboratory and bioinformatic components of next-generation sequencing and sequence analysis. Participants used next-generation technology to sequence real genetic material, then used a variety of bioinformatics software tools to assemble those sequences, compare and align them to other sequences, predict genes, and visualize the genome. Due to the success of the 2012 workshop, the second workshop, planned for this summer, is expected to be larger in scale and to include even more participants. It will furthermore include several additional bioinformatics tools and tasks. Since participants will be simultaneously running intensive bioinformatics computing tasks, the resources required will exceed the capacity of the single twelve-core server used to support the workshop last year. One particular resource that appears promising to meet our intensive computational needs is the XSEDE grid computing network, a follow-on to the TeraGrid project designed specifically for “e-Science” and scientific computing. Many of the systems in the XSEDE network already support some of the software used within our workshop; however, many of the programs we will demonstrate have not previously been installed on tested on the XSEDE network. We will describe our experiences porting these applications to, and deploying them on, XSEDE. We will also discuss the challenges that the HPC approach presents for teaching and learning, particularly the complexities of navigating between time-sharing systems and remote job scheduling.

We propose a new efficient cryptography-based secure comparison protocol for comparing secrets that are additively split between two parties. Our solution, based on homomorphic cryptosystems, needs 2N + 6 invocations of secure multiplications when the two secrets are numbers in the range [0; 2N); previous solutions required 12N + O(1) secure multiplications. The protocol provides substantial performance improvement in privacy preserving data mining protocols that use comparison as a primitive operation. In particular, we experimentally evaluate the performance of our secure comparison protocol in the implementation of a secure k-means clustering protocol applied to several real datasets.

Background In eukaryotes, pre-mRNA molecules undergo splicing, which is the removal of sequences called introns to produce mature mRNA transcripts whose open reading frames (ORFs) may then be translated to proteins. Often this splicing step may be performed in many ways a situation known as alternative splicing [1,2] that can be described by structures such as splice graphs [3]. Alternative splicings, even those that differ only slightly, may result in proteins with substantially different biological properties [4,5].

We propose a secure approximate comparison protocol and develop a practical privacy-preserving two-party k-means clustering algorithm over vertically partitioned dataset. Experiments with to real datasets show that the accuracy of clustering achieved with our privacy preserving protocol is similar to the standard (non-secure) kmeans function in MATLAB.

Mean shift, an iterative technique for identifying the local maxima of a probability density function, has been successfully used as a clustering method in computer vision and image processing. We apply the mean shift technique to the high dimensional space of phylogeny trees. The basic idea behind this technique is to, given a set of sample points, shift each point in the direction of the gradient of the underlying density function in an iterative manner until the points concentrate at the local maxima of the density function and form natural clusters [ 1 ]. We have developed software named MSCTrees based on a variant of the mean shift method, called the adaptive mean shift [ 2 ], to perform cluster analysis on a set of multidimensional data points corresponding to phylogenetic trees. MSCTrees has two components: a C program called ms_cluster which implements a clustering algorithm based on the adaptive mean shift method, and a Perl script called cluster_trees.pl , which converts phylogenetic trees to multidimensional data points and calls ms_cluster to perform cluster analysis on the resulting points. The ms_cluster program, developed in C for optimized performance, takes a set of multidimensional data points as input, and outputs the clusters of the input points together with the cluster centers. The ms_cluster performs the following steps: 1) calculate the adaptive bandwidth for each data point using the k -Nearest Neighbor ( k NN) method; 2) initialize a set of points using the values of the original data points; 3) shift the set of initialized points to new locations based on the mean shift vectors computed at each point; 4) repeat step (3) until all points have converged; 5) merge points that have converged to the same locations into clusters. Four auto-optimized (and user-definable) parameters have been implemented to control the mean shift clustering process. The cluster_trees.pl script uses the BioPerl modules to parse a set of phylogenetic trees as the input. It maps a phylogenetic tree to a multidimensional data point by calculating the pair-wise distances between the leaves of the tree as the dimensional values of the resulting point. The script produces as output clusters of phylogenetic trees resulting from the clustering of their corresponding data points.

We have developed software called Phylotree as a toolkit for running experiments to study gene cophylogenies for genome evolution using distance-based methods. In particular, the toolkit has been instrumental in conducting processing-heavy experiments with the new “difference of means” statistical method. Phylotree was used to run experiments using simulated data as well as biological sequences of well known host and parasite species, and is distributed with data and configuration files allowing these experiments to be reproduced.

Figure 1 CPU-time and wait-time composite.Figure 1 shows the summation of CPU-time (blue) and queue wait-time (red) in minutes as the number of nodes and database segments increase.

A consensus sequence is a single sequence that represents characteristics of a family of sequences. Such synopses are most commonly used in the bioinformatics for sequence analysis. For example, algorithms that determine high quality consensus sequences are useful to construct a multiple alignment and consequently, a sequence logo (another representation that attempts to capture the important features of sequences). The determination of optimal consensus sequences is NP-hard (Gusfield). We present two new algorithms and compare them to earlier, published methods of determining consensus sequences. The first, CONSENSIZE, is an application of the genetic algorithm scheme (GAS). The other is a simple steepest descent search, usually not very useful for NP-hard problems, but surprisingly successful for this application. We discuss both algorithms and experimentally compare their accuracy and efficiency with the simulated annealing, multiple alignment and center string approaches. Test results are presented on both synthetic data and biological sequences.

Much of the recent research in DNA computing has focused on designing better overall techniques for computation, or implementing the techniques in simulation or a wet-lab in order to show the viability of these techniques for solving small SAT problems. In this paper, we examine a major obstacle to using DNA computing to solve larger, real-world SAT problems for which the correct answer is not already known. In particular, we ask the following question: Given the results of a DNA computation, how does one determine the answer to the underlying SAT problem, and how does one examine the confidence of this answer? We examine this question in detail for selection-based DNA computing, and show that it is non-trivial to answer. We then introduce a method we call "decision thresholds" for answering it which can be applied to any variation of selection-based DNA computing. Furthermore, we provide an example by applying this method to the technique of using a network of microreactors employing negative selection of ssDNA.

We present a new algorithm for the two-center problem: “Given a set S of n points in the real plane, find two closed discs whose union contains all of the points and the radius of the larger disc is minimized.” An almost quadratic O(n2logn) solution is given. The previously best known algorithms for the two-center problem have time complexity O(n2log3n). The solution is based on a new geometric characterization of the optimal discs and on a searching scheme with so-called lazy evaluation. The algorithm is simple and does not assume general position of the input points. The importance of the problem is known in various practical applications including transportation, station placement, and facility location.

Results of neighborhood graphs are surveyed. Properties, bounds on the size, algorithms, and variants of the neighborhood graphs are discussed. Numerous applications including computational morphology, spatial analysis, pattern classification, and databases for computer vision are described.>

A new geometrical transform, skewed-projection, is introduced. This transform is applied to design a new algorithm for a common transversal problem for families of polyhedra in R3. The time and space analysis, using Davenport-Schinzel sequences, is given.

Two new algorithms finding relative neighborhood graph RNG(V) for a set V of n points are presented. The first algorithm solves this problem for input points in (R2,Lp) metric space in time O(n a(n,n)) if the Delaunay triangulation DT(V) is given. This time performance is achieved due to attractive and natural application of FIND-UNION data structure to represent so-called elimination forest of edges in DT(V). The second algorithm solves the relative neighborhood graph problem in (Rd,Lp), 1