Open access journal

Operating at lower frequencies than systems such as Wi-Fi, TVWS wireless communication can enable long-range communication in rural communities and can more easily penetrate obstacles (vegetation, terrains). Thus, it is appealing to scenarios where line-of-sight is not always guaranteed. In particular, TVWS communication is a good candidate for supporting precision agriculture such as camera-based plant phenotyping and sensor-based analysis of plant behaviour. Yet there lacks in-depth real-world measurement data on the behavior of TVWS wireless channels in agriculture farms. To fill this gap, we use the field-deployed TVWS network of CyNet to measure TVWS channel behaviour in the Curtiss Research Farm in Ames, Iowa, where the landscape is predominantly composed of soybean and corn fields. We investigate the impact that crop diversity (soybean vs. corn), height and density of corn fields, antennas’ placement and variations of temperature and humidity have on the spatiotemporal behaviour of TVWS channels. This study also helps identify path loss models that best reflect radio propagation characteristics of TVWS systems in corn farms for different antenna heights.

Phenomics is an emerging branch of modern biology that uses high throughput phenotyping tools to capture multiple environmental and phenotypic traits, often at massive spatial and temporal scales. The resulting high dimensional data represent a treasure trove of information for providing an in-depth understanding of how multiple factors interact and contribute to the overall growth and behavior of different genotypes. However, computational tools that can parse through such complex data and aid in extracting plausible hypotheses are currently lacking. In this article, we present Hyppo-X, a new algorithmic approach to visually explore complex phenomics data and in the process characterize the role of environment on phenotypic traits. We model the problem as one of unsupervised structure discovery, and use emerging principles from algebraic topology and graph theory for discovering higher-order structures of complex phenomics data. We present an open source software which has interactive visualization capabilities to facilitate data navigation and hypothesis formulation. We test and evaluate Hyppo-X on two real-world plant (maize) data sets. Our results demonstrate the ability of our approach to delineate divergent subpopulation-level behavior. Notably, our approach shows how environmental factors could influence phenotypic behavior, and how that effect varies across different genotypes and different time scales. To the best of our knowledge, this effort provides one of the first approaches to systematically formalize the problem of hypothesis extraction for phenomics data. Considering the infancy of the phenomics field, tools that help users explore complex data and extract plausible hypotheses in a data-guided manner will be critical to future advancements in the use of such data.

Next-generation sequencing (NGS) has revolutionized the detection of structural variation in genome. Among NGS strategies, read depth is widely used and paramorphism information contained inside is generally ignored. We develop an algorithm that can fully exploit both read depth and paramorphism information. We embed mutation procedure in our system model for estimating prior likelihood of single nucleotide base. Hidden Markov model (HMM) is used to connect single base into segments and belief propagation algorithm is performed for the optimal solution of the HMM model. Simulations show promising results in detecting important types of structural variation. We have applied the algorithm on the maize B73 and MO17 genome data and compared the results with those obtained from array CGH method based micro-array data. Inconsistency between the two sets of data is discussed.

The advent of high-throughput short read technology is revolutionizing life sciences by providing an inexpensive way to sequence genomes at high coverage. Exploiting this technology requires the development of a de novo short read assembler, which is an important open problem that is garnering significant research effort. Current methods are largely limited to microbial organisms, whose genomes are two to three orders of magnitude smaller than complex mammalian and plant genomes. In this paper, we present the design and development of a parallel de novo short read assembler that can scale to large genomes with high coverage. Our approach is based on the string graph formulation. Input reads are mapped to short paths, and the genome is reconstructed as a superpath anchored by distance constraints inferred from read pairs. Our method can handle a mixture of multiple read sizes and multiple paired read distances. We present parallel algorithms for string graph construction, string graph compaction, graph based error detection and removal, and computing aggregate summarization of paired read links across graph edges. Using this, we navigate the final graph structure to reproduce large contiguous sequences from the underlying genome. We present a validation of our framework on experimental and simulated data from multiple known genomes and present scaling results on IBM Blue Gene/L.

BACKGROUND: Few microarrays have been quantitatively calibrated to identify optimal hybridization conditions because it is difficult to precisely determine the hybridization characteristics of a microarray using biologically variable cDNA samples. RESULTS: Using synthesized samples with known concentrations of specific oligonucleotides, a series of microarray experiments was conducted to evaluate microarrays designed by PICKY, an oligo microarray design software tool, and to test a direct microarray calibration method based on the PICKY-predicted, thermodynamically closest nontarget information. The complete set of microarray experiment results is archived in the GEO database with series accession number GSE14717. Additional data files and Perl programs described in this paper can be obtained from the website http://www.complex.iastate.edu under the PICKY Download area. CONCLUSION: PICKY-designed microarray probes are highly reliable over a wide range of hybridization temperatures and sample concentrations. The microarray calibration method reported here allows researchers to experimentally optimize their hybridization conditions. Because this method is straightforward, uses existing microarrays and relatively inexpensive synthesized samples, it can be used by any lab that uses microarrays designed by PICKY. In addition, other microarrays can be reanalyzed by PICKY to obtain the thermodynamically closest nontarget information for calibration.

BACKGROUND: The de novo assembly of genomes and transcriptomes from short sequences is a challenging problem. Because of the high coverage needed to assemble short sequences as well as the overhead of modeling the assembly problem as a graph problem, the methods for short sequence assembly are often validated using data from BACs or small sized prokaryotic genomes. RESULTS: We present a parallel method for transcriptome assembly from large short sequence data sets. Our solution uses a rigorous graph theoretic framework and tames the computational and space complexity using parallel computers. First, we construct a distributed bidirected graph that captures overlap information. Next, we compact all chains in this graph to determine long unique contigs using undirected parallel list ranking, a problem for which we present an algorithm. Finally, we process this compacted distributed graph to resolve unique regions that are separated by repeats, exploiting the naturally occurring coverage variations arising from differential expression. CONCLUSION: We demonstrate the validity of our method using a synthetic high coverage data set generated from the predicted coding regions of Zea mays. We assemble 925 million sequences consisting of 40 billion nucleotides in a few minutes on a 1024 processor Blue Gene/L. Our method is the first fully distributed method for assembling a non-hierarchical short sequence data set and can scale to large problem sizes.

BACKGROUND: A primary reason for using two-color microarrays is that the use of two samples labeled with different dyes on the same slide, that bind to probes on the same spot, is supposed to adjust for many factors that introduce noise and errors into the analysis. Most users assume that any differences between the dyes can be adjusted out by standard methods of normalization, so that measures such as log ratios on the same slide are reliable measures of comparative expression. However, even after the normalization, there are still probe specific dye and slide variation among the data. We define a method to quantify the amount of the dye-by-probe and slide-by-probe interaction. This serves as a diagnostic, both visual and numeric, of the existence of probe-specific dye bias. We show how this improved the performance of two-color array analysis for arrays for genomic analysis of biological samples ranging from rice to human tissue. RESULTS: We develop a procedure for quantifying the extent of probe-specific dye and slide bias in two-color microarrays. The primary output is a graphical diagnostic of the extent of the bias which called ECDF (Empirical Cumulative Distribution Function), though numerical results are also obtained. CONCLUSION: We show that the dye and slide biases were high for human and rice genomic arrays in two gene expression facilities, even after the standard intensity-based normalization, and describe how this diagnostic allowed the problems causing the probe-specific bias to be addressed, and resulted in important improvements in performance. The R package LMGene which contains the method described in this paper has been available to download from Bioconductor.

A genetic map is an ordering of genetic markers calculated from a population of known lineage. While traditionally a map has been generated from a single population for each species, recently researchers have created maps from multiple populations. In the face of these new data, we address the need to find a consensus map--a map that combines the information from multiple partial and possibly inconsistent input maps. We model each input map as a partial order and formulate the consensus problem as finding a median partial order. Finding the median of multiple total orders (preferences or rankings)is a well studied problem in social choice. We choose to find the median using the weighted symmetric difference distance, a more general version of both the symmetric difference distance and the Kemeny distance. Finding a median order using this distance is NP-hard. We show that for our chosen weight assignment, a median order satisfies the positive responsiveness, extended Condorcet,and unanimity criteria. Our solution involves finding the maximum acyclic subgraph of a weighted directed graph. We present a method that dynamically switches between an exact branch and bound algorithm and a heuristic algorithm, and show that for real data from closely related organisms, an exact median can often be found. We present experimental results using seven populations of the crop plant Zea mays.

Assembly of large genomes from tens of millions of short genomic fragments is computationally demanding requiring hundreds of gigabytes of memory and tens of thousands of CPU hours. New gene-enrichment sequencing strategies are expected to further exacerbate this situation. In this paper, we present a massively parallel genome assembly framework. The unique features of our approach include space-efficient and on-demand algorithms that consume only linear space, and heuristic strategies that reduce the number of expensive pairwise sequence alignments while maintaining assembly quality. As part of the ongoing efforts in maize genome sequencing, we applied our assembly framework to the largest available collection of maize genomic data. We report the partitioning of more than 1.6 million fragments of over 1.25 billion nucleotides total size into genomic islands in 2 hours on 1,024 processors of an IBM BlueGene/L supercomputer.

MOTIVATION: Scanning parameters are often overlooked when optimizing microarray experiments. A scanning approach that extends the dynamic data range by acquiring multiple scans of different intensities has been developed. RESULTS: Data from each of three scan intensities (low, medium, high) were analyzed separately using multiple scan and linear regression approaches to identify and compare the sets of genes that exhibit statistically significant differential expression. In the multiple scan approach only one-third of the differentially expressed genes were shared among the three intensities, and each scan intensity identified unique sets of differentially expressed genes. The set of differentially expressed genes from any one scan amounted to < 70% of the total number of genes identified in at least one scan. The average signal intensity of genes that exhibited statistically significant changes in expression was highest for the low-intensity scan and lowest for the high-intensity scan, suggesting that low-intensity scans may be best for detecting expression differences in high-signal genes, while high-intensity scans may be best for detecting expression differences in low-signal genes. Comparison of the differentially expressed genes identified in the multiple scan and linear regression approaches revealed that the multiple scan approach effectively identifies a subset of statistically significant genes that linear regression approach is unable to identify. Quantitative RT-PCR (qRT-PCR) tests demonstrated that statistically significant differences identified at all three scan intensities can be verified. AVAILABILITY: The data presented can be viewed at http://www.ncbi.nlm.nih.gov/geo/ under GEO accession no. GSE3017.

Summary: PathBinderH allows users to make queries that retrieve sentences and the abstracts containing them from PubMed. Another aspect of PathBinderH is that users can specify biological taxa in order to limit searches by mentioning either the specified taxa, or their subordinate taxa, in the biological taxonomy. Although the current project requires this function only for plant taxa, the principle is extensible to the entire taxonomy. Availability: www.plantgenomics.iastate.edu/PathBinderH. Source code and databases on request. Contact: [email protected] Supplementary information: A tutorial is at the tool Website. A longer paper is at class.ee.iastate.edu/berleant/s/paperPathBinderHreport.pdf

This presents results on training both finite state classifiers and interpolated Markov models as classifiers for polymerase chain reaction primers. The goal of the study is to find techniques to decrease the number of primers that fail to amplify correctly within a large genomics project. Standard primer design packages already select primers in a manner consistent with current knowledge of the biophysics of DNA. The classifiers trained in this effort are used to capture lab and organism specific features of primer data and are used to postprocess the output of standard primer design packages. The finite state classifiers in this study are trained with a novel evolutionary algorithm that uses an incremental fitness reward system and multipopulation hybridization. This hybridization is akin to population seeding, not the more usual hybridization of evolutionary computation with other techniques. The interpolated Markov model is a form of Markov model that adapts to data rich and data sparse portions of the training set by using a variable order in its modeling. The interpolated Markov models exhibited slightly superior performance and trains with far higher speed. The finite state classifiers provide a substantially different classification, however, and require less training data.

Abstract Motivation: Many large genomes are getting sequenced nowadays. Biologists are eager to start microarray analysis taking advantage of all known genes of a species, but existing microarray design tools were very inefficient for large genomes. Also, many existing tools operate in a batch mode that does not assure best designs. Results: Picky is an efficient oligo microarray design tool for large genomes. Picky integrates novel computer science techniques and the best known nearest-neighbor parameters to quickly identify sequence similarities and estimate their hybridization properties. Oligos designed by Picky are computationally optimized to guarantee the best specificity, sensitivity and uniformity under the given design constrains. Picky can be used to design arrays for whole genomes, or for only a subset of genes. The latter can still be screened against a whole genome to attain the same quality as a whole genome array, thereby permitting low budget, pathway-specific experiments to be conducted with large genomes. Picky is the fastest oligo array design tool currently available to the public, requiring only a few hours to process large gene sets from rice, maize or human. Availability: Picky is independent of any external software to execute, is designed for non-programmers to easily operate through a graphical user interface, and is made available for all major computing platforms (e.g. Mac, Windows and Linux) at http://www.complex.iastate.edu. Supplementary information: A short paper detailing the probability analysis for Picky, a program that implements functions mentioned in the paper, and the output of the program are available online from the Publisher's website.

UNLABELLED: Because the bulk of the maize (Zea mays L.) genome consists of repetitive sequences, sequencing efforts are being targeted to its 'gene-rich' fraction. Traditional assembly programs are inadequate for this approach because they are optimized for a uniform sampling of the genome and inherently lack the ability to differentiate highly similar paralogs. RESULTS: We report the development of bioinformatics tools for the accurate assembly of the maize genome. This software, which is based on innovative parallel algorithms to ensure scalability, assembled 730,974 genomic survey sequences fragments in 4 h using 64 Pentium III 1.26 GHz processors of a commodity cluster. Algorithmic innovations are used to reduce the number of pairwise alignments significantly without sacrificing quality. Clone pair information was used to estimate the error rate for improved differentiation of polymorphisms versus sequencing errors. The assembly was also used to evaluate the effectiveness of various filtering strategies and thereby provide information that can be used to focus subsequent sequencing efforts.