Daniel Ashlock

A New Resource for Cereal Genomics: 22K Barley GeneChip Comes of Age

In recent years, access to complete genomic sequences, coupled with rapidly accumulating data related to RNA and protein expression patterns, has made it possible to determine comprehensively how genes contribute to complex phenotypes. However, for major crop plants, publicly available, standard platforms for parallel expression analysis have been limited. We report the conception and design of the new publicly available, 22K Barley1 GeneChip probe array, a model for plants without a fully sequenced genome. Array content was derived from worldwide contribution of 350,000 high-quality ESTs from 84 cDNA libraries, in addition to 1,145 barley (Hordeum vulgare) gene sequences from the National Center for Biotechnology Information nonredundant database. Conserved sequences expressed in seedlings of wheat (Triticum aestivum), oat (Avena strigosa), rice (Oryza sativa), sorghum (Sorghum bicolor), and maize (Zea mays) were identified that will be valuable in the design of arrays across grasses. To enhance the usability of the data, BarleyBase, a MIAME-compliant, MySQL relational database, serves as a public repository for raw and normalized expression data from the Barley1 GeneChip probe array. Interconnecting links with PlantGDB and Gramene allow BarleyBase users to perform gene predictions using the 21,439 non-redundant Barley1 exemplar sequences or cross-species comparison at the genome level, respectively. We expect that this first generation array will accelerate hypothesis generation and gene discovery in disease defense pathways, responses to abiotic stresses, development, and evolutionary diversity in monocot plants.

Preferential partner selection in an evolutionary study of Prisoner's Dilemma.

Partner selection is an important process in many social interactions, permitting individuals to decrease the risks associated with cooperation. In large populations, defectors may escape punishment by roving from partner to partner, but defectors in smaller populations risk social isolation. We investigate these possibilities for an evolutionary Prisoners Dilemma in which agents use expected payoffs to choose and refuse partners. In comparison to random or round-robin partner matching, we find that the average payoffs attained with preferential partner selection tend to be more narrowly confined to a few isolated payoff regions. Most ecologies evolve to essentially full cooperative behavior, but when agents are intolerant of defections, or when the costs of refusal and social isolation are small, we also see the emergence of wallflower ecologies in which all agents are socially isolated. Between these two extremes, we see the emergence of ecologies whose agents tend to engage in a small number of defections followed by cooperation thereafter. The latter ecologies exhibit a plethora of interesting social interaction patterns.

Graph-based evolutionary algorithms

Evolutionary algorithms use crossover to combine information from pairs of solutions and use selection to retain the best solutions. Ideally, crossover takes distinct good features from each of the two structures involved. This process creates a conflict: progress results from crossing over structures with different features, but crossover produces new structures that are like their parents and so reduces the diversity on which it depends. As evolution continues, the algorithm searches a smaller and smaller portion of the search space. Mutation can help maintain diversity but is not a panacea for diversity loss. This paper explores evolutionary algorithms that use combinatorial graphs to limit possible crossover partners. These graphs limit the speed and manner in which information can spread giving competing solutions time to mature. This use of graphs is a computationally inexpensive method of picking a global level of tradeoff between exploration and exploitation. The results of using 26 graphs with a diverse collection of graphical properties are presented. The test problems used are: one-max, the De Jong functions, the Griewangk function in three to seven dimensions, the self-avoiding random walk problem in 9, 12, 16, 20, 25, 30, and 36 dimensions, the plus-one-recall-store (PORS) problem with n=15,16, and 17, location of length-six one-error-correcting DNA barcodes, and solving a simple differential equation semi-symbolically. The choice of combinatorial graph has a significant effect on the time-to-solution. In the cases studied, the optimal choice of graph improved solution time as much as 63-fold with typical impact being in the range of 15% to 100% variation. The graph yielding superior performance is found to be problem dependent. In general, the optimal graph diameter increases and the optimal average degree decreases with the complexity and difficulty of the fitness landscape. The use of diverse graphs as population structures for a collection of problems also permits a classification of the problems. A phylogenetic analysis of the problems using normalized time to solution on each graph groups the numerical problems as a clade together with one-max; self-avoiding walks form a clade with the semisymbolic differential equation solution; and the PORS and DNA barcode problems form a superclade with the numerical problems but are substantially distinct from them. This novel form of analysis has the potential to aid researchers choosing problems for a test suite

Genetic dissection of intermated recombinant inbred lines using a new genetic map of maize.

A new genetic map of maize, ISU–IBM Map4, that integrates 2029 existing markers with 1329 new indel polymorphism (IDP) markers has been developed using intermated recombinant inbred lines (IRILs) from the intermated B73 × Mo17 (IBM) population. The website http://magi.plantgenomics.iastate.edu provides access to IDP primer sequences, sequences from which IDP primers were designed, optimized marker-specific PCR conditions, and polymorphism data for all IDP markers. This new gene-based genetic map will facilitate a wide variety of genetic and genomic research projects, including map-based genome sequencing and gene cloning. The mosaic structures of the genomes of 91 IRILs, an important resource for identifying and mapping QTL and eQTL, were defined. Analyses of segregation data associated with markers genotyped in three B73/Mo17-derived mapping populations (F2, Syn5, and IBM) demonstrate that allele frequencies were significantly altered during the development of the IBM IRILs. The observations that two segregation distortion regions overlap with maize flowering-time QTL suggest that the altered allele frequencies were a consequence of inadvertent selection. Detection of two-locus gamete disequilibrium provides another means to extract functional genomic data from well-characterized plant RILs.

Understanding representational sensitivity in the iterated prisoner's dilemma with fingerprints

The iterated prisoners dilemma is a widely used computational model of cooperation and conflict. Many studies report emergent cooperation in populations of agents trained to play prisoners dilemma with an evolutionary algorithm. This study varies the representation of the evolving agents resulting in levels of emergent cooperation ranging from 0% to over 90%. The representations used in this study are directly encoded finite-state machines, cellularly encoded finite-state machines, feedforward neural networks, if-skip-action lists, parse trees storing two types of Boolean functions, lookup tables, Boolean function stacks, and Markov chains. An analytic tool for rapidly identifying agent strategies and comparing across representations called a fingerprint is used to compare the more complex representations. Fingerprints provide functional signatures of an agents strategy in a manner that is independent of the agents representation. This study demonstrates conclusively that choice of a representation dominates agent behavior in evolutionary prisoners dilemma. This in turn suggests that any soft computing system intended to simulate behavior must be concerned with the representation issue

DNA Sequence-Based „Bar Codes” for Tracking the Origins of Expressed Sequence Tags from a Maize cDNA Library Constructed Using Multiple mRNA Sources

To enhance gene discovery, expressed sequence tag (EST) projects often make use of cDNA libraries produced using diverse mixtures of mRNAs. As such, expression data are lost because the origins of the resulting ESTs cannot be determined. Alternatively, multiple libraries can be prepared, each from a more restricted source of mRNAs. Although this approach allows the origins of ESTs to be determined, it requires the production of multiple libraries. A hybrid approach is reported here. A cDNA library was prepared using 21 different pools of maize (Zea mays) mRNAs. DNA sequence „bar codes” were added during first-strand cDNA synthesis to uniquely identify the mRNA source pool from which individual cDNAs were derived. Using a decoding algorithm that included error correction, it was possible to identify the source mRNA pool of more than 97% of the ESTs. The frequency at which a bar code is represented in an EST contig should be proportional to the abundance of the corresponding mRNA in the source pool. Consistent with this, all ESTs derived from several genes (zein and adh1) that are known to be exclusively expressed in kernels or preferentially expressed under anaerobic conditions, respectively, were exclusively tagged with bar codes associated with mRNA pools prepared from kernel and anaerobically treated seedlings, respectively. Hence, by allowing for the retention of expression data, the bar coding of cDNA libraries can enhance the value of EST projects.

Nearly Identical Paralogs: Implications for Maize (Zea mays L.) Genome Evolution

As an ancient segmental tetraploid, the maize (Zea mays L.) genome contains large numbers of paralogs that are expected to have diverged by a minimum of 10% over time. Nearly identical paralogs (NIPs) are defined as paralogous genes that exhibit ≥98% identity. Sequence analyses of the “gene space” of the maize inbred line B73 genome, coupled with wet lab validation, have revealed that, conservatively, at least ∼1% of maize genes have a NIP, a rate substantially higher than that in Arabidopsis. In most instances, both members of maize NIP pairs are expressed and are therefore at least potentially functional. Of evolutionary significance, members of many NIP families also exhibit differential expression. The finding that some families of maize NIPs are closely linked genetically while others are genetically unlinked is consistent with multiple modes of origin. NIPs provide a mechanism for the maize genome to circumvent the inherent limitation that diploid genomes can carry at most two “alleles” per “locus.” As such, NIPs may have played important roles during the evolution and domestication of maize and may contribute to the success of long-term selection experiments in this important crop species.

Fingerprinting: Visualization and Automatic Analysis of Prisoner's Dilemma Strategies

Fingerprinting is a technique for generating a representation-independent functional signature for a game playing agent. Fingerprints can be used to compare agents across representations in an automatic fashion. The theory of fingerprints is developed for software agents that play the iterated prisoners dilemma. Examples of the technique for computing fingerprints are given. This paper summarizes past results and introduces the following new results. Fingerprints of prisoners dilemma strategies that are represented as finite-state machines must be rational functions. An example of a strategy that does not have a finite-state representation and which does not have a rational fingerprint function is given: the majority strategy. It is shown that the AllD- and AllC-based fingerprints can be derived from the tit-for-tat fingerprint by a simple substitution. Fingerprints for four new probe strategies are introduced, generalizing previous work in which tit-for-tat is the sole probe strategy. A trial comparison is made of evolved prisoners dilemma strategies across three representations: finite-state machines, feedforward neural nets, and lookup tables. Fingerprinting demonstrates that all three representations sample the strategy space in a radically different manner, even though the neural nets and lookup tables parameters are alternate encodings of the same strategy space. This space of strategies is also a subset of those encoded by the finite-state representation. Shortcomings of the fingerprint technique are outlined, with illustrative examples, and possible paths to overcome these shortcomings are given.

Search-Based Procedural Generation of Maze-Like Levels

A correctly designed dynamic programming algorithm can be used as a fitness function to permit the evolution of maze-like levels for use in games. This study compares multiple representations for evolvable mazes including direct, as well as positive and negative indirect representations. The first direct representation simply specifies, with a binary gene, which squares of a grid are obstructed. The second paints the maze grid and passage is allowed only between colors that are the same or adjacent in a rainbow. The positive and negative representations are developmental and evolve directions for adding barriers or digging “tunnels.” These representations are tested with a design space of fitness functions that automatically generate levels with controllable properties. Fitness function design is the most difficult part of automatic level generation and this study gives a simple framework for designing fitness functions that permits substantial control over the character of the mazes that evolve. This technique relies on using checkpoints within the maze to characterize the connectivity and path lengths within the level. Called checkpoint-based fitness, these fitness functions are built on a menu of properties that can be rewarded. The choice of which qualities are rewarded, in turn, specifies within broad limits the characteristics of the mazes to be evolved. Three of the representations are found to benefit from a technique called sparse initialization in which a maze starts mostly empty and variation operators fill in details while increasing fitness. Different representations are found to produce mazes with very different appearances, even when the same fitness function is used. The example fitness functions designed around dynamic programming with checkpoints are found to permit substantial control over the properties of the evolved mazes.

MULTI-K: accurate classification of microarray subtypes using ensemble k-means clustering

BackgroundUncovering subtypes of disease from microarray samples has important clinical implications such as survival time and sensitivity of individual patients to specific therapies. Unsupervised clustering methods have been used to classify this type of data. However, most existing methods focus on clusters with compact shapes and do not reflect the geometric complexity of the high dimensional microarray clusters, which limits their performance.ResultsWe present a cluster-number-based ensemble clustering algorithm, called MULTI-K, for microarray sample classification, which demonstrates remarkable accuracy. The method amalgamates multiple k-means runs by varying the number of clusters and identifies clusters that manifest the most robust co-memberships of elements. In addition to the original algorithm, we newly devised the entropy-plot to control the separation of singletons or small clusters. MULTI-K, unlike the simple k-means or other widely used methods, was able to capture clusters with complex and high-dimensional structures accurately. MULTI-K outperformed other methods including a recently developed ensemble clustering algorithm in tests with five simulated and eight real gene-expression data sets.ConclusionThe geometric complexity of clusters should be taken into account for accurate classification of microarray data, and ensemble clustering applied to the number of clusters tackles the problem very well. The C++ code and the data sets tested are available from the authors.