Complex sequencing rules of birdsong can be explained by simple hidden Markov processes

Complex sequencing rules observed in birdsongs provide an opportunity to investigate the neural mechanism for generating complex sequential behaviors. To relate the findings from studying birdsongs to other sequential behaviors such as human speech and musical performance, it is crucial to characterize the statistical properties of the sequencing rules in birdsongs. However, the properties of the sequencing rules in birdsongs have not yet been fully addressed. In this study, we investigate the statistical properties of the complex birdsong of the Bengalese finch (Lonchura striata var. domestica). Based on manual-annotated syllable labeles, we first show that there are significant higher-order context dependencies in Bengalese finch songs, that is, which syllable appears next depends on more than one previous syllable. We then analyze acoustic features of the song and show that higher-order context dependencies can be explained using first-order hidden state transition dynamics with redundant hidden states. This model corresponds to hidden Markov models (HMMs), well known statistical models with a large range of application for time series modeling. The song annotation with these models with first-order hidden state dynamics agreed well with manual annotation, the score was comparable to that of a second-order HMM, and surpassed the zeroth-order model (the Gaussian mixture model; GMM), which does not use context information. Our results imply that the hierarchical representation with hidden state dynamics may underlie the neural implementation for generating complex behavioral sequences with higher-order dependencies.     

Segregation distortion in Arabidopsis C24/Col-0 and Col-0/C24 recombinant inbred line populations is due to reduced fertility caused by epistatic interaction of two loci

A new large set of reciprocal recombinant inbred lines (RILs) was created between the Arabidopsis accessions Col-0 and C24 for quantitative trait mapping approaches, consisting of 209 Col-0 x C24 and 214 C24 x Col-0 F(7 )RI lines. Genotyping was performed using 110 evenly distributed framework single nucleotide polymorphism markers, yielding a genetic map of 425.70 cM, with an average interval of 3.87 cM. Segregation distortion (SD) was observed in several genomic regions during the construction of the genetic map. Linkage disequilibrium analysis revealed an association between a distorted region at the bottom of chromosome V and a non-distorted region on chromosome IV. A detailed analysis of the RILs for these two regions showed that an SD occurred when homozygous Col-0 alleles on chromosome IV coincided with homozygous C24 alleles at the bottom of chromosome V. Using nearly isogenic lines segregating for the distorted region we confirmed that this genotypic composition leads to reduced fertility and fitness.     

AtMap1: a DNA microarray for genomic deletion mapping in Arabidopsis thaliana

We have designed a novel tiling array, AtMap1, for genomic deletion mapping. AtMap1 is a 60-mer oligonucleotide microarray consisting of 42 497 data probes designed from the genomic sequence of Arabidopsis thaliana Col-0. The average probe interval is 2.8 kb. The performance of the AtMap1 array was assessed using the deletion mutants mag2-2, rot3-1 and zig-2. Eight of the probes showed threefold lower signals in mag2-2 than Col-0. Seven of these probes were located in one region on chromosome 3. We considered these adjacent probes to represent one deletion. This deletion was consistent with a reported deleted region. The other probe was located near the end of chromosome 4. A newly identified deletion around the probe was confirmed by PCR. We also detected the responsible deletions for rot3-1 and zig-2. Thus we concluded that the AtMap1 array was sufficiently sensitive to identify a deletion without any a priori knowledge of the deletion. An analysis of the result of hybridization of Ler and previously reported polymorphism data revealed that the signal decrease tended to depend on the overlap size of sequence polymorphisms. Mutation mapping is time-consuming, laborious and costly. The AtMap1 array removes these limitations.     

A collection of INDEL markers for map-based cloning in seven Arabidopsis accessions

The availability of a comprehensive set of resources including an entire annotated reference genome, sequenced alternative accessions, and a multitude of marker systems makes Arabidopsis thaliana an ideal platform for genetic mapping. PCR markers based on INsertions/DELetions (INDELs) are currently the most frequently used polymorphisms. For the most commonly used mapping combination, Columbia×Landsberg erecta (Col-0×Ler-0), the Cereon polymorphism database is a valuable resource for the generation of polymorphic markers. However, because the number of markers available in public databases for accessions other than Col-0 and Ler-0 is extremely low, mapping using other accessions is far from straightforward. This issue arose while cloning mutations in the Wassilewskija (Ws-4) background. In this work, approaches are described for marker generation in Ws-4 x Col-0. Complementary strategies were employed to generate 229 INDEL markers. Firstly, existing Col-0/Ler-0 Cereon predicted polymorphisms were mined for transferability to Ws-4. Secondly, Ws-0 ecotype Illumina sequence data were analyzed to identify INDELs that could be used for the development of PCR-based markers for Col-0 and Ws-4. Finally, shotgun sequencing allowed the identification of INDELs directly between Col-0 and Ws-4. The polymorphism of the 229 markers was assessed in seven widely used Arabidopsis accessions, and PCR markers that allow a clear distinction between the diverged Ws-0 and Ws-4 accessions are detailed. The utility of the markers was demonstrated by mapping more than 35 mutations in a Col-0×Ws-4 combination, an example of which is presented here. The potential contribution of next generation sequencing technologies to more traditional map-based cloning is discussed.     

An HMM-Based Comparative Genomic Framework for Detecting Introgression in Eukaryotes

One outcome of interspecific hybridization and subsequent effects of evolutionary forces is introgression, which is the integration of genetic material from one species into the genome of an individual in another species. The evolution of several groups of eukaryotic species has involved hybridization, and cases of adaptation through introgression have been already established. In this work, we report on PhyloNet-HMM—a new comparative genomic framework for detecting introgression in genomes. PhyloNet-HMM combines phylogenetic networks with hidden Markov models (HMMs) to simultaneously capture the (potentially reticulate) evolutionary history of the genomes and dependencies within genomes. A novel aspect of our work is that it also accounts for incomplete lineage sorting and dependence across loci. Application of our model to variation data from chromosome 7 in the mouse (Mus musculus domesticus) genome detected a recently reported adaptive introgression event involving the rodent poison resistance gene Vkorc1, in addition to other newly detected introgressed genomic regions. Based on our analysis, it is estimated that about 9% of all sites within chromosome 7 are of introgressive origin (these cover about 13 Mbp of chromosome 7, and over 300 genes). Further, our model detected no introgression in a negative control data set. We also found that our model accurately detected introgression and other evolutionary processes from synthetic data sets simulated under the coalescent model with recombination, isolation, and migration. Our work provides a powerful framework for systematic analysis of introgression while simultaneously accounting for dependence across sites, point mutations, recombination, and ancestral polymorphism.     

Bayesian Spatiotemporal Inference in Functional Magnetic Resonance Imaging

Mapping of the human brain by means of functional magnetic resonance imaging (fMRI) is an emerging field in cognitive and clinical neuroscience. Current techniques to detect activated areas of the brain mostly proceed in two steps. First, conventional methods of correlation, regression, and time series analysis are used to assess activation by a separate, pixelwise comparison of the fMRI signal time courses to the reference function of a presented stimulus. Spatial aspects caused by correlations between neighboring pixels are considered in a separate second step, if at all. The aim of this article is to present hierarchical Bayesian approaches that allow one to simultaneously incorporate temporal and spatial dependencies between pixels directly in the model formulation. For reasons of computational feasibility, models have to be comparatively parsimonious, without oversimplifying. We introduce parametric and semiparametric spatial and spatiotemporal models that proved appropriate and illustrate their performance applied to visual fMRI data.     

S locus genes and the evolution of self-fertility in Arabidopsis thaliana

Loss of self-incompatibility (SI) in Arabidopsis thaliana was accompanied by inactivation of genes required for SI, including S-LOCUS RECEPTOR KINASE (SRK) and S-LOCUS CYSTEINE-RICH PROTEIN (SCR), coadapted genes that constitute the SI specificity-determining S haplotype. Arabidopsis accessions are polymorphic for PsiSRK and PsiSCR, but it is unknown if the species harbors structurally different S haplotypes, either representing relics of ancestral functional and structurally heteromorphic S haplotypes or resulting from decay concomitant with or subsequent to the switch to self-fertility. We cloned and sequenced the S haplotype from C24, in which self-fertility is due solely to S locus inactivation, and show that this haplotype was produced by interhaplotypic recombination. The highly divergent organization and sequence of the C24 and Columbia-0 (Col-0) S haplotypes demonstrate that the A. thaliana S locus underwent extensive structural remodeling in conjunction with a relaxation of selective pressures that once preserved the integrity and linkage of coadapted SRK and SCR alleles. Additional evidence for this process was obtained by assaying 70 accessions for the presence of C24- or Col-0-specific sequences. Furthermore, analysis of SRK and SCR polymorphisms in these accessions argues against the occurrence of a selective sweep of a particular allele of SCR, as previously proposed.     

Using hidden Markov models to analyze gene expression time course data

MOTIVATION: Cellular processes cause changes over time. Observing and measuring those changes over time allows insights into the how and why of regulation. The experimental platform for doing the appropriate large-scale experiments to obtain time-courses of expression levels is provided by microarray technology. However, the proper way of analyzing the resulting time course data is still very much an issue under investigation. The inherent time dependencies in the data suggest that clustering techniques which reflect those dependencies yield improved performance. RESULTS: We propose to use Hidden Markov Models (HMMs) to account for the horizontal dependencies along the time axis in time course data and to cope with the prevalent errors and missing values. The HMMs are used within a model-based clustering framework. We are given a number of clusters, each represented by one Hidden Markov Model from a finite collection encompassing typical qualitative behavior. Then, our method finds in an iterative procedure cluster models and an assignment of data points to these models that maximizes the joint likelihood of clustering and models. Partially supervised learning--adding groups of labeled data to the initial collection of clusters--is supported. A graphical user interface allows querying an expression profile dataset for time course similar to a prototype graphically defined as a sequence of levels and durations. We also propose a heuristic approach to automate determination of the number of clusters. We evaluate the method on published yeast cell cycle and fibroblasts serum response datasets, and compare them, with favorable results, to the autoregressive curves method.     

Reconstructing the duplication history of tandemly repeated genes

We present a novel approach to deal with the problem of reconstructing the duplication history of tandemly repeated genes that are supposed to have arisen from unequal recombination. We first describe the mathematical model of evolution by tandem duplication and introduce duplication histories and duplication trees. We then provide a simple recursive algorithm which determines whether or not a given rooted phylogeny can be a duplication history and another algorithm that simulates the unequal recombination process and searches for the best duplication trees according to the maximum parsimony criterion. We use real data sets of human immunoglobulins and T-cell receptors to validate our methods and algorithms. Identity between most parsimonious duplication trees and most parsimonious phylogenies for the same data, combined with the agreement with additional knowledge about the sequences, such as the presence of polymorphisms, shows strong evidence that our reconstruction procedure provides good insights into the duplication histories of these loci.     

State-space analysis of time-varying higher-order spike correlation for multiple neural spike train data

Precise spike coordination between the spiking activities of multiple neurons is suggested as an indication of coordinated network activity in active cell assemblies. Spike correlation analysis aims to identify such cooperative network activity by detecting excess spike synchrony in simultaneously recorded multiple neural spike sequences. Cooperative activity is expected to organize dynamically during behavior and cognition; therefore currently available analysis techniques must be extended to enable the estimation of multiple time-varying spike interactions between neurons simultaneously. In particular, new methods must take advantage of the simultaneous observations of multiple neurons by addressing their higher-order dependencies, which cannot be revealed by pairwise analyses alone. In this paper, we develop a method for estimating time-varying spike interactions by means of a state-space analysis. Discretized parallel spike sequences are modeled as multi-variate binary processes using a log-linear model that provides a well-defined measure of higher-order spike correlation in an information geometry framework. We construct a recursive Bayesian filter/smoother for the extraction of spike interaction parameters. This method can simultaneously estimate the dynamic pairwise spike interactions of multiple single neurons, thereby extending the Ising/spin-glass model analysis of multiple neural spike train data to a nonstationary analysis. Furthermore, the method can estimate dynamic higher-order spike interactions. To validate the inclusion of the higher-order terms in the model, we construct an approximation method to assess the goodness-of-fit to spike data. In addition, we formulate a test method for the presence of higher-order spike correlation even in nonstationary spike data, e.g., data from awake behaving animals. The utility of the proposed methods is tested using simulated spike data with known underlying correlation dynamics. Finally, we apply the methods to neural spike data simultaneously recorded from the motor cortex of an awake monkey and demonstrate that the higher-order spike correlation organizes dynamically in relation to a behavioral demand.     

Sequence analysis of the complete mitochondrial DNA in 10 commonly used inbred rat strains

Rat remains a major biomedical model system for common, complex diseases. The rat continues to gain importance as a model system with the completion of its full genomic sequence. Although the genomic sequence has generated much interest, only three complete sequences of the rat mitochondria exist. Therefore, to increase the knowledge of the rat genome, the entire mitochondrial genomes (16,307–16,315 bp) from 10 inbred rat strains (that are standard laboratory models around the world) and 2 wild rat strains were sequenced. We observed a total of 195 polymorphisms, 32 of which created an amino acid change (nonsynonymous substitutions) in 12 of the 13 protein coding genes within the mitochondrial genome. There were 11 single nucleotide polymorphisms within the tRNA genes, six in the 12S rRNA, and 12 in the 16S rRNA including 3 insertions/deletions. We found 14 single nucleotide polymorphisms and 2 insertion/deletion polymorphisms in the D-loop. The inbred rat strains cluster phylogenetically into three distinct groups. The wild rat from Tokyo grouped closely with five inbred strains in the phylogeny, whereas the wild rat from Milwaukee was not closely related to any inbred strain. These data will enable investigators to rapidly assess the potential impact of the mitochondria in these rats on the physiology and the pathophysiology of phenotypes studied in these strains. Moreover, these data provide information that may be useful as new animal models, which result in novel combinations of nuclear and mitochondrial genomes, are developed. genome; mitochondria     

Construction and analysis of 2 reciprocal Arabidopsis introgression line populations

Two new large reciprocal sets of introgression lines (ILs) were created between the Arabidopsis accessions Col-0 and C24. In both sets (78 ILs with Col-0 background and 62 ILs with C24 background), the donor segments cover almost the entire genome with an average substitution size of 18.3 cM. In addition to the basic sets of ILs, further subILs were developed for 2 genomic regions allowing better mapping resolution. SubILs carrying donor segments with candidate genes for flowering time and reduced fertility were used to demonstrate the usefulness of the reciprocal ILs for quantitative trait loci detection and fine mapping. For subIL development at high resolution around the reduced fertility locus, we used modified CelI-based assays in one-well format for both marker development and genotyping. This serves as a very flexible and cost-effective approach.     

Establishment of a high-efficiency SNP-based framework marker set for Arabidopsis

The major goal of this project was the establishment of a tool for rapid mapping of new mutations and genotyping in Arabidopsis consisting of at least 100 evenly spaced framework markers. We assembled a single nucleotide polymorphism (SNP)-based marker set consisting of 112 polymorphic sites with average spacing of 1.15 Mbp derived from an SNP database that we recently developed. This information was used to set up efficient SNP detection reactions based on multiplexed primer extension assays. The 112 Columbia (Col-0)/C24 framework markers were used to assemble 18 multiplexed SNaPshot assays with which up to eight separate loci can be genotyped in a single-tube/single-capillary format. In addition, for 110 framework markers matrix-assisted laser desorption/ionization time of flight (MALDI-ToF) assays have been established for high throughput analyses. We demonstrated the usefulness and the robustness of both procedures of this tool by genotyping 48 BC3F1 individuals created between the accessions Col-0 and C24. Subsets of 10-62 of the established markers discriminate between various combinations of the accessions Col-0, C24, Landsberg erecta (Ler), Cape Verdi Islands (Cvi) and Niederzenz (Nd). Using a subset of 17 evenly distributed and established SNP markers that are also polymorphic between Ler and Col-0, we were able to rapidly map a mutant gene (tbr1) to an interval of 2.3 Mbp in an Ler (tbr1) x Col-0 cross.     

Natural Variation in Stomatal Responses to Environmental Changes among Arabidopsis thaliana Ecotypes

Stomata are small pores surrounded by guard cells that regulate gas exchange between plants and the atmosphere. Guard cells integrate multiple environmental signals and control the aperture width to ensure appropriate stomatal function for plant survival. Leaf temperature can be used as an indirect indicator of stomatal conductance to environmental signals. In this study, leaf thermal imaging of 374 Arabidopsis ecotypes was performed to assess their stomatal responses to changes in environmental CO2 concentrations. We identified three ecotypes, Köln (Kl-4), Gabelstein (Ga-0), and Chisdra (Chi-1), that have particularly low responsiveness to changes in CO2 concentrations. We next investigated stomatal responses to other environmental signals in these selected ecotypes, with Col-0 as the reference. The stomatal responses to light were also reduced in the three selected ecotypes when compared with Col-0. In contrast, their stomatal responses to changes in humidity were similar to those of Col-0. Of note, the responses to abscisic acid, a plant hormone involved in the adaptation of plants to reduced water availability, were not entirely consistent with the responses to humidity. This study demonstrates that the stomatal responses to CO2 and light share closely associated signaling mechanisms that are not generally correlated with humidity signaling pathways in these ecotypes. The results might reflect differences between ecotypes in intrinsic response mechanisms to environmental signals.