Revealing hidden plant diversity in arid environments

Estimating total plant diversity in extreme or hyperarid environments can be challenging, as adaptations to pronounced climate variability include evading prolonged stress periods through seeds or specialized underground organs. Short‐term surveys of these ecosystems are thus likely poor estimators of actual diversity. Here we develop a multimethod strategy to obtain a more complete understanding of plant diversity from a community in the Atacama Desert. We explicitly test environmental DNA‐based techniques (eDNA) to see if they can reveal the observed and ‘hidden' (dormant or locally rare) species. To estimate total plant diversity, we performed long‐term traditional surveys during eight consecutive years, including El Niño and La Niña events, we then analyzed eDNA from soil samples using high‐throughput sequencing. We further used soil pollen analysis and soil seed bank germination assays to identify ‘hidden' species. Each approach offers different subsets of current biodiversity at different taxonomic, spatial and temporal resolution, with a total of 92 taxa identified along the transect. Traditional field surveys identified 77 plant species over eight consecutive years. Observed community composition greatly varies interannually, with only 22 species seen every year. eDNA analysis revealed 37 taxa, eight of which were ‘hidden' in our field surveys. Soil samples contain a viable seed bank of 21 taxa. Soil pollen (27 taxa) and eDNA analysis show affinities with vegetation at the landscape scale but a weak relationship to local plot diversity. Multimethod approaches (including eDNA) in deserts are valuable tools that add to a comprehensive assessment of biodiversity in such extreme environments, where using a single method or observations over a few years is insufficient. Our results can also explain the resilience of Atacama plant communities as ‘hidden' taxa may have been active in the recent past or could even emerge in the future as accelerated global environmental change continues unabated.     

Revealing the hidden complexities of mtDNA inheritance

Mitochondrial DNA (mtDNA) is a pivotal tool in molecular ecology, evolutionary and population genetics. The power of mtDNA analyses derives from a relatively high mutation rate and the apparent simplicity of mitochondrial inheritance (maternal, without recombination), which has simplified modelling population history compared to the analysis of nuclear DNA. However, in biology things are seldom simple, and advances in DNA sequencing and polymorphism detection technology have documented a growing list of exceptions to the central tenets of mitochondrial inheritance, with paternal leakage, heteroplasmy and recombination now all documented in multiple systems. The presence of paternal leakage, recombination and heteroplasmy can have substantial impact on analyses based on mtDNA, affecting phylogenetic and population genetic analyses, estimates of the coalescent and the myriad of other parameters that are dependent on such estimates. Here, we review our understanding of mtDNA inheritance, discuss how recent findings mean that established ideas may need to be re‐evaluated, and we assess the implications of these new‐found complications for molecular ecologists who have relied for decades on the assumption of a simpler mode of inheritance. We show how it is possible to account for recombination and heteroplasmy in evolutionary and population analyses, but that accurate estimates of the frequencies of biparental inheritance and recombination are needed. We also suggest how nonclonal inheritance of mtDNA could be exploited, to increase the ways in which mtDNA can be used in analyses.     

Revealing the hidden mechanisms of smoke-induced fallopian tubal implantation

Ectopic pregnancy (EP) is an enigmatic reproductive disorder. Although tubal EP is difficult to predict, several hypotheses about its etiology have been proposed. In retrospective case-control studies, smoking is associated with an increased rate of EPs in the fallopian tube. Studies of experimental animals in vivo and human fallopian tubal tissues in vitro have suggested mechanisms of fallopian tubal damage and dysfunction induced by nicotine and other smoking-related chemicals that may explain this association. However, the pathogenesis of smoking-induced modulation of implantation leading to tubal EP is largely unknown. Because cigarette/tobacco smoke adversely affects the success of intrauterine implantation, there is a great need to determine how embryo implantation occurs in the fallopian tube in female smokers of reproductive age.     

Revealing hidden evolutionary capacity to cope with global change

The extent to which global change will impact the long‐term persistence of species depends on their evolutionary potential to adapt to future conditions. While the number of studies that estimate the standing levels of adaptive genetic variation in populations under predicted global change scenarios is growing all the time, few studies have considered multiple environments simultaneously and even fewer have considered evolutionary potential in multivariate context. Because conditions will not be constant, adaptation to climate change is fundamentally a multivariate process so viewing genetic variances and covariances over multivariate space will always be more informative than relying on bivariate genetic correlations between traits. A multivariate approach to understanding the evolutionary capacity to cope with global change is necessary to avoid misestimating adaptive genetic variation in the dimensions in which selection will act. We assessed the evolutionary capacity of the larval stage of the marine polychaete Galeolaria caespitosa to adapt to warmer water temperatures. Galeolaria is an important habitat‐forming species in Australia, and its earlier life‐history stages tend to be more susceptible to stress. We used a powerful quantitative genetics design that assessed the impacts of three temperatures on subsequent survival across over 30 000 embryos across 204 unique families. We found adaptive genetic variation in the two cooler temperatures in our study, but none in the warmest temperature. Based on these results, we would have concluded that this species has very little capacity to evolve to the warmest temperature. However, when we explored genetic variation in multivariate space, we found evidence that larval survival has the potential to evolve even in the warmest temperatures via correlated responses to selection across thermal environments. Future studies should take a multivariate approach to estimating evolutionary capacity to cope with global change lest they misestimate a species’ true adaptive potential.     

Model of hidden heterogeneity in longitudinal data

Variables measured in longitudinal studies of aging and longevity do not exhaust the list of all factors affecting health and mortality transitions. Unobserved factors generate hidden variability in susceptibility to diseases and death in populations and in age trajectories of longitudinally measured indices. Effects of such heterogeneity can be manifested not only in observed hazard rates but also in average trajectories of measured indices. Although effects of hidden heterogeneity on observed mortality rates are widely discussed, their role in forming age patterns of other aging-related characteristics (average trajectories of physiological state, stress resistance, etc.) is less clear. We propose a model of hidden heterogeneity to analyze its effects in longitudinal data. The approach takes the presence of hidden heterogeneity into account and incorporates several major concepts currently developing in aging research (allostatic load, aging-associated decline in adaptive capacity and stress-resistance, age-dependent physiological norms). Simulation experiments confirm identifiability of model's parameters.     

Improved scoring of functional groups from gene expression data by decorrelating GO graph structure

MOTIVATION: The result of a typical microarray experiment is a long list of genes with corresponding expression measurements. This list is only the starting point for a meaningful biological interpretation. Modern methods identify relevant biological processes or functions from gene expression data by scoring the statistical significance of predefined functional gene groups, e.g. based on Gene Ontology (GO). We develop methods that increase the explanatory power of this approach by integrating knowledge about relationships between the GO terms into the calculation of the statistical significance. RESULTS: We present two novel algorithms that improve GO group scoring using the underlying GO graph topology. The algorithms are evaluated on real and simulated gene expression data. We show that both methods eliminate local dependencies between GO terms and point to relevant areas in the GO graph that remain undetected with state-of-the-art algorithms for scoring functional terms. A simulation study demonstrates that the new methods exhibit a higher level of detecting relevant biological terms than competing methods.     

Uncovering hidden phylogenetic consensus in large data sets

Many of the steps in phylogenetic reconstruction can be confounded by “rogue” taxa—taxa that cannot be placed with assurance anywhere within the tree, indeed, whose location within the tree varies with almost any choice of algorithm or parameters. Phylogenetic consensus methods, in particular, are known to suffer from this problem. In this paper, we provide a novel framework to define and identify rogue taxa. In this framework, we formulate a bicriterion optimization problem, the relative information criterion, that models the net increase in useful information present in the consensus tree when certain taxa are removed from the input data. We also provide an effective greedy heuristic to identify a subset of rogue taxa and use this heuristic in a series of experiments, with both pathological examples from the literature and a collection of large biological data sets. As the presence of rogue taxa in a set of bootstrap replicates can lead to deceivingly poor support values, we propose a procedure to recompute support values in light of the rogue taxa identified by our algorithm; applying this procedure to our biological data sets caused a large number of edges to move from “unsupported” to “supported” status, indicating that many existing phylogenies should be recomputed and reevaluated to reduce any inaccuracies introduced by rogue taxa. We also discuss the implementation issues encountered while integrating our algorithm into RAxML v7.2.7, particularly those dealing with scaling up the analyses. This integration enables practitioners to benefit from our algorithm in the analysis of very large data sets (up to 2,500 taxa and 10,000 trees, although we present the results of even larger analyses).     

Revealing biological information using data structuring and automated learning

The intermediary steps between a biological hypothesis, concretized in the input data, and meaningful results, validated using biological experiments, commonly employ bioinformatics tools. Starting with storage of the data and ending with a statistical analysis of the significance of the results, every step in a bioinformatics analysis has been intensively studied and the resulting methods and models patented. This review summarizes the bioinformatics patents that have been developed mainly for the study of genes, and points out the universal applicability of bioinformatics methods to other related studies such as RNA interference. More specifically, we overview the steps undertaken in the majority of bioinformatics analyses, highlighting, for each, various approaches that have been developed to reveal details from different perspectives. First we consider data warehousing, the first task that has to be performed efficiently, optimizing the structure of the database, in order to facilitate both the subsequent steps and the retrieval of information. Next, we review data mining, which occupies the central part of most bioinformatics analyses, presenting patents concerning differential expression, unsupervised and supervised learning. Last, we discuss how networks of interactions of genes or other players in the cell may be created, which help draw biological conclusions and have been described in several patents.     

Evolutionary information hidden in a single protein structure

The knowledge of conserved sequences in proteins is valuable in identifying functionally or structurally important residues. Generating the conservation profile of a sequence requires aligning families of homologous sequences and having knowledge of their evolutionary relationships. Here, we report that the conservation profile at the residue level can be quantitatively derived from a single protein structure with only backbone information. We found that the reciprocal packing density profiles of protein structures closely resemble their sequence conservation profiles. For a set of 554 nonhomologous enzymes, 74% (408/554) of the proteins have a correlation coefficient > 0.5 between these two profiles. Our results indicate that the three-dimensional structure, instead of being a mere scaffold for positioning amino acid residues, exerts such strong evolutionary constraints on the residues of the protein that its profile of sequence conservation essentially reflects that of its structural characteristics.     

The interpretation of hidden support in combined data phylogenetics

In phylogenetic analysis, support for a given clade is ‘hidden’ when isolated partitions support that clade less than in the analysis of combined data sets. In such simultaneous analyses, signal common to the majority of partitions dominates the topology at the expense of any signal idiosyncratic to each partition. This process is often referred to as synergy and is commonly used to validate the combination of disparate data partitions. We investigate the behaviour of hidden branch support (HBS), partitioned branch support (PBS) and hidden synapomorphy (HS) as measures of hidden support using artificial, real and experimentally manipulated phylogenetic data sets. Our analyses demonstrate that high levels of both HBS and HS can be obtained by combining data with little shared phylogenetic signal. This finding is in agreement with the original intent of hidden support metrics, which essentially quantify the extent of data set interaction, both through the dispersion of homoplasy and revelation of underlying shared signal (positive data synergy). High levels of HBS alone are insufficient to justify data combination. We advocate the use of multiple hidden support measures to distinguish between the dispersion of homoplasy and positive data synergy, and to better interpret data interactions. Furthermore, we suggest two criteria that help identify hidden support resulting from homoplasy dispersion: first, when total support decreases with the addition of a data partition and second, when total HBS per unit total support (TS) per node is similar to that derived from randomized data.     

Linear models for microarray data analysis: hidden similarities and differences.

In the past several years many linear models have been proposed for analyzing two-color microarray data. As presented in the literature, many of these models appear dramatically different. However, many of these models are reformulations of the same basic approach to analyzing microarray data. This paper demonstrates the equivalence of some of these models. Attention is directed at choices in microarray data analysis that have a larger impact on the results than the choice of linear model.     

A high-order graph generating self-organizing structure

A large class of neural network models have their units organized in a lattice with fixed topology or generate their topology during the learning process. These network models can be used as neighborhood preserving map of the input manifold, but such a structure is difficult to manage since these maps are graphs with a number of nodes that is just one or two orders of magnitude less than the number of input points (i.e., the complexity of the map is comparable with the complexity of the manifold) and some hierarchical algorithms were proposed in order to obtain a high-level abstraction of these structures. In this paper a general structure capable to extract high order information from the graph generated by a large class of self-organizing networks is presented. This algorithm will allow to build a two layers hierarchical structure starting from the results obtained by using the suitable neural network for the distribution of the input data. Moreover the proposed algorithm is also capable to build a topology preserving map if it is trained using a graph that is also a topology preserving map.     

Applications of graph theory in protein structure identification

There is a growing interest in the identification of proteins on the proteome wide scale. Among different kinds of protein structure identification methods, graph-theoretic methods are very sharp ones. Due to their lower costs, higher effectiveness and many other advantages, they have drawn more and more researchers' attention nowadays. Specifically, graph-theoretic methods have been widely used in homology identification, side-chain cluster identification, peptide sequencing and so on. This paper reviews several methods in solving protein structure identification problems using graph theory. We mainly introduce classical methods and mathematical models including homology modeling based on clique finding, identification of side-chain clusters in protein structures upon graph spectrum, and de novo peptide sequencing via tandem mass spectrometry using the spectrum graph model. In addition, concluding remarks and future priorities of each method are given.     

Clustering gene expression data using graph separators

Recent work has used graphs to modelize expression data from microarray experiments, in view of partitioning the genes into clusters. In this paper, we introduce the use of a decomposition by clique separators. Our aim is to improve the classical clustering methods in two ways: first we want to allow an overlap between clusters, as this seems biologically sound, and second we want to be guided by the structure of the graph to define the number of clusters. We test this approach with a well-known yeast database (Saccharomyces cerevisiae). Our results are good, as the expression profiles of the clusters we find are very coherent. Moreover, we are able to organize into another graph the clusters we find, and order them in a fashion which turns out to respect the chronological order defined by the the sporulation process.     

Sequence structure of hidden 10.4-base repeat in the nucleosomes of C. elegans

By measuring prevailing distances between YY, YR, RR, and RY dinucleotides in the large database of the nucleosome DNA fragments from C. elegans, the consensus sequence structure of the nucleosome DNA repeat of C. elegans was reconstructed: (YYYYYRRRRR)n. An actual period was estimated to be 10.4 bases. The pattern is fully consistent with the nucleosome DNA patterns of other eukaryotes, as established earlier, and, thus, the YYYYYRRRRR repeat can be considered as consensus nucleosome DNA sequence repeat across eukaryotic species. Similar distance analysis for [A, T] dinucleotides suggested the related pattern (TTTYTARAAA)n where the TT and AA dinucleotides display rather out of phase behavior, contrary to the "AA or TT" in-phase periodicity, considered in some publications. A weak 5-base periodicity in the distribution of TA dinucleotides was detected.     

Revealing the structure of the oxygen-evolving core dimer of photosystem II by cryoelectron crystallography.

Here we present cryoelectron crystallographic analysis of an isolated dimeric oxygen-evolving complex of photosystem II (at a resolution of approximately 0.9 nm), revealing that the D1-D2 reaction center (RC) proteins are centrally located between the chlorophyll-binding proteins, CP43 and CP47. This conclusion supports the hypothesis that photosystems I and II have similar structural features and share a common evolutionary origin. Additional density connecting the two halves of the dimer, which was not observed in a recently described CP47-RC complex that did not include CP43, may be attributed to the small subunits that are involved in regulating secondary electron transfer, such as PsbH. These subunits are possibly also required for stabilization of the dimeric photosystem II complex. This complex, containing at least 29 transmembrane helices in its asymmetric unit, represents one of the largest membrane protein complexes studied at this resolution.     

Efficient calculation of interval scores for DNA copy number data analysis.

DNA amplifications and deletions characterize cancer genome and are often related to disease evolution. Microarray-based techniques for measuring these DNA copy-number changes use fluorescence ratios at arrayed DNA elements (BACs, cDNA, or oligonucleotides) to provide signals at high resolution, in terms of genomic locations. These data are then further analyzed to map aberrations and boundaries and identify biologically significant structures. We develop a statistical framework that enables the casting of several DNA copy number data analysis questions as optimization problems over real-valued vectors of signals. The simplest form of the optimization problem seeks to maximize phi(I) = Sigmanu(i)/radical|I| over all subintervals I in the input vector. We present and prove a linear time approximation scheme for this problem, namely, a process with time complexity O (nepsilon(-2)) that outputs an interval for which phi(I) is at least Opt/alpha(epsilon), where Opt is the actual optimum and alpha(epsilon) --> 1 as epsilon --> 0. We further develop practical implementations that improve the performance of the naive quadratic approach by orders of magnitude. We discuss properties of optimal intervals and how they apply to the algorithm performance. We benchmark our algorithms on synthetic as well as publicly available DNA copy number data. We demonstrate the use of these methods for identifying aberrations in single samples as well as common alterations in fixed sets and subsets of breast cancer samples.