Christian N. S. Pedersen

Opposing plant community responses to warming with and without herbivores

If controls over primary productivity and plant community composition are mainly environmental, as opposed to biological, then global change may result in large-scale alterations in ecosystem structure and function. This view appears to be favored among investigations of plant biomass and community responses to experimental and observed warming. In far northern and arctic ecosystems, such studies predict increasing dominance of woody shrubs with future warming and emphasize the carbon (C)-sequestration potential and consequent atmospheric feedback potential of such responses. In contrast to previous studies, we incorporated natural herbivory by muskoxen and caribou into a 5-year experimental investigation of arctic plant community response to warming. In accordance with other studies, warming increased total community biomass by promoting growth of deciduous shrubs (dwarf birch and gray willow). However, muskoxen and caribou reduced total community biomass response, and responses of birch and willow, to warming by 19%, 46%, and 11%, respectively. Furthermore, under warming alone, the plant community shifted after 5 years away from graminoid-dominated toward dwarf birch-dominated. In contrast, where herbivores grazed, plant community composition on warmed plots did not differ from that on ambient plots after 5 years. These results highlight the potentially important and overlooked influences of vertebrate herbivores on plant community response to warming and emphasize that conservation and management of large herbivores may be an important component of mitigating ecosystem response to climate change.

RNA pseudoknot prediction in energy-based models.

RNA molecules are sequences of nucleotides that serve as more than mere intermediaries between DNA and proteins, e.g., as catalytic molecules. Computational prediction of RNA secondary structure is among the few structure prediction problems that can be solved satisfactorily in polynomial time. Most work has been done to predict structures that do not contain pseudoknots. Allowing pseudoknots introduces modeling and computational problems. In this paper we consider the problem of predicting RNA secondary structures with pseudoknots based on free energy minimization. We first give a brief comparison of energy-based methods for predicting RNA secondary structures with pseudoknots. We then prove that the general problem of predicting RNA secondary structures containing pseudoknots is NP complete for a large class of reasonable models of pseudoknots.

Fast evaluation of internal loops in RNA secondary structure prediction.

MOTIVATION Though not as abundant in known biological processes as proteins, RNA molecules serve as more than mere intermediaries between DNA and proteins. Research in the last 15 years demonstrates that RNA molecules serve in many roles, including catalysis. Furthermore, RNA secondary structure prediction based on free energy rules for stacking and loop formation remains one of the few major breakthroughs in the field of structure prediction, as minimum free energy structures and related quantities can be computed with full mathematical rigor. However, with the current energy parameters, the algorithms used hitherto suffer the disadvantage of either employing heuristics that risk (though highly unlikely) missing the optimal structure or becoming prohibitively time consuming for moderate to large sequences. RESULTS We present a new method to evaluate internal loops utilizing currently used energy rules. This method reduces the time complexity of this part of the structure prediction from O(n4) to O(n3), thus reducing the overall complexity to O(n3). Even when the size of evaluated internal loops is bounded by k (a commonly used heuristic), the method presented has a competitive edge by reducing the time complexity of internal loop evaluation from O(k2n2) to O(kn2). The method also applies to the calculation of the equilibrium partition function. AVAILABILITY Source code for an RNA secondary structure prediction program implementing this method is available at ftp://www.ibc.wustl.edu/pub/zuker/zuker .tar.Z

Pseudoknots in RNA secondary structures

RNA molecules are sequences of nucleotides that serve as more than mere intermediaries between DNA and proteins, e.g. as catalytic molecules. Computational prediction of RNA secondary structure is among the few structure prediction problems that can be solved satisfactory in polynomial time. Most work has been done to predict structures that do not contain pseudoknots. Allowing pseudoknots introduce modelling and computational problems. In this paper we consider the problem of predicting RNA secondary structure when certain types of pseudoknots are allowed. We first present an algorithm that in time &Ogr;(n5) and space &Ogr;(n3) predicts the secondary structure of an RNA sequence of length n in a model that allows certain kinds of pseudoknots. We then prove that the general problem of predicting RNA secondary structure containing pseudoknots is NP-complete for a large class of reasonable models of pseudoknots.

Novel variation and de novo mutation rates in population-wide de novo assembled Danish trios

Building a population-specific catalogue of single nucleotide variants (SNVs), indels and structural variants (SVs) with frequencies, termed a national pan-genome, is critical for further advancing clinical and public health genetics in large cohorts. Here we report a Danish pan-genome obtained from sequencing 10 trios to high depth (50 × ). We report 536k novel SNVs and 283k novel short indels from mapping approaches and develop a population-wide de novo assembly approach to identify 132k novel indels larger than 10 nucleotides with low false discovery rates. We identify a higher proportion of indels and SVs than previous efforts showing the merits of high coverage and de novo assembly approaches. In addition, we use trio information to identify de novo mutations and use a probabilistic method to provide direct estimates of 1.27e−8 and 1.5e−9 per nucleotide per generation for SNVs and indels, respectively.

CoaSim: A flexible environment for simulating genetic data under coalescent models

BackgroundCoalescent simulations are playing a large role in interpreting large scale intra-specific sequence or polymorphism surveys and for planning and evaluating association studies. Coalescent simulations of data sets under different models can be compared to the actual data to test the importance of different evolutionary factors and thus get insight into these.ResultsWe have created the CoaSim application as a flexible environment for Monte Carlo simulation of various types of genetic data under equilibrium and non-equilibrium coalescent processes for a variety of applications. Interaction with the tool is through the Guile version of the Scheme scripting language. Scheme scripts for many standard and advanced applications are provided and these can easily be modified by the user for a much wider range of applications. A graphical user interface with less functionality and flexibility is also included. It is primarily intended as an exploratory and educational toolConclusionCoaSim is a powerful tool because of its flexibility and ease of use. This is illustrated through very varied uses of the application, e.g. evaluation of association mapping methods, parametric bootstrapping, and design and choice of markers for specific questions

Evolution of Plant P-Type ATPases

Five organisms having completely sequenced genomes and belonging to all major branches of green plants (Viridiplantae) were analyzed with respect to their content of P-type ATPases encoding genes. These were the chlorophytes Ostreococcus tauri and Chlamydomonas reinhardtii, and the streptophytes Physcomitrella patens (a non-vascular moss), Selaginella moellendorffii (a primitive vascular plant), and Arabidopsis thaliana (a model flowering plant). Each organism contained sequences for all five subfamilies of P-type ATPases. Whereas Na+ and H+ pumps seem to mutually exclude each other in flowering plants and animals, they co-exist in chlorophytes, which show representatives for two kinds of Na+ pumps (P2C and P2D ATPases) as well as a primitive H+-ATPase. Both Na+ and H+ pumps also co-exist in the moss P. patens, which has a P2D Na+-ATPase. In contrast to the primitive H+-ATPases in chlorophytes and P. patens, the H+-ATPases from vascular plants all have a large C-terminal regulatory domain as well as a conserved Arg in transmembrane segment 5 that is predicted to function as part of a backflow protection mechanism. Together these features are predicted to enable H+ pumps in vascular plants to create large electrochemical gradients that can be modulated in response to diverse physiological cues. The complete inventory of P-type ATPases in the major branches of Viridiplantae is an important starting point for elucidating the evolution in plants of these important pumps.

The consensus string problem and the complexity of comparing hidden Markov models

The basic theory of hidden Markov models was developed and applied to problems in speech recognition in the late 1960s, and has since then been applied to numerous problems, e.g. biological sequence analysis. Most applications of hidden Markov models are based on efficient algorithms for computing the probability of generating a given string, or computing the most likely path generating a given string. In this paper we consider the problem of computing the most likely string, or consensus string, generated by a given model, and its implications on the complexity of comparing hidden Markov models. We show that computing the consensus string, and approximating its probability within any constant factor, is NP-hard, and that the same holds for the closely related labeling problem for class hidden Markov models. Furthermore, we establish the NP-hardness of comparing two hidden Markov models under the L∞- and L1-norms. We discuss the applicability of the technique used for proving the hardness of comparing two hidden Markov models under the L1-norm to other measures of distance between probability distributions. In particular, we show that it cannot be used for proving NP-hardness of determining the Kullback-Leibler distance between two hidden Markov models, or of comparing them under the Lk-norm for any fixed even integer k.

Molecular Docking with Ligand Attached Water Molecules

A novel approach to incorporate water molecules in protein-ligand docking is proposed. In this method, the water molecules display the same flexibility during the docking simulation as the ligand. The method solvates the ligand with the maximum number of water molecules, and these are then retained or displaced depending on energy contributions during the docking simulation. Instead of being a static part of the receptor, each water molecule is a flexible on/off part of the ligand and is treated with the same flexibility as the ligand itself. To favor exclusion of the water molecules, a constant entropy penalty is added for each included water molecule. The method was evaluated using 12 structurally diverse protein-ligand complexes from the PDB, where several water molecules bridge the ligand and the protein. A considerable improvement in successful docking simulations was found when including flexible water molecules solvating hydrogen bonding groups of the ligand. The method has been implemented in the docking program Molegro Virtual Docker (MVD).

Rapid Neighbour-Joining

The neighbour-joining method reconstructs phylogenies by iteratively joining pairs of nodes until a single node remains. The criterion for which pair of nodes to merge is based on both the distance between the pair and the average distance to the rest of the nodes. In this paper, we present a new search strategy for the optimisation criteria used for selecting the next pair to merge and we show empirically that the new search strategy is superior to other state-of-the-art neighbour-joining implementations.