ACCURACY OF PHYLOGENETIC-ESTIMATION METHODS UNDER UNEQUAL EVOLUTIONARY RATES

A comparative study of the accuracy of three different approaches to phylogenetic estimation was made on simulated data with differing rates of change in different lineages. The three approaches were maximum likelihood, maximum parsimony, and phenetic clustering. The data were generated by simulating genetic drift with different population sizes over a simple four-species tree topology. Although the accuracy of all three approaches was found to be dependent on the number of loci (characters), maximum likelihood was found to perform considerably and consistently better than maximum parsimony or phenetic clustering.     

Dissecting the molecular mechanism of drosophila odorant receptors through activity modeling and comparative analysis

To gain insight into the molecular mechanism of odorant receptors (ORs) in Drosophila species, we developed a Quantitative Structure Activity Relationship (QSAR) model that predicts experimentally measured electrophysiological activities between 24 D. melanogaster ORs and 108 odorants. Although the model is limited by the tested odorants,analyzing the model allowed dissection of specific topological and chemical properties necessary for an odorant to elicit excitatory or inhibitory receptor response. Linear odorants with five to eight nonhydrogen atoms at the main chain and hydrogen‐bond acceptor and/or hydrogen‐bond donor at its ends were found to stimulate strong excitatory response. A comparative sequence analysis of 90 ORs in 15 orthologous groups identified 15 putative specificity‐determining residues (SDRs) and 15 globally conserved residues that we postulate as functionally key residues. Mapping to a model of secondary structure resulted in 14 out of 30 key residues locating to the transmembrane (TM) domains. Twelve residues, including six SDRs and six conserved residues, are located at the extracellular halves of the TM domains. Combining the evidence from the QSAR modeling and the comparative sequence analysis, we hypothesize that the Drosophila ORs accept odorants into a binding pocket located on the extracellular halves of its TM domains. The QSAR modeling suggests that the binding pocket is around 15 Å in depth and about 6 Å in width. Twelve mainly polar or charged key residues, both SDRs and conserved, are located inthis pocket and postulated to distinguish docked odorants via primarily geometry fitting and hydrogen‐bond interaction. Proteins 2010. © 2009 Wiley‐Liss, Inc.     

Sniper: improved SNP discovery by multiply mapping deep sequenced reads

SNP (single nucleotide polymorphism) discovery using next-generation sequencing data remains difficult primarily because of redundant genomic regions, such as interspersed repetitive elements and paralogous genes, present in all eukaryotic genomes. To address this problem, we developed Sniper, a novel multi-locus Bayesian probabilistic model and a computationally efficient algorithm that explicitly incorporates sequence reads that map to multiple genomic loci. Our model fully accounts for sequencing error, template bias, and multi-locus SNP combinations, maintaining high sensitivity and specificity under a broad range of conditions. An implementation of Sniper is freely available at .     

A combinatorial toolbox for protein sequence design and landscape analysis in the grand canonical model.

In modern biology, one of the most important research problems is to understand how protein sequences fold into their native 3D structures. To investigate this problem at a high level, one wishes to analyze the protein landscapes, i.e., the structures of the space of all protein sequences and their native 3D structures. Perhaps the most basic computational problem at this level is to take a target 3D structure as input and design a fittest protein sequence with respect to one or more fitness functions of the target 3D structure. We develop a toolbox of combinatorial techniques for protein landscape analysis in the Grand Canonical model of Sun, Brem, Chan, and Dill. The toolbox is based on linear programming, network flow, and a linear-size representation of all minimum cuts of a network. It not only substantially expands the network flow technique for protein sequence design in Kleinberg's seminal work but also is applicable to a considerably broader collection of computational problems than those considered by Kleinberg. We have used this toolbox to obtain a number of efficient algorithms and hardness results. We have further used the algorithms to analyze 3D structures drawn from the Protein Data Bank and have discovered some novel relationships between such native 3D structures and the Grand Canonical model.     

Patterns of sequence conservation in presynaptic neural genes

Background  

The neuronal synapse is a fundamental functional unit in the central nervous system of animals. Because synaptic function is evolutionarily conserved, we reasoned that functional sequences of genes and related genomic elements known to play important roles in neurotransmitter release would also be conserved.     

Estimating genomic coexpression networks using first-order conditional independence

We describe a computationally efficient statistical framework for estimating networks of coexpressed genes. This framework exploits first-order conditional independence relationships among gene-expression measurements to estimate patterns of association. We use this approach to estimate a coexpression network from microarray gene-expression measurements from Saccharomyces cerevisiae. We demonstrate the biological utility of this approach by showing that a large number of metabolic pathways are coherently represented in the estimated network. We describe a complementary unsupervised graph search algorithm for discovering locally distinct subgraphs of a large weighted graph. We apply this algorithm to our coexpression network model and show that subgraphs found using this approach correspond to particular biological processes or contain representatives of distinct gene families.     

ACCURACY OF ESTIMATED PHYLOGENIES: EFFECTS OF TREE TOPOLOGY AND EVOLUTIONARY MODEL

A simulation study was carried out to investigate the relative importance of tree topology (both balance and stemminess), evolutionary rates (constant, varying among characters, and varying among lineages), and evolutionary models in determining the accuracy with which phylogenetic trees can be estimated. The three evolutionary context models were phyletic (characters can change at each simulated time step), speciational (changes are possible only at the time of speciation into two daughter lineages), and punctuational (changes occur at the time of speciation but only in one of the daughter lineages). UPGMA clustering and maximum parsimony (“Wagner trees”) methods for estimating phylogenies were compared. All trees were based on eight recent OTUs. The three evolutionary context models were found to have the largest influence on accuracy of estimates by both methods. The next most important effect was that of the stemminess × context interaction. Maximum parsimony and UPGMA performed worst under the punctuational models. Under the phyletic model, trees with high stemminess values could be estimated more accurately and balanced trees were slightly easier to estimate than unbalanced ones. Overall, maximum parsimony yielded more accurate trees than UPGMA—but that was expected for these simulations since many more characters than OTUs were used. Our results suggest that the great majority of estimated phylogenetic trees are likely to be quite inaccurate; they underscore the inappropriateness of characterizing current phylogenetic methods as being for reconstruction rather than for estimation.