Many complex disease syndromes, such as asthma, consist of a large number of highly related, rather than independent, clinical or molecular phenotypes. This raises a new technical challenge in identifying genetic variations associated simultaneously with correlated traits. In this study, we propose a new statistical framework called graph-guided fused lasso (GFlasso) to directly and effectively incorporate the correlation structure of multiple quantitative traits such as clinical metrics and gene expressions in association analysis. Our approach represents correlation information explicitly among the quantitative traits as a quantitative trait network (QTN) and then leverages this network to encode structured regularization functions in a multivariate regression model over the genotypes and traits. The result is that the genetic markers that jointly influence subgroups of highly correlated traits can be detected jointly with high sensitivity and specificity. While most of the traditional methods examined each phenotype independently and combined the results afterwards, our approach analyzes all of the traits jointly in a single statistical framework. This allows our method to borrow information across correlated phenotypes to discover the genetic markers that perturb a subset of the correlated traits synergistically. Using simulated datasets based on the HapMap consortium and an asthma dataset, we compared the performance of our method with other methods based on single-marker analysis and regression-based methods that do not use any of the relational information in the traits. We found that our method showed an increased power in detecting causal variants affecting correlated traits. Our results showed that, when correlation patterns among traits in a QTN are considered explicitly and directly during a structured multivariate genome association analysis using our proposed methods, the power of detecting true causal SNPs with possibly pleiotropic effects increased significantly without compromising performance on non-pleiotropic SNPs. An association study examines a phenotype against genotypic variations over a large set of individuals in order to find the genetic variant that gives rise to the variation in the phenotype. Many complex disease syndromes consist of a large number of highly related clinical phenotypes, and the patient cohorts are routinely surveyed with a large number of traits, such as hundreds of clinical phenotypes and genome-wide profiling of thousands of gene expressions, many of which are correlated. However, most of the conventional approaches for association mapping or eQTL analysis consider a single phenotype at a time instead of taking advantage of the relatedness of traits by analyzing them jointly. Assuming that a group of tightly correlated traits may share a common genetic basis, in this paper, we present a new framework for association analysis that searches for genetic variations influencing a group of correlated traits. We explicitly represent the correlation information in multiple quantitative traits as a quantitative trait network and directly incorporate this network information to scan the genome for association. Our results on simulated and asthma data show that our approach has a significant advantage in detecting associations when a genetic marker perturbs synergistically a group of traits. Many complex disease syndromes, such as diabetes, asthma, and cancer, consist of a large number of highly related, rather than independent, clinical phenotypes. Differences between these syndromes involve a complex interplay of a large number of genomic variations that perturb the function of disease-related genes in the context of a regulatory network, rather than each gene individually Numerous recent studies have shown that it is often more informative to map intermediate steps in disease processes, such as various disease-related clinical traits or expression levels of genes of interest, rather than merely the binary case/control disease status, to genetic marker loci quantitative trait network (QTN). From a visual inspection of this network, it is apparent that it contains several groups of inter-correlated traits that are connected with weighted edges among them. Further investigation reveals that each subnetwork in this QTN corresponds to different clinical aspects of asthma, such as quality of life , asthma symptoms , and lung physiology . It is natural for one to suspect that such highly correlated traits in a subnetwork may share some common genetic causes, and that analyzing a group of traits in each subnetwork jointly rather than each trait independently may help to better uncover such causes.In several recent attempts on expression quantitative trait locus (eQTL) mapping, a significant focus has been placed on identifying modules of co-expressed genes and the genotype markers that perturb the whole module rather than a single gene. For example, a genotype variation in a putative transcription factor is likely to affect the expression levels of all of the genes regulated by this common transcription factor. Under this scenario, once a group of genes are mapped to a common locus in the genome, it is possible to examine whether the locus harbors a transcription factor that targets the group of genes jointly in order to understand the functional relationship between the genotype marker and the gene module e.g., . Anotherad hoc cluster analysis for finding gene modules from the co-regulation network. Subsequently, each cluster would undergo an examination to determine whether it contains a significantly large fraction of genes that are mapped to a common locus in the genome with a potential pleiotropic effect. This primitive approach is essentially a multitude of single-marker/single-trait analyses which involved no direct integration of information across traits within a QTN during the association tests themselves, since the clustering information was used only in the post-processing step.Recent advances in high-throughput sequencing and molecular profiling technologies have made it both affordable and efficient to observe DNA sequence variations over millions of genomic loci, to measure the abundance of transcripts of virtually all known coding sequences, and to measure a wide range of clinical traits in various disease populations In a different approach to eQTL mapping, a module network graph-guided fused lasso (GFlasso), that can effectively address the general problem of association mapping of multivariate traits related as a quantitative trait network. Instead of using a two-stage method that performs single-trait analyses and combines the results afterwards in light of clusters of traits, our method directly infers markers with a pleiotropic effect by combining information across multiple traits in a single statistical framework, and does not require subnetworks or trait clusters to be extracted a priori or at any point of running the algorithm. The proposed GFlasso approach represents the correlation pattern in multiple traits explicitly as a QTN, and searches for genotype markers that are significantly and jointly associated with multiple highly correlated traits that often appear as a densely connected subnetwork within the whole network. Indeed, the extent of the “jointness” in a marker-to-multitrait association is automatically determined by the connectivities among traits in the QTN, and is subject to the modulation of the strengths of the trait correlations. Thus, the clustering information is just one form of relationship implicitly captured in the network, as the QTN is strictly richer than a trait-cluster. In addition, a QTN may carry other relational information such as weak correlations, heterogeneous  correlations, and pathways, etc. For example, the QTN of asthma-related traits in We believe that explicitly incorporating the molecular and/or clinical phenotype network as a trait correlation structure while searching for genetic associations can significantly increase the power of detecting pleiotropic effects. In this article, we present a new statistical approach, called lasso. Lasso achieves “sparsistancy” in the estimated model by setting the regression coefficients for irrelevant markers to exactly zero graph-constrained fused lasso  variations among a large number of candidate polymorphic loci, we use a linear regression formalism with an topology , and gra the QTN . In thisThe problem of estimating the regression coefficients in GFlasso involves solving a convex program, in which a global optimum solution can be efficiently obtained by exploring the large body of existing work on fast algorithms for convex optimization. In this article, we develop a fast coordinate-descent algorithm to estimate the regression coefficients under GFlasso, from which markers relevant to the (possibly multiple) traits in questions can be identified from the non-zero elements in the estimated regression coefficients. The results on two datasets, one simulated from HapMap SNP markers and the other collected from the SARP asthma patients, show that our method has a significantly greater power with fewer false positives in detecting pleiotropic effects of markers than other methods that do not exploit the correlation structure in traits.To capture correlated genome associations to a QTN, we employ a multivariate linear regression model as the basic model for trait responses given inputs of genome variations such as SNPs, with the addition of a sparsity-biasing regularizer to encourage selection of truly relevant SNPs in the presence of many irrelevant ones. Then, we introduce an additional regularizer of fusion penalty to encourage the sharing of association patterns from a common SNP to multiple inter-related traits.There is a large literature on multivariate linear regression in statistics In a standard regression approach for a single-trait association analysis, we assume a linear relationship between the covariates (SNPs) and each response (trait) parameterized by a set of regression coefficients, and estimate the parameters by optimizing a loss function defined on SNP-trait samples given the parameters. Based on the magnitudes of estimated regression coefficients, we draw conclusions on which SNPs are most significantly associated with the given trait. When data are available for multiple traits, we can apply this single-trait approach to each trait separately as we detail below.Let In a typical genome-wide association mapping, one examines a large number of marker loci with the goal of identifying only a small number of markers associated with the given phenotype. A naive application of the method in Eqn 2 to association mapping with large The lasso for multiple-trait association mapping defined in Eqn 3 is equivalent to solving a set of In order to estimate the association strengths jointly for multiple correlated traits while maintaining sparsity, we introduce another penalty term called graph-guided fusion penalty into the lasso framework. This novel penalty makes use of the complex correlation pattern among the traits represented as a QTN, and encourages the traits which appear highly correlated in the QTN to be influenced by a common set of genetic markers. Thus, the GFlasso estimate of the regression coefficients reveals joint associations of each SNP with the correlated traits in the entire subnetwork as well as associations with each individual trait.relevance network, and has been widely used as a representation of gene interaction networks We assume that a QTN, denoted by Below, we first introduce Given a QTN, it is reasonable to assume that if two traits are highly correlated and connected with an edge in the QTN, their variations across individuals are more likely to be explained by genetic variations at the same loci. In The idea of using a fusion penalty has been first proposed in the classical regression problem for a univariate response  and high-dimensional covariates to fuse the regression coefficients of two adjacent covariates when the covariates are assumed to be ordered such as in time When applied locally to a pair of regression coefficients for each edge When this edge-level fusion penalty is applied to all of the edges in the entire QTN as in the graph-guided fusion penalty, the overall effect is that differences of regression coefficients, by combining information among multiple correlated traits according to the topology of the QTN. As we demonstrate in our simulation study, in a typical association study that involves many irrelevant SNPs, this bias towards sparsity in the difference of regression coefficients for neighboring traits helps increase power while reducing false positives, since the information of a SNP being relevant or irrelevant is shared across traits. A balance among the three terms in Eqn 4 that jointly define the objective function, the regression error, the sparsity penalty, and the fusion penalty, will be reached if the optimal regularization parameters Although, in principle, the graph-guided fusion penalty has a smoothing effect on the rows of Now, we describe an enhanced version of Compared to The optimization problems in Eqn 4 and Eqn 5 are convex, and can be formulated as a quadratic programming problem using the similar approach for solving the fused lasso problem In this section, we describe a procedure for obtaining estimates of the regression coefficients in This coordinate-descent procedure finds the optimal The coordinate-descent algorithm for We determine the initial values We performed a simulation study to evaluate the power of the proposed GFlasso methods, and compared the results with those from single-marker/single-trait regression analyses as well as other multivariate regression methods.We simulated genotype data of 50 SNPs for 250 individuals based on the HapMap data Given the simulated genotype, we set the number of phenotypes to 10, and simulated the matrix of true regression coefficients by first choosing SNP-trait pairs with true associations and assigning values for the strengths of associations for the selected pairs as we describe below. We assumed three groups of correlated traits of sizes 3, 3, and 4. Three causal SNPs were randomly selected for the first group of traits, and four causal SNPs were selected for each of the other two groups, so that the shared relevant SNPs induce correlation among the traits within each cluster. In addition, we assumed another causal SNP for traits in both of the first two clusters in order to model the situation of a higher-level correlation structure across two subnetworks. Finally, we assumed one additional causal SNP for all of the phenotypes. In our simulation study, we assumed that shared causal SNPs are the only factors that induce correlations among traits, although in general there might be other genotypic effects or environmental factors that influence the correlation structure among traits.Once the SNP-trait pairs with true association were selected, we considered the following two cases of association strengths for these pairs, while setting the rest of the regression coefficients to 0.The regression coefficients for all of the SNP-trait pairs with true association were set to the same value. This corresponds to the situation where the basic assumption of the fusion penalty holds, and each SNP has the same effect across the traits in each subnetwork.The regression coefficients for the SNP-trait pairs with true associations were set to different values randomly generated from a uniform distribution over an interval Then, we simulated phenotype data based on the linear regression model with noise distributed as We compared the results from the GFlasso methods with those from other methods given below:We used  into a smaller number of variables that explain most of the variability in the original data, performs a standard multivariate regression on each of the transformed output separately, and then transforms the estimated regression coefficients back into the original space For methods that require a specification of the values of the regularization parameters such as ridge regression, lasso, and the GFlasso methods, we used As an illustrative example of the behaviors of the different methods, a graphical display of the QTN and the estimated QTL sets Next, we systematically and quantitatively evaluated the performance of the association methods based on two criteria, sensitivity/specificity on the uncovered QTL sets First, we varied the sample size of the dataset to see how the sample size affects the performance of the different methods for association analysis. We used datasets of sizes 50, 100, 150, 200, and 250, with association strength fixed at 0.5 for all associated SNP-trait pairs (Case 1), and we set the threshold We examined how varying the signal-to-noise ratio affects the performances of the different methods. We simulated datasets with regression coefficients set to 0.3, 0.5, 0.8, and 1.0, respectively, with sample size Next, we examined the sensitivity of the GFlasso methods to how the trait correlation network is generated, by varying the threshold entirely . When thrlapping . OverallGiven the SNP-trait pairs that the association methods found as associated, and the corresponding regression coefficients, we computed prediction errors to see if these SNPs with non-zero regression coefficients had a predictive power for traits of previously unseen individuals. reshold  . In thisSince the fusion penalty tends to fuse the regression coefficients to be the same value within a densely connected subgraph, one may suspect that the bias introduced by this penalty can reduce the power when the true association strengths of a SNP to different traits are not the same within each subgraph. In order to examine how the performance is affected in this case, we considered the situation where the association strengths of each causal SNP are not uniform across traits within each subnetwork, but vary within the interval of The scalability of our methods can be assessed from IL-4R gene that spans a 40 kb region on chromosome 16. This gene has been previously shown to be implicated in severe asthma PHASEIL-4R gene were associated with a subnetwork of correlated traits rather than an individual trait. We standardized measurements for each phenotype to have mean 0 and standard deviation 1 so that their values were roughly in the same range across phenotypes.We applied our methods to a dataset collected from 543 asthma patients as a part of the Severe Asthma Research Program (SARP) Before searching for associations between SNPs and traits, we first examined the correlation structure in the 53 clinical traits in question. We first computed the pairwise correlations between these traits as depicted in A comprehensive comparison of QTL mapping using GFlasso and other methods is presented in IL-4R, and codes for amino-acid changes in the intracellular signaling portion of the receptor. It has been previously found to be associated with severe asthma and its traits for lung physiology As can be seen from In addition, the results from the single-marker analyses in  to zero . When weBecause of the fusion penalty, the regression coefficients estimated by our methods formed a block structure, where each block corresponds to a SNP associated with several correlated traits. It is clear that the horizontal bars in In order to see how the threshold In summary, the GFlasso methods identified the previously known causal SNP (SNP Q551R) as significantly associated with the lung physiology traits, while maintaining an overall sparse pattern in estimated regression coefficients to reduce false positives. The property of the GFlasso estimates having a block structure for a SNP jointly associated with a set of correlated traits led to an interesting new hypothesis that two additional SNPs (rs3024660 and rs3024622) on the upstream of SNP Q551R may be jointly influencing the same set of traits on lung physiology as SNP Q551R, which may be validated in a future follow-up study.When multiple phenotypes are involved in association mapping, it is important to combine the information across phenotypes and make use of the full information available in data in order to achieve the maximum power. Most of the previous approaches either considered each phenotype separately, or used relatively primitive types of phenotype correlation structures such as surrogate phenotypes transformed through PCA or the mean values of subgroups of phenotypes found by clustering algorithms. Networks or graphs have been extensively studied as a representation of the correlation structure of phenotypes such as gene expression or clinical traits because they provide a flexible and explicit form of representation for capturing dependencies In this article, we proposed a new family of regression methods called GFlasso that directly incorporates the correlation structure represented as a QTN and uses this information to guide the estimation process. These methods considered a multitude of phenotypes jointly, and estimated a joint association model in a single statistical framework. Often, we are interested in detecting genetic variations that perturb a sub-module of phenotypes rather than a single phenotype, and GFlasso achieved this through a fusion penalty, in addition to the lasso penalty, that encourages parsimony in the estimated model. The fusion penalty locally fused two regression coefficients for a pair of correlated phenotypes, and this effect propagated through edges of the QTN, effectively applying fusion to all of the phenotypes within each subgraph. The fusion penalty in GFlasso introduced a bias that the amount of influence of a shared QTL is similar over the set of correlated traits in order to increase the power for detecting weak signal and reduce false positives. The simulation results showed that the benefit of information sharing due to the fusion penalty outweighed the risk of low-variance bias on fused regression coefficients when in reality the magnitudes of the coefficients can be highly variable. Perhaps a more effective approach and a promising future direction would be to encourage each SNP marker to be jointly relevant or irrelevant to the subset of correlated traits, but still allow the marker to have a different amount of influence on each of the traits. This would reduce the bias introduced by the fusion penalty and further improve the performance of GFlasso, since the only information shared across correlated traits is the sparsity pattern but not the magnitudes of the regression coefficients.We have used a simple scheme of a thresholded correlation graph for learning the QTN of phenotypes to be used in GFlasso. Many different types of network-learning algorithms have been developed previously. For example, graphical Gaussian models (GGMs) Geronemo and Lirnet, both the module network structure and the markers of regulators regulating the modules were learned simultaneously, although these methods only focused on modeling the relationship between regulators and target genes In this study, we assumed that the graph structure of a QTN is available from a pre-processing step. One of the possible extensions of the proposed method is to learn the QTN and the regression coefficients jointly by combining GFlasso with the graphical lasso For any new multivariate genetic-association methods, a natural question is whether the new method can scale to a genome-scale analysis. The current implementation of GFlasso leaves this to be determined by a user-specified tradeoffs between power and computation time. As shown in Finally, it is important to point out that as of now GFlasso considers only dependencies among phenotypes, and does not assume any dependencies among the markers. Since recombinations break chromosomes during meiosis at non-random sites, segments of chromosomes rather than an individual nucleotide are inherited as a unit from ancestors to descendants, creating a relatively low diversity in observed haplotypes than would be expected if each allele were inherited independently. Thus, SNPs in high LD are likely to be jointly associated with a phenotype in a regression-based penetrance function. In our future research, we plan to apply the same idea of the graph-guided fusion penalty for phenotypes to incorporate the LD structure among genotypes. It is straightforward to introduce another fusion penalty for correlated markers based on the genotype correlation graph and weight each term in the penalty using values that reflect the recombination rates and distances between each pair of genetic markers. This would allow a genome-phenome association analysis for identifying a block of correlated markers influencing a set of correlated phenotypes.http://www.sailing.cs.cmu.edu/gflasso.html. A preliminary version of this method was presented at the 19th international conference on intelligent systems for molecular biology (ISMB 2009).Software for our proposed method is available at


The number of algorithms available to predict ligand-protein interactions is large and ever-increasing. The number of test cases used to validate these methods is usually small and problem dependent. Recently, several databases have been released for further understanding of protein-ligand interactions, having the Protein Data Bank as backend support. Nevertheless, it appears to be difficult to test docking methods on a large variety of complexes. In this paper we report the development of a new database of protein-ligand complexes tailored for testing of docking algorithms.Using a new definition of molecular contact, small ligands contained in the 2005 PDB edition were identified and processed. The database was enriched in molecular properties. In particular, an automated typing of ligand atoms was performed. A filtering procedure was applied to select a non-redundant dataset of complexes. Data mining was performed to obtain information on the frequencies of different types of atomic contacts. Docking simulations were run with the program DOCK.We compiled a large database of small ligand-protein complexes, enriched with different calculated properties, that currently contains more than 6000 non-redundant structures. As an example to demonstrate the value of the new database, we derived a new set of chemical matching rules to be used in the context of the program DOCK, based on contact frequencies between ligand atoms and points representing the protein surface, and proved their enhanced efficiency with respect to the default set of rules included in that program..The new database constitutes a valuable resource for the development of knowledge-based docking algorithms and for testing docking programs on large sets of protein-ligand complexes. The new chemical matching rules proposed in this work significantly increase the success rate in DOCKing simulations. The database developed in this work is available at  Improving our understanding of protein-ligand interactions at the molecular level plays an important role in the discovery process of new drug candidates. The Protein Data Bank (PDB)  is the mCurrent virtual screening methodologies for in-silico discovery process of new leads rely on databases of chemical complexes with structural information and also chemical, physical and biological properties, when available. Examples of these datasets are the Cambridge Structural Database , NCI 3]3], ZINC Several databases containing information on ligand molecules found in the PDB, and on protein-ligand interactions  may be a complicated task for several reasons. First, there is not explicit information in the PDB entries that would make clear which records contain atomic data belonging to each small molecule, if present in the structure. Usually, small ligands are listed within HETATM records, but this doesn't apply to amino acids or nucleic acids, which may also constitute small ligands. Furthermore, some small molecules are a mixture of amino acids and/or nucleic acids with other chemical groups, being listed both under HETATM and ATOM records. On the other hand, some of the small molecules present in the PDB, especially many sugars, are covalently bound to proteins, being therefore not of interest for the purpose of this work. The solution given here to these problems was to analyse the atomic connectivity within a PDB entry.We wrote our own program  to process the whole PDB. For peptides and nucleotide chains, we set a limit of eight and four residues, respectively, to consider them as ligands. First, all the amino acid and/or nucleotide chains present in an entry were identified. Entries containing nucleotide chains having more than four residues were not further analysed. Water molecules and metal ions were stored apart. Hydrogen atoms, if present, were deleted .The remaining heteroatoms, as well as the small peptides and nucleotide chains were all processed together and grouped by their connectivity into fragments (molecules). Two atoms were considered to be covalently bonded if the distance between them was less than 2.1 Å. This number was selected to cover the larger covalent distances we found in the PDB (between sulphur atoms: 2 – 2.05 Å). Fragments having less than 10 atoms were removed, as well as those fragments that were covalently bonded to a protein chain.Ligands were classified in seven categories according to their chemical nature: peptide, nucleotide, hetero, peptide-nucleotide, peptide-hetero, nucleotide-hetero and peptide-nucleotide-hetero. Ligand residues listed under the ATOM keyword in the PDB files were identified as peptide or nucleotide by checking their residue names. Every residue listed under the HETATM keyword was included in the broad category of 'hetero'.Atomic contacts between two molecules are typically determined based on a distance criterion. Thus, in several reports a contact between two atoms is registered if the distance between them is below a fixed cut-off value. For example, in Relibase ,11, a vaA and the carbon atom represented in this figure would be considered as being in contact if a purely distance criterion is used. Following our definition, however, they are not contacting each other.In this work, we introduced a new definition of atomic contact that takes into account a possible screening of the direct interaction between two atoms, produced by a third atom that is covalently bonded to one of them, as illustrated in Figure -2 and a radius of 1.4 Å for the rolling probe. The van der Waals atomic radii were taken (with just a slight rounding) from the AMBER united-atom force field [Our definition of atomic contact takes also into account a distance criterion, but with an additional refinement. In our approach, the Connolly's molecular surfaces  of the tce field .2 for each of the two atoms  the distance between them is not greater than the sum of their van der Waals radii plus a tolerance distance (0.5 Å), and b) if both atoms have associated surface points located around a line joining their centres, and covering at least an area of 1.5 ÅFollowing this definition, the program complex_info computes, for each protein-ligand complex, a list of atomic contacts, as well as a more concise list of the protein residues that make contact with the ligand.The program complex_info also determines which of the water molecules, if present in a PDB entry, are located at a protein-ligand interface. In addition, it gives the total surface area of the ligand and the surface areas buried on the ligand and on the protein as a result of the interaction. This analysis was based on the calculated Connolly's surfaces, since his program MS DOT  specifieFor each water oxygen that is close to the ligand (distance < 3.5 Å), its Connolly surface was computed taking into account all the surrounding atoms within a cut-off distance of 8 Å. Water molecules having > 90% of their surface buried by ligand and protein atoms were considered as interface water. The same type of analysis was performed for any metal ion present in the structure.The calculation of the ligand surface buried by the protein was performed including the interface water molecules and ions as if they were part of the protein. Conversely, the interface water and ions were considered as extensions of the ligand surface to compute the area of the protein surface buried by the ligand.When studying molecular interfaces it is important to consider the nature of the atomic interactions, for example, the presence of hydrogen bonds, salt bridges or hydrophobic contacts. For this purpose, it is necessary to know what chemical properties the atoms at the molecular interface have. This information, however, is not included in a PDB file. In particular, the absence of hydrogen atoms in most PDB structures makes it difficult in many cases to determine whether a polar atom can be a donor or an acceptor of a hydrogen bond. In order to overcome these problems, we implemented a procedure for automatic atom type assignment.et al. [For ligand atoms, a few atom types were defined on top of the chemical elements for nitrogen, oxygen and carbon  to differentiate between sp2 and sp3 hybridizations. If an atom has three connections to heavy atoms and the absolute improper dihedral is smaller than 18 degrees, then it is sp2 hybridized . NewThe assignment of 'donor' and 'acceptor' atom types performed by Babel 1.6 for some nitrogen atoms, which depends on their protonation, was not always correct. This kind of problem appeared mainly with heterocyclic structures. To improve the capabilities of Babel to protonate correctly these structures, we included some empirical rules generally accepted in organic chemistry .Finally, the protonation of nitrogen atoms in ligand ring structures was checked against the crystal structure of the corresponding protein-ligand complex. The influence of every polar protein atom contacting with a ring nitrogen in a ligand was evaluated. This evaluation took into account the distances between the nitrogen and the protein atoms and the angle formed by a possible hydrogen bond donated by the ligand nitrogen. The influence is positive if hydrogen placement favours the formation of a hydrogen bond, but is negative if the added hydrogen is facing a hydrogen from a donor atom in the protein, as illustrated in Figure Once a molecule is protonated Babel 1.6 can also assign partial charges to atoms, using the Gasteiger algorithm . The infTwo reference datasets were used to test the typing of ligand atoms with the modified Babel-1.6 program: the DOCK 4.0 dataset , consistA filtering procedure was applied to the collected protein-ligand complexes aiming to create a non-redundant dataset for data mining. The term "non-redundant", for this particular work, means that the dataset should not contain entries for which both the structures of the ligand and the protein-binding site are similar. Following this principle, the comparison of the complexes was focused mainly on the protein amino acids and the ligand atoms that are in contact with each other. Thus, we compared the ligand structures and the protein-ligand contact tables. The comparison took into account the atom types previously assigned.First, the filtering procedure was performed within each PDB entry. Afterwards, all pairs of protein-ligand complexes obtained after the first round were compared against each other. If both the ligand structures and the contact tables for two complexes were similar, the complex with lower resolution was discarded.Comparison of the ligand structures was centred on the atoms contacting the protein. From the list of protein-ligand contacts computed by our contact definition, a graph representation of the interaction is built to find the molecular similarity between two structures. The maximum common sub-graph (MCS) between two structures is used to express molecular similarity. From the MCS, an analysis of the protein-binding site is carried out to eliminate redundancies. Since finding the MCS is a NP-complete graph-matching problem, it is computationally expensive to locate an optimal mapping in a realistic time frame without the application of heuristics. Several approaches have been reported to tackle this kind of problem. Here, our heuristic to find MCS starts with a clique detection algorithm, as described in , which cThe assignment of atom types carried out in this work, together with all the structural information stored in the database, allows performing different types of studies regarding the chemical nature of the protein-ligand interactions. The extracted knowledge may have important applications, for example, in the design of docking algorithms. In this paper, we explored the data mining possibilities given by our new database of protein-ligand complexes by compiling statistics about the frequencies of interaction between different protein and ligand atom types, and using the obtained data to tune the chemical matching filter implemented in the well known program DOCK .ij) for different pairs of ligand-protein atom types, e.g. N_d-acceptor, which was defined as follows:In this study we introduced the term "relative contact frequency" (FCrij = k Cij/(pi pj), where Cij is the number of contacts counted for ligand atom type "i" and protein atom type "j", divided by the total number of computed ligand-protein contacts; pi and pj are the proportions in which atom types i and j, respectively, are found in the database; and k is a normalization constant, computed so that the sum of the frequencies by all protein-ligand pairs yields one.FCrTo test the performance of different versions of the chemical filter implemented in the program DOCK, we ran a series of simulations with this program for a selected set of hundreds of protein-ligand complexes.The algorithm implemented in the program DOCK uses a representation of the binding site based on points ("spheres"), which are intended to occupy positions favourable for ligand atoms to be placed there. Then, sets of ligand atoms are matched onto sets of spheres to generate different orientations of the ligand . A chemiModifying the chemical filter that comes by default with the program DOCK was easily done by just replacing the original input files containing atom type definitions and matching rules.To create a negative image of the active site, we used our own method for generating binding site points, called "attached points" (ATPTS) . In thisATPTS binding site representations were generated in a totally automated way, following the algorithm described in . The selSince we were interested only in the functioning of the orientation part of the program DOCK with different versions of the chemical filter, we did not perform any scoring of the generated ligand geometries. Instead, we ran "truncated" docking simulations – the code of the program was slightly modified to output to a file the RMSD value, with respect to the crystal structure geometry, of every generated ligand orientation, and then skip the scoring routines. The docking simulations were carried out also in automated way, using simple scripts.We processed the whole PDB release of August 2005 (32069 files) using our program 'complex_info' in order to detect ligand molecules bound to proteins, extract a variety of information on these protein-ligand complexes and calculate several atomic and molecular properties. As result, 22513 complexes from 10694 files were extracted and analysed.One important part of the calculations performed on the extracted protein-ligand complexes was the assignment of atom types to both the protein and the ligand. We defined a simple set of atom types, as described in the Methods section Table , aiming Typing of protein atoms was easily done from the PDB atom names. Typing of ligand atoms, however, presented some difficulties due mainly to the imperfect stereo chemistry of ligand molecules extracted from the PDB and imprecisions in the protonation of nitrogen atoms.sp3-hybridized, has an average bond angle higher than the cut-off value set in the Babel-1.6 code for this type of hybridization. For this reason, it was assigned an incorrect sp2 hybridization. By checking the improper dihedral angle formed by C2 and its connected atoms C1, C6 and N5, a correct sp3 hybridization was assigned. For the Moxiraprine molecule .Once the atom typing was performed for all the extracted complexes, a filtering procedure was carried out with the purpose of creating a non-redundant dataset, following the principles delineated in the Methods section. First, obvious redundancies such as repeated complexes at different resolutions and repeated copies of the same ligand within an entry were eliminated. In most cases, these copies are found for proteins having several identical binding sites, for instance, the cholera toxin B pentamer (entry 3chb), or for entries containing copies of the same complex in the asymmetric unit of the crystal. In either case, the protein-ligand contact patterns displayed by the different ligand copies are almost identical.On the other hand, there are several small groups of entries containing the same protein, but in complex with different ligands, or mutants of the same protein in complex with the same or different ligands. We did not eliminate any of these complexes a priori because each of them might contribute different protein-ligand contacts. For instance, different versions of the same protein very often contain mutations in the binding site region, which most likely results in distinct new contacts with the ligand.After a first filtering round where copies of the same ligand within the same PDB entry were removed, 12360 complexes out of 22513 remained. Afterward, a second filtering round was carried out where all complexes were compared against each other. As result, 6586 complexes were obtained.All information about extracted protein-ligand complexes was stored in a SQL server database, as illustrated in Figure 2 for all complexes. The buried ligand surface is always slightly smaller than the corresponding buried protein surface because the ligand is embedded within the protein site and because of the presence, in many cases, of water molecules at the interface. Figure As shown in Figure Some of these properties are correlated, for example, Figure sp2-hybridized "aromatic nitrogens" (N_r) have a low contact ratio, due to the fact that their three valences are occupied by heavy atoms that screen most part of their atomic surface. Negatively charged oxygens have the highest ratio of interaction (~75%).Also by doing simple queries to the database, it is possible to know how the different atom types defined in this work are represented in the PDB ligands, and their ratio of participation in contacts with the protein receptors. Table We further performed a series of data mining and docking simulations, in order to show the potentialities of our database in terms of extracting useful data that could be incorporated into knowledge-based docking algorithms, and testing these algorithms using a large number of protein-ligand complexes. For this purpose we used two different non-redundant data sets compiled from the database. One of them, called PDB2003 and containing 4243 complexes, was obtained from the complexes deposited in the PDB before March 2003, using the filtering procedure explained in the Methods section, while the second dataset, called PDB2005 and having 2931 complexes, was obtained from the rest of the structures, up to August 2005. PDB2003 was used to derive statistics on protein-ligand interactions by atom type, whereas PDB2005 was employed in testing newly designed chemical matching rules, as described below.We further queried the database to determine the number of interatomic contacts by both ligand and protein atom types. Table We then defined what we called "relative contact frequencies" (FCr) for ligand-protein atom type pairs, aiming at removing the biases in contact frequencies caused by the differences in atom type populations. The relative contact frequencies were obtained by normalising the values displayed in Table After the performed normalization, it turned out that atomic interaction pairs formed by a positively charged nitrogen in the ligand and a negatively charged atom in the protein (an oxygen atom from the carboxyl group of asparagine or glutamine) have the highest relative frequency in protein-ligand complexes. In general, positively charged and hydrogen bond donor nitrogen atoms in ligands have high relative frequencies of interaction with hydrogen bond acceptors and negatively charged atoms in the protein. On the other hand, negatively charged oxygens in ligands interact with high relative frequencies with positively charged nitrogens and hydroxyl oxygens. Ligand hydroxyl oxygens are more prompt to donate a hydrogen bond that accepting one from the protein.In summary, interactions between charged and polar atoms have much higher relative frequencies than interactions with or between hydrophobic or aromatic atoms. This means that when present both in the ligand molecule and in the protein binding site, nitrogens and oxygens have a much higher probability of being involved in interactions with the protein than individual carbon atoms.To reflect these relative contact preferences by atom type in a discrete way, the interactions were classified into three categories: highly probable, probable and less probable, as defined in Table With the aim of making a practical use of the obtained probabilities of interaction by atom types, we designed a set of chemical complementarity rules that could be used as a filter to discard wrong ligand orientations by a docking program, such as DOCK .As explained in Methods, DOCK uses a representation of the binding site based on points. Then sets of ligand atoms are matched onto sets of points to generate different orientations of the ligand. A chemical filter implemented in the program helps discarding matches that do not fulfil a defined set of chemical (or matching) rules, thus speeding up the calculations.Here we used our own method to generate binding site points, called "attached points" (ATPTS), as briefly explained in Methods and in more details in ref. . Each ATThus, ATPTS binding site representations were generated for the 4243 protein-ligand complexes included in the PDB2003 dataset, in a totally automated way. The calculation time per structure was, on average, less than 0.1 s.mp). A matching atom-point pair is formed by a site point and a ligand atom being within certain "matching cut-off distance" (1.5 Å) from each other. Then we showed that for the vast majority of the tested complexes, more than 70% of the ligand atoms that are in contact with the protein are matched by ATPTS points. Here we repeated the same type of calculation for the PDB2003 dataset and obtained roughly the same result (not shown).The quality of the ATPTS representation of protein binding sites, in terms of its success in matching point and ligand atom positions for the correct  orientation of ligand molecules, was thoroughly demonstrated in a previous paper . For thiIn this work, taking profit of the atom type data included in the database, we could also evaluate the quality of the formed matching pairs, according to the classification of the atomic interactions defined in Table If the number of matching pairs with probable and very probable interactions is high enough, the stringency of the matching rules to be used in the docking runs may be increased, for example, by enabling only matching of very probable pairs in the ligand orientation process. This would significantly accelerate the calculations by reducing the number of ligand orientations to generate and evaluate. On the other hand, the use of these rules must allow the generation of a sufficient number of correct orientations, otherwise the program may fail in predicting a right solution.DOCK), which are much more permissive.Here we defined two different sets of matching rules: one that permits very probable and probable interactions (R1), and a second set that permits only very probable matches (R2). Then we tested their performance as compared with the chemical complementarity rules coming with the DOCK program . Ligands were kept rigid in these simulations.For all the selected complexes we ran "truncated" docking simulations, as explained in Methods. No scoring was carried out since we were not interested in the performance of the scoring functions, but only in evaluating the number of correct ligand orientations that are produced by the program. The maximum number of orientations to be generated was limited to 10In order to check the efficiency of the chemical rules, we computed the number of "good" ligand-receptor configurations generated by the DOCK program in the ligand orientation process. As "good orientations" were considered those whose RMSD value with respect to the crystal geometry was less than 2 Å.0) and the number of "good" orientations obtained.The simulations for the 542 complexes took just a few hours. A comparison of the results obtained for the three sets of matching rules is shown in Figure When using its default set of chemical matching rules, the program DOCK fails in producing a good solution for about 30% of the complexes Figure , even th5 orientations for most of the complexes (Figure Using the R1 set of matching rules significantly improves the performance of the simulations, with only a very few complexes having their best orientation with an RMSD > 2 Å Figure . For this Figure . The aveFinally, when the R2 rules were employed, a good solution was always found Figure . For thi5, or even more unfavourably, within the first 103 explored conformations, can be small.On the other hand, the fact that using the matching rules given by default for the DOCK runs made the program fail in about 30% of the tested cases causes concern. Since the ensemble of ligand atom-site point matches produced with the R1 or R2 rules must be a subset of the ensemble of matches produced using more permissive rules, or not using any filter at all, the reason for the failure must be that the maximum allowed number of ligand orientations was not enough to give the program the chance of exploring the right sector of the orientational space. Even for small and medium size ligands, the total number of atom-point combinations that can be produced may be so large, that the probability of obtaining correct matches within the first 10We compiled a large database of small ligand-protein complexes derived from the PDB that currently contains more than 6000 non-redundant structures. In processing the PDB structures, we characterized in details the protein-ligand interactions, using a new definition based on the proximity of the molecular surfaces. Furthermore, the structural data was enriched with chemical properties that we calculated using our own as well as existing algorithms. As an example to demonstrate the value of the new database for data mining and testing of docking algorithms, we derived a new set of chemical matching rules to be used in the context of the program DOCK, based on contact frequencies between ligand atoms and points representing the protein surface, and proved their enhanced efficiency with respect to the default set of rules included in the program. One important finding in this study is the high rate of failure of the program DOCK in finding orientations close to the crystal structure when using the default chemical filter, even in the case when the maximum number of allowed ligand orientations is set 100 times larger than the default value.LAD worked in the typing of ligand atoms, the filtering of the data to create non-redundant sets, the data mining to extract several statistics from the database and in the design and testing of the new rules for chemical matching. He also was in charge of most of the manuscript writing. LA designed and leaded the work to structure the data into a SQL server. PM designed and built a user-friendly graphical interface with wide possibilities for querying the database on a web server. EM conceived, designed and coordinated the project, created the program to identify and extract structural information on the protein-ligand complexes, and took an active part in manuscript writing. The four authors read and approved the final version of the manuscript.


Systematic reviews of randomized trials that include measurements of health-related quality of life potentially provide critical information for patient and clinicians facing challenging health care decisions. When, as is most often the case, individual randomized trials use different measurement instruments for the same construct , authors typically report differences between intervention and control in standard deviation units . This approach has statistical limitations (it is influenced by the heterogeneity of the population) and is non-intuitive for decision makers. We suggest an alternative approach: reporting results in minimal important difference units . This approach provides a potential solution to both the statistical and interpretational problems of existing methods. Health-related quality of life (HRQL) is increasingly recognized as an important outcome in randomized trials. Disease-specific HRQL instruments provide critical information because of their ability to detect small but important treatment effects ,2. TypicClinical trial investigators use different HRQL instruments for various reasons, including their familiarity with an instrument. This creates challenges for meta-analysts seeking summary estimates in systematic reviews of trials addressing the same or similar HRQL constructs. Choices include reporting summary estimates for each separate measurement instrument, or pooling across instruments. The former approach is less appealing in that it leaves the clinician with multiple imprecise estimates of effect.A widely used approach to providing summary estimates across instruments - an approach endorsed by the Cochrane Collaboration - involves dividing mean differences between intervention and control in each study by the study's standard deviation (SD) and calculating what are called "standardized mean differences" (SMDs) or "effect sizes". Ultimately, systematic reviews using this approach will present the magnitude of treatment effects as SD units [This approach provides a single pooled estimate of treatment effect but leaves two problems. One problem is that if the heterogeneity of patients is different in different studies, the SD will vary across studies. Therefore, given the same true difference in HRQL between intervention and control groups, trials with more heterogeneous patients and similar scores on the HRQL instrument of interest will show apparently - but spuriously - smaller effects than trials enrolling less heterogeneous patients.The second problem is that interpretation of the magnitude of effect when represented as SD units is challenging. Although rules of thumb - the most frequently used guide tells us that an effect size of 0.2 represents a small difference, 0.5 a moderate difference, and 0.8 a large difference .One strategy to address similar problems in interpretation of results from individual trials that report on HRQL measures involves the minimal important difference (MID) ,8. The MIf the MID has been established for two or more instruments, systematic review authors could report the results of each study in "MID units" instead of SD units . Standardization using MIDs may provide a uniform metric that both circumvents the fragile assumption regarding similar variability in study populations across trials that is required for the SMD and facilitates interpretation by both clinicians and patients. In the remainder of this article, we will illustrate both the current and proposed methods using data from a systematic review of respiratory rehabilitation in COPD . AlthougHealth Related Quality of Life scores are typically treated as continuous. In meta-analysis of continuous data, the mean difference (MD), or the "difference in means" is the measure of the absolute difference between the mean value in each arm in a parallel group clinical trial. When outcome measurements in all trials are made on the same scale, a well-established inverse variance meta-analysis method can be used to combine results across trials and obtain a pooled MD .When investigators have relied on different instruments measuring the same or similar construct, it is necessary to transform or standardize the trial results to a uniform scale before they can be combined in a meta-analysis. The common approach to the problem is to calculate the SMDs for each trial  and pool across trials.A potential solution to the limitations of SMD is to substitute the MID for the usual denominator of the SMD, the SD. That is, we divide the MD by the MID that was established for the instrument used in the trial. As a result, rather than obtaining an estimate in SD units, we obtain an estimate in MID units.When we standardize by dividing the MD by the MID, we alter the scale on which we are performing our meta-analysis. In doing so, we also need to account for the changes that the standardization has on the standard error and weights associated with each standardized trial outcome. In the accompanying appendix  separately for each of the four domains of the CRQ and each of the three domains of the SGRQ, as well as an overall score for each instrument. For the CRQ, most of the included trials did not report the overall mean (SD). To resolve this, we generated the overall mean (SD) for each trial using the domain data provided by the Cochrane review  as well as the total score exceeded the MID, as did the lower limit of the confidence interval for each domain  . For the% Figure . To avoi2 = 32%  and for the SGRQ, 1.53 . For both measures the common effect size exceeded 1.0 indicating that the intervention effect is, on average, appreciably greater than the MID. With respect to the lower confidence interval around the common effect size, the CRQ results exceeded 1.0, whereas the SGRQ did not. We can thus be confident, on the basis of the studies using the CRQ, that the mean effect exceeds the MID whereas, for the SGRQ we cannot. Combining all studies in MID units yields an overall pooled estimate of 1.75 , IThe point estimate in MID units suggests a large effect (approaching 2 MIDs) and the lower 95% CI is greater than 1, suggesting that it is implausible that the mean effect is less than the MID  by reporting results in MID units as opposed to SD units. Future studies involving formal simulation techniques might consider evaluating I2 estimations when considering SD units vs MID units.The major strength of our method is that it avoids the problems associated with heterogeneity of between-study variances . The MID unit approach prevents introducing inconsistency depending on the SD of each included trial and provides results that are likely to facilitate intuitive interpretation by clinicians and patients. Of interest, the statistical heterogeneity as measured by the IOur method requires that previous investigations have generated an estimate of the MID; this is true for only a limited number of HRQL measures. Nevertheless, MIDs are being increasingly established for instruments used to evaluate common illnesses ,19. If aEven if the MID is available, application of the method in particular instances may present challenges. In the example we have used, the CRQ was not originally developed to provide an overall summary score, and for this reason the majority of included trials calculated estimates for each domain separately. We felt comfortable with this strategy because previous work has demonstrated that an overall score is sensible and likely remains valid and responsive . An addiSystematic reviews and meta-analyses of randomized trials that employ HRQL instruments provide the least biased and most precise summary estimates of the impact of interventions on patients' lives. When, however, individual randomized trials use different measurement instruments for the same construct, existing methods for combining across studies are plagued by statistical and interpretational limitations. The MID approach provides a potential solution to the limitations of the existing methods.HJS and GHG are authors of the Chronic Respiratory Questionnaire.BCJ participated in design of the study, data extraction, data analysis, interpretation of the results and drafted the manuscript. KT participated in the design of the study, developed the statistical framework for data analysis and participated in the interpretation of the results. HJS, FX, MHM, VMM participated in the design of study and interpretation of the results. GHG participated in the design of study, developed the statistical framework for data analysis and participated in the interpretation of the results. All authors critically revised the article and approved the version to be published.Total CRQ score formulas and MID units formulas. Total CRQ score formulas: Standard errors and standard deviations for total CRQ scores. MID units formulas: Pooling MID standardized mean differencesClick here for file


The human phylome, which includes evolutionary relationships of all human proteins and their homologs among thirty-nine fully sequenced eukaryotes, is reconstructed. Phylogenomics analyses serve to establish evolutionary relationships among organisms and their genes. A phylome, the complete collection of all gene phylogenies in a genome, constitutes a valuable source of information, but its use in large genomes still constitutes a technical challenge. The use of phylomes also requires the development of new methods that help us to interpret them.We reconstruct here the human phylome, which includes the evolutionary relationships of all human proteins and their homologs among 39 fully sequenced eukaryotes. Phylogenetic techniques used include alignment trimming, branch length optimization, evolutionary model testing and maximum likelihood and Bayesian methods. Although differences with alternative topologies are minor, most of the trees support the Coelomata and Unikont hypotheses as well as the grouping of primates with laurasatheria to the exclusion of rodents. We assess the extent of gene duplication events and their relationship with the functional roles of the protein families involved. We find support for at least one, and probably two, rounds of whole genome duplications before vertebrate radiation. Using a novel algorithm that is independent from a species phylogeny, we derive orthology and paralogy relationships of human proteins among eukaryotic genomes.Topological variations among phylogenies for different genes are to be expected, highlighting the danger of gene-sampling effects in phylogenomic analyses. Several links can be established between the functions of gene families duplicated at certain phylogenetic splits and major evolutionary transitions in those lineages. The pipeline implemented here can be easily adapted for use in other organisms. The complete sequencing of the human genome represented a major breakthrough for the genome era ,2. SinceUsing a different, gene-based approach that aims at maximizing both the coverage over the human genome and the taxon-sampling among fully sequenced eukaryotic genomes, we have developed a fully automated pipeline Figure  to reconThe availability of such a comprehensive collection of evolutionary histories of protein-coding human genes constitutes a valuable source of information that allows us to test several evolutionary hypotheses. For this purpose, we investigated the consistency of the individual phylogenies within the phylome with alternative evolutionary scenarios, namely those involving the relative positions of rodents and primates, amoebozoans and opisthokonts and, finally, insects, nematodes and chordates. We also scanned the human phylome for cases of putative horizontally transferred genes and found that such topologies are never highly supported, indicating that they are rather the result of phylogenetic artifacts. Moreover, we provide estimates for the number of gene duplications that have occurred at different evolutionary stages in the eukaryotic lineages leading to hominids and found several over-represented functional classes in the different duplication events. Finally, we explored an alternative, fully automated algorithm to infer orthology relationships from phylogenetic trees that does not require a fully resolved species phylogeny and, therefore, is less sensitive to topological variations. The choice for this novel methodology for orthology prediction is based on the fact that alternative tree reconciliation methods have difficulties in accounting for inherent phylogenetic noise, divergences in evolutionary histories for different genes and the low resolution level of available species trees. As will be shown below, the high degree of topological variation found in the human phylome for all scenarios considered also supports the choice of alternatives to classic tree reconciliation methods. All in all, the results presented here constitute a preliminary but broad overview of the evolutionary history of the human genome, which is not taken as an average or represented by a limited number of genes, but instead is regarded as a complex mosaic of thousands of individual phylogenies.Dictyostellum discoideum), two plants , two apicomplexans , and three excavates (the diplomonad Guillardia theta and the kinetoplastids Leishmania major and Paramecium tetraurelia). This distribution of species makes our set especially suitable for addressing the evolution of protein families among the opisthokonts. It covers, therefore, a period that is rich in important evolutionary innovations, from the origin of apoptotic pathways [The human phylome presented here is derived from the proteins encoded by 39 publicly available eukaryotic genomes Table . This sepathways  to the epathways .To derive a phylome from the abovementioned proteome database we applied a phylogenetic pipeline to each human protein. This fully automated pipeline  emulates the manual workflow used by phylogeneticists: from sequence, through alignment, to phylogenetic reconstruction. It starts with a sequence search against the proteome database to retrieve groups of significantly similar proteins that are then aligned. Alignments are automatically trimmed to remove gap-rich regions. The subsequent phylogenetic reconstruction combines NJ, ML and Bayesian methods. Firstly, a NJ tree is constructed with BioNJ , and secBoth ML and Bayesian analyses are model-based approaches that can provide divergent results when different evolutionary models are assumed. Several authors have shown that the use of an appropriate model is crucial for the reconstruction of correct phylogenies and that the origin of the sequences involved  is not always a good predictor of the most appropriate model ,24. ApplAmong the models tested, JTT was chosen as the best fitting model in a majority of the trees , followed by WAG , Blosum62  and VT . MtREV was chosen as the best model in ten out of the thirteen mitochondrial-encoded human proteins. Surprisingly, the phylogenies of subunit 6 of NADH dehydrogenase and subunits 1 and 2 of cytochrome oxidase were best fitted by JTT, Blosum62 and WAG models, respectively.To assess whether a tree produced by the NJ approach has sufficient predictive value for the model selection step, we compared the model chosen by the full ML approach  to the model selected when the likelihood of the seed NJ tree was assessed under different models, allowing for branch-length optimization. In 86.7% of the cases the model chosen by both methods was the same. This confirms and extends earlier results  and, morRecent advances in resolving the tree of eukaryotes are converging into a model that comprises a few large super-groups . DespiteA phylome represents a broader, yet more complex to interpret, reconstruction of the evolution of an organism, since it comprises the phylogenies of all its genes. Most notably, the availability of a phylome opens the possibility for studying the relationships among species in a different way: that of quantifying the fraction of individual phylogenies whose topologies are consistent with a given hypothesis. Here we explored this methodology by specifically contrasting a number of evolutionary relationships that are controversial to some extent. We chose three different scenarios for which there is some level of controversy in the literature and that involve three different depths of the eukaryotic tree Figure . Namely,Perhaps one of the most debated issues regarding the tree of eukaryotes is the relative position of arthropods, nematodes and chordates. Traditionally, comparative anatomy placed arthropods and chordates in the coelomata clade, which contained animals with a true body cavity, while pseudocoelomates such as nematodes occupied a more basal position. However, phylogenetic analyses of 18S and 28S rRNAs supported an alternative view that grouped nematodes and arthropods, dubbed ecdysozoa, to the exclusion of chordates . Since tOur results Figure  show a pThe finding that all three possible topologies, including the one widely considered as wrong in the literature, are supported by a significant number of trees illustrates the inherent difficulty of resolving the species phylogeny from gene phylogenies. We have found similar topological diversity in the three scenarios considered (see below) and also, to smaller degrees, in apparently undisputed evolutionary relationships (results not shown). Similar results showing variability in the relative positions of arthropods, nematodes and chordates have also been found in topological analyses of the phylogenies of 507 eukaryotic orthologous groups  and of 1The phylogenetic relationship among placental mammals has attracted great interest in recent years . A stillIn our analyses the results seem to favor the basal position of rodents, although the difference with the alternative hypothesis of a clade grouping rodents and primates is not great Figure . From thDictyostellum discoideum, has been included, together with representatives from three other major groups, including excavates , plants  and chromoalveolates . We scanned the phylome for trees supporting the grouping of opisthokonts with each of the other major groups, provided that at least four of the five major groups were represented in the tree  and subsequently mapped them onto a species phylogeny that marks the major branching points in the lineage leading to hominids Figure . The relCiona intestinalis) and vertebrates. This observation is consistent with previous results supporting the existence of at least one round, and probably two rounds, of whole genome duplications before the radiation of vertebrates [Ciona). The second largest peak appears at the base of the metazoans, after their split with fungi. The relatively large duplication rate (0.58 duplications per tree) at this point could be interpreted as a result of a WGD at the base of metazoan evolution or, alternatively, an accumulation of smaller scale duplications. To the best of our knowledge, the possibility of a WGD at the base of metazoan evolution has not been proposed in the literature [The highest peak in gene duplication events corresponds to the base of chordate evolution, after the split of urochordates . Due to space limitations we represent only a fraction of the over-represented terms for the category 'biological process'  terms that are significantly over-represented in the set of genes that underwent duplications during the different stages of eukaryotic evolution. We performed this analysis automatically with the aid of the program Fatigo+, from the Babelomics suite . At each' Figure . A complIn most evolutionary stages, we found several terms from different GO levels and categories that are significantly over-represented. Of these, some are specific to a given evolutionary transition , while others are over-represented in a series of consecutive stages . Providing links between the over-represented terms and the functional or morphological transitions characteristic of each stage is not straightforward. Nevertheless, some terms do suggest the expansion of some physiological processes at a given evolutionary time. Terms related to maintenance of complex cellular structures, such as 'organelle organization and biogenesis', 'cytoskeleton', 'cellular organization' or 'cellular localization', are over-represented in genes duplicated before the divergence of fungi and metazoans, suggesting major transitions in cellular organization common to all opisthokonts. The expansion of the process 'small GTPase signal transduction' in almost all major stages from the origin of opisthokonts to the vertebrates indicates a continuous expansion of signaling cascades that is likely related to the increasing level of multi-cellularity and tissue differentiation observed at these evolutionary stages. Similarly, protein families related to 'G-protein coupled receptor signaling pathway' were expanded before the amniota and mammalian radiations. Also remarkable are the consecutive waves of expansion observed for the 'immune response' and related terms. They have occurred at every split from the origin of tetrapods to the origin of primates and suggest an increasing sophistication of the immune system. Xenobiotic metabolism terms are also over-represented in genes duplicated in primates. As noted before , the sopThe specific association of terms such as 'transmission of nerve pulse' or 'nervous system development' with families duplicated just before the vertebrate expansion is consistent with the development of a complex nervous system as compared to that of simpler chordates. Later on, the expansion of 'sensory perception' and related terms in the lineages leading to amniota, mammals and primates indicates increasing sophistication of the senses. Similarly, the term 'epidermis development' is over-represented in genes duplicated in tetrapods. This might be related to major skin modifications, which potentially allowed the conquering of the terrestrial environment by this group.The extent and scope of horizontal gene transfer (HGT) events among organisms has been the subject of intense debate . The emeThe species represented in our phylome include organisms that are tightly linked to human, either because they are pathogens , or used as a source of food . A recent transfer from any of these species to the human genome could, in principle, be detected as a human protein being placed in a 'wrong' phylogenetic context. However, caution must be taken when interpreting phylogenies, since such topologies can also be explained by alternative processes such as multiple gene-loss or lack of phylogenetic resolution.et al. [To find such putative cases we scanned the human phylome to detect trees in which the phylogenetic position of the human seed protein could suggest a possible HGT event. For this purpose we applied a series of increasingly stringent filters. These filters consisted in identifying trees in which: the human seed protein has non-primate proteins as nearest phylogenetic neighbors; such topology cannot be explained simply by the loss of the orthologous sequences in the other primates or multiple losses in mammalian groups; the partition suggesting the HGT is supported by a high posterior probability (>0.9) in the Bayesian analysis; and that partition is also supported by ML analysis. This methodology bears some similarity to that proposed by Hallet et al.  in that A total of 99 trees (0.47%) passed the first two filters, thus having a topology that could be explained by an HGT event. However, only 8 of these trees had a posterior probability supporting the HGT partition of 0.9 or higher in the Bayesian analysis, and none of these was supported by the ML analyses, indicating that the partitions suggesting the horizontal transfer are not strongly supported.We interpret these results as a lack of evidence supporting the existence of human genes originating from recent horizontal transfers from the lineages considered and argue that the observed HGT-like topologies are rather the result of phylogenetic artifacts. This interpretation is consistent with the generally adopted view that horizontal gene transfers among multi-cellular eukaryotes is virtually absent due to the existing natural barriers that prevent transferred genes from reaching the germ-line .Caenorhabditis elegans. Additionally, for historical or practical reasons, alternative model species are used to investigate specific systems or pathways. Such is the case with the use of Neurospora crassa, Yarrowia lipolytica and Bos taurus models in the characterization of the multiprotein enzyme NADH:Ubiquinone oxidoreductase (Complex I), in which an intricate evolution and the use of different naming schemas in the various species complicate the transfer of knowledge among investigators studying the different model species [Although an increasing number of genome-wide experimental datasets is becoming available for human, most experimental analyses are performed in model species such as mouse, fruit fly, yeast and the nematode  species .Comparative genomics can be used for transferring functional information across species, a process that requires the establishment of evolutionary relationships among genes encoded in the different genomes. Such relationships are best established by means of detecting orthology, rather than just homology. Orthologs are a special case of homologous genes that diverged from a common ancestor through speciation events, in contrast to paralogs, which originate from duplication events . Since oThe need for detecting orthology at a genome-scale has triggered the development of a variety of automatic approaches that identify orthology relationships by means of similarity searches. The first and still most widely used such method is based on the detection of best reciprocal hits (BRHs), that is, pairs of sequences from different species that are, reciprocally, the best hit of each other in a sequence search . ExtensiConsidering the high degree of topological diversity observed in the human phylome (see above), we reasoned that any algorithm based on reconciliation with a specific species tree would inevitably infer false duplication events in the trees showing topologies that depart from the canonical species tree. Therefore, we decided to explore an alternative, fully automated approach that does not require a fully resolved species phylogeny and a reconciliation phase. The algorithm  uses the level of overlap in the species connected to two related nodes to decide whether their parental node represents a duplication or speciation event. The full list of predicted ortology and paralogy relationships is provided as supplementary material .We compared our predictions with those from other algorithms by using a recent reference dataset comprising 67 human-mouse and 45 human-worm orthologous pairs from five multi-gene families . ConsideMost remarkable are the big differences encountered in the positive predictive values. These range from 2.8% (KOG) to 86.61%  and 95.24% (Ensembl). Altogether, the results show that phylogeny-based orthology detection methods can provide substantial improvement in terms of positive predictive value when sophisticated phylogenetic pipelines are implemented. Note that the other phylogeny-based method included , uses NJ trees. The low rate of false positive prediction achieved by sophisticated phylogeny-based methods makes them especially suited for cases in which orthology prediction is used for the transfer of functional annotations among model species. In such cases, minimizing the level of wrong assignments, which will lead to wrong annotations, is more important than reaching a high coverage at a cost of many false assignments.We have shown the feasibility of reconstructing complex phylomes, comprising the evolutionary histories of all genes from a given species and their homologs in dozens of other genomes. The pipeline and genome sampling is fully automated and can easily be tailored for specific needs, therefore paving the way for the reconstructions of other phylomes using different parameters or species sampling. Because of its significance, we have initially applied this pipeline to the human genome. The resulting phylome constitutes a valuable dataset that can be explored by the research community. In the near future we are planning to implement this and other phylomes in a fully searchable database. To illustrate the potential of the human phylome we have performed several analyses, but many others can be envisaged. Overall, our results indicate that there is a great topological diversity affecting the three unresolved scenarios that we have discussed . This and other recent findings  reinforcMapping speciation and duplication events on the complete phylome has allowed us to derive a comprehensive set of orthology and paralogy relationships among the genomes involved. The results obtained in the benchmark analysis show that although automatic methods for phylogeny-based detection of orthology are progressing in the right direction, there is still room for improvement both in the algorithms and the quality of the trees. Taking into account the levels of topological diversity mentioned above, it follows that the algorithms for phylogeny-based orthology detection need to cope with levels of topological uncertainty.The results obtained in the present analysis are consistent with the existence of at least one round of whole genome duplication occurring before the radiation of verebrates . If a reCandida albicans [N. crassa [C. reinhardtii [Caenorhabditis briggsae, Giberella zeae, Debaromyces hansenii and Leishmania major have apparently not been deposited in the public databases and, therefore, are missing from this study. The final proteome database contains 542,423 unique protein sequences from 39 different genomes . Only sequences that aligned with a continuous region longer than 50% of the query sequence were selected.For each human protein a Smith-Waterman  search wSets of homologous protein sequences were aligned using MUSCLE 3.6 . PositioNJ trees were derived using scoredist distances as implemented in BioNJ . ML treeTo obtain support values of all tree partitions, the ML tree produced by the best-fitting model was used as a seed for a Bayesian analysis by running Mr Bayes  for 100,A final tree produced by this Bayesian reconstruction consists of a consensus phylogeny, using the 'halfcompat' option in MrBayes, in which partitions with a posterior probability lower than 0.5 are collapsed. The alignments and all trees produced for each human protein are made available as supplemental information . Unless We used a phylogeny-based algorithm to detect duplication and speciation events on the trees. In contrast to alternative phylogeny-based methods that use reconciliation of the gene tree with the species tree to infer duplication events, our approach does not require any previous fully resolved species topology. The only evolutionary information required is that used to root the trees to define a polarity so each internal node is connected to two children nodes. The orthology prediction algorithm was run independently for each human gene using the tree generated using its protein sequence as a seed. The algorithm was implemented in a series of python scripts specifically developed for this project.To map duplication and speciation events on an internal node of the tree, the algorithm proceeds as follows. First, two tree partitions are defined that contain the sequences connected to each of the two children nodes. Second, a species-overlap score is defined between the two partitions as follows: species common to both partitions/species in any of the partitions. Third, if the score is higher than a given threshold the node is mapped as a duplication event, otherwise it is considered a speciation event. In the present study the species-overlap threshold was set to 0.0 - that is, no common species between the two partitions were allowed - because this produced the best results in the benchmark. The algorithm does so for all internal nodes in the tree. Once all the nodes in the tree are marked as a duplication or speciation event, the algorithm establishes orthology relationships between the seed protein and other proteins in the tree. For each protein, the algorithm tracks the nodes that connect it to the seed protein and establishes an orthology relationship only if this connection proceeds exclusively through speciation nodes, disregarding intra-specific duplications. After mapping speciation and duplication nodes onto the phylogeny, several situations may arise in which orthology relationships are not one-to-one relationships, but rather one-to-many or many-to-many.To root the trees the following procedure was used. The species present in the tree were grouped according to the branching pattern of the tree in Figure The duplication events detected by the algorithm described above can be assigned to different evolutionary periods by examining the species represented after the duplication event. To do so we used as a reference a set of clearly defined phylogenetic relationships that mark the major branching points in the lineage leading to hominids Figure . For eacThe orthology detection algorithm can detect only duplications that occurred after the root of the tree; for example, if a tree is rooted in a fungal sequence, only duplications that occurred in the metazoan lineage could be detected. Therefore, to compare the results obtained at the different evolutionary stages we computed the relative number of duplication events per gene at each branching point. This was done by dividing the number of duplication events mapped at a particular evolutionary stage by the number of trees rooted at a deeper branching point; for example, duplications that occurred at the base of metazoans were divided by the number of trees rooted on either a fungal or a non-opisthokont sequence.Homo sapiens, Macacca mulata, Pan troglodites) and any sequence (s) from rodents  within a larger partition that contains these sequence plus any sequence (s) from Laurasatherians . Sequences from other species are not allowed in the partition and the presence of the seed sequence in the partition is required. This algorithm has been implemented in a series of Python scripts developed for this project.The algorithm used here to search for specific topologies within the phylome is described elsewhere . In brieIn the topology scanning analyses presented here we discarded the trees based on alignments in which less than 100 columns were left after applying the gap filter. This procedure eliminated 1,714 (7.9%) from the total phylome.The reference set used in a recent benchmark of orthology assignment methods  is used The following additional data are available with the on-line version of this paper. Additional data file Over-represented GO terms in the duplicatons depicted in Figure 3Click here for fileOrthologs predicted for every human proteinClick here for file