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How do instantaneous rate models of insertion-deletion processes relate to distributions over pairwise sequence alignments? The only exactly-solved model is the 1991 Thorne....., We introduce a systematic method of approximating finite-time transition probabilities for continuous-time insertion-deletion models on sequences. The method uses automata theory to describe the action of an infinitesimal evolutionary generator on a probability distribution over alignments, where both the generator and the alignment distribution can be represented by pair hidden Markov models (HMMs). In general, combining HMMs in this way induces a multiplication of their state spaces; to control this, we introduce a coarse-graining operation to keep the state space at a constant size. This leads naturally to ordinary differential equations for the evolution of the transition probabilities of the approximating pair HMM. The TKF91 model emerges as an exact solution to these equations for the special case of single-residue indels. For the more general case of multiple-residue indels, the equations can be solved by numerical integration. Using simulated data, we show that the resulting distribution over alignments, when compared to previous approximations, is a better fit over a broader range of parameters. We also propose a related approach to develop differential equations for sufficient statistics to estimate the underlying instantaneous indel rates by expectation maximization. Our code and data are available at https://github.com/ihh/trajectory-likelihood.

Background Detecting copy number variations (CNVs) and copy number alterations (CNAs) based on whole-genome sequencing data is important for personalized genomics and treatment. CNVnator is one of the most popular tools for CNV/CNA discovery and analysis based on read depth. Findings Herein, we present an extension of CNVnator developed in Python—CNVpytor. CNVpytor inherits the reimplemented core engine of its predecessor and extends visualization, modularization, performance, and functionality. Additionally, CNVpytor uses B-allele frequency likelihood information from single-nucleotide polymorphisms and small indels data as additional evidence for CNVs/CNAs and as primary information for copy number–neutral losses of heterozygosity. Conclusions CNVpytor is significantly faster than CNVnator—particularly for parsing alignment files (2–20 times faster)—and has (20–50 times) smaller intermediate files. CNV calls can be filtered using several criteria, annotated, and merged over multiple samples. Modular architecture allows it to be used in shared and cloud environments such as Google Colab and Jupyter notebook. Data can be exported into JBrowse, while a lightweight plugin version of CNVpytor for JBrowse enables nearly instant and GUI-assisted analysis of CNVs by any user. CNVpytor release and the source code are available on GitHub at https://github.com/abyzovlab/CNVpytor under the MIT license.

Understanding population connectivity within a species as well as potential interactions with its close relatives is crucial to define management units and to derive efficient management actions. However, although genetics can reveal mismatches between biological and management units and other relevant but hidden information such as species misidentification or hybridization, the uptake of genetic methods by the fisheries management process is far from having been consolidated. Here, we have assessed the power of genetics to better understand the population connectivity of white (Lophius piscatorius) and its interaction with its sister species, the black anglerfish (Lophius budegassa). Our analyses, based on thousands of genome-wide single nucleotide polymorphisms, show three findings that are crucial for white anglerfish management. We found (i) that white anglerfish is likely composed of a single panmictic population throughout the Northeast Atlantic, challenging the three-stock based management, (ii) that a fraction of specimens classified as white anglerfish using morphological characteristics are genetically identified as black anglerfish (L. budegassa), and iii) that the two Lophius species naturally hybridize leading to a population of hybrids of up to 20% in certain areas. Our results set the basics for a genetics-informed white anglerfish assessment framework that accounts for stock connectivity, revises and establishes new diagnostic characters for Lophius species identification, and evaluates the effect of hybrids in the current and future assessments of the white anglerfish. Furthermore, our study contributes to provide additional evidence of the potentially negative consequences of ignoring genetic data for assessing fisheries resources., SI

Nanopore technology allows for direct sequencing of individual DNA duplexes. However, its higher error rate compared to other sequencing methods has limited its application in situations where deep coverage is unavailable, such as detection of rare variants or characterization of highly polymorphic samples. In principle, 2X coverage is available even for single duplexes, using Oxford Nanopore Technologies’ 1D2protocol or related methods which sequence both strands of the duplex consecutively. Using both strands should improve accuracy; however, most neural network basecaller architectures are designed to operate on single strands. We have developed a general approach for improving accuracy of 1D2and related protocols by finding the consensus of two neural network basecallers, by combining a constrained profile-profile alignment with a heuristic variant of beam search. When run on a basecalling neural network we trained, our consensus algorithm improves median basecall accuracy from 86.2% (for single-read decoding) to 92.1% (for pair decoding). Our software can readily be adapted to work with the output of other basecallers, such as the recently released Bonito basecaller. Although Bonito operates only on individual strands and was not designed to leverage the 1D2protocol, our method lifts its median accuracy from 93.3% to 97.7%, more than halving the median error rate. This surpasses the maximum accuracy achievable with Guppy, an alternate basecaller which was designed to include pair decoding of 1D2reads. Our software PoreOver, including both our neural network basecaller and our consensus pair decoder (which can be separably applied to improve other basecallers), is implemented in Python 3 and C++11 and is freely available athttps://github.com/jordisr/poreover.

Genome annotation is the process of identifying the location and function of a genome’s encoded features. Improving the biological accuracy of annotation is a complex and iterative process requiring researchers to review and incorporate multiple sources of information such as transcriptome alignments, predictive models based on sequence profiles, and comparisons to features found in related organisms. Because rapidly decreasing costs are enabling an ever-growing number of scientists to incorporate sequencing as a routine laboratory technique, there is widespread demand for tools that can assist in the deliberative analytical review of genomic information. To this end, Apollo is an open source software package that enables researchers to efficiently inspect and refine the precise structure and role of genomic features in a graphical browser-based platform.In this paper we first outline some of Apollo’s newer user interface features, which were driven by the needs of this expanding genomics community. These include support for real-time collaboration, allowing distributed users to simultaneously edit the same encoded features while also instantly seeing the updates made by other researchers on the same region in a manner similar to Google Docs. Its technical architecture enables Apollo to be integrated into multiple existing genomic analysis pipelines and heterogeneous laboratory workflow platforms. Finally, we consider the implications that Apollo and related applications may have on how the results of genome research are published and made accessible. Source: https://github.com/GMOD/ApolloLicense (BSD-3): https://github.com/GMOD/Apollo/blob/master/LICENSE.mdDocker: https://hub.docker.com/r/gmod/apollo/tags/, https://github.com/GMOD/docker-apolloRequirements: JDK 1.8, Node v6.0+User guide: http://genomearchitect.org; technical guide: http://genomearchitect.readthedocs.io/en/latest/Mailing list: apollo@lists.lbl.gov

Neutral models which assume ecological equivalence between species provide null models for community assembly. In Hubbell's Unified Neutral Theory of Biodiversity (UNTB), many local communities are connected to a single metacommunity through differing immigration rates. Our ability to fit the full multi-site UNTB has hitherto been limited by the lack of a computationally tractable and accurate algorithm. We show that a large class of neutral models with this mainland-island structure but differing local community dynamics converge in the large population limit to the hierarchical Dirichlet process. Using this approximation we developed an efficient Bayesian fitting strategy for the multi-site UNTB. We can also use this approach to distinguish between neutral local community assembly given a non-neutral metacommunity distribution and the full UNTB where the metacommunity too assembles neutrally. We applied this fitting strategy to both tropical trees and a data set comprising 570,851 sequences from 278 human gut microbiomes. The tropical tree data set was consistent with the UNTB but for the human gut neutrality was rejected at the whole community level. However, when we applied the algorithm to gut microbial species within the same taxon at different levels of taxonomic resolution, we found that species abundances within some genera were almost consistent with local community assembly. This was not true at higher taxonomic ranks. This suggests that the gut microbiota is more strongly niche constrained than macroscopic organisms, with different groups adopting different functional roles, but within those groups diversity may at least partially be maintained by neutrality.We also observed a negative correlation between body mass index and immigration rates within the family Ruminococcaceae., Comment: 50 pages, 5 figures, 2 tables and submitted to Proceedings of the IEEE

It has been discovered that there appears to exist a close relationship between relative differences in total solar irradiance and the atmospheric temperature, at a pressure of 1 bar, on all three terrestrial-type bodies which possess thick atmospheres. The apparent relationship is through the quaternary root of total solar irradiance at 1 bar, and applies to the planetary bodies Venus, Earth and Titan. The relationship is so close that the average surface atmospheric temperature of Earth can be easily calculated to within 1 Kelvin (0.5%) of the correct figure by the knowledge of only two numbers, neither of which are related to the Earth’s atmosphere. These are; the atmospheric temperature in the Venusian atmosphere at 1 bar, and the top-of-atmosphere solar insolation of the two planets. A similar relationship in atmospheric temperatures is found to exist, through insolation differences alone, between the atmospheric temperatures at 1 bar of the planetary bodies Titan and Earth, and Venus and Titan. This relationship exists despite the widely varying atmospheric greenhouse gas content, and the widely varying albedos of the three planetary bodies. This result is consistent with previous research with regards to atmospheric temperatures and their relationship to the molar mass version of the ideal gas law, in that this work also points to a climate sensitivity to CO2 - or to any other ‘greenhouse’ gas - which is close to or at zero. It is more confirmation that the main determinants of atmospheric temperatures in the regions of terrestrial planetary atmospheres which are >0.1 bar, is overwhelmingly the result of two factors; solar insolation and atmospheric pressure. There appears to be no measurable, or what may be better termed ‘anomalous’ warming input from a class of gases which have up until the present, been incorrectly labelled as ‘greenhouse’ gases.

Motivation Reconstruction of ancestral sequence histories, and estimation of parameters like indel rates, are improved by using explicit evolutionary models and summing over uncertain alignments. The previous best tool for this purpose (according to simulation benchmarks) was ProtPal, but this tool was too slow for practical use. Results Historian combines an efficient reimplementation of the ProtPal algorithm with performance-improving heuristics from other alignment tools. Simulation results on fidelity of rate estimation via ancestral reconstruction, along with evaluations on the structurally informed alignment dataset BAliBase 3.0, recommend Historian over other alignment tools for evolutionary applications. Availability and Implementation Historian is available at https://github.com/evoldoers/historian under the Creative Commons Attribution 3.0 US license.

We describe an open source, portable, JavaScript-based genome browser, JBrowse, that can be used to navigate genome annotations over the web. JBrowse helps preserve the user's sense of location by avoiding discontinuous transitions, instead offering smoothly animated panning, zooming, navigation, and track selection. Unlike most existing genome browsers, where the genome is rendered into images on the webserver and the role of the client is restricted to displaying those images, JBrowse distributes work between the server and client and therefore uses significantly less server overhead than previous genome browsers. We report benchmark results empirically comparing server- and client-side rendering strategies, review the architecture and design considerations of JBrowse, and describe a simple wiki plug-in that allows users to upload and share annotation tracks.

Recently attention has been turned to the problem of reconstructing complete ancestral sequences from large multiple alignments. Successful generation of these genome-wide reconstructions will facilitate a greater knowledge of the events that have driven evolution. We present a new evolutionary alignment modeler, called “Ortheus,” for inferring the evolutionary history of a multiple alignment, in terms of both substitutions and, importantly, insertions and deletions. Based on a multiple sequence probabilistic transducer model of the type proposed by Holmes, Ortheus uses efficient stochastic graph-based dynamic programming methods. Unlike other methods, Ortheus does not rely on a single fixed alignment from which to work. Ortheus is also more scaleable than previous methods while being fast, stable, and open source. Large-scale simulations show that Ortheus performs close to optimally on a deep mammalian phylogeny. Simulations also indicate that significant proportions of errors due to insertions and deletions can be avoided by not assuming a fixed alignment. We additionally use a challenging hold-out cross-validation procedure to test the method; using the reconstructions to predict extant sequence bases, we demonstrate significant improvements over using closest extant neighbor sequences. Accompanying this paper, a new, public, and genome-wide set of Ortheus ancestor alignments provide an intriguing new resource for evolutionary studies in mammals. As a first piece of analysis, we attempt to recover “fossilized” ancestral pseudogenes. We confidently find 31 cases in which the ancestral sequence had a more complete sequence than any of the extant sequences.

Phylo-grammars, probabilistic models combining Markov chain substitution models with stochastic grammars, are powerful models for annotating structured features in multiple sequence alignments and analyzing the evolution of those features. In the past, these methods have been cumbersome to implement and modify. xrate provides means for the rapid development of phylo-grammars (using a simple file format) and automated parameterization of those grammars from training data (via the Expectation Maximization algorithm). xREI (pron. ‘X-ray’) is an intuitive, flexible AJAX (Asynchronous Javascript And XML) web interface to xrate providing grammar visualization tools as well as access to xrate’s training and annotation functionality. It is hoped that this application will serve as a valuable tool to those developing phylo-grammars, and as a means for the exploration and dissemination of such models. xREI is available at http:// harmony.biowiki.org/xrei/

In the past, 2 kinds of Markov models have been considered to describe protein sequence evolution. Codon-level models have been mechanistic with a small number of parameters designed to take into account features, such as transition-transversion bias, codon frequency bias, and synonymous-nonsynonymous amino acid substitution bias. Amino acid models have been empirical, attempting to summarize the replacement patterns observed in large quantities of data and not explicitly considering the distinct factors that shape protein evolution. We have estimated the first empirical codon model (ECM). Previous codon models assume that protein evolution proceeds only by successive single nucleotide substitutions, but our results indicate that model accuracy is significantly improved by incorporating instantaneous doublet and triplet changes. We also find that the affiliations between codons, the amino acid each encodes and the physicochemical properties of the amino acids are main factors driving the process of codon evolution. Neither multiple nucleotide changes nor the strong influence of the genetic code nor amino acids' physicochemical properties form a part of standard mechanistic models and their views of how codon evolution proceeds. We have implemented the ECM for likelihood-based phylogenetic analysis, and an assessment of its ability to describe protein evolution shows that it consistently outperforms comparable mechanistic codon models. We point out the biological interpretation of our ECM and possible consequences for studies of selection.

© 2015 Macmillan Publishers Limited Adeno-associated virus (AAV) vectors have achieved clinical efficacy in treating several diseases. However, enhanced vectors are required to extend these landmark successes to other indications and protein engineering approaches may provide the necessary vector improvements to address such unmet medical needs. To generate new capsid variants with potentially enhanced infectious properties and to gain insights into AAV’s evolutionary history, we computationally designed and experimentally constructed a putative ancestral AAV library. Combinatorial variations at 32 amino acid sites were introduced to account for uncertainty in their identities. We then analyzed the evolutionary flexibility of these residues, the majority of which have not been previously studied, by subjecting the library to iterative selection on a representative cell line panel. The resulting variants exhibited transduction efficiencies comparable to the most efficient extant serotypes and, in general, ancestral libraries were broadly infectious across the cell line panel, indicating that they favored promiscuity over specificity. Interestingly, putative ancestral AAVs were more thermostable than modern serotypes and did not use sialic acids, galactose or heparan sulfate proteoglycans for cellular entry. Finally, variants mediated 19- to 31-fold higher gene expression in the muscle compared with AAV1, a clinically used serotype for muscle delivery, highlighting their promise for gene therapy.Gene Therapy advance online publication, 13 August 2015; doi:10.1038/gt.2015.74.

Adeno-associated virus (AAV) vectors have demonstrated clinical efficacy in treating several diseases. Improved vectors may extend these landmark successes to other indications, and protein engineering approaches are enhancing the gene delivery properties of AAV vectors to address such unmet medical needs. To access new viral sequences with potentially enhanced infectious properties, and to gain insights into AAV's evolutionary history, we computationally designed and experimentally characterized an ancestral reconstruction of the AAV capsid. To this end, we first generated a phylogenetic tree of AAV sequences through Bayesian Markov chain Monte Carlo simulations, then selected a node for reconstruction based on its relative confidence value and proximity to AAV serotypes of clinical interest. We next used HandAlign, a Markov chain Monte Carlo alignment sampler, to predict the ancestral sequence of the most likely alignment at the selected node. This generated a reconstruction where most amino acids were predicted with high confidence, while 32 of the positions were predicted with low confidence. In order to address uncertainty in these positions, we synthesized the inferred ancestral capsid as a large combinatorial library incorporating degenerate residues at the 32 variable amino acid sites. We then characterized the evolutionary flexibility of these residues, the majority of which have not been previously studied, by subjecting the library to six rounds of selection on a panel of representative cell lines. The resulting variants exhibited transduction efficiencies comparable to the most efficient extant serotypes, and in general the selected ancestral libraries were broadly infectious across the cell line panel, indicating that they favor promiscuity over specificity. Unlike many current serotypes, these ancestral AAVs do not utilize sialic acids, galactose, or heparan sulfate proteoglycans for cellular entry. Additionally, ancestral libraries were found to be as susceptible to neutralizing antibodies as extant serotypes, suggesting that such ancestral variant candidates exhibit immunogenic properties similar to modern descendants. Interestingly, ancestral AAVs retained infectivity at temperatures that completely ablated infectivity of modern serotypes; this higher thermostability may have supported increased evolvability through mutational tolerance. Finally, selected variants mediated 19-31 fold higher gene expression in muscle compared to AAV1, a serotype clinically utilized for muscle delivery, highlighting their potential promise for gene therapy.

Modeling sequence evolution on phylogenetic trees is a useful technique in computational biology. Especially powerful are models which take account of the heterogeneous nature of sequence evolution according to the "grammar" of the encoded gene features. However, beyond a modest level of model complexity, manual coding of models becomes prohibitively labor-intensive. We demonstrate, via a set of case studies, the new built-in model-prototyping capabilities of XRate (macros and Scheme extensions). These features allow rapid implementation of phylogenetic models which would have previously been far more labor-intensive. XRate's new capabilities for lineage-specific models, ancestral sequence reconstruction, and improved annotation output are also discussed. XRate's flexible model-specification capabilities and computational efficiency make it well-suited to developing and prototyping phylogenetic grammar models. XRate is available as part of the DART software package: http://biowiki.org/DART ., 34 pages, 3 figures, glossary of XRate model terminology

We introduce Dirichlet multinomial mixtures (DMM) for the probabilistic modelling of microbial metagenomics data. This data can be represented as a frequency matrix giving the number of times each taxa is observed in each sample. The samples have different size, and the matrix is sparse, as communities are diverse and skewed to rare taxa. Most methods used previously to classify or cluster samples have ignored these features. We describe each community by a vector of taxa probabilities. These vectors are generated from one of a finite number of Dirichlet mixture components each with different hyperparameters. Observed samples are generated through multinomial sampling. The mixture components cluster communities into distinct 'metacommunities', and, hence, determine envirotypes or enterotypes, groups of communities with a similar composition. The model can also deduce the impact of a treatment and be used for classification. We wrote software for the fitting of DMM models using the 'evidence framework' (http://code.google.com/p/microbedmm/). This includes the Laplace approximation of the model evidence. We applied the DMM model to human gut microbe genera frequencies from Obese and Lean twins. From the model evidence four clusters fit this data best. Two clusters were dominated by Bacteroides and were homogenous; two had a more variable community composition. We could not find a significant impact of body mass on community structure. However, Obese twins were more likely to derive from the high variance clusters. We propose that obesity is not associated with a distinct microbiota but increases the chance that an individual derives from a disturbed enterotype. This is an example of the 'Anna Karenina principle (AKP)' applied to microbial communities: disturbed states having many more configurations than undisturbed. We verify this by showing that in a study of inflammatory bowel disease (IBD) phenotypes, ileal Crohn's disease (ICD) is associated with a more variable community.

Summary: We describe handalign, a software package for Bayesian reconstruction of phylogenetic history. The underlying model of sequence evolution describes indels and substitutions. Alignments, trees and model parameters are all treated as jointly dependent random variables and sampled via Metropolis–Hastings Markov chain Monte Carlo (MCMC), enabling systematic statistical parameter inference and hypothesis testing. handalign implements several different MCMC proposal kernels, allows sampling from arbitrary target distributions via Hastings ratios, and uses standard file formats for trees, alignments and models. Availability and Implementation: Installation and usage instructions are at http://biowiki.org/HandAlign Contact: ihh@berkeley.edu Supplementary information:Supplementary material is available at Bioinformatics online.

The Multiple Sequence Alignment (MSA) is a computational abstraction that represents a partial summary either of indel history, or of structural similarity. Taking the former view (indel history), it is possible to use formal automata theory to generalize the phylogenetic likelihood framework for finite substitution models (Dayhoff's probability matrices and Felsenstein's pruning algorithm) to arbitrary-length sequences. In this paper, we report results of a simulation-based benchmark of several methods for reconstruction of indel history. The methods tested include a relatively new algorithm for statistical marginalization of MSAs that sums over a stochastically-sampled ensemble of the most probable evolutionary histories. For mammalian evolutionary parameters on several different trees, the single most likely history sampled by our algorithm appears less biased than histories reconstructed by other MSA methods. The algorithm can also be used for alignment-free inference, where the MSA is explicitly summed out of the analysis. As an illustration of our method, we discuss reconstruction of the evolutionary histories of human protein-coding genes., 28 pages, 15 figures. arXiv admin note: text overlap with arXiv:1103.4347

International audience; During the last decade there has been a great increase in the number of noncoding RNA genes identified, including new classes such as microRNAs and piRNAs. There is also a large growth in the amount of experimental characterization of these RNA components. Despite this growth in information, it is still difficult for researchers to access RNA data, because key data resources for noncoding RNAs have not yet been created. The most pressing omission is the lack of a comprehensive RNA sequence database, much like UniProt, which provides a comprehensive set of protein knowledge. In this article we propose the creation of a new open public resource that we term RNAcentral, which will contain a comprehensive collection of RNA sequences and fill an important gap in the provision of biomedical databases. We envision RNA researchers from all over the world joining a federated RNAcentral network, contributing specialized knowledge and databases. RNAcentral would centralize key data that are currently held across a variety of databases, allowing researchers instant access to a single, unified resource. This resource would facilitate the next generation of RNA research and help drive further discoveries, including those that improve food production and human and animal health. We encourage additional RNA database resources and research groups to join this effort. We aim to obtain international network funding to further this endeavor.

XRATE implements algorithms for comparative annotation, ancestral reconstruction, evolutionary rate estimation, and simulation. Its modeling repertoire includes phylogenetic stochastic context-free grammars and phylo-hidden Markov models. Following earlier tests of XRATE as a machine-learning tool suitable for alignment annotation, we now report the first tests of XRATE as a precise quantitative instrument for estimating evolutionary rates. We implement a codon model similar to that of Goldman and Yang (1994) (A codon-based model of nucleotide substitution for protein-coding DNA sequences. Mol Biol Evol 11: 725-736) and show that XRATE's parameter estimates are consistent with those of PAML. To demonstrate its utility, we apply the model to measure the difference in selective strength (omega) between intracellular and secreted regions of type I transmembrane proteins. In 215 of 303 instances, a complex model with individual omega for each region provides a better fit to the data than the simpler single omega value model. Secreted portions of type I transmembrane proteins show an elevation in omega similar to that seen for secreted protein genes. Less stringent purifying selection is thus a general property of the extracellular milieu, rather than being specific to only soluble and secreted proteins.

Motivation: Whole-genome screens suggest that eukaryotic genomes are dense with non-coding RNAs (ncRNAs). We introduce a novel approach to RNA multiple alignment which couples a generative probabilistic model of sequence and structure with an efficient sequence annealing approach for exploring the space of multiple alignments. This leads to a new software program, Stemloc-AMA, that is both accurate and specific in the alignment of multiple related RNA sequences. Results: When tested on the benchmark datasets BRalibase II and BRalibase 2.1, Stemloc-AMA has comparable sensitivity to and better specificity than the best competing methods. We use a large-scale random sequence experiment to show that while most alignment programs maximize sensitivity at the expense of specificity, even to the point of giving complete alignments of non-homologous sequences, Stemloc-AMA aligns only sequences with detectable homology and leaves unrelated sequences largely unaligned. Such accurate and specific alignments are crucial for comparative-genomics analysis, from inferring phylogeny to estimating substitution rates across different lineages. Availability: Stemloc-AMA is available from http://biowiki.org/StemLocAMA as part of the dart software package for sequence analysis. Contact: lpachter@math.berkeley.edu; ihh@berkeley.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Affiliations des auteurs : cf page 216 de l'article; International audience; Comparative analysis of multiple genomes in a phylogenetic framework dramatically improves the precision and sensitivity of evolutionary inference, producing more robust results than single-genome analyses can provide. The genomes of 12 Drosophila species, ten of which are presented here for the first time (sechellia, simulans, yakuba, erecta, ananassae, persimilis, willistoni, mojavensis, virilis and grimshawi), illustrate how rates and patterns of sequence divergence across taxa can illuminate evolutionary processes on a genomic scale. These genome sequences augment the formidable genetic tools that have made Drosophila melanogaster a pre-eminent model for animal genetics, and will further catalyse fundamental research on mechanisms of development, cell biology, genetics, disease, neurobiology, behaviour, physiology and evolution. Despite remarkable similarities among these Drosophila species, we identified many putatively non-neutral changes in protein-coding genes, non-coding RNA genes, and cis-regulatory regions. These may prove to underlie differences in the ecology and behaviour of these diverse species.

We report the generation and analysis of functional data from multiple, diverse experiments performed on a targeted 1% of the human genome as part of the pilot phase of the ENCODE Project. These data have been further integrated and augmented by a number of evolutionary and computational analyses. Together, our results advance the collective knowledge about human genome function in several major areas. First, our studies provide convincing evidence that the genome is pervasively transcribed, such that the majority of its bases can be found in primary transcripts, including non-protein-coding transcripts, and those that extensively overlap one another. Second, systematic examination of transcriptional regulation has yielded new understanding about transcription start sites, including their relationship to specific regulatory sequences and features of chromatin accessibility and histone modification. Third, a more sophisticated view about chromatin structure has emerged, including its interrelationship with DNA replication and transcriptional regulation. Finally, integration of these new sources of information, in particular with respect to mammalian evolution based on inter- and intra-species sequence comparisons, has yielded novel mechanistic and evolutionary insights about the functional landscape of the human genome. Together, these studies are defining a path forward to pursue a more-comprehensive characterisation of human genome function.

A key component of the ongoing ENCODE project involves rigorous comparative sequence analyses for the initially targeted 1% of the human genome. Here, we present orthologous sequence generation, alignment, and evolutionary constraint analyses of 23 mammalian species for all ENCODE targets. Alignments were generated using four different methods; comparisons of these methods reveal large-scale consistency but substantial differences in terms of small genomic rearrangements, sensitivity (sequence coverage), and specificity (alignment accuracy). We describe the quantitative and qualitative trade-offs concomitant with alignment method choice and the levels of technical error that need to be accounted for in applications that require multisequence alignments. Using the generated alignments, we identified constrained regions using three different methods. While the different constraint-detecting methods are in general agreement, there are important discrepancies relating to both the underlying alignments and the specific algorithms. However, by integrating the results across the alignments and constraint-detecting methods, we produced constraint annotations that were found to be robust based on multiple independent measures. Analyses of these annotations illustrate that most classes of experimentally annotated functional elements are enriched for constrained sequences; however, large portions of each class (with the exception of protein-coding sequences) do not overlap constrained regions. The latter elements might not be under primary sequence constraint, might not be constrained across all mammals, or might have expendable molecular functions. Conversely, 40% of the constrained sequences do not overlap any of the functional elements that have been experimentally identified. Together, these findings demonstrate and quantify how many genomic functional elements await basic molecular characterization.

We present a new probabilistic model of sequence evolution, allowing indels of arbitrary length, and give sequence alignment algorithms for our model. Previously implemented evolutionary models have allowed (at most) single-residue indels or have introduced artifacts such as the existence of indivisible "fragments." We compare our algorithm to these previous methods by applying it to the structural homology dataset HOMSTRAD, evaluating the accuracy of (1) alignments and (2) evolutionary time estimates. With our method, it is possible (for the first time) to integrate probabilistic sequence alignment, with reliability indicators and arbitrary gap penalties, in the same framework as phylogenetic reconstruction. Our alignment algorithm requires that we evaluate the likelihood of any specific path of mutation events in a continuous-time Markov model, with the event times integrated out. To this effect, we introduce a "trajectory likelihood" algorithm (Appendix A). We anticipate that this algorithm will be useful in more general contexts, such as Markov Chain Monte Carlo simulations.

The hydrolytic haloalkane dehalogenases are promising bioremediation and biocatalytic agents. Two general classes of dehalogenases have been reported from Xanthobacter and Rhodococcus. While these enzymes share 30% amino acid sequence identity, they have significantly different substrate specificities and halide-binding properties. We report the 1.5 A resolution crystal structure of the Rhodococcus dehalogenase at pH 5.5, pH 7.0, and pH 5.5 in the presence of NaI. The Rhodococcus and Xanthobacter enzymes have significant structural homology in the alpha/beta hydrolase core, but differ considerably in the cap domain. Consistent with its broad specificity for primary, secondary, and cyclic haloalkanes, the Rhodococcus enzyme has a substantially larger active site cavity. Significantly, the Rhodococcus dehalogenase has a different catalytic triad topology than the Xanthobacter enzyme. In the Xanthobacter dehalogenase, the third carboxylate functionality in the triad is provided by D260, which is positioned on the loop between beta7 and the penultimate helix. The carboxylate functionality in the Rhodococcus catalytic triad is donated from E141. A model of the enzyme cocrystallized with sodium iodide shows two iodide binding sites; one that defines the normal substrate and product-binding site and a second within the active site region. In the substrate and product complexes, the halogen binds to the Xanthobacter enzyme via hydrogen bonds with the N(eta)H of both W125 and W175. The Rhodococcusenzyme does not have a tryptophan analogous to W175. Instead, bound halide is stabilized with hydrogen bonds to the N(eta)H of W118 and to N(delta)H of N52. It appears that when cocrystallized with NaI the Rhodococcus enzyme has a rare stable S-I covalent bond to S(gamma) of C187.

Web Apollo is the first instantaneous, collaborative genomic annotation editor available on the web. One of the natural consequences following from current advances in sequencing technology is that there are more and more researchers sequencing new genomes. These researchers require tools to describe the functional features of their newly sequenced genomes. With Web Apollo researchers can use any of the common browsers (for example, Chrome or Firefox) to jointly analyze and precisely describe the features of a genome in real time, whether they are in the same room or working from opposite sides of the world.

Abstract 2129 The developmental switch in human β-like globin gene subtype from fetal (γ) to adult (β) that begins at birth foreshadows the onset of the hemoglobinopathies, β-thalassemia and sickle cell disease (SCD). In the clinical setting it is established that β-thalassemia and SCD patients with hereditary persistence of fetal hemoglobin mutations enjoy a significant amelioration of disease severity due to the continued expression of γ-globin. This has prompted the search for therapeutic strategies to reverse γ-globin gene silencing. Central to the mechanism of γ-gene silencing is DNA methylation, which marks critical CpG dinucleotides flanking the γ-gene transcriptional initiation site in adult bone marrow erythroid cells. These marks are established by recruitment of DNMT3A, a DNA methyltransferase, to the γ-globin promoter by protein arginine methyltransferase 5 (PRMT5)[Zhao Q et al. Nat Struct Mol Biol. 2009;16(3):304–311]. PRMT5 catalyses the symmetric dimethylation of arginine 3 of Histone 4 (H4R3me2), which serves as a template for direct binding of DNMT3A and the subsequent DNA methylation of the γ-gene promoter. Loss of PRMT5 or its enzymatic activity is sufficient to induce demethylation of the CpG dinucleotides and reactivation of γ-globin gene expression [Rank, G., et al. Blood, 116(9), 1585–92]. Based on these observations we hypothesize that small molecule inhibitors of PRMT5 activity could provide a beneficial treatment for β-thalassemia and SCD. To identify small molecule inhibitors of PRMT5 a high throughput screen (HTS) was performed. Both radiometric and non-radiometric assay formats were developed to support the screening campaign. The radiometric assay format measures the ability of PRMT5 purified from K562 cells to catalyse the labelling of a short peptide based on the N-terminal sequence of Histone H4 by 3H-Methyl-S-Adenosyl-L-methionine (SAM). In contrast, the non-radiometric assay format employs recombinant PRMT5/MEP50 and measures the production of S-adenosyl-L-homocysteine (SAH), which is generated by PRMT5-catalysed methylation of H4 peptide. SAH is measured with Transcreener EPIGEN” and the assay is formatted in 1536-well microtitre plates in a total assay volume of 4 μL. Using these assays, a chemical library of 350,000 lead-like molecules and known pharmacologically active agents was screened to identify inhibitors of PRMT5 methyltransferase activity. A number of compounds with low micromolar or submicromolar inhibitory activity were identified by the HTS campaign, and six were selected for re-synthesis. The inhibitory activity of five of the six compounds was confirmed. To provide an initial appraisal of inhibitor selectivity the five active compounds were subsequently tested against a panel of enzymes consisting of 23 protein and DNA methyltransferases and 12 kinases. These compounds were found to be remarkably selective PRMT5 inhibitors, inhibition of MLL4 being the only significant off-target activity noted for one of the scaffolds. We have established a critical path for selection and progression of new chemical analogues which entails testing the compounds for: i) inhibition of PRMT5, other protein methyl transferases and kinases; ii) the ability to induce expression of γ-globin mRNA in the K562 erythroleukemic cell line; iii) the ability to induce expression of γ-globin mRNA in adult bone marrow erythroid cells; and iv) the induction of γ-globin in vivo in β-YAC mice, a transgenic model which carries the 250-kb human globin locus. In parallel, the physicochemical, metabolism, and pharmacokinetic properties of the most promising compounds are also determined. Medicinal chemistry efforts have now produced molecules with > 100-fold increased inhibitory potency against PRMT5 compared to the original hits, and preliminary results indicate that the more potent compounds have the ability to induce γ-globin mRNA in our cell based models. These early results illustrate the potential of PRMT5 inhibitors as a novel approach for the treatment of β-thalassemia and sickle cell disease. Disclosures: No relevant conflicts of interest to declare.

We performed benchmarks of phylogenetic grammar-based ncRNA gene prediction, experimenting with eight different models of structural evolution and two different programs for genome alignment. We evaluated our models using alignments of twelve Drosophila genomes. We find that ncRNA prediction performance can vary greatly between different gene predictors and subfamilies of ncRNA gene. Our estimates for false positive rates are based on simulations which preserve local islands of conservation; using these simulations, we predict a higher rate of false positives than previous computational ncRNA screens have reported. Using one of the tested prediction grammars, we provide an updated set of ncRNA predictions for D. melanogaster and compare them to previously-published predictions and experimental data. Many of our predictions show correlations with protein-coding genes. We found significant depletion of intergenic predictions near the 3' end of coding regions and furthermore depletion of predictions in the first intron of protein-coding genes. Some of our predictions are colocated with larger putative unannotated genes: for example, 17 of our predictions showing homology to the RFAM family snoR28 appear in a tandem array on the X chromosome; the 4.5 Kbp spanned by the predicted tandem array is contained within a FlyBase-annotated cDNA.

We propose a novel method for detecting sites of molecular recombination in multiple alignments. Our approach is a compromise between previous extremes of computationally prohibitive but mathematically rigorous methods and imprecise heuristic methods. Using a combined algorithm for estimating tree structure and hidden Markov model parameters, our program detects changes in phylogenetic tree topology over a multiple sequence alignment. We evaluate our method on benchmark datasets from previous studies on two recombinant pathogens, Neisseria and HIV-1, as well as simulated data. We show that we are not only able to detect recombinant regions of vastly different sizes but also the location of breakpoints with great accuracy. We show that our method does well inferring recombination breakpoints while at the same time maintaining practicality for larger datasets. In all cases, we confirm the breakpoint predictions of previous studies, and in many cases we offer novel predictions., Author Summary In viral and bacterial pathogens, recombination has the ability to combine fitness-enhancing mutations. Accurate characterization of recombinant breakpoints in newly sequenced strains can provide information about the role of this process in evolution, for example, in immune evasion. Of particular interest are situations of an admixture of pathogen subspecies, recombination between whose genomes may change the apparent phylogenetic tree topology in different regions of a multiple-genome alignment. We describe an algorithm that can pinpoint recombination breakpoints to greater accuracy than previous methods, allowing detection of both short recombinant regions and long-range multiple crossovers. The algorithm is appropriate for the analysis of fast-evolving pathogen sequences where repeated substitutions may be observed at a single site in a multiple alignment (violating the “infinite sites” assumption inherent to some other breakpoint-detection algorithms). Simulations demonstrate the practicality of our implementation for alignments of longer sequences and more taxa than previous methods.

For the purposes of finding and aligning noncoding RNA gene- and cis-regulatory elements in multiple-genome datasets, it is useful to be able to derive multi-sequence stochastic grammars (and hence multiple alignment algorithms) systematically, starting from hypotheses about the various kinds of random mutation event and their rates. Here, we consider a highly simplified evolutionary model for RNA, called "The TKF91 Structure Tree" (following Thorne, Kishino and Felsenstein's 1991 model of sequence evolution with indels), which we have implemented for pairwise alignment as proof of principle for such an approach. The model, its strengths and its weaknesses are discussed with reference to four examples of functional ncRNA sequences: a riboswitch (guanine), a zipcode (nanos), a splicing factor (U4) and a ribozyme (RNase P). As shown by our visualisations of posterior probability matrices, the selected examples illustrate three different signatures of natural selection that are highly characteristic of ncRNA: (i) co-ordinated basepair substitutions, (ii) co-ordinated basepair indels and (iii) whole-stem indels. Although all three types of mutation "event" are built into our model, events of type (i) and (ii) are found to be better modeled than events of type (iii). Nevertheless, we hypothesise from the model's performance on pairwise alignments that it would form an adequate basis for a prototype multiple alignment and genefinding tool.

The dsRNA gene segment coding for the major outer shell glycoprotein of a human rotavirus (Hu/Australia/5/77, serotype 2) was converted into DNA and cloned into the PstI site of the plasmid pBR322. The cloned gene was sequenced and found to be 1062 bp long with one long open reading frame capable of coding for a protein 326 amino-acids. When this gene sequence was compared to the published sequences of the corresponding genes of two animal rotaviruses, SA11 (simian) and UK (bovine), all three were found to be closely related (74-78%). The predicted amino-acid sequences of the three genes were also highly conserved (75-86%), despite the fact that the three viruses belong to different serotypes.

Transposable elements (TEs), or transposons, form a major fraction of the eukaryotic genome (Kidwell and Lisch 2001). Dismissed for some time as junk DNA, these repetitive sequences are now recognized for their diverse evolutionary roles. In this issue of Genome Research, Bao and Eddy (2002) describe a software tool (RECON) for de novo annotation of transposons in genomic sequence, offering new possibilities for discovery to biologists interested in TE evolution as well as a practical tool for masking repetitive DNA from genomic annotation pipelines. In this commentary, I begin by reviewing why transposons are relevant to studies of genome evolution. I then outline the advances of Bao and Eddy’s method from previous work, highlighting certain exemplary features of the RECON method. Finally I describe some of the open questions of transposable element evolution that may be more easily addressed by large-scale bioinformatics and functional genomics approaches as RECON, and more tools like it, become available.

Three tools for simulating genome evolution are presented: for neutrally evolving DNA, for phylogenetic context-free grammars and for richly structured syntenic blocks of genome sequence., Controlled simulations of genome evolution are useful for benchmarking tools. However, many simulators lack extensibility and cannot measure parameters directly from data. These issues are addressed by three new open-source programs: GSIMULATOR (for neutrally evolving DNA), SIMGRAM (for generic structured features) and SIMGENOME (for syntenic genome blocks). Each offers algorithms for parameter measurement and reconstruction of ancestral sequence. All three tools out-perform the leading neutral DNA simulator (DAWG) in benchmarks. The programs are available at .