Large Sequence Alignments Research Articles

Rapid alignment updating with Extensiphy

Abstract High‐throughput sequencing has become commonplace in evolutionary studies. Large, rapidly collected genomic datasets are used to capture biodiversity and for monitoring global and national scale disease transmission patterns, among many other applications. Updating homologous sequence datasets with new samples is cumbersome, requiring excessive program runtimes and data processing. We describe Extensiphy, a bioinformatics tool to efficiently update multiple sequence alignments with whole‐genome short‐read data. Extensiphy performs reference based sequence assembly and alignment in one process while maintaining the alignment length of the original alignment. Input data‐types for Extensiphy are any multiple sequence alignment in fasta format and whole‐genome, short‐read fastq sequences. To validate Extensiphy, we compared its results to those produced by two other methods that construct whole‐genome scale multiple sequence alignments. We measured our comparisons by analysing program runtimes, base‐call accuracy, dataset retention in the presence of missing data and phylogenetic accuracy. We found that Extensiphy rapidly produces high‐quality updated sequence alignments while preventing alignment shrinkage due to missing data. Phylogenies estimated from alignments produced by Extensiphy show similar accuracy to other commonly used alignment construction methods. Extensiphy is suitable for updating large sequence alignments and is ideal for studies of biodiversity, ecology and epidemiological monitoring efforts.

Coevolutionary Analysis of Protein Subfamilies by Sequence Reweighting.

Extracting structural information from sequence co-variation has become a common computational biology practice in the recent years, mainly due to the availability of large sequence alignments of protein families. However, identifying features that are specific to sub-classes and not shared by all members of the family using sequence-based approaches has remained an elusive problem. We here present a coevolutionary-based method to differentially analyze subfamily specific structural features by a continuous sequence reweighting (SR) approach. We introduce the underlying principles and test its predictive capabilities on the Response Regulator family, whose subfamilies have been previously shown to display distinct, specific homo-dimerization patterns. Our results show that this reweighting scheme is effective in assigning structural features known a priori to subfamilies, even when sequence data is relatively scarce. Furthermore, sequence reweighting allows assessing if individual structural contacts pertain to specific subfamilies and it thus paves the way for the identification specificity-determining contacts from sequence variation data.

DECIPHER: harnessing local sequence context to improve protein multiple sequence alignment.

BackgroundAlignment of large and diverse sequence sets is a common task in biological investigations, yet there remains considerable room for improvement in alignment quality. Multiple sequence alignment programs tend to reach maximal accuracy when aligning only a few sequences, and then diminish steadily as more sequences are added. This drop in accuracy can be partly attributed to a build-up of error and ambiguity as more sequences are aligned. Most high-throughput sequence alignment algorithms do not use contextual information under the assumption that sites are independent. This study examines the extent to which local sequence context can be exploited to improve the quality of large multiple sequence alignments.ResultsTwo predictors based on local sequence context were assessed: (i) single sequence secondary structure predictions, and (ii) modulation of gap costs according to the surrounding residues. The results indicate that context-based predictors have appreciable information content that can be utilized to create more accurate alignments. Furthermore, local context becomes more informative as the number of sequences increases, enabling more accurate protein alignments of large empirical benchmarks. These discoveries became the basis for DECIPHER, a new context-aware program for sequence alignment, which outperformed other programs on large sequence sets.ConclusionsPredicting secondary structure based on local sequence context is an efficient means of breaking the independence assumption in alignment. Since secondary structure is more conserved than primary sequence, it can be leveraged to improve the alignment of distantly related proteins. Moreover, secondary structure predictions increase in accuracy as more sequences are used in the prediction. This enables the scalable generation of large sequence alignments that maintain high accuracy even on diverse sequence sets. The DECIPHER R package and source code are freely available for download at DECIPHER.cee.wisc.edu and from the Bioconductor repository.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0749-z) contains supplementary material, which is available to authorized users.

K-Pax2: Bayesian identification of cluster-defining amino acid positions in large sequence datasets.

The recent growth in publicly available sequence data has introduced new opportunities for studying microbial evolution and spread. Because the pace of sequence accumulation tends to exceed the pace of experimental studies of protein function and the roles of individual amino acids, statistical tools to identify meaningful patterns in protein diversity are essential. Large sequence alignments from fast-evolving micro-organisms are particularly challenging to dissect using standard tools from phylogenetics and multivariate statistics because biologically relevant functional signals are easily masked by neutral variation and noise. To meet this need, a novel computational method is introduced that is easily executed in parallel using a cluster environment and can handle thousands of sequences with minimal subjective input from the user. The usefulness of this kind of machine learning is demonstrated by applying it to nearly 5000 haemagglutinin sequences of influenza A/H3N2.Antigenic and 3D structural mapping of the results show that the method can recover the major jumps in antigenic phenotype that occurred between 1968 and 2013 and identify specific amino acids associated with these changes. The method is expected to provide a useful tool to uncover patterns of protein evolution.

πBUSS: a parallel BEAST/BEAGLE utility for sequence simulation under complex evolutionary scenarios.

BackgroundSimulated nucleotide or amino acid sequences are frequently used to assess the performance of phylogenetic reconstruction methods. BEAST, a Bayesian statistical framework that focuses on reconstructing time-calibrated molecular evolutionary processes, supports a wide array of evolutionary models, but lacked matching machinery for simulation of character evolution along phylogenies.ResultsWe present a flexible Monte Carlo simulation tool, called πBUSS, that employs the BEAGLE high performance library for phylogenetic computations to rapidly generate large sequence alignments under complex evolutionary models. πBUSS sports a user-friendly graphical user interface (GUI) that allows combining a rich array of models across an arbitrary number of partitions. A command-line interface mirrors the options available through the GUI and facilitates scripting in large-scale simulation studies. πBUSS may serve as an easy-to-use, standard sequence simulation tool, but the available models and data types are particularly useful to assess the performance of complex BEAST inferences. The connection with BEAST is further strengthened through the use of a common extensible markup language (XML), allowing to specify also more advanced evolutionary models. To support simulation under the latter, as well as to support simulation and analysis in a single run, we also add the πBUSS core simulation routine to the list of BEAST XML parsers.ConclusionsπBUSS offers a unique combination of flexibility and ease-of-use for sequence simulation under realistic evolutionary scenarios. Through different interfaces, πBUSS supports simulation studies ranging from modest endeavors for illustrative purposes to complex and large-scale assessments of evolutionary inference procedures. Applications are not restricted to the BEAST framework, or even time-measured evolutionary histories, and πBUSS can be connected to various other programs using standard input and output format.

Integrated Hardware Architecture for Efficient Computation of the $n$-Best Bio-Sequence Local Alignments in Embedded Platforms

A flexible hardware architecture that implements a set of new and efficient techniques to significantly reduce the computational requirements of the commonly used Smith-Waterman sequence alignment algorithm is presented. Such innovative techniques use information gathered by the hardware accelerator during the computation of the alignment scores to constrain the size of the subsequence that has to be post-processed in the traceback phase using a general purpose processor (GPP). Moreover, the proposed structure is also capable of computing the n-best local alignments according to the Waterman-Eggert algorithm, becoming the first hardware architecture that is able to simultaneously evaluate the n-best alignments of a given sequence pair, by incorporating a set of ordering units that work in parallel with the systolic array. A complete alignment system was developed and implemented in a Virtex-4 FPGA, by integrating the proposed accelerator architecture with a Leon3 GPP. The obtained experimental results demonstrate that the proposed system is flexible and allows the alignment of large sequences in memory constrained systems. As an example, a speedup of 17 was obtained with the conceived system when compared with a regular implementation of the LALIGN35 program running on an Intel Core2 Duo processor running at a 40 × higher frequency.

Statistical Potentials for Hairpin and Internal Loops Improve the Accuracy of the Predicted RNA Structure

RNA is directly associated with a growing number of functions within the cell. The accurate prediction of different RNA higher-order structures from their nucleic acid sequences will provide insight into their functions and molecular mechanics. We have been determining statistical potentials for a collection of structural elements that is larger than the number of structural elements determined with experimentally determined energy values. The experimentally derived free energies and the statistical potentials for canonical base-pair stacks are analogous, demonstrating that statistical potentials derived from comparative data can be used as an alternative energetic parameter. A new computational infrastructure—RNA Comparative Analysis Database (rCAD)—that utilizes a relational database was developed to manipulate and analyze very large sequence alignments and secondary-structure data sets. Using rCAD, we determined a richer set of energetic parameters for RNA fundamental structural elements including hairpin and internal loops. A new version of RNAfold was developed to utilize these statistical potentials. Overall, these new statistical potentials for hairpin and internal loops integrated into the new version of RNAfold demonstrated significant improvements in the prediction accuracy of RNA secondary structure.

Bayesian Estimation of Divergence Times from Large Sequence Alignments

Bayesian estimation of divergence times from molecular sequences relies on sophisticated Markov chain Monte Carlo techniques, and Metropolis-Hastings (MH) samplers have been successfully used in that context. This approach involves heavy computational burdens that can hinder the analysis of large phylogenomic data sets. Reliable estimation of divergence times can also be extremely time consuming, if not impossible, for sequence alignments that convey weak or conflicting phylogenetic signals, emphasizing the need for more efficient sampling methods. This article describes a new approach that estimates the posterior density of substitution rates and node times. The prior distribution of rates accounts for their potential autocorrelation along lineages, whereas priors on node ages are modeled with uniform densities. Also, the likelihood function is approximated by a multivariate normal density. The combination of these components leads to convenient mathematical simplifications, allowing the posterior distribution of rates and times to be estimated using a Gibbs sampling algorithm. The analysis of four real-world data sets shows that this sampler outperforms the standard MH approach and demonstrates the suitability of this new method for analyzing large and/or difficult data sets.

Streptococcus agalactiae DNA polymerase I is an efficient reverse transcriptase

Genome annotations result generally from large sequence alignments by bioinformatics. Large scale biochemical data are more difficult to obtain. They derive for example from directed protein evolution experiments by selection. A previously reported directed enzyme evolution experiment by in vitro selection of Stoffel fragment variants of Taq DNA polymerase I was used here to predict the activities of Streptococcus agalactiae DNA polymerase I. The reverse transcriptase activity of S. agalactiae DNA polymerase I was measured and the kinetic parameters for this RNA-dependent DNA polymerase are given. RNA-templated DNA repair is suggested as a possible biological function for this biochemical activity.

Evolutionary Interactions between N-Linked Glycosylation Sites in the HIV-1 Envelope

The addition of asparagine (N)-linked polysaccharide chains (i.e., glycans) to the gp120 and gp41 glycoproteins of human immunodeficiency virus type 1 (HIV-1) envelope is not only required for correct protein folding, but also may provide protection against neutralizing antibodies as a “glycan shield.” As a result, strong host-specific selection is frequently associated with codon positions where nonsynonymous substitutions can create or disrupt potential N-linked glycosylation sites (PNGSs). Moreover, empirical data suggest that the individual contribution of PNGSs to the neutralization sensitivity or infectivity of HIV-1 may be critically dependent on the presence or absence of other PNGSs in the envelope sequence. Here we evaluate how glycan–glycan interactions have shaped the evolution of HIV-1 envelope sequences by analyzing the distribution of PNGSs in a large-sequence alignment. Using a “covarion”-type phylogenetic model, we find that the rates at which individual PNGSs are gained or lost vary significantly over time, suggesting that the selective advantage of having a PNGS may depend on the presence or absence of other PNGSs in the sequence. Consequently, we identify specific interactions between PNGSs in the alignment using a new paired-character phylogenetic model of evolution, and a Bayesian graphical model. Despite the fundamental differences between these two methods, several interactions are jointly identified by both. Mapping these interactions onto a structural model of HIV-1 gp120 reveals that negative (exclusive) interactions occur significantly more often between colocalized glycans, while positive (inclusive) interactions are restricted to more distant glycans. Our results imply that the adaptive repertoire of alternative configurations in the HIV-1 glycan shield is limited by functional interactions between the N-linked glycans. This represents a potential vulnerability of rapidly evolving HIV-1 populations that may provide useful glycan-based targets for neutralizing antibodies.

SEAVIEW and PHYLO_WIN: two graphic tools for sequence alignment and molecular phylogeny.

SEAVIEW and PHYLO_WIN are two graphic tools for X Windows-Unix computers dedicated to sequence alignment and molecular phylogenetics. SEAVIEW is a sequence alignment editor allowing manual or automatic alignment through an interface with CLUSTALW program. Alignment of large sequences with extensive length differences is made easier by a dot-plot-based routine. The PHYLO_WIN program allows phylogenetic tree building according to most usual methods (neighbor joining with numerous distance estimates, maximum parsimony, maximum likelihood), and a bootstrap analysis with any of them. Reconstructed trees can be drawn, edited, printed, stored, evaluated according to numerous criteria. Taxonomic species groups and sets of conserved regions can be defined by mouse and stored into sequence files, thus avoiding multiple data files. Both tools are entirely mouse driven. On-line help makes them easy to use. They are freely available by anonymous ftp at biom3.univ-lyon1.fr/pub/ mol_phylogeny or http:@acnuc.univ-lyon1.fr/, or by e-mail to galtier@biomserv.univ-lyon1.fr.

ALMA, an editor for large sequence alignments

A dedicated sequence editor, ALMA, was developed for aligning many sequences of proteins or RNA molecules or longer DNA fragments. Like previously published editors, ALMA is menu directed, screen oriented, and offers similarity and consensus display. ALMA has the additional features of collective movement of sequences, acceptance of input from many sources including structure files and databases, secondary structure display, and easy merging of alignments. In order to maintain sequence integrity and save disk space, gaps and sequences are stored separately. Automatic recovery of a session is possible. Finally, the program allows interaction between manual and automatic alignment.