Experimental strategies to assess the biological ramifications of multiple drivers of global ocean change—A review

Marine life is controlled by multiple physical and chemical drivers and by diverse ecological processes. Many of these oceanic properties are being altered by climate change and other anthropogenic pressures. Hence, identifying the influences of multifaceted ocean change, from local to global scales, is a complex task. To guide policy‐making and make projections of the future of the marine biosphere, it is essential to understand biological responses at physiological, evolutionary and ecological levels. Here, we contrast and compare different approaches to multiple driver experiments that aim to elucidate biological responses to a complex matrix of ocean global change. We present the benefits and the challenges of each approach with a focus on marine research, and guidelines to navigate through these different categories to help identify strategies that might best address research questions in fundamental physiology, experimental evolutionary biology and community ecology. Our review reveals that the field of multiple driver research is being pulled in complementary directions: the need for reductionist approaches to obtain process‐oriented, mechanistic understanding and a requirement to quantify responses to projected future scenarios of ocean change. We conclude the review with recommendations on how best to align different experimental approaches to contribute fundamental information needed for science‐based policy formulation.     

A hidden Markov model for progressive multiple alignment

MOTIVATION: Progressive algorithms are widely used heuristics for the production of alignments among multiple nucleic-acid or protein sequences. Probabilistic approaches providing measures of global and/or local reliability of individual solutions would constitute valuable developments. RESULTS: We present here a new method for multiple sequence alignment that combines an HMM approach, a progressive alignment algorithm, and a probabilistic evolution model describing the character substitution process. Our method works by iterating pairwise alignments according to a guide tree and defining each ancestral sequence from the pairwise alignment of its child nodes, thus, progressively constructing a multiple alignment. Our method allows for the computation of each column minimum posterior probability and we show that this value correlates with the correctness of the result, hence, providing an efficient mean by which unreliably aligned columns can be filtered out from a multiple alignment.     

Surface‐based protein binding pocket similarity

Protein similarity comparisons may be made on a local or global basis and may consider sequence information or differing levels of structural information. We present a local three‐dimensional method that compares protein binding site surfaces in full atomic detail. The approach is based on the morphological similarity method which has been widely applied for global comparison of small molecules. We apply the method to all‐by‐all comparisons two sets of human protein kinases, a very diverse set of ATP‐bound proteins from multiple species, and three heterogeneous benchmark protein binding site data sets. Cases of disagreement between sequence‐based similarity and binding site similarity yield informative examples. Where sequence similarity is very low, high pocket similarity can reliably identify important binding motifs. Where sequence similarity is very high, significant differences in pocket similarity are related to ligand binding specificity and similarity. Local protein binding pocket similarity provides qualitatively complementary information to other approaches, and it can yield quantitative information in support of functional annotation. Proteins 2011; © 2011 Wiley‐Liss, Inc.     

Alignment of protein sequences by their profiles

The accuracy of an alignment between two protein sequences can be improved by including other detectably related sequences in the comparison. We optimize and benchmark such an approach that relies on aligning two multiple sequence alignments, each one including one of the two protein sequences. Thirteen different protocols for creating and comparing profiles corresponding to the multiple sequence alignments are implemented in the SALIGN command of MODELLER. A test set of 200 pairwise, structure-based alignments with sequence identities below 40% is used to benchmark the 13 protocols as well as a number of previously described sequence alignment methods, including heuristic pairwise sequence alignment by BLAST, pairwise sequence alignment by global dynamic programming with an affine gap penalty function by the ALIGN command of MODELLER, sequence-profile alignment by PSI-BLAST, Hidden Markov Model methods implemented in SAM and LOBSTER, pairwise sequence alignment relying on predicted local structure by SEA, and multiple sequence alignment by CLUSTALW and COMPASS. The alignment accuracies of the best new protocols were significantly better than those of the other tested methods. For example, the fraction of the correctly aligned residues relative to the structure-based alignment by the best protocol is 56%, which can be compared with the accuracies of 26%, 42%, 43%, 48%, 50%, 49%, 43%, and 43% for the other methods, respectively. The new method is currently applied to large-scale comparative protein structure modeling of all known sequences.     

Gene set analysis of genome-wide association studies: methodological issues and perspectives

Recent studies have demonstrated that gene set analysis, which tests disease association with genetic variants in a group of functionally related genes, is a promising approach for analyzing and interpreting genome-wide association studies (GWAS) data. These approaches aim to increase power by combining association signals from multiple genes in the same gene set. In addition, gene set analysis can also shed more light on the biological processes underlying complex diseases. However, current approaches for gene set analysis are still in an early stage of development in that analysis results are often prone to sources of bias, including gene set size and gene length, linkage disequilibrium patterns and the presence of overlapping genes. In this paper, we provide an in-depth review of the gene set analysis procedures, along with parameter choices and the particular methodology challenges at each stage. In addition to providing a survey of recently developed tools, we also classify the analysis methods into larger categories and discuss their strengths and limitations. In the last section, we outline several important areas for improving the analytical strategies in gene set analysis.     

A Novel Heuristic for Local Multiple Alignment of Interspersed DNA Repeats

Pairwise local sequence alignment methods have been the prevailing technique to identify homologous nucleotides between related species. However, existing methods that identify and align all homologous nucleotides in one or more genomes have suffered from poor scalability and limited accuracy. We propose a novel method that couples a gapped extension heuristic with an efficient filtration method for identifying interspersed repeats in genome sequences. During gapped extension, we use the MUSCLE implementation of progressive global multiple alignment with iterative refinement. The resulting gapped extensions potentially contain alignments of unrelated sequence. We detect and remove such undesirable alignments using a hidden Markov model (HMM) to predict the posterior probability of homology. The HMM emission frequencies for nucleotide substitutions can be derived from any time-reversible nucleotide substitution matrix. We evaluate the performance of our method and previous approaches on a hybrid data set of real genomic DNA with simulated interspersed repeats. Our method outperforms a related method in terms of sensitivity, positive predictive value, and localizing boundaries of homology. The described methods have been implemented in freely available software, Repeatoire, available from: http://wwwabi.snv.jussieu.fr/public/Repeatoire.     

A diagnostic for collaborative monitoring in forest landscape restoration

Monitoring is crucial to meet the goals of the major global forest landscape restoration (FLR) initiatives that are underway. If members of the global FLR community are going to learn from one another, a multi‐scalar, multi‐site monitoring approach is needed to generate information that can provide the basis for social learning and adaptive management, both of which are essential processes for FLR. This requires reframing and expanding the perspective of monitoring so that compliance monitoring is just one component of a multidimensional approach where collaborative monitoring and compliance‐oriented monitoring are complementary. However, FLR planners and implementers often lack experience in applying collaborative approaches in multi‐stakeholder settings, and there are few tools that show how to implement FLR or to engage in collaborative monitoring in FLR. Through a literature review, we identified the factors that contribute to successful collaborative monitoring in FLR and synthesized them into a diagnostic that was vetted by 20 global experts. The result is a checklist of 42 core success factors to be assessed at local, subnational, and national levels at different stages in the planning and implementation of FLR. The tool has practical application by providing guidance on best practices: specifically, how to start collaborative monitoring, and more generally, how to plan, prepare for, and evaluate FLR activities. This diagnostic complements other diagnostics, such as those used to identify FLR sites, as it can identify preexisting strengths and weaknesses in new initiatives, or pinpoint problems with ongoing implementation. The diagnostic explicitly addresses issues of scale, including multiple sites, governance levels, and changes over time.     

An efficient strategy for extensive integration of diverse biological data for protein function prediction

MOTIVATION: With the increasing availability of diverse biological information, protein function prediction approaches have converged towards integration of heterogeneous data. Many adapted existing techniques, such as machine-learning and probabilistic methods, which have proven successful on specific data types. However, the impact of these approaches is hindered by a couple of factors. First, there is little comparison between existing approaches. This is in part due to a divergence in the focus adopted by different works, which makes comparison difficult or even fuzzy. Second, there seems to be over-emphasis on the use of computationally demanding machine-learning methods, which runs counter to the surge in biological data. Analogous to the success of BLAST for sequence homology search, we believe that the ability to tap escalating quantity, quality and diversity of biological data is crucial to the success of automated function prediction as a useful instrument for the advancement of proteomic research. We address these problems by: (1) providing useful comparison between some prominent methods; (2) proposing Integrated Weighted Averaging (IWA)--a scalable, efficient and flexible function prediction framework that integrates diverse information using simple weighting strategies and a local prediction method. The simplicity of the approach makes it possible to make predictions based on on-the-fly information fusion. RESULTS: In addition to its greater efficiency, IWA performs exceptionally well against existing approaches. In the presence of cross-genome information, which is overwhelming for existing approaches, IWA makes even better predictions. We also demonstrate the significance of appropriate weighting strategies in data integration.     

Integrated landscape approaches to managing social and environmental issues in the tropics: learning from the past to guide the future

Poverty, food insecurity, climate change and biodiversity loss continue to persist as the primary environmental and social challenges faced by the global community. As such, there is a growing acknowledgement that conventional sectorial approaches to addressing often inter‐connected social, environmental, economic and political challenges are proving insufficient. An alternative is to focus on integrated solutions at landscape scales or ‘landscape approaches’. The appeal of landscape approaches has resulted in the production of a significant body of literature in recent decades, yet confusion over terminology, application and utility persists. Focusing on the tropics, we systematically reviewed the literature to: (i) disentangle the historical development and theory behind the framework of the landscape approach and how it has progressed into its current iteration, (ii) establish lessons learned from previous land management strategies, (iii) determine the barriers that currently restrict implementation of the landscape approach and (iv) provide recommendations for how the landscape approach can contribute towards the fulfilment of the goals of international policy processes. This review suggests that, despite some barriers to implementation, a landscape approach has considerable potential to meet social and environmental objectives at local scales while aiding national commitments to addressing ongoing global challenges.     

Navigating within thiamine diphosphate-dependent decarboxylases: Sequences,structures, functional positions,and binding sites

Thiamine diphosphate-dependent decarboxylases catalyze both cleavage and formation of C C bonds in various reactions, which have been assigned to different homologous sequence families. This work compares 53 ThDP-dependent decarboxylases with known crystal structures. Both sequence and structural information were analyzed synergistically and data were analyzed for global and local properties by means of statistical approaches (principle component analysis and principal coordinate analysis) enabling complexity reduction. The different results obtained both locally and globally, that is, individual positions compared with the overall protein sequence or structure, revealed challenges in the assignment of separated homologous families. The methods applied herein support the comparison of enzyme families and the identification of functionally relevant positions. The findings for the family of ThDP-dependent decarboxylases underline that global sequence identity alone is not sufficient to distinguish enzyme function. Instead, local sequence similarity, defined by comparisons of structurally equivalent positions, allows for a better navigation within several groups of homologous enzymes. The differentiation between homologous sequences is further enhanced by taking structural information into account, such as BioGPS analysis of the active site properties or pairwise structural superimpositions. The methods applied herein are expected to be transferrable to other enzyme families, to facilitate family assignments for homologous protein sequences.     

COCO-CL: hierarchical clustering of homology relations based on evolutionary correlations

MOTIVATION: Determining orthology relations among genes across multiple genomes is an important problem in the post-genomic era. Identifying orthologous genes can not only help predict functional annotations for newly sequenced or poorly characterized genomes, but can also help predict new protein-protein interactions. Unfortunately, determining orthology relation through computational methods is not straightforward due to the presence of paralogs. Traditional approaches have relied on pairwise sequence comparisons to construct graphs, which were then partitioned into putative clusters of orthologous groups. These methods do not attempt to preserve the non-transitivity and hierarchic nature of the orthology relation. RESULTS: We propose a new method, COCO-CL, for hierarchical clustering of homology relations and identification of orthologous groups of genes. Unlike previous approaches, which are based on pairwise sequence comparisons, our method explores the correlation of evolutionary histories of individual genes in a more global context. COCO-CL can be used as a semi-independent method to delineate the orthology/paralogy relation for a refined set of homologous proteins obtained using a less-conservative clustering approach, or as a refiner that removes putative out-paralogs from clusters computed using a more inclusive approach. We analyze our clustering results manually, with support from literature and functional annotations. Since our orthology determination procedure does not employ a species tree to infer duplication events, it can be used in situations when the species tree is unknown or uncertain. CONTACT: jothi@mail.nih.gov, przytyck@mail.nih.gov SUPPLEMENTARY INFORMATION: Supplementary materials are available at Bioinformatics online.     

Correlation and process in species distribution models: bridging a dichotomy

Within the field of species distribution modelling an apparent dichotomy exists between process‐based and correlative approaches, where the processes are explicit in the former and implicit in the latter. However, these intuitive distinctions can become blurred when comparing species distribution modelling approaches in more detail. In this review article, we contrast the extremes of the correlative–process spectrum of species distribution models with respect to core assumptions, model building and selection strategies, validation, uncertainties, common errors and the questions they are most suited to answer. The extremes of such approaches differ clearly in many aspects, such as model building approaches, parameter estimation strategies and transferability. However, they also share strengths and weaknesses. We show that claims of one approach being intrinsically superior to the other are misguided and that they ignore the process–correlation continuum as well as the domains of questions that each approach is addressing. Nonetheless, the application of process‐based approaches to species distribution modelling lags far behind more correlative (process‐implicit) methods and more research is required to explore their potential benefits. Critical issues for the employment of species distribution modelling approaches are given, together with a guideline for appropriate usage. We close with challenges for future development of process‐explicit species distribution models and how they may complement current approaches to study species distributions.     

Progressive multiple alignment using sequence triplet optimizations and three-residue exchange costs

In this paper we demonstrate a practical approach to construct progressive multiple alignments using sequence triplet optimizations rather than a conventional pairwise approach. Using the sequence triplet alignments progressively provides a scope for the synthesis of a three-residue exchange amino acid substitution matrix. We develop such a 20 x 20 x 20 matrix for the first time and demonstrate how its use in optimal sequence triplet alignments increases the sensitivity of building multiple alignments. Various comparisons were made between alignments generated using the progressive triplet methods and the conventional progressive pairwise procedure. The assessment of these data reveal that, in general, the triplet based approaches generate more accurate sequence alignments than the traditional pairwise based procedures, especially between more divergent sets of sequences.     

Model Systems versus Outgroups Alternative Approaches to the Study of Head Development and Evolution

There is widespread recognition of a recent coming togetherof developmental and evolutionary biology in the study of problemsof mutual interest. Contemporary studies into the developmentand evolution of the head largely comprise two parallel approaches,or research strategies the model systems approach and the comparativeapproach. The two strategies share the same general goal—greaterunderstanding of cranial development and evolution—buttypically emphasize different problems, ask different questions,and employ different methods, reflecting the contrasting backgroundsand biases of each group of investigators, there has been relativelylittle true synthesis. Each strategy is making important andvalid contributions, but both have limitations. Resolution ofmany fundamental and long-standing problems in cranial developmentand evolution will require a combined approach that incorporatesthe technical and conceptual strengths of each discipline.     

GPRM: A genetic programming approach to finding common RNA secondary structure elements

RNA molecules play an important role in many biological activities. Knowing its secondary structure can help us better understand the molecule's ability to function. The methods for RNA structure determination have traditionally been implemented through biochemical, biophysical and phylogenetic analyses. As the advance of computer technology, an increasing number of computational approaches have recently been developed. They have different goals and apply various algorithms. For example, some focus on secondary structure prediction for a single sequence; some aim at finding a global alignment of multiple sequences. Some predict the structure based on free energy minimization; some make comparative sequence analyses to determine the structure. In this paper, we describe how to correctly use GPRM, a genetic programming approach to finding common secondary structure elements in a set of unaligned coregulated or homologous RNA sequences. GPRM can be accessed at http://bioinfo.cis.nctu.edu.tw/service/gprm/.