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ABSTRACT: BACKGROUND: Whether or not a mutant allele in a population is under selection is an important issue in population genetics, and various neutrality tests have been invented so far to detect selection. However, detection of negative selection has been notoriously difficult, partly because negatively selected alleles are usually rare in the population and have little impact on either population dynamics or the shape of the gene genealogy. Recently, through studies of genetic disorders and genome-wide analyses, many structural variations were shown to occur recurrently in the population. Such "recurrent mutations" might be revealed as deleterious by exploiting the signal of negative selection in the gene genealogy enhanced by their recurrence. RESULTS: Motivated by the above idea, we devised two new test statistics. One is the total number of mutants at a recurrently mutating locus among sampled sequences, which is tested conditionally on the number of forward mutations mapped on the sequence genealogy. The other is the size of the most common class of identical-by-descent mutants in the sample, again tested conditionally on the number of forward mutations mapped on the sequence genealogy. To examine the performance of these two tests, we simulated recurrently mutated loci each flanked by sites with neutral single nucleotide polymorphisms (SNPs), with no recombination. Using neutral recurrent mutations as null models, we attempted to detect deleterious recurrent mutations. Our analyses demonstrated high powers of our new tests under constant population size, as well as their moderate power to detect selection in expanding populations. We also devised a new maximum parsimony algorithm that, given the states of the sampled sequences at a recurrently mutating locus and an incompletely resolved genealogy, enumerates mutation histories with a minimum number of mutations while partially resolving genealogical relationships when necessary. CONCLUSIONS: With their considerably high powers to detect negative selection, our new neutrality tests may open new venues for dealing with the population genetics of recurrent mutations as well as help identifying some types of genetic disorders that may have escaped identification by currently existing methods.
BMC Genetics 05/2013; 14(1):37. · 2.47 Impact Factor
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Christian Schmitt,
Peter Atkinson,
Erica Sodergren,
Qingsong Zhu,
Jennifer Hume,
Shalini N Jhangiani,
Mimi N Chandrabose,
Jim Beidler,
Michael Schoppmeier,
Selina Vattahil, [......],
Jeremy A Lynch,
Peer Bork,
Rodrigo Nunes da Fonseca,
Manuel Aranda,
Nico Posnien,
Riyue Bao,
Rolf Reuter,
Anke Beermann,
Nicola Berns,
Johannes B Schinko
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ABSTRACT: It has been suggested that the mammalian genome is composed mainly of long compositionally homogeneous domains. Such domains are frequently identified using recursive segmentation algorithms based on the Jensen-Shannon divergence. However, a common difficulty with such methods is deciding when to halt the recursive partitioning and what criteria to use in deciding whether a detected boundary between two segments is real or not. We demonstrate that commonly used halting criteria are intrinsically biased, and propose IsoPlotter, a parameter-free segmentation algorithm that overcomes such biases by using a simple dynamic halting criterion and tests the homogeneity of the inferred domains. IsoPlotter was compared with an alternative segmentation algorithm, D(JS), using two sets of simulated genomic sequences. Our results show that IsoPlotter was able to infer both long and short compositionally homogeneous domains with low GC content dispersion, whereas D(JS) failed to identify short compositionally homogeneous domains and sequences with low compositional dispersion. By segmenting the human genome with IsoPlotter, we found that one-third of the genome is composed of compositionally nonhomogeneous domains and the remaining is a mixture of many short compositionally homogeneous domains and relatively few long ones.
Nucleic Acids Research 08/2010; 38(15):e158. · 8.03 Impact Factor
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ABSTRACT: Evaluating the accuracy of multiple sequence alignment (MSA) is critical for virtually every comparative sequence analysis that uses an MSA as input. Here we present the GUIDANCE web-server, a user-friendly, open access tool for the identification of unreliable alignment regions. The web-server accepts as input a set of unaligned sequences. The server aligns the sequences and provides a simple graphic visualization of the confidence score of each column, residue and sequence of an alignment, using a color-coding scheme. The method is generic and the user is allowed to choose the alignment algorithm (ClustalW, MAFFT and PRANK are supported) as well as any type of molecular sequences (nucleotide, protein or codon sequences). The server implements two different algorithms for evaluating confidence scores: (i) the heads-or-tails (HoT) method, which measures alignment uncertainty due to co-optimal solutions; (ii) the GUIDANCE method, which measures the robustness of the alignment to guide-tree uncertainty. The server projects the confidence scores onto the MSA and points to columns and sequences that are unreliably aligned. These can be automatically removed in preparation for downstream analyses. GUIDANCE is freely available for use at http://guidance.tau.ac.il.
Nucleic Acids Research 05/2010; 38(Web Server issue):W23-8. · 8.03 Impact Factor
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ABSTRACT: Multiple sequence alignment (MSA) is the basis for a wide range of comparative sequence analyses from molecular phylogenetics to 3D structure prediction. Sophisticated algorithms have been developed for sequence alignment, but in practice, many errors can be expected and extensive portions of the MSA are unreliable. Hence, it is imperative to understand and characterize the various sources of errors in MSAs and to quantify site-specific alignment confidence. In this paper, we show that uncertainties in the guide tree used by progressive alignment methods are a major source of alignment uncertainty. We use this insight to develop a novel method for quantifying the robustness of each alignment column to guide tree uncertainty. We build on the widely used bootstrap method for perturbing the phylogenetic tree. Specifically, we generate a collection of trees and use each as a guide tree in the alignment algorithm, thus producing a set of MSAs. We next test the consistency of every column of the MSA obtained from the unperturbed guide tree with respect to the set of MSAs. We name this measure the "GUIDe tree based AligNment ConfidencE" (GUIDANCE) score. Using the Benchmark Alignment data BASE benchmark as well as simulation studies, we show that GUIDANCE scores accurately identify errors in MSAs. Additionally, we compare our results with the previously published Heads-or-Tails score and show that the GUIDANCE score is a better predictor of unreliably aligned regions.
Molecular Biology and Evolution 03/2010; 27(8):1759-67. · 5.55 Impact Factor
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ABSTRACT: The isochore theory depicts the genomes of warm-blooded vertebrates as a mosaic of long genomic regions that are characterized by relatively homogeneous GC content. In the absence of genomic data, the GC content at third-codon positions of protein-coding genes (GC3) was commonly used as a proxy for the GC content of isochores. Oddly, in the postgenomic era, GC3 is still sometimes used as a proxy for the GC composition of isochores. Here, we use genic and genomic sequences from human, chimpanzee, cow, mouse, rat, chicken, and zebrafish to show that GC3 only explains a very small proportion of the variation in GC content of long genomic sequences flanking the genes (GCf), and what little correlation there is between GC3 and GCf was found to decay rapidly with distance from the gene. The coefficient of variation of GC3 was found to be much larger than that of GCf and, therefore, GC3 and GCf values are not comparable with each other. Comparisons of orthologous gene pairs from 1) human and chimpanzee and 2) mouse and rat show strong correlations between their GC3 values, but very weak correlations between their GCf values. We conclude that the GC content of third-codon position cannot be used as stand-in for isochoric composition.
Molecular Biology and Evolution 06/2009; 26(8):1829-33. · 5.55 Impact Factor
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ABSTRACT: Published estimates of the proportion of positively selected genes (PSGs) in human vary over three orders of magnitude. In mammals, estimates of the proportion of PSGs cover an even wider range of values. We used 2,980 orthologous protein-coding genes from human, chimpanzee, macaque, dog, cow, rat, and mouse as well as an established phylogenetic topology to infer the fraction of PSGs in all seven terminal branches. The inferred fraction of PSGs ranged from 0.9% in human through 17.5% in macaque to 23.3% in dog. We found three factors that influence the fraction of genes that exhibit telltale signs of positive selection: the quality of the sequence, the degree of misannotation, and ambiguities in the multiple sequence alignment. The inferred fraction of PSGs in sequences that are deficient in all three criteria of coverage, annotation, and alignment is 7.2 times higher than that in genes with high trace sequencing coverage, "known" annotation status, and perfect alignment scores. We conclude that some estimates on the prevalence of positive Darwinian selection in the literature may be inflated and should be treated with caution.
Genome Biology and Evolution 01/2009; 1:114-8. · 4.62 Impact Factor
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ABSTRACT: Same-strand overlapping genes may occur in frameshifts of one (phase 1) or two nucleotides (phase 2). In previous studies of bacterial genomes, long phase-1 overlaps were found to be more numerous than long phase-2 overlaps. This bias was explained by either genomic location or an unspecified selection advantage. Models that focused on the ability of the two genes to evolve independently did not predict this phase bias. Here, we propose that a purely compositional model explains the phase bias in a more parsimonious manner. Same-strand overlapping genes may arise through either a mutation at the termination codon of the upstream gene or a mutation at the initiation codon of the downstream gene. We hypothesized that given these two scenarios, the frequencies of initiation and termination codons in the two phases may determine the number for overlapping genes.
We examined the frequencies of initiation- and termination-codons in the two phases, and found that termination codons do not significantly differ between the two phases, whereas initiation codons are more abundant in phase 1. We found that the primary factors explaining the phase inequality are the frequencies of amino acids whose codons may combine to form start codons in the two phases. We show that the frequencies of start codons in each of the two phases, and, hence, the potential for the creation of overlapping genes, are determined by a universal amino-acid frequency and species-specific codon usage, leading to a correlation between long phase-1 overlaps and genomic GC content.
Our model explains the phase bias in same-strand overlapping genes by compositional factors without invoking selection. Therefore, it can be used as a null model of neutral evolution to test selection hypotheses concerning the evolution of overlapping genes.
Biology Direct 09/2008; 3:36. · 4.02 Impact Factor
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ABSTRACT: We characterize pairwise and multiple sequence alignment (MSA) errors by comparing true alignments from simulations of sequence evolution with reconstructed alignments. The vast majority of reconstructed alignments contain many errors. Error rates rapidly increase with sequence divergence, thus, for even intermediate degrees of sequence divergence, more than half of the columns of a reconstructed alignment may be expected to be erroneous. In closely related sequences, most errors consist of the erroneous positioning of a single indel event and their effect is local. As sequences diverge, errors become more complex as a result of the simultaneous mis-reconstruction of many indel events, and the lengths of the affected MSA segments increase dramatically. We found a systematic bias towards underestimation of the number of gaps, which leads to the reconstructed MSA being on average shorter than the true one. Alignment errors are unavoidable even when the evolutionary parameters are known in advance. Correct reconstruction can only be guaranteed when the likelihood of true alignment is uniquely optimal. However, true alignment features are very frequently sub-optimal or co-optimal, with the result that optimal albeit erroneous features are incorporated into the reconstructed MSA. Progressive MSA utilizes a guide-tree in the reconstruction of MSAs. The quality of the guide-tree was found to affect MSA error levels only marginally.
Gene 07/2008; 441(1-2):141-7. · 2.34 Impact Factor
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Stephen Richards,
Richard A Gibbs,
George M Weinstock,
Susan J Brown,
Robin Denell,
Richard W Beeman,
Richard Gibbs,
Gregor Bucher,
Markus Friedrich,
Cornelis J P Grimmelikhuijzen, [......],
Joachim Schachtner,
Peter Verleyen,
Florian Raible,
Kimberly K O Walden,
Sergio Angeli,
Sylvain Forêt,
Stefan Schuetz,
Ryszard Maleszka,
Sherry C Miller,
Daniela Grossmann
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ABSTRACT: Tribolium castaneum is a member of the most species-rich eukaryotic order, a powerful model organism for the study of generalized insect development, and an important pest of stored agricultural products. We describe its genome sequence here. This omnivorous beetle has evolved the ability to interact with a diverse chemical environment, as shown by large expansions in odorant and gustatory receptors, as well as P450 and other detoxification enzymes. Development in Tribolium is more representative of other insects than is Drosophila, a fact reflected in gene content and function. For example, Tribolium has retained more ancestral genes involved in cell-cell communication than Drosophila, some being expressed in the growth zone crucial for axial elongation in short-germ development. Systemic RNA interference in T. castaneum functions differently from that in Caenorhabditis elegans, but nevertheless offers similar power for the elucidation of gene function and identification of targets for selective insect control.
Nature 05/2008; 452(7190):949-55. · 36.28 Impact Factor
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Stephen Richards,
Richard A. Gibbs,
George M. Weinstock,
Susan J. Brown,
Robin Denell,
Richard W. Beeman,
Richard Gibbs,
Gregor Bucher,
Markus Friedrich,
Cornelis J. P. Grimmelikhuijzen, [......],
Joachim Schachtner,
Peter Verleyen,
Florian Raible,
Kimberly K. O. Walden,
Sergio Angeli,
Sylvain For|[ecirc]|t,
Stefan Schuetz,
Ryszard Maleszka,
Sherry C. Miller,
Daniela Grossmann
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ABSTRACT: Tribolium castaneum is a member of the most species-rich eukaryotic order, a powerful model organism for the study of generalized insect development, and an important pest of stored agricultural products. We describe its genome sequence here. This omnivorous beetle has evolved the ability to interact with a diverse chemical environment, as shown by large expansions in odorant and gustatory receptors, as well as P450 and other detoxification enzymes. Development in Tribolium is more representative of other insects than is Drosophila, a fact reflected in gene content and function. For example, Tribolium has retained more ancestral genes involved in cell–cell communication than Drosophila, some being expressed in the growth zone crucial for axial elongation in short-germ development. Systemic RNA interference in T. castaneum functions differently from that in Caenorhabditis elegans, but nevertheless offers similar power for the elucidation of gene function and identification of targets for selective insect control.
Nature 03/2008; 452(7190):949-955. · 36.28 Impact Factor
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ABSTRACT: Inferring the intensity of positive selection in protein-coding genes is important since it is used to shed light on the process of adaptation. Recently, it has been reported that overlapping genes, which are ubiquitous in all domains of life, seem to exhibit inordinate degrees of positive selection. Here, we present a new method for the simultaneous estimation of selection intensities in overlapping genes. We show that the appearance of positive selection is caused by assuming that selection operates independently on each gene in an overlapping pair, thereby ignoring the unique evolutionary constraints on overlapping coding regions. Our method uses an exact evolutionary model, thereby voiding the need for approximation or intensive computation. We test the method by simulating the evolution of overlapping genes of different types as well as under diverse evolutionary scenarios. Our results indicate that the independent estimation approach leads to the false appearance of positive selection even though the gene is in reality subject to negative selection. Finally, we use our method to estimate selection in two influenza A genes for which positive selection was previously inferred. We find no evidence for positive selection in both cases.
PLoS ONE 02/2008; 3(12):e3996. · 4.09 Impact Factor
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ABSTRACT: The question of multiple sequence alignment quality has received much attention from developers of alignment methods. Less forthcoming, however, are practical measures for quantifying alignment reliability in real life settings. Here, we present a method to identify and quantify uncertainties in multiple sequence alignments. The proposed method is based upon the observation that under any objective function or evolutionary model, some portions of reconstructed alignments are uniquely optimal, while other parts constitute an arbitrary choice from a set of co-optimal alternatives. The co-optimal portions of reconstructed alignments are, thus, at most half as reliable as the uniquely optimal portions. For pairwise alignments, this irreducible uncertainty can be quantified by the comparison of the high-road and low-road alignments, which form the cooptimality envelope for the two sequences. We extend this approach for the case of progressive multiple sequence alignment by forming a large set of equally likely co-optimal alignments that bracket the co-optimality space. This set can, then, be used to derive a series of local reliability measures for any candidate alignment. The resulting reliability measures can be used as predictors and classifiers of alignment errors. We report a simulation study that demonstrates the superior power of the proposed local reliability measures.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing 02/2008;
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ABSTRACT: The question of multiple sequence alignment quality has received much attention from developers of alignment methods. Less forthcoming, however, are practical measures for addressing alignment quality issues in real life settings. Here, we present a simple methodology to help identify and quantify the uncertainties in multiple sequence alignments and their effects on subsequent analyses. The proposed methodology is based upon the a priori expectation that sequence alignment results should be independent of the orientation of the input sequences. Thus, for totally unambiguous cases, reversing residue order prior to alignment should yield an exact reversed alignment of that obtained by using the unreversed sequences. Such "ideal" alignments, however, are the exception in real life settings, and the two alignments, which we term the heads and tails alignments, are usually different to a greater or lesser degree. The degree of agreement or discrepancy between these two alignments may be used to assess the reliability of the sequence alignment. Furthermore, any alignment dependent sequence analysis protocol can be carried out separately for each of the two alignments, and the two sets of results may be compared with each other, providing us with valuable information regarding the robustness of the whole analytical process. The heads-or-tails (HoT) methodology can be easily implemented for any choice of alignment method and for any subsequent analytical protocol. We demonstrate the utility of HoT for phylogenetic reconstruction for the case of 130 sequences belonging to the chemoreceptor superfamily in Drosophila melanogaster, and by analysis of the BaliBASE alignment database. Surprisingly, Neighbor-Joining methods of phylogenetic reconstruction turned out to be less affected by alignment errors than maximum likelihood and Bayesian methods.
Molecular Biology and Evolution 07/2007; 24(6):1380-3. · 5.55 Impact Factor
