CiteULike: adacier's library [17 articles]

What is principal component analysis?

Markus Ringnér — 2008-03-09T04:13:08-00:00

Nature Biotechnology, Vol. 26, No. 3., pp. 303-304.

Simulating DNA coding sequence evolution with EvolveAGene 3

Barry Hall — 2008-03-10T00:47:43-00:00

Mol Biol Evol (12 January 2008), msn008.

Phylogenetic reconstruction based upon multiple alignments of molecular sequences is important to most branches of modern biology and is central to molecular evolution. Understanding the historical relationships among macromolecules depends upon computer programs that implement a variety of analytical methods. Because it is impossible to know those historical relationships with certainty, assessment of the accuracy of methods and the programs that implement them requires the use of programs that realistically simulate the evolution of DNA sequences EvolveAGene 3 is a realistic coding sequence simulation program that separates mutation from selection and allows the user to set selection conditions, including variable regions of selection intensity within the sequence and variation in intensity of selection over branches. Variation includes base substitutions, insertions and deletions. To the best of my knowledge it is the only program available that simulates the evolution of intact coding sequences. Output includes the true tree and true alignments of the resulting coding sequence and corresponding protein sequences. A log file reports the frequencies of each kind of base substitution, the ratio of transition to transversion substitutions, the ratio of indel to base substitution mutations, and the numbers of silent and amino acid replacement mutations. The realism of the data sets has been assessed by comparing the dN/dS ratio, the ratio of transition to transversion substitutions, and the ratio of indel to base substitution mutations of the simulated data sets with those parameters of real data sets from the "gold standard" BaliBase collection of structural alignments. Results show that the data sets produced by EvolveAGene 3 are very similar to real data sets, and EvolveAGene 3 is therefore a realistic simulation program that can be used to evaluate a variety of programs and methods in molecular evolution. 10.1093/molbev/msn008

Worldwide Human Relationships Inferred from Genome-Wide Patterns of Variation

Jun Li — 2008-02-22T08:51:45-00:00

Science, Vol. 319, No. 5866. (22 February 2008), pp. 1100-1104.

Human genetic diversity is shaped by both demographic and biological factors and has fundamental implications for understanding the genetic basis of diseases. We studied 938 unrelated individuals from 51 populations of the Human Genome Diversity Panel at 650,000 common single-nucleotide polymorphism loci. Individual ancestry and population substructure were detectable with very high resolution. The relationship between haplotype heterozygosity and geography was consistent with the hypothesis of a serial founder effect with a single origin in sub-Saharan Africa. In addition, we observed a pattern of ancestral allele frequency distributions that reflects variation in population dynamics among geographic regions. This data set allows the most comprehensive characterization to date of human genetic variation. 10.1126/science.1153717

Calculating Bootstrap Probabilities of Phylogeny Using Multilocus Sequence Data

Tae-Kun Seo — 2008-04-15T04:09:39-00:00

Mol Biol Evol, Vol. 25, No. 5. (1 May 2008), pp. 960-971.

Phylogeny estimation is extremely crucial in the study of molecular evolution. The increase in the amount of available genomic data facilitates phylogeny estimation from multilocus sequence data. Although maximum likelihood and Bayesian methods are available for phylogeny reconstruction using multilocus sequence data, these methods require heavy computation, and their application is limited to the analysis of a moderate number of genes and taxa. Distance matrix methods present suitable alternatives for analyzing huge amounts of sequence data. However, the manner in which distance methods can be applied to multilocus sequence data remains unknown. Here, we suggest new procedures to estimate molecular phylogeny using multilocus sequence data and evaluate its significance in the framework of the distance method. We found that concatenation of the multilocus sequence data may result in incorrect phylogeny estimation with an extremely high bootstrap probability (BP), which is due to incorrect estimation of the distances and intentional ignorance of the intergene variations. Therefore, we suggest that the distance matrices for multilocus sequence data be estimated separately and these matrices be subsequently combined to reconstruct phylogeny instead of phylogeny reconstruction using concatenated sequence data. To calculate the BPs of the reconstructed phylogeny, we suggest that 2-stage bootstrap procedures be adopted; in this, genes are resampled followed by resampling of the sequence columns within the resampled genes. By resampling the genes during calculation of BPs, intergene variations are properly considered. Via simulation studies and empirical data analysis, we demonstrate that our 2-stage bootstrap procedures are more suitable than the conventional bootstrap procedure that is adopted after sequence concatenation. 10.1093/molbev/msn043

Alignment Uncertainty and Genomic Analysis

Karen Wong — 2008-01-25T07:09:02-00:00

Science, Vol. 319, No. 5862. (25 January 2008), pp. 473-476.

The statistical methods applied to the analysis of genomic data do not account for uncertainty in the sequence alignment. Indeed, the alignment is treated as an observation, and all of the subsequent inferences depend on the alignment being correct. This may not have been too problematic for many phylogenetic studies, in which the gene is carefully chosen for, among other things, ease of alignment. However, in a comparative genomics study, the same statistical methods are applied repeatedly on thousands of genes, many of which will be difficult to align. Using genomic data from seven yeast species, we show that uncertainty in the alignment can lead to several problems, including different alignment methods resulting in different conclusions. 10.1126/science.1151532

What are artificial neural networks?

Anders Krogh — 2008-02-08T00:40:34-00:00

Nature Biotechnology, Vol. 26, No. 2., pp. 195-197.

Interaction networks for systems biology.

Samuel Bader — 2008-02-25T10:41:21-00:00

FEBS Lett (15 February 2008)

Cellular functions are almost always the result of the coordinated action of several proteins, interacting in protein complexes, pathways or networks. Progress made in devising suitable tools for analysis of protein-protein interactions, have recently made it possible to chart interaction networks on a large-scale. The aim of this review is to provide a short overview of the most promising contributions of interaction networks to human biology, structural biology and human genetics.

Bayesian support is larger than bootstrap support in phylogenetic inference: a mathematical argument

Tom Britton — 2008-03-16T15:10:15-00:00

Math Med Biol, Vol. 24, No. 4. (1 December 2007), pp. 401-411.

In phylogenetic inference, the support of an estimated phylogenetic tree topology and its interior branches is usually measured either with non-parametric bootstrap support (BS) values or with Bayesian posterior probabilities (BPPs). Extensive empirical evidence indicates that BPP values are systematically larger than BS when measured on the same data set, but there are no theoretical results supporting such a systematic difference. In the present note, we give a heuristic mathematical argument supporting the empirically observed phenomenon. The argument uses properties of the marginal and profile likelihoods of the normal distribution. The heuristic arguments are supported in a simulation study evaluating different steps in the argument. 10.1093/imammb/dqm008

phylogenetic comparative analysis

2008-05-02T02:13:06-00:00

Tempo and mode in evolution: phylogenetic inertia, adaptation and comparative methods

SP Blomberg — 2008-05-02T02:10:55-00:00

Journal of Evolutionary Biology, Vol. 15, No. 6. (2002), pp. 899-910.

Abstract Before the Evolutionary Synthesis, 'phylogenetic inertia' was associated with theories of orthogenesis, which claimed that organisms possessed an endogenous perfecting principle. The concept in the modern literature dates to Simpson (1944), who used 'evolutionary inertia' as a description of pattern in the fossil record. Wilson (1975) used 'phylogenetic inertia' to describe population-level or organismal properties that can affect the course of evolution in response to selection. Many current authors now view phylogenetic inertia as an alternative hypothesis to adaptation by natural selection when attempting to explain interspecific variation, covariation or lack thereof in phenotypic traits. Some phylogenetic comparative methods have been claimed to allow quantification and testing of phylogenetic inertia. Although some existing methods do allow valid tests of whether related species tend to resemble each other, which we term 'phylogenetic signal', this is simply pattern recognition and does not imply any underlying process. Moreover, comparative data sets generally do not include information that would allow rigorous inferences concerning causal processes underlying such patterns. The concept of phylogenetic inertia needs to be defined and studied with as much care as 'adaptation'.

The comparative method in conservation biology

Diana Fisher — 2008-05-02T01:54:55-00:00

Trends in Ecology & Evolution, Vol. 19, No. 7. (1 July 2004), pp. 391-398.

The phylogenetic comparative approach is a statistical method for analyzing correlations between traits across species. Whilst it has revolutionized evolutionary biology, can it work for conservation biology? Although it is correlative, advocates of the comparative method hope that it will reveal general mechanisms in conservation, provide shortcuts for prioritizing conservation research, and enable us to predict which species will experience (or create) problems in the future. Here, we ask whether these stated management goals are being achieved. We conclude that comparative methods are stimulating research into the ecological mechanisms underlying conservation, and are providing information for preemptive screening of problem species. But comparative analyses of extinction risk to date have tended to be too broad in scope to provide shortcuts to conserving particular endangered species. Correlates of vulnerability to conservation problems are often taxon, region and threat specific, so models must be narrowly focused to be of maximum practical use.

APE: Analyses of Phylogenetics and Evolution in R language

Emmanuel Paradis — 2008-05-02T01:53:49-00:00

Bioinformatics, Vol. 20, No. 2. (22 January 2004), pp. 289-290.

Summary: Analysis of Phylogenetics and Evolution (APE) is a package written in the R language for use in molecular evolution and phylogenetics. APE provides both utility functions for reading and writing data and manipulating phylogenetic trees, as well as several advanced methods for phylogenetic and evolutionary analysis (e.g. comparative and population genetic methods). APE takes advantage of the many R functions for statistics and graphics, and also provides a flexible framework for developing and implementing further statistical methods for the analysis of evolutionary processes. Availability: The program is free and available from the official R package archive at http://cran.r-project.org/src/contrib/PACKAGES.html#ape. APE is licensed under the GNU General Public License. 10.1093/bioinformatics/btg412

Phylogeny Shape and the Phylogenetic Comparative Method

Emilia Martins — 2008-05-02T01:53:50-00:00

Systematic Biology, Vol. 51, No. 6. (2002), pp. 873-880.

Phylogenetic comparative methods and the geography of speciation

Jonathan Losos — 2007-12-09T03:26:18-00:00

Trends in Ecology & Evolution, Vol. 18, No. 5. (May 2003), pp. 220-227.

The geography of speciation has long been contentious. In recent years, phylogenetic approaches have been proposed to determine the geographical mode of speciation. If reliable, these methods not only provide a means of settling the debate about the geography of speciation, but also indicate that sympatric speciation is surprisingly common and that peripatric speciation is relatively rare. Similar to any phylogenetic inference, reconstructions of speciation mode are only useful if the underlying assumptions of the method are met. In this case, the key assumption is that the geographical range of both extant and ancestral species at the time of speciation can be inferred from present-day distributions. We discuss whether, and under what circumstances, such assumptions could be met. We conclude that interspecific phylogenies are unable to test alternative hypotheses concerning the geography of speciation rigorously because of the lability of geographical ranges and the lack of correlation between the role of adaptive processes and geographical mode of speciation.

Phylogenetic comparative methods and the geographic range size – body size relationship in new world terrestrial carnivora

José Diniz-Filho — 2008-05-02T01:49:45-00:00

Evolutionary Ecology, Vol. 16, No. 4. (1 July 2002), pp. 351-367.

Most recent papers avoid describing macroecological relationships and interpreting then without a previous control of non-independence in data caused by phylogenetic patterns in data. In this paper, we analyzed the geographic range size – body size relationship for 70 species of New World terrestrial Carnivora (‘fissipeds’) using various phylogenetic comparative methods and simulation procedures to assess their statistical performance. Autocorrelation analyses suggested a strong phylogenetic pattern for body size, but not for geographic range size. The correlation between the two traits was estimated using standard Pearson correlation across species (TIPS) and four different comparative methods: Felsenstein's independent contrasts (PIC), autoregressive method (ARM), phylogenetic eigenvector regression (PVR) and phylogenetic generalized least-squares (PGLS). The correlation between the two variables was significant for all methods, except PIC, in such a way that ecological mechanisms (i.e., minimum viable population or environmental heterogeneity- physiological homeostasis), could be valid explanations for the relationship. Simulations using different O-U processes for each trait were run in order to estimate true Type I errors of each method. Type I errors at 5% were similar for all phylogenetic methods (always lower than 8%), but equal to 13.1% for TIPS. PIC usually performs better than all other methods under Brownian motion evolution, but not in this case using a more complex combination of evolutionary models. So, recent claims that using independent contrasts in ecological research can be too conservative are correct but, on the other hand, using simple across-species correlation is too liberal even under the more complex evolutionary models exhibited by the traits analyzed here.

From Genotype to Phenotype: Systems Biology Meets Natural Variation

Philip Benfey — 2008-04-24T22:05:03-00:00

Science, Vol. 320, No. 5875. (25 April 2008), pp. 495-497.

The promise that came with genome sequencing was that we would soon know what genes do, particularly genes involved in human diseases and those of importance to agriculture. We now have the full genomic sequence of human, chimpanzee, mouse, chicken, dog, worm, fly, rice, and cress, as well as those for a wide variety of other species, and yet we still have a lot of trouble figuring out what genes do. Mapping genes to their function is called the "genotype-to-phenotype problem," where phenotype is whatever is changed in the organism when a gene's function is altered. 10.1126/science.1153716

Adaptation and the comparative method

Emília Martins — 2008-05-02T01:33:53-00:00

Trends in Ecology & Evolution, Vol. 15, No. 7. (1 July 2000), pp. 296-299.

Over the past two decades, it has become widely accepted that phylogenies need to be incorporated into statistical analyses of interspecific data. However, recent debate has focused on whether it is appropriate to apply phylogenetic comparative methods (PCMs) to the study of adaptation. Although some of the criticisms are serious, it is premature to stop applying PCMs altogether. New statistical methods designed explicitly for the comparative study of adaptation overcome these criticisms and offer fresh insights into the evolution of phenotypes.