Origins of plastids and glyceraldehyde-3-phosphate dehydrogenase genes in the green-colored dinoflagellate Lepidodinium chlorophorum

The dinoflagellate Lepidodinium chlorophorum possesses "green" plastids containing chlorophylls a and b (Chl a+b), unlike most dinoflagellate plastids with Chl a+c plus a carotenoid peridinin (peridinin-containing plastids). In the present study we determined 8 plastid-encoded genes from Lepidodinium to investigate the origin of the Chl a+b-containing dinoflagellate plastids. The plastid-encoded gene phylogeny clearly showed that Lepidodinium plastids were derived from a member of Chlorophyta, consistent with pigment composition. We also isolated three different glyceraldehyde-3-phosphate dehydrogenase (GAPDH) genes from Lepidodinium-one encoding the putative cytosolic "GapC" enzyme and the remaining two showing affinities to the "plastid-targeted GapC" genes. In a GAPDH phylogeny, one of the plastid-targeted GapC-like sequences robustly grouped with those of dinoflagellates bearing peridinin-containing plastids, while the other was nested in a clade of the homologues of haptophytes and dinoflagellate genera Karenia and Karlodinium bearing "haptophyte-derived" plastids. Since neither host nor plastid phylogeny suggested an evolutionary connection between Lepidodinium and Karenia/Karlodinium, a lateral transfer of a plastid-targeted GapC gene most likely took place from a haptophyte or a dinoflagellate with haptophyte-derived plastids to Lepidodinium. The plastid-targeted GapC data can be considered as an evidence for the single origin of plastids in haptophytes, cryptophytes, stramenopiles, and alveolates. However, in the light of Lepidodinium GAPDH data, we need to closely examine whether the monophyly of the plastids in the above lineages inferred from plastid-targeted GapC genes truly reflects that of the host lineages.     

Selection of evolutionary models for phylogenetic hypothesis testing using parametric methods

Recent molecular studies have incorporated the parametric bootstrap method to test a priori hypotheses when the results of molecular based phylogenies are in conflict with these hypotheses. The parametric bootstrap requires the specification of a particular substitutional model, the parameters of which will be used to generate simulated, replicate DNA sequence data sets. It has been both suggested that, (a) the method appears robust to changes in the model of evolution, and alternatively that, (b) as realistic model of DNA substitution as possible should be used to avoid false rejection of a null hypothesis. Here we empirically evaluate the effect of suboptimal substitution models when testing hypotheses of monophyly with the parametric bootstrap using data sets of mtDNA cytochrome oxidase I and II (COI and COII) sequences for Macaronesian Calathus beetles, and mitochondrial 16S rDNA and nuclear ITS2 sequences for European Timarcha beetles. Whether a particular hypothesis of monophyly is rejected or accepted appears to be highly dependent on whether the nucleotide substitution model being used is optimal. It appears that a parameter rich model is either equally or less likely to reject a hypothesis of monophyly where the optimal model is unknown. A comparison of the performance of the Kishino–Hasegawa (KH) test shows it is not as severely affected by the use of suboptimal models, and overall it appears to be a less conservative method with a higher rate of failure to reject null hypotheses.     

Inference in response-adaptive clinical trials when the enrolled population varies over time

A common assumption of data analysis in clinical trials is that the patient population, as well as treatment effects, do not vary during the course of the study. However, when trials enroll patients over several years, this hypothesis may be violated. Ignoring variations of the outcome distributions over time, under the control and experimental treatments, can lead to biased treatment effect estimates and poor control of false positive results. We propose and compare two procedures that account for possible variations of the outcome distributions over time, to correct treatment effect estimates, and to control type-I error rates. The first procedure models trends of patient outcomes with splines. The second leverages conditional inference principles, which have been introduced to analyze randomized trials when patient prognostic profiles are unbalanced across arms. These two procedures are applicable in response-adaptive clinical trials. We illustrate the consequences of trends in the outcome distributions in response-adaptive designs and in platform trials, and investigate the proposed methods in the analysis of a glioblastoma study.     

EFFECT OF TAXON SAMPLING,CHARACTER WEIGHTING,AND COMBINED DATA ON THE INTERPRETATION OF RELATIONSHIPS AMONG THE HETEROKONT ALGAE1

Nuclear ribosomal small subunit and chloroplast rbcL sequence data for heterokont algae and potential outgroup taxa were analyzed separately and together using maximum parsimony. A series of taxon sampling and character weighting experiments was performed. Traditional classes (e.g. diatoms, Phaeophyceae, etc.) were monophyletic in most analyses of either data set and in analyses of combined data. Relationships among classes and of heterokont algae to outgroup taxa were sensitive to taxon sampling. Bootstrap (BS) values were not always predictive of stability of nodes in taxon sampling experiments or between analyses of different data sets. Reweighting sites by the rescaled consistency index artificially inflates BS values in the analysis of rbcL data. Inclusion of the third codon position from rbcL enhanced signal despite the superficial appearance of mutational saturation. Incongruence between data sets was largely due to placement of a few problematic taxa, and so data were combined. BS values for the combined analysis were much higher than for analyses of each data set alone, although combining data did not improve support for heterokont monophyly.     

Using connectivity to identify climatic drivers of local adaptation: a response to Macdonald et al.

Macdonald et al. (Ecol. Lett., 21, 2018, 207–216) proposed an analytical framework for identifying evolutionary processes underlying trait‐environment relationships observed in natural populations. Here, we propose an expanded and refined framework based on simulations and bootstrap‐based approaches, and we elaborate on an important statistical caveat common to most datasets.     

Analysis of genetic relationships between 10 cattle breeds with 17 microsatellites

To guide genetic conservation programmes with objective criteria, general genetic variability has to be taken into account. This study was conducted to determine the genetic variation between 10 cattle breeds by using 17 microsatellite loci and 13 biochemical markers (11 blood groups, the transferrin and β-casein loci). Microsatellite loci were amplified in 31–50 unrelated individuals from 10 cattle breeds: Charolais, Limousin, Breton Black Pied, Parthenais, Montbéliard, Vosgien, Maine-Anjou, Normande, Jersey and Holstein. Neighbor-joining trees were calculated from genetic distance estimates. The robustness of tree topology was obtained by bootstrap resampling of loci. A total of 210 alleles of the 17 microsatellites were detected in this study and average heterozygosities ranged from 0·53 in the Jersey breed to 0·66 in the Parthenais breed. In general, low bootstrap values were obtained: with the 17 microsatellites, the highest bootstrap values concerned the Holstein/Maine-Anjou grouping with an occurrence of 74%; with the biochemical markers, this node had an occurrence of 79% and the Charolais/Limousin grouping appeared with an occurrence of 74%; when microsatellites and biochemical polymorphism were analysed together, the occurrence of the Holstein/Maine-Anjou grouping was 90% and that of the Charolais/Limousin grouping was 42%. These results suggest that 30 microsatellites, a number currently considered as sufficient to distinguish closely related breeds is, in fact, probably insufficient.     

RST Calc: a collection of computer programs for calculating estimates of genetic differentiation from microsatellite data and determining their significance

R _ST, an analogue of F _ST, provides a convenient approach for estimating levels of genetic differentiation from microsatellite data. This paper examines current approaches for calculating estimates of R _ST and suggests a weighting scheme based on the transformation of allele sizes at loci across data sets. Combined within an analysis of variance framework this scheme yields an estimator of R _ST analogous to the θ estimator of F _ST. Software for the IBM-PC is described which carries out such calculations and assesses the significance of R _ST or Nm estimates using bootstrap and permutation tests.     

Identification of protein-protein binding sites by incorporating the physicochemical properties and stationary wavelet transforms into pseudo amino acid composition

With the explosive growth of protein sequences entering into protein data banks in the post-genomic era, it is highly demanded to develop automated methods for rapidly and effectively identifying the protein–protein binding sites (PPBSs) based on the sequence information alone. To address this problem, we proposed a predictor called iPPBS-PseAAC, in which each amino acid residue site of the proteins concerned was treated as a 15-tuple peptide segment generated by sliding a window along the protein chains with its center aligned with the target residue. The working peptide segment is further formulated by a general form of pseudo amino acid composition via the following procedures: (1) it is converted into a numerical series via the physicochemical properties of amino acids; (2) the numerical series is subsequently converted into a 20-D feature vector by means of the stationary wavelet transform technique. Formed by many individual “Random Forest” classifiers, the operation engine to run prediction is a two-layer ensemble classifier, with the 1st-layer voting out the best training data-set from many bootstrap systems and the 2nd-layer voting out the most relevant one from seven physicochemical properties. Cross-validation tests indicate that the new predictor is very promising, meaning that many important key features, which are deeply hidden in complicated protein sequences, can be extracted via the wavelets transform approach, quite consistent with the facts that many important biological functions of proteins can be elucidated with their low-frequency internal motions. The web server of iPPBS-PseAAC is accessible at http://www.jci-bioinfo.cn/iPPBS-PseAAC, by which users can easily acquire their desired results without the need to follow the complicated mathematical equations involved.     

Differentiation of fourHordeum sect.Stenostachys spp. (Poaceae: Triticeae) using multivariate morphometrics

Field collections and 296 herbarium sheets were examined for 27 morphometric variables. A priori species identifcation was based on geographical distribution except forH. californicum, a diploid species primarily occurring in California and differing from the much more widespread tetraploidH. brachyantherum that thrives in N. America and N.E. Asia;H. capense grows in S. Africa andH. secalinum mainly in Europe. Various cluster analyses were used followed by cluster recovery verification. Classificatory discriminant analysis and validation by the bootstrap yielded 85–90% overall total correct classification of the four species. Canonical analysis revealed thatH. californicum occupies an intermediate phenetic position among the other three distinct species. Factors of shape differences were unravelled and portrayed by shearing. A revised key to species was drawn up.