Implications from a 28S rRNA gene fragment for the phylogenetic relationships of halichondrid sponges (Porifera: Demospongiae)

Halichondrida is a pivotal demosponge order of which the classification underwent major changes in the recent history. The monophyly of this order and its intra-ordinal phylogeny cannot be reliably determined on the basis of morphology. Here we present a 28Sr RNA gene tree of selected halichondrids, which supports the hypothesis of halichondrid non-monophyly and elucidates further inter-ordinal relationships. We enlarged the analysis by previously published sequences, discuss how previous analyses suffer from taxon bias and analyse the resulting phylogenetic implications. Most halichondrid families (in particular Axinellidae und Dictyonellidae) cluster polyphyletic and the molecular classification of several genera does not agree with the current (morphological) system.     

An evaluation of support for order-level monophyly and interrelationships within the class Demospongiae using partial data from the large subunit rDNA and cytochrome oxidase subunit I

Large subunit ribosomal DNA (LSU rDNA) sequence data from 120 taxa and cytochrome oxidase subunit 1(COI) sequence data from 27 taxa are analyzed separately and together to estimate the internal phylogeny of the class Demospongiae and to evaluate how consistent these data are with pre-existing hypotheses of relationship concerning order-level monophyly and relationships. The monophyly of Porifera is only slightly inconsistent with LSU data, which do not support the monophyly of the class Demospongiae regardless of the inclusion or exclusion of Homoscleromopha (this result is likely due to the placement of a single hexactinellid taxon within the Demospongiae), however, no LSU support is found for the monophyly of Silicea (Demospongiae+Hexactinellida) unless homoscleromorphs are excluded. Neither the subclasses Ceractinomorpha and Tetractinomorpha, nor the orders Halichondrida, Hadromerida, and Haplosclerida are supported as monophyletic under any data partition. The haplosclerid suborders Haplosclerina and Petrosina are supported as monophyletic to the exclusion of the suborder Spongillina, and the orders Dictyoceratida, Verongida, Poecilosclerida, Astrophorida, Spirophorida, Homosclerophorida, and Agelasida are largely reconstructed as monophyletic, with the exception of few anomalously placed taxa. Few inter-order relationships are strongly supported by any data partition, but there is moderate support for a verongid+chondrosid clade and a tetractinellid+halichondrid clade. Furthermore, LSU data strongly support the existence of two novel clades that do not correspond to the existing classification and that show no morphological uniformity. Finally, every data partition supports the monophyly of a clade that includes the order Agelasida, some members of the genus Axinella, and two taxa tentatively identified as belonging to the orders Hadromerida and Halichondrida.     

Data mining on DNA sequences of hepatitis B virus

Extraction of meaningful information from large experimental data sets is a key element in bioinformatics research. One of the challenges is to identify genomic markers in Hepatitis B Virus (HBV) that are associated with HCC (liver cancer) development by comparing the complete genomic sequences of HBV among patients with HCC and those without HCC. In this study, a data mining framework, which includes molecular evolution analysis, clustering, feature selection, classifier learning, and classification, is introduced. Our research group has collected HBV DNA sequences, either genotype B or C, from over 200 patients specifically for this project. In the molecular evolution analysis and clustering, three subgroups have been identified in genotype C and a clustering method has been developed to separate the subgroups. In the feature selection process, potential markers are selected based on Information Gain for further classifier learning. Then, meaningful rules are learned by our algorithm called the Rule Learning, which is based on Evolutionary Algorithm. Also, a new classification method by Nonlinear Integral has been developed. Good performance of this method comes from the use of the fuzzy measure and the relevant nonlinear integral. The nonadditivity of the fuzzy measure reflects the importance of the feature attributes as well as their interactions. These two classifiers give explicit information on the importance of the individual mutated sites and their interactions toward the classification (potential causes of liver cancer in our case). A thorough comparison study of these two methods with existing methods is detailed. For genotype B, genotype C subgroups C1, C2, and C3, important mutation markers (sites) have been found, respectively. These two classification methods have been applied to classify never-seen-before examples for validation. The results show that the classification methods have more than 70 percent accuracy and 80 percent sensitivity for most data sets, which are considered high as an initial scanning method for liver cancer diagnosis.     

New fungal classification and their applications in medicine

Fungi are gaining importance with the increased incidence of invasive, often fatal mycoses, in immunocompromised patients. In addition, the increased number of emerging opportunistic pathogens has prompted interest in studies pertaining to fungal classification. The traditional methods of identification and classification of these microorganisms are based on the morphology of the sexual and asexual reproductive structures. Recently, this system of classification has been shown incongruent with approaches that better establish phylogenetic relationships among these organisms. Novel aspects of fungal reclassification are described as follows: 1) the position of fungi in the historical classification systems, 2) the recognition of their polyphyly, based on comparative studies of nucleic acid sequences and on analysis of descent lines and, 3) the contribution of molecular phylogeny to medical mycology, including identification of mycotic agents, their molecular epidemiology, prevention of infection and methods of diagnosis.     

Phylogenetic relationships of the Sparidae (Teleostei: Percoidei) and implications for convergent trophic evolution

A phylogenetic analysis of the majority of sparid genera and representatives of the sparoid families Centracanthidae, Lethrinidae and Nemipteridae is presented using 87 predominately osteological characters. The Sparidae constitute a monophyletic grouping, with the inclusion of the centracanthid Spicara smaris , which nests deep within the ingroup. The phylogeny was then used to investigate agreement with the most recent molecular study, taxonomic stability of subfamilial classification and the evolution of feeding strategies. Results show that the incongruence between morphological and molecular data appears largely to be an artifact of errors in rooting. However, there appears to be real and substantial conflict between the molecular tree and the morphological data, which is not attributable to the different positions of the least congruent taxa. The data support the molecular hypothesis that none of the subfamilial classification, based on dentition and trophic specialization, is monophyletic, and should be rejected pending further taxonomic revision. The phylogeny supports multiple independent origins of trophic types and it is suggested that the evolutionary plasticity of the oral teeth of sparids has been fundamental to the adaptive radiation of this family compared to their closest allies. ©2002 The Linnean Society of London, Biological Journal of the Linnean Society , 2002, 76 , 269–301.     

Evolutionary history of the Tricladida and the Platyhelminthes: an up-to-date phylogenetic and systematic account

Within the free-living platyhelminths, the triclads, or planarians, are the best-known group, largely as a result of long-standing and intensive research on regeneration, pattern formation and Hox gene expression. However, the group's evolutionary history has been long debated, with controversies ranging from their phyletic structure and position within the Metazoa to the relationships among species within the Tricladida. Over the the last decade, with the advent of molecular phylogenies, some of these issues have begun to be resolved. Here, we present an up-to-date summary of the main phylogenetic changes and novelties with some comments on their evolutionary implications. The phylum has been split into two groups, and the position of the main group (the Rhabdithophora and the Catenulida), close to the Annelida and the Mollusca within the Lophotrochozoa, is now clear. Their internal relationships, although not totally resolved, have been clarified. Tricladida systematics has also experienced a revolution since the implementation of molecular data. The terrestrial planarians have been demonstrated to have emerged from one of the freshwater families, giving a different view of their evolution and greatly altering their classification. The use of molecular data is also facilitating the identification of Tricladida species by DNA barcoding, allowing better knowledge of their distribution and genetic diversity. Finally, molecular phylogenetic and phylogeographical analyses, taking advantage of recent data, are beginning to give a clear picture of the recent history of the Dugesia and Schmidtea species in the Mediterranean.     

Molecular phylogenies challenge the classification of Polymastiidae (Porifera,Demospongiae) based on morphology

Polymastiidae Gray, 1867 is a worldwide distributed sponge family, which has a great significance for understanding of the demosponge deep phylogeny since the former order Hadromerida Topsent, 1894 has been recently split based on the molecular evidence and a new separate order has been established for the polymastiids. However, molecular data obtained from Polymastiidae so far are scarce, while the phylogenetic reconstruction based on morphology has faced a deficit of characters along with the vagueness of their states. The present study is a phylogenetic reconstruction of Polymastiidae based on novel data on two molecular markers, cytochrome oxidase subunit I and large subunit ribosomal DNA, obtained from a broad set of species. Monophyly of the family and nonmonophyly of four polymastiid genera are revealed, suggesting a high level of homoplasy of morphological characters, which are therefore not an appropriate base for the natural classification of Polymastiidae. Although the presented phylogenies cannot yet provide an alternative classification scheme, several strongly supported clades, which may be used as reference points in future classification, are recovered and three taxonomic actions are proposed: transfer of one species from Radiella to Polymastia Bowerbank, 1862; transfer of three species from Radiella Schmidt, 1870 to Spinularia Gray, 1867; and the consequent abandonment of Radiella.     

基于多组学数据的早期宫颈鳞状细胞癌分类

从分子层面对泛癌进行研究已经得到了很大的进展，但是对宫颈鳞状细胞癌的分子分类研究仍然需要更多的探索. 为了找到宫颈鳞状细胞癌潜在的子类，本文提出了一个基于多维组学数据的癌症亚型分类分析流程. 通过统计学方法对癌症基因组图谱（The Cancer Genome Atlas，TCGA）宫颈鳞状细胞癌的mRNA表达数据、小分子核糖核酸（microRNA，miRNA）表达数据、DNA甲基化数据以及拷贝数变异数据4个维度包含的分子进行筛选，然后对筛选后的分类特征进行整合聚类，进一步筛选能够区分不同子类的关键分类特征，并使用这些关键分类特征建立宫颈鳞状细胞癌分类模型. 本研究为宫颈鳞状细胞癌分子层面子类的识别提供了分析流程，得到了两个临床生存水平具有显著性差异的宫颈鳞状细胞癌子类，并确定了8个宫颈鳞状细胞癌的关键分类特征. 本研究中识别的宫颈鳞状细胞癌子类和关键分类特征为宫颈鳞状细胞癌早期分类及分类标志物的鉴定提供了重要参考.     

A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification

Background  

Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray technology with several molecular signatures on their way toward clinical deployment. Use of the most accurate classification algorithms available for microarray gene expression data is a critical ingredient in order to develop the best possible molecular signatures for patient care. As suggested by a large body of literature to date, support vector machines can be considered "best of class" algorithms for classification of such data. Recent work, however, suggests that random forest classifiers may outperform support vector machines in this domain.     

Galápagos' Opuntia (prickly pear) cacti: extensive morphological diversity, low genetic variability

Due to the pronounced morphological variation and geographical distribution of Galápagos' Opuntia cacti, numerous hypotheses have been advanced regarding their radiation, diversification, and classification. The currently accepted classification is based on morphology and recognizes six species and fourteen varieties, but the plasticity of many of the characteristics renders any morphological taxonomy problematic. Our analysis of previously published morphological data agrees only partially with the current classification. We present the first molecular phylogeny of these plants. Multiple DNA sequences indicate little genetic distinction among the currently identified species, despite restricted gene flow and limited long distance dispersal within the archipelago. No clear relationship exists between morphological and genetic differences. These results suggest that both molecular and morphological data should be used in conservation planning.  © 2009 The Linnean Society of London, Biological Journal of the Linnean Society , 2009, 96 , 451–461.     

Bayesian analysis of population structure based on linked molecular information

The Bayesian model-based approach to inferring hidden genetic population structures using multilocus molecular markers has become a popular tool within certain branches of biology. In particular, it has been shown that heterogeneous data arising from genetically dissimilar latent groups of individuals can be effectively modelled using an unsupervised classification formulation. However, most currently employed models ignore potential linkage within the employed molecular information, and can therefore lead to biased inferences under certain circumstances. Utilizing the general theory of graphical models, we develop a framework that accounts for dependences both within linked molecular marker loci and DNA sequence data. Due to a high level of sequence conservation among eukaryotic species, the latter aspect is particularly relevant for analyzing rapidly evolving microbial species. The advantages of incorporating the dependence due to linkage in the classification models are illustrated by analyses of both simulated data and real samples of Bacillus cereus.     

Ancestral state reconstruction reveals multiple independent evolution of diagnostic morphological characters in the "Higher Oribatida" (Acari), conflicting with current classification schemes

Background  

The use of molecular genetic data in phylogenetic systematics has revolutionized this field of research in that several taxonomic groupings defined by traditional taxonomic approaches have been rejected by molecular data. The taxonomic classification of the oribatid mite group Circumdehiscentiae ("Higher Oribatida") is largely based on morphological characters and several different classification schemes, all based upon the validity of diagnostic morphological characters, have been proposed by various authors. The aims of this study were to test the appropriateness of the current taxonomic classification schemes for the Circumdehiscentiae and to trace the evolution of the main diagnostic traits (the four nymphal traits scalps, centrodorsal setae, sclerits and wrinkled cuticle plus octotaxic system and pteromorphs both in adults) on the basis of a molecular phylogenetic hypothesis by means of parsimony, likelihood and Bayesian approaches.     

Topology-based cancer classification and related pathway mining using microarray data

Cancer classification is the critical basis for patient-tailored therapy, while pathway analysis is a promising method to discover the underlying molecular mechanisms related to cancer development by using microarray data. However, linking the molecular classification and pathway analysis with gene network approach has not been discussed yet. In this study, we developed a novel framework based on cancer class-specific gene networks for classification and pathway analysis. This framework involves a novel gene network construction, named ordering network, which exhibits the power-law node-degree distribution as seen in correlation networks. The results obtained from five public cancer datasets showed that the gene networks with ordering relationship are better than those with correlation relationship in terms of accuracy and stability of the classification performance. Furthermore, we integrated the ordering networks, classification information and pathway database to develop the topology-based pathway analysis for identifying cancer class-specific pathways, which might be essential in the biological significance of cancer. Our results suggest that the topology-based classification technology can precisely distinguish cancer subclasses and the topology-based pathway analysis can characterize the correspondent biochemical pathways even if there are subtle, but consistent, changes in gene expression, which may provide new insights into the underlying molecular mechanisms of tumorigenesis.     

Karyology, generic delineation and dysploidy in the genera Carduncellus, Carthamus and Phonus (Asteraceae)

Within the Mediterranean complex Carduncellus Carthamus Phonus , taxonomic classification has proven problematical. A recent molecular analysis has clarified the generic delineation, while a revision of the abundant karyological data has suggested that correlation of taxonomic classification and karyology would be of great interest. In this paper, a confirmation of chromosome numbers in the complex is offered, with 44 populations of 30 taxa studied. Six of the counts are new. A short analysis of the correlation between base chromosome numbers and generic classification follows. The main conclusion is that descending dysploidy is the main mechanism of karyological evolution in the complex, as has been the case in other genera of the subtribe.     

Classification using partial least squares with penalized logistic regression

MOTIVATION: One important aspect of data-mining of microarray data is to discover the molecular variation among cancers. In microarray studies, the number n of samples is relatively small compared to the number p of genes per sample (usually in thousands). It is known that standard statistical methods in classification are efficient (i.e. in the present case, yield successful classifiers) particularly when n is (far) larger than p. This naturally calls for the use of a dimension reduction procedure together with the classification one. RESULTS: In this paper, the question of classification in such a high-dimensional setting is addressed. We view the classification problem as a regression one with few observations and many predictor variables. We propose a new method combining partial least squares (PLS) and Ridge penalized logistic regression. We review the existing methods based on PLS and/or penalized likelihood techniques, outline their interest in some cases and theoretically explain their sometimes poor behavior. Our procedure is compared with these other classifiers. The predictive performance of the resulting classification rule is illustrated on three data sets: Leukemia, Colon and Prostate.     

Reassessment of morphological characteristics in freshwater eels (genus Anguilla, Anguillidae) shows congruence with molecular phylogeny estimates

The morphology-based phylogeny of freshwater eels, proposed by V. Ege in 1939, has been accepted as the basis of eel classification since that time. However, this has been called into question by recent molecular studies. Most of the morphological characteristics recognized by Ege are morphometric. Since methods for the application of morphometric data to phylogeny construction have not been fully established, it is unclear whether the observed discrepancies between morphological and molecular data arise from intrinsic differences or from flawed analyses. Here, we have used two methods to assemble evolutionary trees from distance matrices constructed according to Ege's data, the neighbor-joining (NJ) method and the minimum network (MinNet) method; the latter is based on an evolutionary algorithm. After reanalysing Ege's morphological data, we found that both methods gave results consistent with those based on molecular data, although not with Ege's original classification. Therefore, we speculate that some morphological features Ege used to subdivide the eel groups may not be synapomorphic as he proposed, but symplesiomorphic or convergent . The method developed here may prove useful for constructing phylogeny for taxon groups where only continuous morphometric characteristics are recognized, such as the freshwater eels.     

Sequential linear neighborhood propagation for semi-supervised protein function prediction

Predicting protein function is one of the most challenging problems of the post-genomic era. The development of experimental methods for genome scale analysis of molecular interaction networks has provided new approaches to inferring protein function. In this paper we introduce a new graph-based semi-supervised classification algorithm Sequential Linear Neighborhood Propagation (SLNP), which addresses the problem of the classification of partially labeled protein interaction networks. The proposed SLNP first constructs a sequence of node sets according to their shortest distance to the labeled nodes, and then predicts the function of the unlabel proteins from the set closer to labeled one, using Linear Neighborhood Propagation. Its performance is assessed on the Saccharomyces cerevisiae PPI network data sets, with good results compared with three current state-of-the-art algorithms, especially in settings where only a small fraction of the proteins are labeled.     

PCA disjoint models for multiclass cancer analysis using gene expression data

MOTIVATION: Microarray expression profiling appears particularly promising for a deeper understanding of cancer biology and to identify molecular signatures supporting the histological classification schemes of neoplastic specimens. However, molecular diagnostics based on microarray data presents major challenges due to the overwhelming number of variables and the complex, multiclass nature of tumor samples. Thus, the development of marker selection methods, that allow the identification of those genes that are most likely to confer high classification accuracy of multiple tumor types, and of multiclass classification schemes is of paramount importance. RESULTS: A computational procedure for marker identification and for classification of multiclass gene expression data through the application of disjoint principal component models is described. The identified features represent a rational and dimensionally reduced base for understanding the basic biology of diseases, defining targets for therapeutic intervention, and developing diagnostic tools for the identification and classification of multiple pathological states. The method has been tested on different microarray data sets obtained from various human tumor samples. The results demonstrate that this procedure allows the identification of specific phenotype markers and can classify previously unseen instances in the presence of multiple classes.     

Short interspersed elements (SINEs) in plants: origin, classification, and use as phylogenetic markers

Short interspersed elements (SINEs) are a class of dispersed mobile sequences that use RNA as an intermediate in an amplification process called retroposition. The presence-absence of a SINE at a given locus has been used as a meaningful classification criterion to evaluate phylogenetic relations among species. We review here recent developments in the characterisation of plant SINEs and their use as molecular makers to retrace phylogenetic relations among wild and cultivated Oryza and Brassica species. In Brassicaceae, further use of SINE markers is limited by our partial knowledge of endogenous SINE families (their origin and evolution histories) and by the absence of a clear classification. To solve this problem, phylogenetic relations among all known Brassicaceae SINEs were analyzed and a new classification, grouping SINEs in 15 different families, is proposed. The relative age and size of each Brassicaceae SINE family was evaluated and new phylogenetically supported subfamilies were described. We also present evidence suggesting that new potentially active SINEs recently emerged in Brassica oleracea from the shuffling of preexisting SINE portions. Finally, the comparative evolution history of SINE families present in Arabidopsis thaliana and Brassica oleracea revealed that SINEs were in general more active in the Brassica lineage. The importance of these new data for the use of Brassicaceae SINEs as molecular markers in future applications is discussed.