Lipid classification, structures and tools

The study of lipids has developed into a research field of increasing importance as their multiple biological roles in cell biology, physiology and pathology are becoming better understood. The Lipid Metabolites and Pathways Strategy (LIPID MAPS) consortium is actively involved in an integrated approach for the detection, quantitation and pathway reconstruction of lipids and related genes and proteins at a systems-biology level. A key component of this approach is a bioinformatics infrastructure involving a clearly defined classification of lipids, a state-of-the-art database system for molecular species and experimental data and a suite of user-friendly tools to assist lipidomics researchers. Herein, we discuss a number of recent developments by the LIPID MAPS bioinformatics core in pursuit of these objectives.     

Topological classification of RNA structures

We present a novel topological classification of RNA secondary structures with pseudoknots. It is based on the topological genus of the circular diagram associated to the RNA base-pair structure. The genus is a positive integer number whose value quantifies the topological complexity of the folded RNA structure. In such a representation, planar diagrams correspond to pure RNA secondary structures and have zero genus, whereas non-planar diagrams correspond to pseudoknotted structures and have higher genus. The topological genus allows for the definition of topological folding motifs, similar in spirit to those introduced and commonly used in protein folding. We analyze real RNA structures from the databases Worldwide Protein Data Bank and Pseudobase and classify them according to their topological genus. For simplicity, we limit our analysis by considering only Watson-Crick complementary base pairs and G-U wobble base pairs. We compare the results of our statistical survey with existing theoretical and numerical models. We also discuss possible applications of this classification and show how it can be used for identifying new RNA structural motifs.     

ConStruct: Improved construction of RNA consensus structures

Background  

Aligning homologous non-coding RNAs (ncRNAs) correctly in terms of sequence and structure is an unresolved problem, due to both mathematical complexity and imperfect scoring functions. High quality alignments, however, are a prerequisite for most consensus structure prediction approaches, homology searches, and tools for phylogeny inference. Automatically created ncRNA alignments often need manual corrections, yet this manual refinement is tedious and error-prone.     

Prediction of consensus RNA secondary structures including pseudoknots

Most functional RNA molecules have characteristic structures that are highly conserved in evolution. Many of them contain pseudoknots. Here, we present a method for computing the consensus structures including pseudoknots based on alignments of a few sequences. The algorithm combines thermodynamic and covariation information to assign scores to all possible base pairs, the base pairs are chosen with the help of the maximum weighted matching algorithm. We applied our algorithm to a number of different types of RNA known to contain pseudoknots. All pseudoknots were predicted correctly and more than 85 percent of the base pairs were identified.     

Gene expression data classification using consensus independent component analysis

We propose a new method for tumor classification from gene expression data, which mainly contains three steps. Firstly, the original DNA microarray gene expression data are modeled by independent component analysis （ICA）. Secondly, the most discriminant eigenassays extracted by ICA are selected by the sequential floating forward selection technique. Finally, support vector machine is used to classify the modeling data. To show the validity of the proposed method, we applied it to classify three DNA microarray datasets involving various human normal and tumor tissue samples. The experimental results show that the method is efficient and feasible.     

A topological classification of G-quadruplex structures

A topological classification of most quadruplex structures is proposed, based on two main characteristics: 1) the relative orientation of the strands and 2) the nature of the loops connecting the strands.     

Identification of consensus RNA secondary structures using suffix arrays

Background  

The identification of a consensus RNA motif often consists in finding a conserved secondary structure with minimum free energy in an ensemble of aligned sequences. However, an alignment is often difficult to obtain without prior structural information. Thus the need for tools to automate this process.     

Combining multiple classifications of chemical structures using consensus clustering

Consensus clustering involves combining multiple clusterings of the same set of objects to achieve a single clustering that will, hopefully, provide a better picture of the groupings that are present in a dataset. This Letter reports the use of consensus clustering methods on sets of chemical compounds represented by 2D fingerprints. Experiments with DUD, IDAlert, MDDR and MUV data suggests that consensus methods are unlikely to result in significant improvements in clustering effectiveness as compared to the use of a single clustering method.     

Updated classification of hemangiomas and other vascular anomalies

Vascular anomalies comprise a widely heterogenous group of tumors and malformations. Great confusion has arisen because of the term hemangioma has been and is continued to be used to represent a multitude of vascular entities. This review presents the updated classification of vascular anomalies with the goal of clarifying the term hemangioma. In addition, newer clinical concepts in hemangiomas and other vascular tumors is presented. Hemangioma subtypes and hemangioma variants are also discussed, and a brief review of pyogenic granuloma and Kaposiform hemangioendothelioma is provided. Finally, the immunohistochemical marker GLUT1 is reviewed, a marker that heralds a new era in vascular anomalies research.     

Statistical analysis and parsimonious modelling of dendrograms of in vitro neurones

The processes whereby developing neurones acquire morphological features that are common to entire populations (thereby allowing the definition of neuronal types) are still poorly understood. A mathematical model of neuronal arborizations may be useful to extract basic parameters or organization rules, hence helping to achieve a better understanding of the underlying growth processes. We present a parsimonious statistical model, intended to describe the topological organization of neuritic arborizations with a minimal number of parameters. It is based on a probability of splitting which depends only on the centrifugal order of segments. We compare the predictions made by the model of several topological properties of neurones with the corresponding actual values measured on a sample of honeybee (olfactory) antennal lobe neurones grown in primary culture, described in a previous study. The comparison is performed for three populations of segments corresponding to three neuronal morphological types previously identified and described in this sample. We show that simple assumptions together with the knowledge of a very small number of parameters allow the topological reconstruction of representative (bi-dimensional) biological neurones. We discuss the biological significance (in terms of possible factors involved in the determinism of neuronal types) of both common properties and cell-type specific features, observed on the neurones and predicted by the model. These authors contributed equally to this work.     

A consensus multi-view multi-objective gene selection approach for improved sample classification

Background

High-dimensional flow cytometry and mass cytometry allow systemic-level characterization of more than 10 protein profiles at single-cell resolution and provide a much broader landscape in many biological applications, such as disease diagnosis and prediction of clinical outcome. When associating clinical information with cytometry data, traditional approaches require two distinct steps for identification of cell populations and statistical test to determine whether the difference between two population proportions is significant. These two-step approaches can lead to information loss and analysis bias.

Results

We propose a novel statistical framework, called LAMBDA (Latent Allocation Model with Bayesian Data Analysis), for simultaneous identification of unknown cell populations and discovery of associations between these populations and clinical information. LAMBDA uses specified probabilistic models designed for modeling the different distribution information for flow or mass cytometry data, respectively. We use a zero-inflated distribution for the mass cytometry data based the characteristics of the data. A simulation study confirms the usefulness of this model by evaluating the accuracy of the estimated parameters. We also demonstrate that LAMBDA can identify associations between cell populations and their clinical outcomes by analyzing real data. LAMBDA is implemented in R and is available from GitHub (https://github.com/abikoushi/lambda).

     

Topological classification and enumeration of RNA structures by genus

To an RNA pseudoknot structure is naturally associated a topological surface, which has its associated genus, and structures can thus be classified by the genus. Based on earlier work of Harer–Zagier, we compute the generating function $\mathbf{D}_{g,\sigma }(z)=\sum _{n}\mathbf{d}_{g,\sigma }(n)z^n$ for the number $\mathbf{d}_{g,\sigma }(n)$ of those structures of fixed genus $g$ and minimum stack size $\sigma $ with $n$ nucleotides so that no two consecutive nucleotides are basepaired and show that $\mathbf{D}_{g,\sigma }(z)$ is algebraic. In particular, we prove that $\mathbf{d}_{g,2}(n)\sim k_g\,n^{3(g-\frac{1}{2})} \gamma _2^n$ , where $\gamma _2\approx 1.9685$ . Thus, for stack size at least two, the genus only enters through the sub-exponential factor, and the slow growth rate compared to the number of RNA molecules implies the existence of neutral networks of distinct molecules with the same structure of any genus. Certain RNA structures called shapes are shown to be in natural one-to-one correspondence with the cells in the Penner–Strebel decomposition of Riemann’s moduli space of a surface of genus $g$ with one boundary component, thus providing a link between RNA enumerative problems and the geometry of Riemann’s moduli space.     

Automated functional classification of experimental and predicted protein structures

Background  

Proteins that are similar in sequence or structure may perform different functions in nature. In such cases, function cannot be inferred from sequence or structural similarity.     

Automated motif extraction and classification in RNA tertiary structures

We used a novel graph-based approach to extract RNA tertiary motifs. We cataloged them all and clustered them using an innovative graph similarity measure. We applied our method to three widely studied structures: Haloarcula marismortui 50S (H.m 50S), Escherichia coli 50S (E. coli 50S), and Thermus thermophilus 16S (T.th 16S) RNAs. We identified 10 known motifs without any prior knowledge of their shapes or positions. We additionally identified four putative new motifs.     

RNA secondary structures: comparison and determination of frequently recurring substructures by consensus

A method for assessing the preserved stem - loops of RNA secondarystructures is presented. Frequently recurring helical stemsin a set of secondary structures resulting from the simulatedfolding process of a given RNA are assessed and consensus structuralmotifs can then be selected to construct a secondary structureof the RNA. Alternatively, it can be applied to a series of‘optimal’ and ‘suboptimal’ secondarystructures computed using the dynamic program developed by Williamsand Tinoco. To demonstrate the power and the usefulness of theprogram we give examples of this procedure. Received on October 28, 1987; accepted on April 2, 1989     

Predicting antibody complementarity determining region structures without classification

Antibodies are used extensively in medical and biological research. Their complementarity determining regions (CDRs) define the majority of their antigen binding functionality. CDR structures have been intensively studied and classified (canonical structures). Here we show that CDR structure prediction is no different from the standard loop structure prediction problem and predict them without classification. FREAD, a successful database loop prediction technique, is able to produce accurate predictions for all CDR loops (0.81, 0.42, 0.96, 0.98, 0.88 and 2.25 ? RMSD for CDR-L1 to CDR-H3). In order to overcome the relatively poor predictions of CDR-H3, we developed two variants of FREAD, one focused on sequence similarity (FREAD-S) and another which includes contact information (ConFREAD). Both of the methods improve accuracy for CDR-H3 to 1.34 ? and 1.23 ? respectively. The FREAD variants are also tested on homology models and compared to RosettaAntibody (CDR-H3 prediction on models: 1.98 and 2.62 ? for ConFREAD and RosettaAntibody respectively). CDRs are known to change their structural conformations upon binding the antigen. Traditional CDR classifications are based on sequence similarity and do not account for such environment changes. Using a set of antigen-free and antigen-bound structures, we compared our FREAD variants. ConFREAD which includes contact information successfully discriminates the bound and unbound CDR structures and achieves an accuracy of 1.35 ? for bound structures of CDR-H3.     

Undertaking research in other countries: national ethico-legal barometers and international ethical consensus statements

Is it ethical for scientists to conduct or to benefit from research in another country if that research would be unlawful, or not generally accepted, in their own country?     

An evidence-based consensus for the classification of arbuscular mycorrhizal fungi (Glomeromycota)

The publication of a large number of taxon names at all levels within the arbuscular mycorrhizal fungi (Glomeromycota) has resulted in conflicting systematic schemes and generated considerable confusion among biologists working with these important plant symbionts. A group of biologists with more than a century of collective experience in the systematics of Glomeromycota examined all available molecular–phylogenetic evidence within the framework of phylogenetic hypotheses, incorporating morphological characters when they were congruent. This study is the outcome, wherein the classification of Glomeromycota is revised by rejecting some new names on the grounds that they are founded in error and by synonymizing others that, while validly published, are not evidence-based. The proposed “consensus” will provide a framework for additional original research aimed at clarifying the evolutionary history of this important group of symbiotic fungi.