Large Molecular Datasets Research Articles

Molecular phylogeny of the Neotropical fish genus Tetragonopterus (Teleostei: Characiformes: Characidae)

Tetragonopterinae encompasses characid species of the genus Tetragonopterus, which are widely distributed throughout east of the Andes in South America. While taxonomy has recently clarified the species diversity and molecular evidence strongly supports the monophyly of Tetragonopterus, no interspecific relationship studies are currently available. Here we used a large molecular dataset composed of two mitochondrial and three nuclear loci containing an extensive taxon sampling within the family Characidae and included eleven species of Tetragonopterus to generate the first time-calibrated phylogeny for Tetragonopterinae. Our results support monophyly of the subfamily represented solely by Tetragonopterus and corroborate previous molecular hypothesis of close relationship with Exodon plus Roeboexodon and the subfamily Characinae. Internally, we found Moenkhausia georgiae as sister species to all remaining species followed by T. rarus, being both species endemic to the Guiana Shield drainages. Species-level relationships are first hypothesized and putative morphological apomorphies are discussed as support to monophyletic clades. Our time-calibrated phylogeny suggested an origin of the genus during the Late Oligocene–Early Miocene. We hypothesized that the Andean geological activity followed by transformations in the Amazonian hydrographic scenario during the Miocene may have promoted most of the lineage diversification within the Tetragonopterus.

Formal linguistics as a cue to demographic history.

Beyond its theoretical success, the development of molecular genetics has brought about the possibility of extraordinary progress in the study of classification and in the inference of the evolutionary history of many species and populations. A major step forward was represented by the availability of extremely large sets of molecular data suited to quantitative and computational treatments. In this paper, we argue that even in cognitive sciences, purely theoretical progress in a discipline such as linguistics may have analogous impact. Thus, exactly on the model of molecular biology, we propose to unify two traditionally unrelated lines of linguistic investigation: 1) the formal study of syntactic variation (parameter theory) in the biolinguistic program; 2) the reconstruction of relatedness among languages (phylogenetic taxonomy). The results of our linguistic analysis have thus been plotted against data from population genetics and the correlations have turned out to be largely significant: given a non-trivial set of languages/populations, the description of their variation provided by the comparison of systematic parametric analysis and molecular anthropology informatively recapitulates their history and relationships. As a result, we can claim that the reality of some parametric model of the language faculty and language acquisition/transmission (more broadly of generative grammar) receives strong and original support from its historical heuristic power. Then, on these grounds, we can begin testing Darwin's prediction that, when properly generated, the trees of human populations and of their languages should eventually turn out to be significantly parallel.

On Aculifera: a review of hypotheses in tribute to Christoffer Schander

A review of the phylogenetic relationships among the major molluscan lineages, focusing on Aculifera (Polyplacophora, Caudofoveata, Solenogastres), is provided, centering on the newest developments in phylogenetic analysis of large molecular datasets in the light of the newest fossil discoveries. Support for the monophyly of Aculifera and Aplacophora is found among modern studies, and the anatomical implications for such relationships are discussed, but analyses of target-gene approaches often contradict this clade. The review is concluded with a discussion of the breadth and importance of the work of the late Christoffer Schander to molluscan systematics.

A taxonomic revision of boas (Serpentes: Boidae).

Large molecular datasets including many species and loci have greatly improved our knowledge of snake phylogeny, particularly within the group including boas (Table 1). Recent taxonomic revisions using molecular phylogenies have clarified some of the previously contentious nomenclature of the group (Wilcox et al. 2002; Lawson et al. 2004; Burbrink 2005; Noonan & Chippindale 2006), resulting in a robust taxonomy that is mostly concordant with the phylogeny as currently known, which includes ~85% of described, extant species (Pyron et al. 2013; Reynolds et al. 2014). However, a few unresolved issues remain, related primarily to the rules of the International Code of Zoological Nomenclature (the Code hereafter) and the application of Linnaean ranks (International Commission on Zoological Nomenclature et al. 1999).

Validity of covariance models for the analysis of geographical variation

Summary Due to the availability of large molecular data sets, covariance models are increasingly used to describe the structure of genetic variation as an alternative to more heavily parameterized biological models. We focus here on a class of parametric covariance models that received sustained attention lately and show that the conditions under which they are valid mathematical models have been overlooked so far. We provide rigorous results for the construction of valid covariance models in this family. We also outline how to construct alternative covariance models for the analysis of geographical variation that are both mathematically well behaved and easily implementable. The full R code to reproduce the numerical analysis is available from: http://www2.imm.dtu.dk/~gigu/MEE_GSPB/covariance_validity.html

Abstract IA21: Developing a new functional classification of lung cancer based on tumor acquired vulnerabilities.

Abstract We have been comprehensively screening for “vulnerabilities” that have been acquired during the multi-step pathogenesis of lung cancer cells but are not present in normal lung epithelial cells to identify genetic and chemical perturbations that will selectively kill lung cancer. We think many of these have occurred to allow the lung cancers to undergo/tolerate “oncogene addiction.” We tested a sub-panel of 12-15 non-small cell lung cancer (NSCLC) lines that covers the known molecular spectra of lung cancer with genome wide siRNA and large scale chemical library (~250,000 compounds) and natural products in vitro screens to identify “hits” that will kill (suppress the growth of) lung cancer cells but not normal human bronchial epithelial cells and that also only kill a subset of lung cancer cells providing two types of specificity. “Hits” from these broad screens are then tested (including detailed drug concentration curves) across a large panel of lung cancer lines (~100) representing a variety of lung cancer histologic and molecular oncogenotypes. Other versions of these screens include the intensive use of “mini-libraries” each containing 50 – 150 gene targets by siRNAs or shRNAs, or ~200 defined drugs to explore pathways in detail in tests of over 70 NSCLCs. Examples include: nuclear receptors and their co-regulators (120 genes); cancer stem cell pathways (50 genes); chromatin remodelers (75 genes) and identified lung cancer mutated driver oncogenes (175 genes). In addition to the in vitro tests, we have developed in vivo (xenograft) tests where shRNA mini-libraries are introduced into tumor cells at high representation which are grown as xenografts, analyzed by NexGen sequencing and shRNAs identified that drop out or are retained in xenografts compared to in vitro grown cells to identify vulnerabilities that are only detected in the in vivo situation. All of the data are then related to the large legacy molecular datasets associated with the lung cancer lines (including whole exome sequence analyses and genome wide mRNA, copy number variation, methylation, miR expression data and proteomics data). In addition, detailed chemical and pharmacokinetic analyses for favorable drug properties and subsequent chemical modifications also occur for the chemical compounds to progress those towards potential clinical studies. The results of all of these analyses have identified ~300 new chemical compounds and ~300 genetic hits all of which show selectivity for lung cancer over normal lung cells and selectivity for subtypes of lung cancer. The chemical and genes hits are being compared to the tumor molecular information and integrated in turn through a “connectivity map” type of approach – to identify drugs and gene hits involving the same pathways. The molecular correlates of the tumor lines are related to similar molecular changes in patient derived xenografts and patient tumor specimens to provide a connection of the molecular subtype-selective vulnerabilities (“enrollment biomarkers”) between the preclinical response phenotypes and patient tumor specimens. From these data we find lung cancers can be classified into groups (“clades”) that represent functional vulnerabilities to the gene and chemical compound hits and these in turn can be related to molecular abnormalities in tumors. One example of this is our detailed analyses a matched lung adenocarcinoma/normal lung epithelial cell model derived from the same patient which identified three distinct target/response-indicator pairings that are represented a significant frequencies (6-16%) in the lung adenocarcinoma population (Kim et al. Cell 155:552, 2013). These include three totally novel lung cancer selective targeted therapies: NLRP3 mutation/inflammasome activation-dependent FLIP addiction; co-occurring KRAS and LKB1 mutation-driven COPI addiction; and selective sensitivity to a synthetic indolotriazine that is specified by a seven-gene expression signature. Our panel of “hits” provide the opportunity to identify all potential therapeutic targets for lung cancer, while the molecular correlates will allow “personalization” of these new therapies going forward in preclinical and clinical translation. (Supported by NCI SPORE P50CA70907, NCI CTD2N, CPRIT, UTSW CCSG P30CA142543) Citation Format: John D. Minna, Adi Gazdar, Alexander Augustyn, Rebecca Britt, Ryan Carstens, Patrick Dospoy, Boning Gao, Luc Girard, Suzie Hight, Kenneth Huffman, Jill Larsen, Michael Peyton, Chunli Shao, David Mangelsdorf, Rolf Brekken, Ralph Deberardinis, Pei-Hsuan Chen, Carmen Behrens, Lauren Byers, John Heymach, Jack Roth, Ignacio Wistuba, Yang Xie, Caleb Davis, David Wheeler, Richard Gibbs, Edward Marcotte, Joseph Ready, Deepak Nijhawan, Noelle Williams, Steven McKnight, Bruce Posner, John MacMillan, Michael Roth, Michael White. Developing a new functional classification of lung cancer based on tumor acquired vulnerabilities. [abstract]. In: Proceedings of the AACR-IASLC Joint Conference on Molecular Origins of Lung Cancer; 2014 Jan 6-9; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2014;20(2Suppl):Abstract nr IA21.

Cheminformatics models for inhibitors of Schistosoma mansoni thioredoxin glutathione reductase.

Schistosomiasis is a neglected tropical disease caused by a parasite Schistosoma mansoni and affects over 200 million annually. There is an urgent need to discover novel therapeutic options to control the disease with the recent emergence of drug resistance. The multifunctional protein, thioredoxin glutathione reductase (TGR), an essential enzyme for the survival of the pathogen in the redox environment has been actively explored as a potential drug target. The recent availability of small-molecule screening datasets against this target provides a unique opportunity to learn molecular properties and apply computational models for discovery of activities in large molecular libraries. Such a prioritisation approach could have the potential to reduce the cost of failures in lead discovery. A supervised learning approach was employed to develop a cost sensitive classification model to evaluate the biological activity of the molecules. Random forest was identified to be the best classifier among all the classifiers with an accuracy of around 80 percent. Independent analysis using a maximally occurring substructure analysis revealed 10 highly enriched scaffolds in the actives dataset and their docking against was also performed. We show that a combined approach of machine learning and other cheminformatics approaches such as substructure comparison and molecular docking is efficient to prioritise molecules from large molecular datasets.

Abstract A130: Joint cell-line (CCLE, Sanger) and patient (TCGA) modeling of drug sensitivity reveals novel molecular biomarkers for targeted therapy and conventional chemotherapy.

Abstract Background: The unresponsiveness to anticancer drugs in patients outlines the need to identify novel and robust biomarkers of response to therapy. The recent release of large molecular cell line datasets labeled for drug sensitivity enables the development of such predictors. Cell line based models poorly reflect the extent of molecular heterogeneity and of tumor microenvironment relationships existing at the patient level. We aimed to overcome these limitations by combining cell line datasets with patient cohorts to uncover novel molecular traits associated with drug response at the patient level. Methods: For building our model, we used the following publicly available datasets: CCLE (1057 cell lines, 24 drugs), Sanger (790 cell lines, 138 drugs) and TCGA (patients: breast cancer, colorectal cancer, lung squamous carcinoma, lung adenocarcinoma, ovarian cancer, kidney cancer and melanoma). A joint semi-supervised trained Elastic Net approach was used to model drug sensitivity (both conventional chemotherapy and targeted therapy) using gene-expression, copy-number alteration, and somatic mutation data. We introduced a false discovery rate estimation for selecting significant aberrations. As validation, we used pooled RNAi screens (NKI, Netherlands) and also a dataset of 120 patients prospectively enrolled in the ongoing MOSCATO molecular screening program (Gustave Roussy, France) (presented at ASCO 2013). Results: Drug sensitivity models trained in the entire set of cell lines performed better than models trained using tissue specific cell lines, whatever the drug and the tissue type. Novel molecular traits (mutation, copy number variation) were identified by selecting highly performant models (AUC > .75) across drug and tissue type combinations. We were able to recover all of the recognized landmark biomarkers across drug - tissue combination (e.g. Erlotinib: EGFR mutation in lung adenocarcinoma; Lapatanib: ERBB2 amplification in breast cancer; Vemurafenib: BRAF mutation in melanoma, etc). Moreover, we uncovered novel molecular traits (copy, number mutations) associated (FDR<25%) with drug response (e.g. Erlotinib: CDKN2A deletion in lung adenocarcinoma; Lapatanib: CDH1 deletion in breast cancer; Vemurafenib: FH amplification in melanoma, etc.). The validation of our framework in the pooled RNAi screens of colorectal and lung adenocarcinoma cell lines and in the MOSCATO patient's cohort will be presented. Conclusions: We provide a comprehensive annotation of mutations and copy-number events in tumors predicted to be associated with sensitivity and resistance in 142 anti-cancer drugs and across 7 tumor types. These molecular traits can be used to refine the results of large scale functional genomic experiments such as RNAi screens and ultimately in the early clinical trial setting. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):A130. Citation Format: Charles Ferté, Elias Chaibub Neto, Chong Sun, Cecilia Noecker, Frederic Commo, Olga Nikolova, In Sock Jang, Mehmet Gonen, Benjamin Besse, Fabrice André, Eric Angevin, Ludovic Lacroix, Clement Mazoyer, Antoine Hollebecque, Christophe Massard, Adam Margolin, Roderick Beijersbergen, Stephen Friend, Rene Bernards, Jean-Charles Soria, Justin Guinney. Joint cell-line (CCLE, Sanger) and patient (TCGA) modeling of drug sensitivity reveals novel molecular biomarkers for targeted therapy and conventional chemotherapy. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr A130.

Genome-wide modeling of complex phenotypes in Caenorhabditis elegans and Drosophila melanogaster

BackgroundThe genetic and molecular basis for many intermediate and end stage phenotypes in model systems such as C. elegans and D. melanogaster has long been known to involve pleiotropic effects and complex multigenic interactions. Gene sets are groups of genes that contribute to multiple biological or molecular phenomena. They have been used in the analysis of large molecular datasets such as microarray data, Next Generation sequencing, and other genomic datasets to reveal pleiotropic and multigenic contributions to phenotypic outcomes. Many model systems lack species specific organized phenotype based gene sets to enable high throughput analysis of large molecular datasets.Results and discussionHere, we describe two novel collections of gene sets in C. elegans and D. melanogaster that are based exclusively on genetically determined phenotypes and use a controlled phenotypic ontology. We use these collections to build genome-wide models of thousands of defined phenotypes in both model species. In addition, we demonstrate the utility of these gene sets in systems analysis and in analysis of gene expression-based molecular datasets and show how they are useful in analysis of genomic datasets connecting multigenic gene inputs to complex phenotypes.ConclusionsPhenotypic based gene sets in both C. elegans and D. melanogaster are developed, characterized, and shown to be useful in the analysis of large scale species-specific genomic datasets. These phenotypic gene set collections will contribute to the understanding of complex phenotypic outcomes in these model systems.

HARACNe: improving the accuracy of regulatory model reverse engineering via higher-order data processing inequality tests

A key goal of systems biology is to elucidate molecular mechanisms associated with physiologic and pathologic phenotypes based on the systematic and genome-wide understanding of cell context-specific molecular interaction models. To this end, reverse engineering approaches have been used to systematically dissect regulatory interactions in a specific tissue, based on the availability of large molecular profile datasets, thus improving our mechanistic understanding of complex diseases, such as cancer. In this paper, we introduce high-order Algorithm for the Reconstruction of Accurate Cellular Network (hARACNe), an extension of the ARACNe algorithm for the dissection of transcriptional regulatory networks. ARACNe uses the data processing inequality (DPI), from information theory, to detect and prune indirect interactions that are unlikely to be mediated by an actual physical interaction. Whereas ARACNe considers only first-order indirect interactions, i.e. those mediated by only one extra regulator, hARACNe considers a generalized form of indirect interactions via two, three or more other regulators. We show that use of higher-order DPI resulted in significantly improved performance, based on transcription factor (TF)-specific ChIP-chip data, as well as on gene expression profile following RNAi-mediated TF silencing.

Abstract A37: Cross-cancer evaluation of MYC synthetic lethal targets in The Cancer Genome Atlas

Abstract Overview: Synthetic lethality in cancer has been investigated for the last decade, but only recently do we have the convergence of high throughput RNA interference screening capabilities with large genomic and molecular data sets derived from primary tumor samples that allows for more comprehensive characterization of putative therapeutic targets against multiple cancers. Accurately relating cancer cell line RNA interference synthetic lethal screening data to primary human tumors has proven to be challenging, yet critically important, for improving validation rates and therapeutic efficacy. To this end, we have combined siRNA synthetic lethal screening hits in MYC amplified cell lines [Toyoshima et al. PNAS 2012] with genomic and molecular data from twelve tumor types in The Cancer Genome Atlas (TCGA) to identify and prioritize cross-cancer therapeutic targets with direct evidence for synthetic lethal relationships in both cell line and primary tumor samples. Approach: In order to relate TCGA data to synthetic lethal siRNA screening in cell lines, we began by integrating all data types produced by TCGA, including: clinical data, sample characteristics, somatic mutations, gene expression, DNA methylation, miRNA expression, reverse-phase protein array data, and somatic copy number variation. From these data, feature matrices have been constructed which combine all available information regarding patients and samples for each given tumor type. For each such heterogeneous feature matrix, we have performed statistical analysis to infer associations in the data between MYC amplification and all other molecular features. Hits from MYC synthetic lethal cell line screening were evaluated for statistical significance in pairwise analysis of twelve TCGA tumor types, classes of pairwise data-derived associations as well as properties of network-level topologies were characterized, and relationships between genomic data and clinical features were investigated where possible. Results: Evidence for synthetic lethal relationships between MYC amplification and molecular features (e.g., gene expression and DNA methylation) for numerous experimentally validates hits was present across multiple TCGA tumor types. For example, statistically significant associations between MYC amplification and decreased CSNK1E methylation, indicative of increased expression, were present in head and neck squamous cell carcinoma, cutaneous melanoma, and stomach adenocarcinoma, corroborating the experimental findings of Toyoshima et al. Similarly, PES1 gene expression is increased in breast carcinoma, colon adenocarcinoma, and head and neck squamous cell carcinoma samples with MYC amplification. Synthetic lethal hits had differential expression profiles reflective of both increases and decreases in correlation with MYC amplification. Based on these preliminary findings, the intersection of siRNA cell line screening hits with accompanying statistical associations in primary tumor sample data may enable prioritization of preclinical models of MYC driven cancers. While the present approach has been applied to MYC amplification, synthetic lethal relationships are present for a multitude of significantly mutated, copy number aberrated, or epigenetically silenced genes in TCGA. Citation Format: Brady Bernard, Silvia Cermelli, Carla Grandori, Ilya Shmulevich. Cross-cancer evaluation of MYC synthetic lethal targets in The Cancer Genome Atlas. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Synthetic Lethal Approaches to Cancer Vulnerabilities; May 17-20, 2013; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(5 Suppl):Abstract nr A37.

Composition and interrelationships of a large Neotropical freshwater fish group, the subfamily Cheirodontinae (Characiformes: Characidae): A case study based on mitochondrial and nuclear DNA sequences

Characidae is the most species-rich family of freshwater fishes in the order Characiformes, with more than 1000 valid species that correspond to approximately 55% of the order. Few hypotheses about the composition and internal relationships within this family are available and most fail to reach an agreement. Among Characidae, Cheirodontinae is an emblematic group that includes 18 genera (1 fossil) and approximately 60 described species distributed throughout the Neotropical region. The taxonomic and systematic history of Cheirodontinae is complex, and only two hypotheses about the internal relationships in this subfamily have been reported to date. In the present study, we test the composition and relationships of fishes assigned to Cheirodontinae based on a broad taxonomic sample that also includes some characid incertae sedis taxa that were previously considered to be part of Cheirodontinae. We present phylogenetic analyses of a large molecular dataset of mitochondrial and nuclear DNA sequences. Our results reject the monophyly of Cheirodontinae as previously conceived, as well as the tribes Cheirodontini and Compsurini, and the genera Cheirodon, Compsura, Leptagoniates, Macropsobrycon, Odontostilbe, and Serrapinnus. On the basis of these results we propose: (1) the exclusion of Amazonspinther and Spintherobolus from the subfamily Cheirodontinae since they are the sister-group of all remaining Characidae; (2) the removal of Macropsobrycon xinguensis of the genus Macropsobrycon; (3) the removal of Leptagoniates pi of the genus Leptagoniates; (4) the inclusion of Leptagoniates pi in the subfamily Cheirodontinae; (5) the removal of Cheirodon stenodon of the genus Cheirodon and its inclusion in the subfamily Cheirodontinae under a new genus name; (6) the need to revise the polyphyletic genera Compsura, Odontostilbe, and Serrapinnus; and (7) the division of Cheirodontinae in three newly defined monophyletic tribes: Cheirodontini, Compsurini, and Pseudocheirodontini. Our results suggest that our knowledge about the largest Neotropical fish family, Characidae, still is incipient.

Predictive modeling of anti-malarial molecules inhibiting apicoplast formation

BackgroundMalaria is a major healthcare problem worldwide resulting in an estimated 0.65 million deaths every year. It is caused by the members of the parasite genus Plasmodium. The current therapeutic options for malaria are limited to a few classes of molecules, and are fast shrinking due to the emergence of widespread resistance to drugs in the pathogen. The recent availability of high-throughput phenotypic screen datasets for antimalarial activity offers a possibility to create computational models for bioactivity based on chemical descriptors of molecules with potential to accelerate drug discovery for malaria.ResultsIn the present study, we have used high-throughput screen datasets for the discovery of apicoplast inhibitors of the malarial pathogen as assayed from the delayed death response. We employed machine learning approach and developed computational predictive models to predict the biological activity of new antimalarial compounds. The molecules were further evaluated for common substructures using a Maximum Common Substructure (MCS) based approach.ConclusionsWe created computational models using state-of-the-art machine learning algorithms. The models were evaluated based on multiple statistical criteria. We found Random Forest based approach provides for better accuracy as assessed from ROC curve analysis. We further evaluated the active molecules using a substructure based approach to identify common substructures enriched in the active set. We argue that the computational models generated could be effectively used to screen large molecular datasets to prioritize them for phenotypic screens, drastically reducing cost while improving the hit rate.

Why the Current Insistence on Open Access to Scientific Data? Big Data, Knowledge Production, and the Political Economy of Contemporary Biology

The collection and dissemination of data on human and nonhuman organisms has become a central feature of 21st-century biology and has been endorsed by funding agencies in the United States and Europe as crucial to translating biological research into therapeutic and agricultural innovation. Large molecular data sets, often referred to as “big data,” are increasingly incorporated into digital databases, many of which are freely accessible online. These data have come to be seen as resources that play a key role in mediating global market exchange, thus achieving a prominent social and economic status well beyond science itself. At the same time, calls to make all such data publicly and freely available have garnered strength and visibility, most prominently in the form of the Open Data movement. I discuss these developments by considering the conditions under which data journey across the communities and institutions implicated in globalized biology and biomedicine, and what this indicates about how Internet-based communication and the use of online databases affect scientific research and its role within contemporary society.

Reticulamoeba Is a Long-Branched Granofilosean (Cercozoa) That Is Missing from Sequence Databases

We sequenced the 18S ribosomal RNA gene of seven isolates of the enigmatic marine amoeboflagellate Reticulamoeba Grell, which resolved into four genetically distinct Reticulamoeba lineages, two of which correspond to R. gemmipara Grell and R. minor Grell, another with a relatively large cell body forming lacunae, and another that has similarities to both R. minor and R. gemmipara but with a greater propensity to form cell clusters. These lineages together form a long-branched clade that branches within the cercozoan class Granofilosea (phylum Cercozoa), showing phylogenetic affinities with the genus Mesofila. The basic morphology of Reticulamoeba is a roundish or ovoid cell with a more or less irregular outline. Long and branched reticulopodia radiate from the cell. The reticulopodia bear granules that are bidirectionally motile. There is also a biflagellate dispersal stage. Reticulamoeba is frequently observed in coastal marine environmental samples. PCR primers specific to the Reticulamoeba clade confirm that it is a frequent member of benthic marine microbial communities, and is also found in brackish water sediments and freshwater biofilm. However, so far it has not been found in large molecular datasets such as the nucleotide database in NCBI GenBank, metagenomic datasets in Camera, and the marine microbial eukaryote sampling and sequencing consortium BioMarKs, although closely related lineages can be found in some of these datasets using a highly targeted approach. Therefore, although such datasets are very powerful tools in microbial ecology, they may, for several methodological reasons, fail to detect ecologically and evolutionary key lineages.

Computational analysis and predictive modeling of small molecule modulators of microRNA

BackgroundMicroRNAs (miRNA) are small endogenously transcribed regulatory RNA which modulates gene expression at a post transcriptional level. These small RNAs have now been shown to be critical regulators in a number of biological processes in the cell including pathophysiology of diseases like cancers. The increasingly evident roles of microRNA in disease processes have also motivated attempts to target them therapeutically. Recently there has been immense interest in understanding small molecule mediated regulation of RNA, including microRNA.ResultsWe have used publicly available datasets of high throughput screens on small molecules with potential to inhibit microRNA. We employed computational methods based on chemical descriptors and machine learning to create predictive computational models for biological activity of small molecules. We further used a substructure based approach to understand common substructures potentially contributing to the activity.ConclusionWe generated computational models based on Naïve Bayes and Random Forest towards mining small RNA binding molecules from large molecular datasets. We complement this with substructure based approach to identify and understand potentially enriched substructures in the active dataset. We use this approach to identify miRNA binding potential of a set of approved drugs, suggesting a probable novel mechanism of off-target activity of these drugs. To the best of our knowledge, this is the first and most comprehensive computational analysis towards understanding RNA binding activities of small molecules and predictive modeling of these activities.

The Molecule Cloud - compact visualization of large collections of molecules

BackgroundAnalysis and visualization of large collections of molecules is one of the most frequent challenges cheminformatics experts in pharmaceutical industry are facing. Various sophisticated methods are available to perform this task, including clustering, dimensionality reduction or scaffold frequency analysis. In any case, however, viewing and analyzing large tables with molecular structures is necessary. We present a new visualization technique, providing basic information about the composition of molecular data sets at a single glance.SummaryA method is presented here allowing visual representation of the most common structural features of chemical databases in a form of a cloud diagram. The frequency of molecules containing particular substructure is indicated by the size of respective structural image. The method is useful to quickly perceive the most prominent structural features present in the data set. This approach was inspired by popular word cloud diagrams that are used to visualize textual information in a compact form. Therefore we call this approach “Molecule Cloud”. The method also supports visualization of additional information, for example biological activity of molecules containing this scaffold or the protein target class typical for particular scaffolds, by color coding. Detailed description of the algorithm is provided, allowing easy implementation of the method by any cheminformatics toolkit. The layout algorithm is available as open source Java code.ConclusionsVisualization of large molecular data sets using the Molecule Cloud approach allows scientists to get information about the composition of molecular databases and their most frequent structural features easily. The method may be used in the areas where analysis of large molecular collections is needed, for example processing of high throughput screening results, virtual screening or compound purchasing. Several example visualizations of large data sets, including PubChem, ChEMBL and ZINC databases using the Molecule Cloud diagrams are provided.

Marine turtle mitogenome phylogenetics and evolution

The sea turtles are a group of cretaceous origin containing seven recognized living species: leatherback, hawksbill, Kemp’s ridley, olive ridley, loggerhead, green, and flatback. The leatherback is the single member of the Dermochelidae family, whereas all other sea turtles belong in Cheloniidae. Analyses of partial mitochondrial sequences and some nuclear markers have revealed phylogenetic inconsistencies within Cheloniidae, especially regarding the placement of the flatback. Population genetic studies based on D-Loop sequences have shown considerable structuring in species with broad geographic distributions, shedding light on complex migration patterns and possible geographic or climatic events as driving forces of sea-turtle distribution. We have sequenced complete mitogenomes for all sea-turtle species, including samples from their geographic range extremes, and performed phylogenetic analyses to assess sea-turtle evolution with a large molecular dataset. We found variation in the length of the ATP8 gene and a highly variable site in ND4 near a proton translocation channel in the resulting protein. Complete mitogenomes show strong support and resolution for phylogenetic relationships among all sea turtles, and reveal phylogeographic patterns within globally-distributed species. Although there was clear concordance between phylogenies and geographic origin of samples in most taxa, we found evidence of more recent dispersal events in the loggerhead and olive ridley turtles, suggesting more recent migrations (<1Myr) in these species. Overall, our results demonstrate the complexity of sea-turtle diversity, and indicate the need for further research in phylogeography and molecular evolution.

Evolutionary genomics of host-use in bifurcating demes of RNA virus phi-6.

BackgroundViruses are exceedingly diverse in their evolved strategies to manipulate hosts for viral replication. However, despite these differences, most virus populations will occasionally experience two commonly-encountered challenges: growth in variable host environments, and growth under fluctuating population sizes. We used the segmented RNA bacteriophage ϕ6 as a model for studying the evolutionary genomics of virus adaptation in the face of host switches and parametrically varying population sizes. To do so, we created a bifurcating deme structure that reflected lineage splitting in natural populations, allowing us to test whether phylogenetic algorithms could accurately resolve this ‘known phylogeny’. The resulting tree yielded 32 clones at the tips and internal nodes; these strains were fully sequenced and measured for phenotypic changes in selected traits (fitness on original and novel hosts).ResultsWe observed that RNA segment size was negatively correlated with the extent of molecular change in the imposed treatments; molecular substitutions tended to cluster on the Small and Medium RNA chromosomes of the virus, and not on the Large segment. Our study yielded a very large molecular and phenotypic dataset, fostering possible inferences on genotype-phenotype associations. Using further experimental evolution, we confirmed an inference on the unanticipated role of an allelic switch in a viral assembly protein, which governed viral performance across host environments.ConclusionsOur study demonstrated that varying complexities can be simultaneously incorporated into experimental evolution, to examine the combined effects of population size, and adaptation in novel environments. The imposed bifurcating structure revealed that some methods for phylogenetic reconstruction failed to resolve the true phylogeny, owing to a paucity of molecular substitutions separating the RNA viruses that evolved in our study.

Towards resolving the complete fern tree of life.

In the past two decades, molecular systematic studies have revolutionized our understanding of the evolutionary history of ferns. The availability of large molecular data sets together with efficient computer algorithms, now enables us to reconstruct evolutionary histories with previously unseen completeness. Here, the most comprehensive fern phylogeny to date, representing over one-fifth of the extant global fern diversity, is inferred based on four plastid genes. Parsimony and maximum-likelihood analyses provided a mostly congruent results and in general supported the prevailing view on the higher-level fern systematics. At a deep phylogenetic level, the position of horsetails depended on the optimality criteria chosen, with horsetails positioned as the sister group either of Marattiopsida-Polypodiopsida clade or of the Polypodiopsida. The analyses demonstrate the power of using a ‘supermatrix’ approach to resolve large-scale phylogenies and reveal questionable taxonomies. These results provide a valuable background for future research on fern systematics, ecology, biogeography and other evolutionary studies.