quincunx: an R package to query, download and wrangle PGS Catalog data.

MOTIVATION: The Polygenic Score (PGS) Catalog is a recently established open database of published polygenic scores that, to date, has collected, curated and made available 721 polygenic scores from over 133 publications. The PGS Catalog REST API is the only method allowing programmatic access to this resource. RESULTS: Here, we describe quincunx, an R package that provides the first client interface to the PGS Catalog REST API. quincunx enables users to query and quickly retrieve, filter and integrate metadata associated with polygenic scores, as well as polygenic scoring files in tidy table format. AVAILABILITY AND IMPLEMENTATION: quincunx is freely available under an MIT License, and can be accessed from https://github.com/maialab/quincunx. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

gwasrapidd: an R package to query, download and wrangle GWAS catalog data.

MOTIVATION: The National Human Genome Research Institute Catalog of Published Genome-Wide Association Studies (GWAS) Catalog has collected, curated and made available data from over 7100 studies. The recently developed GWAS Catalog representational state transfer (REST) application programming interface (API) is the only method allowing programmatic access to this resource. RESULTS: Here, we describe gwasrapidd, an R package that provides the first client interface to the GWAS Catalog REST API, representing an important software counterpart to the server-side component. gwasrapidd enables users to quickly retrieve, filter and integrate data with comprehensive bioinformatics analysis tools, which is particularly critical for those looking into functional characterization of risk loci. AVAILABILITY AND IMPLEMENTATION: gwasrapidd is freely available under an MIT License, and can be accessed from https://github.com/ramiromagno/gwasrapidd. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

StemMapper: a curated gene expression database for stem cell lineage analysis.

Transcriptomic data have become a fundamental resource for stem cell (SC) biologists as well as for a wider research audience studying SC-related processes such as aging, embryonic development and prevalent diseases including cancer, diabetes and neurodegenerative diseases. Access and analysis of the growing amount of freely available transcriptomics datasets for SCs, however, are not trivial tasks. Here, we present StemMapper, a manually curated gene expression database and comprehensive resource for SC research, built on integrated data for different lineages of human and mouse SCs. It is based on careful selection, standardized processing and stringent quality control of relevant transcriptomics datasets to minimize artefacts, and includes currently over 960 transcriptomes covering a broad range of SC types. Each of the integrated datasets was individually inspected and manually curated. StemMapper's user-friendly interface enables fast querying, comparison, and interactive visualization of quality-controlled SC gene expression data in a comprehensive manner. A proof-of-principle analysis discovering novel putative astrocyte/neural SC lineage markers exemplifies the utility of the integrated data resource. We believe that StemMapper can open the way for new insights and advances in SC research by greatly simplifying the access and analysis of SC transcriptomic data. StemMapper is freely accessible at http://stemmapper.sysbiolab.eu.

Spatio-temporal dynamics of early somite segmentation in the chicken embryo.

During vertebrate embryo development, the body is progressively segmented along the anterior-posterior (A-P) axis early in development. The rate of somite formation is controlled by the somitogenesis embryo clock (EC), which was first described as gene expression oscillations of hairy1 (hes4) in the presomitic mesoderm of chick embryos with 15-20 somites. Here, the EC displays the same periodicity as somite formation, 90 min, whereas the posterior-most somites (44-52) only arise every 150 minutes, matched by a corresponding slower pace of the EC. Evidence suggests that the rostral-most somites are formed faster, however, their periodicity and the EC expression dynamics in these early stages are unknown. In this study, we used time-lapse imaging of chicken embryos from primitive streak to somitogenesis stages with high temporal resolution (3-minute intervals). We measured the length between the anterior-most and the last formed somitic clefts in each captured frame and developed a simple algorithm to automatically infer both the length and time of formation of each somite. We found that the occipital somites (up to somite 5) form at an average rate of 75 minutes, while somites 6 onwards are formed approximately every 90 minutes. We also assessed the expression dynamics of hairy1 using half-embryo explants cultured for different periods of time. This showed that EC hairy1 expression is highly dynamic prior to somitogenesis and assumes a clear oscillatory behaviour as the first somites are formed. Importantly, using ex ovo culture and live-imaging techniques, we showed that the hairy1 expression pattern recapitulates with the formation of each new pair of somites, indicating that somite segmentation is coupled with EC oscillations since the onset of somitogenesis.

Evolution and diversification of the organellar release factor family.

Translation termination is accomplished by proteins of the Class I release factor family (RF) that recognize stop codons and catalyze the ribosomal release of the newly synthesized peptide. Bacteria have two canonical RFs: RF1 recognizes UAA and UAG, RF2 recognizes UAA and UGA. Despite that these two release factor proteins are sufficient for de facto translation termination, the eukaryotic organellar RF protein family, which has evolved from bacterial release factors, has expanded considerably, comprising multiple subfamilies, most of which have not been functionally characterized or formally classified. Here, we integrate multiple sources of information to analyze the remarkable differentiation of the RF family among organelles. We document the origin, phylogenetic distribution and sequence structure features of the mitochondrial and plastidial release factors: mtRF1a, mtRF1, mtRF2a, mtRF2b, mtRF2c, ICT1, C12orf65, pRF1, and pRF2, and review published relevant experimental data. The canonical release factors (mtRF1a, mtRF2a, pRF1, and pRF2) and ICT1 are derived from bacterial ancestors, whereas the others have resulted from gene duplications of another release factor. These new RF family members have all lost one or more specific motifs relevant for bona fide release factor function but are mostly targeted to the same organelle as their ancestor. We also characterize the subset of canonical release factor proteins that bear nonclassical PxT/SPF tripeptide motifs and provide a molecular-model-based rationale for their retained ability to recognize stop codons. Finally, we analyze the coevolution of canonical RFs with the organellar genetic code. Although the RF presence in an organelle and its stop codon usage tend to coevolve, we find three taxa that encode an RF2 without using UGA stop codons, and one reverse scenario, where mamiellales green algae use UGA stop codons in their mitochondria without having a mitochondrial type RF2. For the latter, we put forward a "stop-codon reinvention" hypothesis that involves the retargeting of the plastid release factor to the mitochondrion.

Allelic expression imbalance of PIK3CA mutations is frequent in breast cancer and prognostically significant.

PIK3CA mutations are the most common in breast cancer, particularly in the estrogen receptor-positive cohort, but the benefit of PI3K inhibitors has had limited success compared with approaches targeting other less common mutations. We found a frequent allelic expression imbalance between the missense mutant and wild-type PIK3CA alleles in breast tumors from the METABRIC (70.2%) and the TCGA (60.1%) projects. When considering the mechanisms controlling allelic expression, 27.7% and 11.8% of tumors showed imbalance due to regulatory variants in cis, in the two studies respectively. Furthermore, preferential expression of the mutant allele due to cis-regulatory variation is associated with poor prognosis in the METABRIC tumors (P = 0.031). Interestingly, ER-, PR-, and HER2+ tumors showed significant preferential expression of the mutated allele in both datasets. Our work provides compelling evidence to support the clinical utility of PIK3CA allelic expression in breast cancer in identifying patients of poorer prognosis, and those with low expression of the mutated allele, who will unlikely benefit from PI3K inhibitors. Furthermore, our work proposes a model of differential regulation of a critical cancer-promoting gene in breast cancer.

Allele-specific miRNA-binding analysis identifies candidate target genes for breast cancer risk.

Most breast cancer (BC) risk-associated single-nucleotide polymorphisms (raSNPs) identified in genome-wide association studies (GWAS) are believed to cis-regulate the expression of genes. We hypothesise that cis-regulatory variants contributing to disease risk may be affecting microRNA (miRNA) genes and/or miRNA binding. To test this, we adapted two miRNA-binding prediction algorithms-TargetScan and miRanda-to perform allele-specific queries, and integrated differential allelic expression (DAE) and expression quantitative trait loci (eQTL) data, to query 150 genome-wide significant ( P ≤ 5 × 10 - 8 ) raSNPs, plus proxies. We found that no raSNP mapped to a miRNA gene, suggesting that altered miRNA targeting is an unlikely mechanism involved in BC risk. Also, 11.5% (6 out of 52) raSNPs located in 3'-untranslated regions of putative miRNA target genes were predicted to alter miRNA::mRNA (messenger RNA) pair binding stability in five candidate target genes. Of these, we propose RNF115, at locus 1q21.1, as a strong novel target gene associated with BC risk, and reinforce the role of miRNA-mediated cis-regulation at locus 19p13.11. We believe that integrating allele-specific querying in miRNA-binding prediction, and data supporting cis-regulation of expression, improves the identification of candidate target genes in BC risk, as well as in other common cancers and complex diseases.

Recent advances in understanding vertebrate segmentation.

Segmentation is the partitioning of the body axis into a series of repeating units or segments. This widespread body plan is found in annelids, arthropods, and chordates, showing it to be a successful developmental strategy for growing and generating diverse morphology and anatomy. Segmentation has been extensively studied over the years. Forty years ago, Cooke and Zeeman published the Clock and Wavefront model, creating a theoretical framework of how developing cells could acquire and keep temporal and spatial information in order to generate a segmented pattern. Twenty years later, in 1997, Palmeirim and co-workers found the first clock gene whose oscillatory expression pattern fitted within Cooke and Zeeman's model. Currently, in 2017, new experimental techniques, such as new ex vivo experimental models, real-time imaging of gene expression, live single cell tracking, and simplified transgenics approaches, are revealing some of the fine details of the molecular processes underlying the inner workings of the segmentation mechanisms, bringing new insights into this fundamental process. Here we review and discuss new emerging views that further our understanding of the vertebrate segmentation clock, with a particular emphasis on recent publications that challenge and/or complement the currently accepted Clock and Wavefront model.

rdml: A Mathematica package for parsing and importing Real-Time qPCR data.

OBJECTIVE: The purpose and objective of the research presented is to provide a package for easy importing of Real-Time PCR data markup language (RDML) data to Mathematica. RESULTS: Real-Time qPCR is the most widely used experimental method for the accurate quantification of gene expression. To enable the straightforward archiving and sharing of qPCR data and its associated experimental information, an XML-based data standard was developed-the Real-Time PCR data markup language (RDML)-devised by the RDML consortium. Here, we present rdml, a package to parse and import RDML data into Mathematica, allowing the quick loading and extraction of relevant data, thus promoting the re-analysis, meta-analysis or experimental re-validation of gene expression data deposited in RDML format.

The biophysical nature of cells: potential cell behaviours revealed by analytical and computational studies of cell surface mechanics.

BACKGROUND: The biophysical characteristics of cells determine their shape in isolation and when packed within tissues. Cells can form regular or irregular epithelial structures, round up and form clusters, or deform and attach to substrates. The acquired shape of cells and tissues is a consequence of (i) internal cytoskeletal processes, such as actin polymerisation and cortical myosin contraction, (ii) adhesion molecules within the cell membrane that interact with substrates and neighbouring cells, and (iii) processes that regulate cell volume. Although these processes seem relatively simple, when combined they unleash a rich variety of cellular behaviour that is not readily understandable outside a theoretical framework. METHODS: We perform a mathematical analysis of a commonly used class of model formalisms that describe cell surface mechanics using an energy-based approach. Predictions are then confirmed through comparison with the computational outcomes of a Vertex model and 2D and 3D simulations of the Cellular Potts model. RESULTS: The analytical study reveals the complete possible spectrum of single cell behaviour and tissue packing in both 2D and 3D, by taking the typical core elements of cell surface mechanics into account: adhesion, cortical tension and volume conservation. We show that from an energy-based description, forces and tensions can be derived, as well as the prediction of cell behaviour and tissue packing, providing an intuitive and biologically relevant mapping between modelling parameters and experiments. CONCLUSIONS: The quantitative cellular behaviours and biological insights agree between the analytical study and the diverse computational model formalisms, including the Cellular Potts model. This illustrates the generality of energy-based approaches for cell surface mechanics and highlights how meaningful and quantitative comparisons between models can be established. Moreover, the mathematical analysis reveals direct links between known biophysical properties and specific parameter settings within the Cellular Potts model.