Admissions for hypoglycaemia after 35 weeks of gestation:perinatal predictors of cost of stay

Multivariate analysis of the clinicopathological and molecular factors associated with FOXD2 methylation (A); DIO3 and FOXD2 mRNA expression in hMSI-H tumoural and normal specimens (B) and external validation using TCGA database showing that MSI-H expressed less FOXD2 than MSI-L/MSS colon carcinomas (C)

Additional file 1: of RGmatch: matching genomic regions to proximal genes in omics data integration

Examples of the output files for some of the compared algorithms. (DOCX 20 kb

Supplemental Material for Newman et al., 2018

<div>Supplementary figures, tables and files for Newman et. al, 2018, "Event Analysis: using transcript events to improve estimates of abundance in RNA-seq data", G3, manuscript# G3/2018/200373R1.</div><div><br></div><div>Additional File 1 contains Supplementary Figures 1-6 and Supplementaty Table 1</div><div><br></div><div>Supplementary File 1 contains the results of simulations to compare the Event Analysis approach to STAR (for junction detection) and iReckon (for transcript identification).</div><div><br></div><div>Supplementary File 2 contains the results of applying Event Analysis to eXpress, and the results of using iReckon to identify possible transcripts in the mouse neural data used in the study.<br></div><div><br></div><div>Supplementary File 3 contains the results of comparing Event Analysis (using Bowtie or SOAP2 as the aligner) against STAR for benchmarking junction detection using the mouse neural data. Results are benchmarked against the set of junctions observed in PacBio-sequenced transcripts in the mouse neural data.<br></div><div><br></div><div>Supplementary File 4 contains the comparison between Event Analysis and iReckon for the mouse neural data, benchmarked against PacBio-sequenced transcripts.</div

Supplemental Material for Newman et al., 2018

Comparison of Event analysis with RSEM to Event Analysis with eXpress and these approaches to transcript estimation with iRecko

Web interface for the eBioKit.

<p>(A) The arrangement of the components that compose the web interface. Users can easily switch between the installed services on the eBioKit using the lateral menu. When users choose an option on the menu, the working area is replaced by the corresponding service and the menu is hidden, allowing users to fully interact with the service. (B), (C), and (D) show the familiar web interface users see when working with Ensembl Mammals, Galaxy, and MRS, respectively, in eBioKit. With the upper toolbar, users can open the services on a secondary window and, more importantly, can get a description as well as download documentation and tutorials for the selected service (E).</p

An example of a lesson in the eBioKit training portal.

<p>The image displays an extract of the “Getting started” lesson for the tutorial “next-generation sequencing (NGS) analysis with Galaxy.” During a tutorial, the students will find multiple exercises that allow them to put into practice the content learned.</p

Global distribution for the eBioKit project.

<p>Numbers indicate the number of eBioKits deployed in each country. European institutions: Swedish University of Agricultural Sciences (SLU) (Sweden) and Medizinische Universität (Austria). South America: Instituto Antártico Chileno (INACH) and Universidad de Magallanes (Chile). Asian institutions: University of Colombo (Sri Lanka) and COMSATS (Pakistan). Africa: Centre de Recherche Medicale et Sanitaire (Niger), The International Institute of Tropical Agriculture (IITA) (Nigeria), University of Buea (Cameroon), Uganda Virus Research Institute (Uganda), BECA/ILRI (Kenya), Pwani University (Kenya), International Centre of Insect Physiology and Ecology (Kenya), Technical University of Kenya (Kenya), University of Dar es Salaam (Tanzania), Mikocheni Agricultural Research Institute (Tanzania), University of Mauritius (Mauritius), University of the Witwatersrand (South Africa), University of Pretoria (South Africa), University of Cape Town (South Africa), and SANBI South African National Bioinformatics Institute (South Africa).</p

Image_1_Comparative Metagenomics Provides Insight Into the Ecosystem Functioning of the Shark Bay Stromatolites, Western Australia.pdf

<p>Stromatolites are organosedimentary build-ups that have formed as a result of the sediment trapping, binding and precipitating activities of microbes. Today, extant systems provide an ideal platform for understanding the structure, composition, and interactions between stromatolite-forming microbial communities and their respective environments. In this study, we compared the metagenomes of three prevalent stromatolite-forming microbial mat types in the Spaven Province of Hamelin Pool, Shark Bay located in Western Australia. These stromatolite-forming mat types included an intertidal pustular mat as well as a smooth and colloform mat types located in the subtidal zone. Additionally, the metagenomes of an adjacent, non-lithifying mat located in the upper intertidal zone were also sequenced for comparative purposes. Taxonomic and functional gene analyses revealed distinctive differences between the lithifying and non-lithifying mat types, which strongly correlated with water depth. Three distinct populations emerged including the upper intertidal non-lithifying mats, the intertidal pustular mats associated with unlaminated carbonate build-ups, and the subtidal colloform and smooth mat types associated with laminated structures. Functional analysis of metagenomes revealed that amongst stromatolite-forming mats there was an enrichment of photosynthesis pathways in the pustular stromatolite-forming mats. In the colloform and smooth stromatolite-forming mats, however, there was an increase in the abundance of genes associated with those heterotrophic metabolisms typically associated with carbonate mineralization, such as sulfate reduction. The comparative metagenomic analyses suggest that stromatolites of Hamelin Pool may form by two distinctive processes that are highly dependent on water depth. These results provide key insight into the potential adaptive strategies and synergistic interactions between microbes and their environments that may lead to stromatolite formation and accretion.</p

The eBioKit, a stand-alone educational platform for bioinformatics

<div><p>Bioinformatics skills have become essential for many research areas; however, the availability of qualified researchers is usually lower than the demand and training to increase the number of able bioinformaticians is an important task for the bioinformatics community. When conducting training or hands-on tutorials, the lack of control over the analysis tools and repositories often results in undesirable situations during training, as unavailable online tools or version conflicts may delay, complicate, or even prevent the successful completion of a training event. The eBioKit is a stand-alone educational platform that hosts numerous tools and databases for bioinformatics research and allows training to take place in a controlled environment. A key advantage of the eBioKit over other existing teaching solutions is that all the required software and databases are locally installed on the system, significantly reducing the dependence on the internet. Furthermore, the architecture of the eBioKit has demonstrated itself to be an excellent balance between portability and performance, not only making the eBioKit an exceptional educational tool but also providing small research groups with a platform to incorporate bioinformatics analysis in their research. As a result, the eBioKit has formed an integral part of training and research performed by a wide variety of universities and organizations such as the Pan African Bioinformatics Network (H3ABioNet) as part of the initiative Human Heredity and Health in Africa (H3Africa), the Southern Africa Network for Biosciences (SAnBio) initiative, the Biosciences eastern and central Africa (BecA) hub, and the International Glossina Genome Initiative.</p></div

Box plots displaying access to technology and expertise in East Asia, Europe, South America, and sub-Saharan Africa.

<p>All data is calculated from the World Bank DataBank [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005616#pcbi.1005616.ref015" target="_blank">15</a>] (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005616#pcbi.1005616.s001" target="_blank">S1 Table</a> for for full data). The vertical lines extend from the 25th and 75th percentiles to the lowest/highest value that is within 1.5 times the distance between the 25th and 75th percentiles (the interquartile range). Data beyond the end of the lines are outliers and plotted as points. Data for fixed broadband internet subscribers (per 100 people) are per 2014; the number of secure servers by 2014 and other data is from a range of years from 2010 to 2015, depending on data availability (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005616#pcbi.1005616.s002" target="_blank">S2 Table</a> for summarized data per country).</p