The 100 € lab A 3-D printable open source platform for fluorescence microscopy, optogenetics and accurate temperature control during behaviour of zebrafish, Drosophila and C. elegans, bioRxiv, 2017-04-01

An Integrative Framework for Detecting Structural Variations in Cancer Genomes, bioRxiv, 2017-03-29

AbstractStructural variants can contribute to oncogenesis through a variety of mechanisms, yet, despite their importance, the identification of structural variants in cancer genomes remains challenging. Here, we present an integrative framework for comprehensively identifying structural variation in cancer genomes. For the first time, we apply next-generation optical mapping, high-throughput chromosome conformation capture (Hi-C), and whole genome sequencing to systematically detect SVs in a variety of cancer cells.Using this approach, we identify and characterize structural variants in up to 29 commonly used normal and cancer cell lines. We find that each method has unique strengths in identifying different classes of structural variants and at different scales, suggesting that integrative approaches are likely the only way to comprehensively identify structural variants in the genome. Studying the impact of the structural variants in cancer cell lines, we identify widespread structural variation events affecting the functions of non-coding sequences in the genome, including the deletion of distal regulatory sequences, alteration of DNA replication timing, and the creation of novel 3D chromatin structural domains.These results underscore the importance of comprehensive structural variant identification and indicate that non-coding structural variation may be an underappreciated mutational process in cancer genomes.

Beyond differences in means robust graphical methods to compare two groups in neuroscience, bioRxiv, 2017-03-28

AbstractIf many changes are necessary to improve the quality of neuroscience research, one relatively simple step could have great pay-offs to promote the adoption of detailed graphical methods, combined with robust inferential statistics. Here we illustrate how such methods can lead to a much more detailed understanding of group differences than bar graphs and t-tests on means. To complement the neuroscientist’s toolbox, we present two powerful tools that can help us understand how groups of observations differ the shift function and the difference asymmetry function. These tools can be combined with detailed visualisations to provide complementary perspectives about the data. We provide implementations in R and Matlab of the graphical tools, and all the examples in the article can be reproduced using R scripts.

Regulation of Life Span by The Gut Microbiota in The Short-Lived African Turquoise Killifish, bioRxiv, 2017-03-28

ABSTRACTGut bacteria occupy the interface between the organism and the external environment, contributing to homeostasis and disease. Yet, the causal role of the gut microbiota during host aging is largely unexplored. Here, using the African turquoise killifish (Nothobranchius furzeri), a naturally short-lived vertebrate, we show that the gut microbiota plays a key role in modulating vertebrate life span. Recolonizing the gut of middle-age individuals with bacteria from young donors resulted in life span extension and delayed behavioral decline. This intervention prevented the decrease in microbial diversity associated with host aging and maintained a young-like gut bacterial community, characterized by overrepresentation of the key genera Exiguobacterium, Planococcus, Propionigenium and Psychrobacter. Our findings demonstrate that the natural microbial gut community of young individuals can causally induce long-lasting beneficial systemic effects that lead to life span extension in a vertebrate model.

Machine Learning-based state-of-the-art methods for the classification of RNA-Seq data, bioRxiv, 2017-03-27

AbstractRNA-Seq measures expression levels of several transcripts simultaneously. The identified reads can be gene, exon, or other region of interest. Various computational tools have been developed for studying pathogen or virus from RNA-Seq data by classifying them according to the attributes in several predefined classes, but still computational tools and approaches to analyze complex datasets are still lacking. The development of classification models is highly recommended for disease diagnosis and classification, disease monitoring at molecular level as well as researching for potential disease biomarkers. In this chapter, we are going to discuss various machine learning approaches for RNA-Seq data classification and their implementation. Advancements in bioinformatics, along with developments in machine learning based classification, would provide powerful toolboxes for classifying transcriptome information available through RNA-Seq data.

UpSetR An R Package for the Visualization of Intersecting Sets and their Properties, bioRxiv, 2017-03-26

AbstractVenn and Euler diagrams are a popular yet inadequate solution for quantitative visualization of set intersections. A scalable alternative to Venn and Euler diagrams for visualizing intersecting sets and their properties is needed. We developed UpSetR, an open source R package that employs a scalable matrix-based visualization to show intersections of sets, their size, and other properties. UpSetR is available at <jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpscran.r-project.orgpackage=UpSetR>httpscran.r-project.orgpackage=UpSetR<jatsext-link> and released under the MIT License. A Shiny app is available at <jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpsgehlenborglab.shinyapps.ioupsetr>httpsgehlenborglab.shinyapps.ioupsetr<jatsext-link>.