The Bioinformatics Open Source Conference (BOSC) 2013

Contact: [email protected]

Influence of the geometry on the agglomeration of a polydisperse binary system of spherical particles

Within the context of the European Horizon 2020 project ACDC, we intend to develop a probabilistic chemical compiler, to aid the construction of three-dimensional agglomerations of artificial hierarchical cellular constructs. These programmable discrete units offer a wide variety of technical innovations, like a portable biochemical laboratory that e.g. produces macromolecular medicine on demand. For this purpose, we have to investigate the agglomeration process of droplets and vesicles under proposed constraints, like confinement. This paper focuses on the influence of the geometry of the initialization and of the container on the agglomeration

Paths in a network of polydisperse spherical droplets

We simulate the movement and agglomeration of oil droplets in water under constraints, like confinement, using a simplified stochastic-hydrodynamic model. In the analysis of the network created by the droplets in the agglomeration, we focus on the paths between pairs of droplets and compare the computational results for various system sizes

Percolation breakdown in binary and ternary monodisperse and polydisperse systems of spherical particles

We perform computer simulations of an agglomeration process for monodisperse and polydisperse systems of spherical particles in a cylindrical container, using a simplified stochastic-hydrodynamic model. We consider a ternary system with three particle types A, B, and C, in which only connections of the type can be forged, while any other connections with particles of the same type or with C-particles are forbidden, and for comparison a binary system with two particle types A and C, in which only connections of the type can be formed. We study the breakdown of the percolation in the agglomeration at the bottom of the cylinder with an increasing fraction of C-particles

Kauffman Model with spatially separated ligation and cleavage reactions

One of the open questions regarding the origin of life is the problem how macromolecules could be created. One possible answer is the existence of autocatalytic sets in which some macromolecules mutually catalyze each other’s formation. This mechanism is theoretically described in the Kauffman model. We introduce and simulate an extension of the Kauffman model, in which ligation and cleavage reactions are spatially separated in different containers connected by diffusion, and provide computational results for instances with and without autocatalytic sets, focusing on the time evolution of the densities of the various molecules. Furthermore, we study the rich behavior of a randomly generated instance containing an autocatalytic metabolism, in which molecules are created by ligation processes and destroyed by cleavage processes and vice versa or generated and destroyed both by ligation processes

Community-driven ELIXIR activities in single-cell omics

Single-cell omics (SCO) has revolutionized the way and the level of resolution by which life science research is conducted, not only impacting our understanding of fundamental cell biology but also providing novel solutions in cutting-edge medical research. The rapid development of single-cell technologies has been accompanied by the active development of data analysis methods, resulting in a plethora of new analysis tools and strategies every year. Such a rapid development of SCO methods and tools poses several challenges in standardization, benchmarking, computational resources and training. These challenges are in line with the activities of ELIXIR, the European coordinated infrastructure for life science data. Here, we describe the current landscape of and the main challenges in SCO data, and propose the creation of the ELIXIR SCO Community, to coordinate the efforts in order to best serve SCO researchers in Europe and beyond. The Community will build on top of national experiences and pave the way towards integrated long-term solutions for SCO research. Keywor

Galaxy Training: A powerful framework for teaching!

There is an ongoing explosion of scientific datasets being generated, brought on by recent technological advances in many areas of the natural sciences. As a result, the life sciences have become increasingly computational in nature, and bioinformatics has taken on a central role in research studies. However, basic computational skills, data analysis, and stewardship are still rarely taught in life science educational programs, resulting in a skills gap in many of the researchers tasked with analysing these big datasets. In order to address this skills gap and empower researchers to perform their own data analyses, the Galaxy Training Network (GTN) has previously developed the Galaxy Training Platform (https://training.galaxyproject.org), an open access, community-driven framework for the collection of FAIR (Findable, Accessible, Interoperable, Reusable) training materials for data analysis utilizing the user-friendly Galaxy framework as its primary data analysis platform. Since its inception, this training platform has thrived, with the number of tutorials and contributors growing rapidly, and the range of topics extending beyond life sciences to include topics such as climatology, cheminformatics, and machine learning. While initially aimed at supporting researchers directly, the GTN framework has proven to be an invaluable resource for educators as well. We have focused our efforts in recent years on adding increased support for this growing community of instructors. New features have been added to facilitate the use of the materials in a classroom setting, simplifying the contribution flow for new materials, and have added a set of train-the-trainer lessons. Here, we present the latest developments in the GTN project, aimed at facilitating the use of the Galaxy Training materials by educators, and its usage in different learning environments

Community-Driven Data Analysis Training for Biology

The primary problem with the explosion of biomedical datasets is not the data, not computational resources, and not the required storage space, but the general lack of trained and skilled researchers to manipulate and analyze these data. Eliminating this problem requires development of comprehensive educational resources. Here we present a community-driven framework that enables modern, interactive teaching of data analytics in life sciences and facilitates the development of training materials. The key feature of our system is that it is not a static but a continuously improved collection of tutorials. By coupling tutorials with a web-based analysis framework, biomedical researchers can learn by performing computation themselves through a web browser without the need to install software or search for example datasets. Our ultimate goal is to expand the breadth of training materials to include fundamental statistical and data science topics and to precipitate a complete re-engineering of undergraduate and graduate curricula in life sciences. This project is accessible at https://training.galaxyproject.org. We developed an infrastructure that facilitates data analysis training in life sciences. It is an interactive learning platform tuned for current types of data and research problems. Importantly, it provides a means for community-wide content creation and maintenance and, finally, enables trainers and trainees to use the tutorials in a variety of situations, such as those where reliable Internet access is unavailable

The SIB Swiss Institute of Bioinformatics' resources: focus on curated databases

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article

Dynamic expression of chromatin modifiers during developmental transitions in mouse preimplantation embryos

During mouse preimplantation development, major changes in cell fate are accompanied by extensive alterations of gene expression programs. Embryos first transition from a maternal to zygotic program and subsequently specify the pluripotent and the trophectodermal cell lineages. These processes are regulated by key transcription factors, likely in cooperation with chromatin modifiers that control histone and DNA methylation. To characterize the spatiotemporal expression of chromatin modifiers in relation to developmental transitions, we performed gene expression profiling of 156 genes in individual oocytes and single blastomeres of developing mouse embryos until the blastocyst stage. More than half of the chromatin modifiers displayed either maternal or zygotic expression. We also detected lineage-specific expression of several modifiers, including Ezh1, Prdm14, Scmh1 and Tet1 underscoring possible roles in cell fate decisions. Members of the SET-domain containing SMYD family showed differential gene expression during preimplantation development. We further observed co-expression of genes with opposing biochemical activities, such as histone methyltransferases and demethylases, suggesting the existence of a dynamic chromatin steady-state during preimplantation development