The amount of information regarding protein-protein interactions (PPI) at a proteomic scale is constantly increasing. This is paralleled with an increase of databases making information available. Consequently there are diverse ways of delivering information about not only PPIs but also regarding the databases themselves. This creates a time consuming obstacle for many researchers working in the field. Our survey provides a valuable tool for researchers to reduce the time necessary to gain a broad overview of PPI-databases and is supported by a graphical representation of data exchange. The graphical representation is made available in cooperation with the team maintaining www.pathguide.org and can be accessed at http://www.pathguide.org/interactions.php in a new Cytoscape web implementation. The local copy of Cytoscape cys file can be downloaded from http://bio.icm.edu.pl/~darman/ppi web page.

Protein-protein interactions are important for the majority of biological processes. A significant number of computational methods have been developed to predict protein-protein interactions using protein sequence, structural and genomic data. Vast experimental data is publicly available on the Internet, but it is scattered across numerous databases. This fact motivated us to create and evaluate new high-throughput datasets of interacting proteins. We extracted interaction data from DIP, MINT, BioGRID and IntAct databases. Then we constructed descriptive features for machine learning purposes based on data from Gene Ontology and DOMINE. Thereafter, four well-established machine learning methods: Support Vector Machine, Random Forest, Decision Tree and Naïve Bayes, were used on these datasets to build an Ensemble Learning method based on majority voting. In cross-validation experiment, sensitivity exceeded 80% and classification/prediction accuracy reached 90% for the Ensemble Learning method. We extended the experiment to a bigger and more realistic dataset maintaining sensitivity over 70%. These results confirmed that our datasets are suitable for performing PPI prediction and Ensemble Learning method is well suited for this task. Both the processed PPI datasets and the software are available at .

During DNA extraction the DNA molecule undergoes physical and chemical shearing, causing the DNA to fragment into shorter and shorter pieces. Under common laboratory conditions this fragmentation yields DNA fragments of 5-35 kilobases (kb) in length. This fragment length is more than sufficient for DNA sequencing using short-read technologies which generate reads 50-600 bp in length, but insufficient for long-read sequencing and linked reads where fragment lengths of more than 40 kb may be desirable.This study provides a theoretical framework for quality management to ensure access to high molecular weight DNA in samples. Shearing can be divided into physical and chemical shearing which generate different patterns of fragmentation. Exposure to physical shearing creates a characteristic fragment length where DNA fragments are cut in half by shear stress. This characteristic length can be measured using gel electrophoresis or instruments for DNA fragment analysis. Chemical shearing generates randomly distributed fragment lengths visible as a smear of DNA below the peak fragment length. By measuring the peak of DNA fragment length and the proportion of very short DNA fragments both sources of shearing can be measured using commonly used laboratory techniques, providing a suitable quantification of DNA integrity of DNA for sequencing with long-read technologies.

Abstract:Biobanks are an organized collection of biological material and associated data. They are a fundamental resource for life science research and contribute to the development of pharmaceutical drugs, diagnostic markers and to a deeper understanding of the genetics that regulate the development of all life on earth.Biobanks are well established in High Income Countries (HIC) and are rapidly emerging in Low and Middle Income Countries (LMIC). Surveys among biobanks operating in a LMIC setting indicate that limited resources and short term funding tied to specific projects threaten the sustainability of the biobanks. Fit-for-purpose biobanks targeting major societal challenges such as HIV and Malaria provide an excellent basis for integrating biobanks with the available research communities in LMIC regions. But to become sustainable for the future it is important that biobanks become an integrated part of local research communities. To achieve this, the cost of operating biobanks must be lowered, templates must be developed to support local ethics committees and researchers must be given the opportunity to build experience in successfully operating biobank based research projects.The B3Africa consortium is based on these conclusions and set up to support biobank based research by creating a cost efficient Laboratory Information Management System (LIMS) for developing biobanks and also contribute to the training and capacity building in the local research community. The technical platform called the eB3Kit is open source and consists of a LIMS and a bioinformatics module based on the eBiokit that allow researchers to take control over the analysis of their own data. Along with the technical platform the consortium will also contribute training and support for the associated infrastructures necessary to regulate the ethical and legal implications of biobank based research.

RNA has become one of the major research topics in molecular biology. As a central player in key processes regulating gene expression, RNA is in the focus of many efforts to decipher the pathways that govern the transition of genetic information to a fully functional cell. As more and more researchers join this endeavour, there is a rapidly growing demand for comprehensive collections of tools that cover the diverse layers of RNA-related research. However, increasing amounts of data, from diverse types of experiments, addressing different aspects of biological questions need to be consolidated and integrated into a single framework. Only then is it possible to connect findings from e.g. RNA-Seq experiments and methods for e.g. target predictions. To address these needs, we present the RNA Workbench 2.0 , an updated online resource for RNA related analysis. With the RNA Workbench we created a comprehensive set of analysis tools and workflows that enables researchers to analyze their data without the need for sophisticated command-line skills. This update takes the established framework to the next level, providing not only a containerized infrastructure for analysis, but also a ready-to-use platform for hands-on training, analysis, data exploration, and visualization. The new framework is available at https://rna.usegalaxy.eu , and login is free and open to all users. The containerized version can be found at https://github.com/bgruening/galaxy-rna-workbench.