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Motivation: The rapid growth of metagenomics sequencing data makes metagenomics increasingly dependent on computational and statistical methods for fast and efficient analysis. Consequently, novel analysis tools for big-data metagenomics are constantly emerging. One of the biggest challenges for researchers occurs in the analysis planning stage: selecting the most suitable metagenomics software tool to gain valuable insights from sequencing data. The building process of data analysis pipelines is often laborious and time-consuming since it requires a deep and critical understanding of how to apply a particular tool to complete a specified metagenomics task. Results: We have addressed this challenge by using machine learning methods to develop a classification system of metagenomics software tools into 13 classes (11 semantic annotations of EDAM and two virus-specific classes) based on the descriptions of the tools. We trained three classifiers (Naive Bayes, Logistic Regression, and Random Forest) using 15 text feature extraction techniques (TF-IDF, GloVe, BERT-based models, and others). The manually curated dataset includes 224 software tools and contains text from the abstract and the methods section of the tools' publications. The best classification performance, with an Area Under the Precision-Recall Curve score of 0.85, is achieved using Logistic regression, BioBERT for text embedding, and text from abstracts only. The proposed system provides accurate and unified identification of metagenomics data analysis tools and tasks, which is a crucial step in the construction of metagenomics data analysis pipelines., 13 pages, 5 figures, 2 tables

International audience; BackgroundPlant viruses cause half of the emerging plant diseases and pose a great threat to agricultural crops worldwide. INEXTVIR Marie-Curie Training Network proposes the use of High-Throughput Sequencing (HTS) technologies for studying at an unprecedented depth the virome of selected agricultural crops and ecosystems across Europe. Our specific aims are: a) detecting the virome present in selected agricultural crops across Europe, b) understanding the biological and ecological impacts of the virome, c) improving virus detection capabilities in plant health diagnostic and certification settings through the development and validation of HTS methods, d) assessing the agronomic and socio-economic impact of virome and translate it into practical decision tools.DescriptionWithin the INEXTVIR project three bioinformatics PhDs are responsible for the development of methods and software tools for virome data analysis. We will evaluate the performance of existing pipelines for the analysis of HTS data for plant virus, while also developing new methods. Furthermore, we will propose an approach to simplify and automate bioinformatics pipelines to be user-friendlier by integrating them within a laboratory data management system and workflow tools. Moreover, we plan to develop a machine learning approach for decision support helping to select the most efficient pipeline based on the type of HTS dataset available and the goal of the analysis.ConclusionsThe simplified bioinformatics pipelines for virome HTS data analysis will make their use easier by researchers with little bioinformatic background. While working on that we are also contributing to a unified project-wide management of HTS data.

Despite the increasingly digital nature of society there are some areas of research that remain firmly rooted in the past; in this case the laboratory notebook, the last remaining paper component of an experiment. Countless electronic laboratory notebooks (ELNs) have been created in an attempt to digitise record keeping processes in the lab, but none of them have become a ‘key player’ in the ELN market, due to the many adoption barriers that have been identified in previous research and further explored in the user studies presented here. The main issues identified are the cost of the current available ELNs, their ease of use (or lack of it) and their accessibility issues across different devices and operating systems. Evidence suggests that whilst scientists willingly make use of generic notebooking software, spreadsheets and other general office and scientific tools to aid their work, current ELNs are lacking in the required functionality to meet the needs of the researchers. In this paper we present our extensive research and user study results to propose an ELN built upon a pre-existing cloud notebook platform that makes use of accessible popular scientific software and semantic web technologies to help overcome the identified barriers to adoption. Electronic supplementary material The online version of this article (doi:10.1186/s13321-017-0221-3) contains supplementary material, which is available to authorized users.