ReMap 2020: a database of regulatory regions from an integrative analysis of Human and Arabidopsis DNA-binding sequencing experiments.

ReMap (http://remap.univ-amu.fr) aims to provide the largest catalogs of high-quality regulatory regions resulting from a large-scale integrative analysis of hundreds of transcription factors and regulators from DNA-binding experiments in Human and Arabidopsis (Arabidopsis thaliana). In this 2020 update of ReMap we have collected, analyzed and retained after quality control 2764 new human ChIP-seq and 208 ChIP-exo datasets available from public sources. The updated human atlas totalize 5798 datasets covering a total of 1135 transcriptional regulators (TRs) with a catalog of 165 million (M) peaks. This ReMap update comes with two unique Arabidopsis regulatory catalogs. First, a catalog of 372 Arabidopsis TRs across 2.6M peaks as a result of the integration of 509 ChIP-seq and DAP-seq datasets. Second, a catalog of 33 histone modifications and variants across 4.5M peaks from the integration of 286 ChIP-seq datasets. All catalogs are made available through track hubs at Ensembl and UCSC Genome Browsers. Additionally, this update comes with a new web framework providing an interactive user-interface, including improved search features. Finally, full programmatically access to the underlying data is available using a RESTful API together with a new R Shiny interface for a TRs binding enrichment analysis tool.

The Ocean Gene Atlas: exploring the biogeography of plankton genes online.

The Ocean Gene Atlas is a web service to explore the biogeography of genes from marine planktonic organisms. It allows users to query protein or nucleotide sequences against global ocean reference gene catalogs. With just one click, the abundance and location of target sequences are visualized on world maps as well as their taxonomic distribution. Interactive results panels allow for adjusting cutoffs for alignment quality and displaying the abundances of genes in the context of environmental features (temperature, nutrients, etc.) measured at the time of sampling. The ease of use enables non-bioinformaticians to explore quantitative and contextualized information on genes of interest in the global ocean ecosystem. Currently the Ocean Gene Atlas is deployed with (i) the Ocean Microbial Reference Gene Catalog (OM-RGC) comprising 40 million non-redundant mostly prokaryotic gene sequences associated with both Tara Oceans and Global Ocean Sampling (GOS) gene abundances and (ii) the Marine Atlas of Tara Ocean Unigenes (MATOU) composed of >116 million eukaryote unigenes. Additional datasets will be added upon availability of further marine environmental datasets that provide the required complement of sequence assemblies, raw reads and contextual environmental parameters. Ocean Gene Atlas is a freely-available web service at: http://tara-oceans.mio.osupytheas.fr/ocean-gene-atlas/.

FAIR-Checker: supporting digital resource findability and reuse with Knowledge Graphs and Semantic Web standards.

The current rise of Open Science and Reproducibility in the Life Sciences requires the creation of rich, machine-actionable metadata in order to better share and reuse biological digital resources such as datasets, bioinformatics tools, training materials, etc. For this purpose, FAIR principles have been defined for both data and metadata and adopted by large communities, leading to the definition of specific metrics. However, automatic FAIRness assessment is still difficult because computational evaluations frequently require technical expertise and can be time-consuming. As a first step to address these issues, we propose FAIR-Checker, a web-based tool to assess the FAIRness of metadata presented by digital resources. FAIR-Checker offers two main facets: a "Check" module providing a thorough metadata evaluation and recommendations, and an "Inspect" module which assists users in improving metadata quality and therefore the FAIRness of their resource. FAIR-Checker leverages Semantic Web standards and technologies such as SPARQL queries and SHACL constraints to automatically assess FAIR metrics. Users are notified of missing, necessary, or recommended metadata for various resource categories. We evaluate FAIR-Checker in the context of improving the FAIRification of individual resources, through better metadata, as well as analyzing the FAIRness of more than 25 thousand bioinformatics software descriptions.