List all resources, or create a new resource.

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            "name": "X-omics ACTION demonstrator multi-omics analysis workflow",
            "description": "This multi-omics data analysis workflow was developed as part of a case study investigating shared patterns between multi-omics data and childhood externalizing behaviour. The modular Nextflow workflow was implemented using FAIR practices. It comprises genome-wide DNA methylation, targeted metabolomics and behaviour data pre-processing, and integrative analysis methods. Individuals of a cohort are clustered using Similarity Network Fusion (SNF), latent feature dimensions are uncovered using different unsupervised methods including Multi-Omics Factor Analysis (MOFA) and Multiple Correspondence Analysis (MCA), and correlations between -omics and phenotype dimensions are determined in downstream analyses including results visualisation.",
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            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3391",
                    "term": "Omics"
                },
                {
                    "uri": "http://edamontology.org/topic_3172",
                    "term": "Metabolomics"
                },
                {
                    "uri": "http://edamontology.org/topic_3173",
                    "term": "Epigenomics"
                },
                {
                    "uri": "http://edamontology.org/topic_3474",
                    "term": "Machine learning"
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                    "uri": "http://edamontology.org/topic_0092",
                    "term": "Data visualisation"
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                "Python",
                "R"
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            "maturity": "Emerging",
            "cost": "Free of charge",
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            "link": [
                {
                    "url": "https://github.com/Xomics/ACTIONdemonstrator_workflow",
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                {
                    "url": "https://doi.org/10.48546/workflowhub.workflow.402.8",
                    "type": [
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                {
                    "doi": "10.1093/gigascience/giad115",
                    "pmid": "38217405",
                    "pmcid": "PMC10787363",
                    "type": [
                        "Other"
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                        "title": "A multi-omics data analysis workflow packaged as a FAIR Digital Object",
                        "abstract": "Background: Applying good data management and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles in research projects can help disentangle knowledge discovery, study result reproducibility, and data reuse in future studies. Based on the concepts of the original FAIR principles for research data, FAIR principles for research software were recently proposed. FAIR Digital Objects enable discovery and reuse of Research Objects, including computational workflows for both humans and machines. Practical examples can help promote the adoption of FAIR practices for computational workflows in the research community. We developed a multi-omics data analysis workflow implementing FAIR practices to share it as a FAIR Digital Object. Findings: We conducted a case study investigating shared patterns between multi-omics data and childhood externalizing behavior. The analysis workflow was implemented as a modular pipeline in the workflow manager Nextflow, including containers with software dependencies. We adhered to software development practices like version control, documentation, and licensing. Finally, the workflow was described with rich semantic metadata, packaged as a Research Object Crate, and shared via WorkflowHub. Conclusions: Along with the packaged multi-omics data analysis workflow, we share our experiences adopting various FAIR practices and creating a FAIR Digital Object. We hope our experiences can help other researchers who develop omics data analysis workflows to turn FAIR principles into practice.",
                        "date": "2024-01-01T00:00:00Z",
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                            {
                                "name": "Niehues A."
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                            {
                                "name": "de Visser C."
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                            {
                                "name": "Hagenbeek F.A."
                            },
                            {
                                "name": "Kulkarni P."
                            },
                            {
                                "name": "Pool R."
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                            {
                                "name": "Karu N."
                            },
                            {
                                "name": "Kindt A.S.D."
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                            {
                                "name": "Singh G."
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                            {
                                "name": "Vermeiren R.R.J.M."
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                                "name": "Boomsma D.I."
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                            {
                                "name": "van Dongen J."
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                            {
                                "name": "T'Hoen P.A.C."
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                            {
                                "name": "van Gool A.J."
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                        "title": "MUSCLE: Multiple sequence alignment with high accuracy and high throughput",
                        "abstract": "We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5.com/muscle. © Oxford University Press 20004; all rights reserved.",
                        "date": "2004-07-09T00:00:00Z",
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                        "authors": [
                            {
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                        "title": "Search and sequence analysis tools services from EMBL-EBI in 2022",
                        "abstract": "The EMBL-EBI search and sequence analysis tools frameworks provide integrated access to EMBL-EBI's data resources and core bioinformatics analytical tools. EBI Search (https://www.ebi.ac.uk/ebisearch) provides a full-text search engine across nearly 5 billion entries, while the Job Dispatcher tools framework (https://www.ebi.ac.uk/services) enables the scientific community to perform a diverse range of sequence analysis using popular bioinformatics applications. Both allow users to interact through user-friendly web applications, as well as via RESTful and SOAP-based APIs. Here, we describe recent improvements to these services and updates made to accommodate the increasing data requirements during the COVID-19 pandemic.",
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                            {
                                "name": "Madeira F."
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                            {
                                "name": "Pearce M."
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                            {
                                "name": "Tivey A.R.N."
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                            {
                                "name": "Basutkar P."
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                                "name": "Lee J."
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                                "name": "Edbali O."
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                                "name": "Madhusoodanan N."
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                        "title": "Clustal Omega",
                        "abstract": "Clustal Omega is a package for making multiple sequence alignments of amino acid or nucleotide sequences, quickly and accurately. It is a complete upgrade and rewrite of earlier Clustal programs. This unit describes how to run Clustal Omega interactively from a command line, although it can also be run online from several sites. The unit describes a basic protocol for taking a set of unaligned sequences and producing a full alignment. There are also protocols for using an external HMM or iteration to help improve an alignment.",
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                                "name": "Sievers F."
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                        "abstract": "The GeneWise method for combining gene prediction and homology searches was applied to the 2.9-Mb region from Drosophila melanogaster. The results from the Genome Annotation Assessment Project (GASP) showed that GeneWise provided reasonably accurate gene predictions. Further investigation indicates that many of the incorrect gene predictions from GeneWise were due to transposons with valid protein-coding genes and the remaining cases are pseudogenes or possible annotation oversights.",
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                        "abstract": "The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online (http://bioinf.spbau.ru/spades). It is distributed as open source software. © Copyright 2012, Mary Ann Liebert, Inc.",
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