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                    "term": "Genotype and phenotype"
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            "description": "APE (the Automated Pipeline Explorer) as a command-line tool and API for automated composition of scientific workflows. APE is easily configured to a new application domain by providing it with a domain ontology and semantically annotated tools. It can then be used to synthesize purpose-specific workflows based on a specification of the available workflow inputs, desired outputs and possibly additional constraints.",
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                    "type": "Software package",
                    "note": "Download APE java library or the CLI",
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                {
                    "url": "https://ape-framework.readthedocs.io/",
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                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Ape: A command-line tool and api for automated workflow composition",
                        "abstract": "Automated workflow composition is bound to take the work with scientific workflows to the next level. On top of today’s comprehensive eScience infrastructure, it enables the automated generation of possible workflows for a given specification. However, functionality for automated workflow composition tends to be integrated with one of the many available workflow management systems, and is thus difficult or impossible to apply in other environments. Therefore we have developed APE (the Automated Pipeline Explorer) as a command-line tool and API for automated composition of scientific workflows. APE is easily configured to a new application domain by providing it with a domain ontology and semantically annotated tools. It can then be used to synthesize purpose-specific workflows based on a specification of the available workflow inputs, desired outputs and possibly additional constraints. The workflows can further be transformed into executable implementations and/or exported into standard workflow formats. In this paper we describe APE v1.0 and discuss lessons learned from applications in bioinformatics and geosciences.",
                        "date": "2020-01-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Kasalica V."
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                            {
                                "name": "Lamprecht A.-L."
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                        ],
                        "journal": "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"
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                    "doi": "10.1021/ACS.JPROTEOME.0C00983",
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                        "title": "APE in the Wild: Automated Exploration of Proteomics Workflows in the bio.tools Registry",
                        "abstract": "The bio.tools registry is a main catalogue of computational tools in the life sciences. More than 17 000 tools have been registered by the international bioinformatics community. The bio.tools metadata schema includes semantic annotations of tool functions, that is, formal descriptions of tools' data types, formats, and operations with terms from the EDAM bioinformatics ontology. Such annotations enable the automated composition of tools into multistep pipelines or workflows. In this Technical Note, we revisit a previous case study on the automated composition of proteomics workflows. We use the same four workflow scenarios but instead of using a small set of tools with carefully handcrafted annotations, we explore workflows directly on bio.tools. We use the Automated Pipeline Explorer (APE), a reimplementation and extension of the workflow composition method previously used. Moving \"into the wild\"opens up an unprecedented wealth of tools and a huge number of alternative workflows. Automated composition tools can be used to explore this space of possibilities systematically. Inevitably, the mixed quality of semantic annotations in bio.tools leads to unintended or erroneous tool combinations. However, our results also show that additional control mechanisms (tool filters, configuration options, and workflow constraints) can effectively guide the exploration toward smaller sets of more meaningful workflows.",
                        "date": "2021-04-02T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Kasalica V."
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                            {
                                "name": "Schwammle V."
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                            {
                                "name": "Palmblad M."
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                            {
                                "name": "Ison J."
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                                "name": "Lamprecht A.-L."
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                        "journal": "Journal of Proteome Research"
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            "description": "Oncoanalyser is an nf-core Nextflow implementation of the tools developed by Hartwig Medical Foundation. The workflow is designed to comprehensively detect all variant types (SNV. MNV, InDel, SV, CNV, Fusions, Viral insertions, Telomere length, Genetic Immune Escape, mutational processes) as well as key tumor characteristics (purity, ploidy, TMB, MSI) from data derived from different short read platforms. The tooling is optimised for paired whole genome samples (90x tumor, 30x normal) but can be run on exome or panel (targeted) data. There is support for GRCh37 and GRCh38 human reference genome builds.",
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                    "uri": "http://edamontology.org/topic_2640",
                    "term": "Oncology"
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                    "uri": "http://edamontology.org/topic_3673",
                    "term": "Whole genome sequencing"
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            "name": "CoreDetector",
            "description": "Flexible and efficient program for core-genome alignment of evolutionary diverse genomes.",
            "homepage": "https://github.com/mfruzan/CoreDetector",
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                            "uri": "http://edamontology.org/operation_3182",
                            "term": "Genome alignment"
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                            "uri": "http://edamontology.org/operation_0491",
                            "term": "Pairwise sequence alignment"
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                            "uri": "http://edamontology.org/operation_0496",
                            "term": "Global alignment"
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                            "uri": "http://edamontology.org/operation_0524",
                            "term": "De-novo assembly"
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                    "uri": "http://edamontology.org/topic_0621",
                    "term": "Model organisms"
                },
                {
                    "uri": "http://edamontology.org/topic_3175",
                    "term": "Structural variation"
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                    "uri": "http://edamontology.org/topic_0084",
                    "term": "Phylogeny"
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                    "term": "Transcription factors and regulatory sites"
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                    "doi": "10.1093/BIOINFORMATICS/BTAD628",
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                    "metadata": {
                        "title": "CoreDetector: a flexible and efficient program for core-genome alignment of evolutionary diverse genomes",
                        "abstract": "Motivation: Whole genome alignment of eukaryote species remains an important method for the determination of sequence and structural variations and can also be used to ascertain the representative non-redundant core-genome sequence of a population. Many whole genome alignment tools were first developed for the more mature analysis of prokaryote species with few current tools containing the functionality to process larger genomes of eukaryotes as well as genomes of more divergent species. In addition, the functionality of these tools becomes computationally prohibitive due to the significant compute resources needed to handle larger genomes. Results: In this research, we present CoreDetector, an easy-to-use general-purpose program that can align the core-genome sequences for a range of genome sizes and divergence levels. To illustrate the flexibility of CoreDetector, we conducted alignments of a large set of closely related fungal pathogen and hexaploid wheat cultivar genomes as well as more divergent fly and rodent species genomes. In all cases, compared to existing multiple genome alignment tools, CoreDetector exhibited improved flexibility, efficiency, and competitive accuracy in tested cases. Availability and implementation: CoreDetector was developed in the cross platform, and easily deployable, Java language. A packaged pipeline is readily executable in a bash terminal without any external need for Perl or Python environments. Installation, example data, and usage instructions for CoreDetector are freely available from https://github.com/mfruzan/CoreDetector.",
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                                "name": "Fruzangohar M."
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                                "name": "Moolhuijzen P."
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                            {
                                "name": "Bakaj N."
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                            {
                                "name": "Taylor J."
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        {
            "name": "StarPep Toolbox",
            "description": "StarPep toolbox is a software for studying the antimicrobial peptides' (AMPs) chemical space with molecular network-based representations and similarity searching models. This application aims to contribute to peptide drug repurposing, development, and optimization.",
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                            "uri": "http://edamontology.org/operation_4009",
                            "term": "Small molecule design"
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                            "uri": "http://edamontology.org/operation_3436",
                            "term": "Aggregation"
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                            "uri": "http://edamontology.org/operation_3432",
                            "term": "Clustering"
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                    "term": "Small molecules"
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                    "uri": "http://edamontology.org/topic_3324",
                    "term": "Infectious disease"
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                    "term": "Machine learning"
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                "Windows"
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                "Java"
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                    "doi": "10.1093/BIOINFORMATICS/BTAD506",
                    "pmid": "37603724",
                    "pmcid": "PMC10469104",
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                    "version": null,
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                    "metadata": {
                        "title": "StarPep Toolbox: an open-source software to assist chemical space analysis of bioactive peptides and their functions using complex networks",
                        "abstract": "Motivation: Antimicrobial peptides (AMPs) are promising molecules to treat infectious diseases caused by multi-drug resistance pathogens, some types of cancer, and other conditions. Computer-aided strategies are efficient tools for the high-throughput screening of AMPs. Results: This report highlights StarPep Toolbox, an open-source and user-friendly software to study the bioactive chemical space of AMPs using complex network-based representations, clustering, and similarity-searching models. The novelty of this research lies in the combination of network science and similarity-searching techniques, distinguishing it from conventional methods based on machine learning and other computational approaches. The network-based representation of the AMP chemical space presents promising opportunities for peptide drug repurposing, development, and optimization. This approach could serve as a baseline for the discovery of a new generation of therapeutics peptides.",
                        "date": "2023-08-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Aguilera-Mendoza L."
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                            {
                                "name": "Ayala-Ruano S."
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                                "name": "Martinez-Rios F."
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                                "name": "Chavez E."
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                            {
                                "name": "Garcia-Jacas C.R."
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                            {
                                "name": "Brizuela C.A."
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                                "name": "Marrero-Ponce Y."
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        {
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                            "term": "Data retrieval"
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                            "uri": "http://edamontology.org/operation_3258",
                            "term": "Transcriptome assembly"
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                    "term": "Transcriptomics"
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                    "term": "Sequencing"
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                        "title": "BioLegato: a programmable, object-oriented graphic user interface",
                        "abstract": "Background: Biologists are faced with an ever-changing array of complex software tools with steep learning curves, often run on High Performance Computing platforms. To resolve the tradeoff between analytical sophistication and usability, we have designed BioLegato, a programmable graphical user interface (GUI) for running external programs. Results: BioLegato can run any program or pipeline that can be launched as a command. BioLegato reads specifications for each tool from files written in PCD, a simple language for specifying GUI components that set parameters for calling external programs. Thus, adding new tools to BioLegato can be done without changing the BioLegato Java code itself. The process is as simple as copying an existing PCD file and modifying it for the new program, which is more like filling in a form than writing code. PCD thus facilitates rapid development of new applications using existing programs as building blocks, and getting them to work together seamlessly. Conclusion: BioLegato applies Object-Oriented concepts to the user experience by organizing applications based on discrete data types and the methods relevant to that data. PCD makes it easier for BioLegato applications to evolve with the succession of analytical tools for bioinformatics. BioLegato is applicable not only in biology, but in almost any field in which disparate software tools need to work as an integrated system.",
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                    "uri": "http://edamontology.org/topic_0199",
                    "term": "Genetic variation"
                },
                {
                    "uri": "http://edamontology.org/topic_3168",
                    "term": "Sequencing"
                }
            ],
            "operatingSystem": [
                "Mac",
                "Linux",
                "Windows"
            ],
            "language": [
                "Java"
            ],
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            "cost": "Free of charge",
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                {
                    "url": "https://lapis-docs.readthedocs.io/",
                    "type": [
                        "User manual"
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            "publication": [
                {
                    "doi": "10.1186/S12859-023-05364-3",
                    "pmid": "37277732",
                    "pmcid": "PMC10240112",
                    "type": [],
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                    "metadata": {
                        "title": "LAPIS is a fast web API for massive open virus sequencing data",
                        "abstract": "Background: Recent epidemic outbreaks such as the SARS-CoV-2 pandemic and the mpox outbreak in 2022 have demonstrated the value of genomic sequencing data for tracking the origin and spread of pathogens. Laboratories around the globe generated new sequences at unprecedented speed and volume and bioinformaticians developed new tools and dashboards to analyze this wealth of data. However, a major challenge that remains is the lack of simple and efficient approaches for accessing and processing sequencing data. Results: The Lightweight API for Sequences (LAPIS) facilitates rapid retrieval and analysis of genomic sequencing data through a REST API. It supports complex mutation- and metadata-based queries and can perform aggregation operations on massive datasets. LAPIS is optimized for typical questions relevant to genomic epidemiology. Using a newly-developed in-memory database engine, it has a high speed and throughput: between 25 January and 4 February 2023, the SARS-CoV-2 instance of LAPIS, which contains 14.5 million sequences, processed over 20 million requests with a mean response time of 411 ms and a median response time of 1 ms. LAPIS is the core engine behind our dashboards on genspectrum.org and we currently maintain public LAPIS instances for SARS-CoV-2 and mpox. Conclusions: Powered by an optimized database engine and available through a web API, LAPIS enhances the accessibility of genomic sequencing data. It is designed to serve as a common backend for dashboards and analyses with the potential to be integrated into common database platforms such as GenBank.",
                        "date": "2023-12-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Chen C."
                            },
                            {
                                "name": "Taepper A."
                            },
                            {
                                "name": "Engelniederhammer F."
                            },
                            {
                                "name": "Kellerer J."
                            },
                            {
                                "name": "Roemer C."
                            },
                            {
                                "name": "Stadler T."
                            }
                        ],
                        "journal": "BMC Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Chaoran Chen",
                    "email": "chaoran.chen@bsse.ethz.ch",
                    "url": null,
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                {
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                    "orcidid": "https://orcid.org/0000-0001-6431-535X",
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            "additionDate": "2024-02-22T12:20:05.220524Z",
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